Loonen_2010

What can we simulate?

Roel Loonen

21 June 2010

MSc-ThesisTechnische Universiteit Eindhoven

AdaptiveShells

Climate Building

R.C.G.M. Loonen

0570677

Building Services

Architecture, Building & Planning

Eindhoven University of Technology

Student:

ID:

Master:

Faculty:

Advisors

prof. dr. ir. J.L.M. Hensen

dr. dipl.-ing. M. Trčka

D. Cóstola, MSc

Climate Adaptive Building Shells

What can we simulate?

21 June 2010

Master of Science Thesis

SUMMARY

Building shells are at the interface between the building interior and ambient climate, and thereforefulfill a number of vital functions that dictate most of the building’s energy consumption. The build-ing’s environment is changing over time (short-term weather conditions, diurnal and seasonal cycles),and this also applies for occupancy and comfort preferences. In spite of these changing conditions,traditional building envelopes are ‘rigid systems’ with fixed thermophysical and optical properties, andhence cannot adapt to this variability. Climate adaptive building shells (CABS) do have the ability torepeatedly and reversibly change their functions, features or behavior over time in response to changesin performance requirements and variable boundary conditions. Therefore, these façade can seize theopportunity to save energy by adapting to prevailing weather conditions, and support comfort levelsby immediately responding to occupants’ wishes. A total number of one hundred CABS concepts hasbeen analyzed in this study which reveals that application of CABS is despite of its promises still ratherlimited.

Successful design and operation of CABS is a complex task. CABS typically are complex systems withtheir relevant phenomena taking place across various physical domains. In addition, CABS have toresolve conflicts and trade-offs between energy consumption and thermal and visual comfort require-ments. Traditional design methods are deemed inadequate to facilitate these insights.

This study explores the role that building performance simulation (BPS) can play in designing build-ings with CABS. The tool is well-suited for this purpose because it promotes further insights in thedynamic interactions between climate, the building and its occupants. By offering the possibility toanswer “what-if” questions, it enables us to predict building performance in different scenarios. Ananalysis of current capabilities and requirements however reveals some space for improvements. Bothflexibility of features to model adaptive behavior, and integration of impacts from the various physicaldomains are open to further development. The work also resulted in a strategy for effective modelingand simulation of CABS.

The applicability of BPS in relation to CABS was exemplified in the case study of Smart Energy Glass.This innovative concept uses a unique switchable coating that makes it possible to effectively controlthe optical properties of a window. By switching transparency in one of three possible states (’bright’,’dark’ and ’translucent’), part of the incoming sunlight is captured and converted into electricity. Basedon a description of underlying physics, an integrated BPS model was developed that uses TRNSYS incombination with DAYSIM. The model was refined and validated in a reduced-scale experimental study.Subsequently, energy consumption and thermal and visual comfort were evaluated under various con-trol strategies and compared to the performance of a conventional building shell. Apparently, the chal-lenge is to control adaptive behavior in such a way that energy demand is reduced to a minimum whilegood indoor environmental quality is retained. In the end, the results helped to identify directions forfuture developments that can contribute to make the concept of SEG a success.

This study concludes that CABS have the ability to become a contributor to satisfy the increasinglystricter energy performance targets without being constrained by the need for concessions in terms ofcomfort. Building performance simulation is a valuable tool that can prevent this latent potential fromcontinuing to remain largely untouched.

i

SAMENVATTING

Gevels bevinden zich op het grensvlak tussen binnen en buiten, en spelen daarom een belangrijkerol in de energiehuishouding van een gebouw. De conventionele gevel heeft echter een voornamelijkbeschermende functie met vaste eigenschappen en kan daardoor niet inspelen op de constant veran-derende gebouwomgeving (weer, dag/nacht cycli, seizoenen) en comfortbehoeften. Adaptieve gevelsdaarentegen hebben wel de mogelijkheid om hun gedrag of eigenschappen te veranderen in de tijd.Door op slimme wijze in te spelen op de buitencondities kan de mogelijkheid worden aangegrepenom energie te besparen. Door daarnaast ook te anticiperen op de wisselende gebruikersbehoeften kantegelijkertijd een gunstig binnenklimaat gewaarborgd blijven. Dit onderzoek heeft onder meer geleidtot een overzicht van een honderdtal adaptieve gevelconcepten, er kan echter geconstateerd wordendat ondanks het veelbelovende concept de toepassing in de praktijk vooralsnog beperkt is.

Het succesvol ontwerpen en regelen van adaptieve gevels blijkt een complexe opgave. Prestaties wor-den bepaald door dynamische invloeden uit verschillende fysische domeinen. Daarnaast dient eenadaptieve gevel op verstandige wijze om te kunnen gaan met de concurrerende of mogelijk zelfs con-flicterende belangen tussen thermisch en visueel comfort enerzijds, en de bijbehorende energievraaganderzijds. Traditionele ontwerpmethoden blijken onvoldoende geschikt om dit inzicht te verschaffen.

In dit onderzoek is ingegaan op de rol die gebouwsimulatie kan spelen in de verdere ontwikkeling vanadaptieve façades. De tools lenen zich uitstekend voor dit doel doordat ze inzicht verschaffen in de dy-namische interacties tussen buitenklimaat, het gebouw en zijn gebruikers. Door de mogelijkheid vanhet beantwoorden van “wat-als” vragen is het mogelijk gebouwprestaties te voorspellen onder verschil-lende scenario’s. Op basis van een analyse van de huidige mogelijkheden en behoeften kan wordenopgemaakt dat er nog wel ruimte voor verbetering bestaat. Zowel de flexibiliteit die de gebruiker heeftvoor het modelleren van adaptief gedrag als de koppeling tussen invloeden uit verschillende fysischedomeinen verdienen nog verdere aandacht. Dit onderzoek heeft onder andere geleid tot een algemeneaanpak voor modelleren en simuleren van gebouwen met adaptieve gevels.

De mogelijkheden van simulatie zijn daarna geïllustreerd aan de hand van Smart Energy Glass. Dit inontwikkeling zijnde, innovatieve type beglazing kan geschakeld worden in drie standen — licht, donkeren diffuus — waarbij een deel van het invallende zonlicht wordt afgevangen en omgezet in elektriciteit.Op basis van een beschrijving van het fysische gedrag is een simulatiemodel ontwikkeld waarin TRNSYSwordt gecombineerd met DAYSIM. Dit model is verder verfijnd en vervolgens gevalideerd met behulpvan een experimentele studie waarbij een prototype was geïnstalleerd in een testgevel en blootgesteldaan het buitenklimaat. De prestaties van het concept zijn in kaart gebracht op basis van integralesimulaties voor verwarming, koeling, daglicht en elektriciteit in verschillende situaties. Hieruit blijktdat de crux is gelegen in de uitdaging om adaptief gedrag zodanig te regelen dat energiegebruik tot eenminimum beperkt blijft maar tegelijkertijd behoud van acceptabel comfort gewaarborgd is. Daarnaastzijn richtingen geformuleerd voor verdere doorontwikkeling die het welslagen van het concept SmartEnergy Glass kunnen bevorderen.

De algemene conclusie is dat adaptieve gevels kunnen bijdragen aan het bereiken van de steeds scher-pere energiedoelstellingen, zonder dat daarbij getornd hoeft te worden aan comfortbeleving. Gebouw-simulatie vormt een onmisbaar gereedschap om dit latente potentieel niet langer onbenut te laten.

iii

ACKNOWLEDGEMENTS

I want to thank and acknowledge several people whose input and influence helped shape this thesis.First of all, I wish to thank prof.dr.ir. Jan Hensen, dr.dipl.-ing. Marija Trcka and Daniel Cóstola, MSc fromEindhoven University of Technology, unit Building Physics and Systems for their continuous advice andsuggestions in the supervision of the project.

The cooperation with Peer+ definitely enriched the work in this thesis. It not only made the ratherabstract concept of CABS much more tangible, but also positively contributed to the multidisciplinarycharacter of the research. I would like to thank Casper van Oosten and Teun Wagenaar for giving me thischance to get an inside look at the innovation process of a real product. I also thank Marcel Demandtfor his assistance and pleasant collaboration during the course of the project.

I gratefully acknowledge the practical support I received in setting up the experimental part of the grad-uation project. I want to thank Paul Verbunt for his constructive thoughts during initiation of, and kindassistance with conducting the experiments in the foreign environment of the laboratory at the depart-ment of Chemical Engineering. Finally, I acknowledge the support from the staff at the BPS laboratory.Without their expertise, the measurements in the test-façade could not have been accomplished.

v

CONTENTS

Summary i

Samenvatting iii

Acknowledgements v

1 Introduction 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Objective and research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.5 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 Climate adaptive building shells 72.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2 CABS performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2.1 Robustness vs Flexibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.2.2 CABS as flexible systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.2.3 Performance benefits of CABS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.3 CABS Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.3.1 Adaptive behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.3.2 Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.3.3 Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.3.4 Barriers and challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.4 CABS Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272.4.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272.4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3 Building Performance Simulation 333.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333.2 Requirements for performance simulation of CABS . . . . . . . . . . . . . . . . . . . . . . . 343.3 Capabilities of BPS tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373.4 Physical domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383.5 Coupling of physical domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423.6 Adaptive features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.6.1 Application-oriented . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443.6.2 General purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

3.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

4 Modeling and simulation of climate adaptive building shells 514.1 Model abstraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514.2 Simulation requirement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

vii

CONTENTS

4.3 Relevant physics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 544.4 Simulation strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 564.5 Verification and Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

5 Modeling Smart Energy Glass 595.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595.2 Real world system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615.3 Conceptual model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

5.3.1 Optical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 635.3.2 Thermal Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 635.3.3 Electrical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

5.4 Simulation model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695.4.1 Simulation strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695.4.2 DAYSIM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 705.4.3 TRNSYS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

6 Smart Energy Glass model validation 736.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 736.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

6.2.1 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 746.2.2 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

6.3 Comparing results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 766.3.1 Daylight model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 766.3.2 Electrical model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

6.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

7 Performance simulation of Smart Energy Glass 837.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

7.1.1 Model setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 837.1.2 Performance indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

7.2 Case 1 - Energy efficient office . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 867.3 Case 2 - Advanced renovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 877.4 Case 3 - SEG in a continental climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 897.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

7.5.1 Dynamic daylight performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 917.5.2 Radiant asymmetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

8 Conclusions 958.1 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 958.2 Recommendations for future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

References 99

A Overview of 100 Climate Adaptive Building Shells 111

B Smart Energy Glass product information 113

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CH

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1INTRODUCTION

1.1 Background

Most people spend up to 90% of their time indoors (Bougdah and Sharples, 2010), which justifies ourstrive for buildings that are as safe, healthy and comfortable as possible. Historically, it has always beenthe building shell that provided shelter and protection against the negative influences caused by erraticexternal conditions. With the advent of artificial lighting and HVAC systems in the twentieth centuryhowever, this important task has gradually been delegated to the building services. As a consequence,the building shell lost its role as moderator for energy and comfort, and it is nowadays more or lessaccepted that buildings pose a major load on our environment. Energy consumption in buildings hasincreased in such a way that it now dominates over the industrial en transportation sectors (Pérez-Lombard et al., 2008). In addition, the built environment contributes as much as one third of totalglobal greenhouse gas emissions.

Buildings are thus seen as one of the main instigators of global warming with all the correspondingimplications, but this threat also introduces high opportunities for the sector. The International Panelon Climate Change (IPCC) labeled Buildings as the sector with the highest potential to mitigate thedetrimental effects in a cost-effective way. However, if the desired targets for greenhouse gas emissionsreduction are to be met, the emissions from the building sector have to be suppressed with much greaterseriousness and vigor than has been the case to date (UNEP, 2009). Continuation of current practicewill likely turn out to be inadequate (Figure 1.1). What is needed is a transformation; a paradigm shiftin the way we envision energy efficiency. Low-energy buildings must become the norm, rather thanthe novelty project, in both new and existing buildings (WBCSD, 2009). Innovations and technologicaldevelopment are supposed to be the catalyst that provides the necessary impulses for this major leapforward.

In this respect, it is worthwhile to reconsider the role of the building shell. Unlike only being a wrap-per, the building shell can be entrusted with multiple vital functions that dictate the building’s energyconsumption and perception of indoor environmental quality. Traditionally, most building shells are‘static’, whereas the climatological boundary conditions and user preferences are constantly changing.As a result, traditional façades cannot adapt to the changes it is exposed to, and this results in a lossopportunity for energy saving and increasing thermal and visual occupant comfort. Climate adaptivebuilding shells (CABS) on the other hand consider the building shell in a fundamentally different way.CABS can actively adapt their behavior over time in response to changing environmental conditionsand performance requirements. Therefore, CABS have the ability to harness the natural energy avail-

1

1. INTRODUCTION

Transformation

Time

Sleepwalking

Too little too late

Tota

l ene

rgy

cons

ump

tion

Figure 1.1: Three scenario’s for energy in buildings (WBCSD, 2009).

able in our environment much more effectively. These adaptive building shells not only offer a highpotential to reduce the energy demand for lighting and space conditioning, but are at the same timealso expected to introduce positive contributions to indoor air quality and thermal and visual comfortlevels.

The relevance of adaptivity in construction elements was also recognized by the recently completedIEA - ECBCS Annex 44, entitled “Integrating Environmentally Responsive Elements in Buildings” (As-chehoug and Andresen, 2008). Annex 44 concludes that the development, application and implemen-tation of responsive building elements∗ is considered to be a necessary step towards further energyefficiency improvements in the built environment

1.2 Problem statement

By exploiting advances in material sciences, and due to the widespread availability of sensors and ac-tuators, there are hardly any technological obstacles for making the building shell adaptive. Despite ofthe anticipated performance benefits, application of CABS in current architectural practice is still verylimited. Those examples that have been built are mainly restricted to either small-scale experimentalprojects or the high-profile, high-budget projects. VMRG, the Dutch façade builders association, everyyear publishes the “high-performance façade book” (van Maarsseveen and van Duijnen, 2008, 2009):An annual overview of the most complex, aesthetic, functional and promising building envelopes, thebranch is proud of. Among the 150 featured projects, there is no example that can be classified as beingCABS.

Making building shells adaptive and more sustainable not only requires technological development butalso faces some design process-related, financial, legislative and social barriers (Hoffman and Henn,2008; Williams and Dair, 2007; Zerkin, 2006). In principle, all these barriers can be traced back to a lackof awareness and fundamental understanding about CABS’ performance. Consequently, there is a greatdemand for effective tools and instruments that can be used in the design process of CABS. These tools

∗Within Annex 44, responsive building elements have been defined as building construction elements that assist to maintainan appropriate balance between optimum interior conditions and environmental performance by reacting in a controlled andholistic manner to changes in external or internal conditions and to occupant intervention.

2

1.3. Objective and research questions

should be able to unambiguously quantify the performance of CABS. Which in turn increases trans-parency about how performance benefits during operation might outweigh first cost arguments. In ad-dition, these tools should be able to rationalize the uncertainty and associated risks that are introducedwith the implementation of CABS. This allows higher ambition levels to be aspired with confidence andhelps to overcome the cautious attitude introduced by the ‘anchoring’ bias towards prescriptive andstandard design solutions (Klotz et al., 2010).

The logic behind CABS seems quite straightforward: buildings that can adapt in response to chang-ing boundary conditions are likely to achieve higher performance throughout the year. Designing andoperating buildings with climate adaptive building shells is however far from trivial. CABS are inher-ently complex systems, consisting of several interrelated components that are working across variousphysical domains. These components together have to deal with trade-offs and resolve conflictive per-formance objectives in real-time. Introduction of CABS moreover changes the traditional way in whichbuildings are designed; CABS are intrinsically dynamic, and therefore it becomes the design of a ‘pro-cess’ rather than an ‘artifact’ (Moloney, 2007). For these reasons, traditional design methods are likelydeemed inappropriate and it will no longer be adequate to rely on past experience or rules of thumb.

Using building performance simulation (BPS), makes it possible to predict why, how, and when a build-ing will consume energy. BPS-tools are able to map the dynamics between the building and its sys-tems, the ambient environment and occupants; and consequently can predict building performance.This instrument enables us to model the complex interactions of a large number of components acrossthe boundaries of multiple physical domains. By doing this, the software tools create virtual buildingmodels that can be used to predict building performance for the following purposes: Choose correctly,understand why, explore possibilities, diagnose problems, identify constraints, develop understand-ing, build consensus, etc (Sokolowski and Banks, 2009). Because of these qualities, BPS can also be avaluable tool for performance prediction in the design stage of CABS, provided that the tool is used in awell-considered way. BPS already proved to be effective for supporting decision-making and code com-pliance checking in the building design process. However the application of BPS in relation to CABS isstill largely unexplored.

1.3 Objective and research questions

This study is embedded within the strive for developing climate adaptive building shells, and focuses onthe building performance simulation area. The primary objective of this study is to determine the rolethat building performance simulation can play in designing buildings with climate adaptive buildingshells. In order to fulfill this objective, the following five research questions are answered:

1. What are the characteristics of CABS and why is BPS needed?

2. What are the possibilities and restrictions of BPS related to CABS?

3. How to model and simulate CABS?

4. What is the performance of CABS?

5. What role can BPS play in CABS development?

1.4 Methodology

Various research methods can be applied in the search for answers to the formulated research ques-tions. Within the scope of this thesis, literature survey, experiments, modeling and simulation are used.The research methodology can be subdivided in three distinct parts, which are described below in moredetail.

3

1. INTRODUCTION

Analysis of CABS

Before the role of BPS in the development of CABS can be indicated, it is essential to first clarify whatCABS really are. Starting with establishing an appropriate definition for CABS, the various concepts andunderlying principles are investigated. For this purpose, an extensive literature review is conductedwhich covers the history of CABS, but also considers cutting-edge and even visionary adaptive buildingshell concepts. This resulted in a list of 100 examples of CABS that have been built, or are in research,design or development stage. A systematic classification method is then employed to indicate the par-ticularities of individual examples but also to extract the overall trends and observations of CABS ingeneral. Subsequently, this information is used to identify the critical elements that are essential forsuccessful performance prediction in CABS.

BPS in relation to CABS

Building performance simulation is used more and more in design of buildings, however, its potentialfor design and operation of CABS is still largely unexplored. Therefore, this part starts with indicat-ing the intrinsic qualities that computational building simulation offers for performance prediction ofCABS, but also highlights the deficiencies and possible ways to work around these problems. Based onthe results of comparative software reviews and a literature survey, the capabilities of specific BPS-toolsin relation to CABS are illustrated. This approach both includes the relevant physical domains and theavailable mechanisms for modeling the adaptive behavior. The knowledge obtained via this process isthen generalized in a universal framework for modeling and simulation of CABS.

Case Study

In order to demonstrate the potential strengths of BPS in relation to CABS in more detail, the casestudy of Smart Energy Glass (SEG) is used. Smart Energy Glass is an innovative, energy generatingwindow-concept that is switchable in three states, and therefore eligible as a representative exampleof CABS. First the concept of Smart Energy Glass is introduced and placed in the context of other in-novative/adaptive window technologies. Following the methodology for modeling and simulation ofCABS, a conceptual model is then derived that describes the adaptive behavior of SEG. The abstrac-tion of the governing physical principles is complemented by conducting laboratory experiments onsmall-scale test samples. The assumptions of this model are validated in a reduced-scale experimentalset-up using prototypes that are implemented in a real façade and exposed to atmospheric conditions.After some refinements, this model is used in a simulation study that is employed to illustrate the ca-pabilities of BPS for performance prediction of CABS. These simulations identify SEG’s energy savingpotential and also demonstrate the role that BPS can play in dealing with trade-offs between energysavings and thermal and visual comfort levels.

1.5 Outline

Figure 1.2 shows a graphical representation of the way this thesis is structured. The approach and find-ings of this study are structured in two distinct parts; the main body of the thesis (numbered chapters),and the CABS overview in the form of Appendix A. In this respect, the term ‘appendix’ might under-state the relevance of the CABS overview. The booklet is believed to be most valuable when it is read inconjunction with the main text, rather than being studied afterwards.

The body of the text is also further subdivided in two main parts. Chapters 2, 3 and 4 deal with CABS ingeneral, whereas Chapters 5, 6 and 7 present the case study of Smart Energy Glass. The outline of thechapters is as follows:

Chapter 2 starts with analyzing the characteristics of CABS and in this way determines the essentialelements that are to be considered in performance prediction of CABS. In Chapter 3, the qualities ofBPS in general and the capabilities of the available tools in particular with regards to performance pre-diction in CABS are specified. Chapter 4 relates the CABS overview to the analysis of BPS’ capabilities

4

1.5. Outline

1 ‐ Introduction

2 ‐ CABS 3 ‐ BPS

4 ‐ Modeling and simulation of CABS

8 ‐ Conclusions

5 ‐ Modeling SEG

6 ‐ Validation

7 ‐ Performance of SEG

A ‐

CABS

Ove

rvie

wCase‐study

Figure 1.2: Structure of the thesis.

as it presents a general methodology to be used for performance prediction in CABS. Chapter 5 startswith building sufficient domain knowledge about Smart Energy Glass and subsequently describes themodeling approach that was used. The credibility of this model’s assumptions is assured in Chapter 6which describes the methodology and outcomes and of the reduced-scale empirical validation studyexposed to atmospheric conditions. Chapter 7 then uses this validated model in an application studythat quantifies CABS’/SEG’s performance benefits in terms of energy saving potential and thermal andvisual comfort. Finally, Chapter 8 concludes with summarizing the findings of the entire study and pro-vides recommendations for future work. This chapter also gives an answer to the question what roleBPS can play in designing buildings with CABS.

5

CH

AP

TE

R

2CLIMATE ADAPTIVE BUILDING SHELLS

2.1 Introduction

Climate adaptive building shells, or CABS, is only one designation for a concept that is described by amultitude of different terms. Figure 2.1 summarizes the variations on the term ‘adaptive’ in this contextfound in literature. Although all these expressions have a somewhat different meaning, they are oftenused interchangeably and in an ad hoc manner. In order to avoid ambiguity, the term CABS is adoptedin this study, and is defined as follows:

A climate adaptive building shell has the ability to repeatedly and reversibly change its func-tions, features or behavior over time in response to changing performance requirements andvariable boundary conditions. By doing this, the building shell effectively seeks to improveoverall building performance in terms of primary energy consumption while maintainingacceptable thermal and visual comfort conditions.

The building shell is interpreted here as those construction elements that form the division betweeninside and outside air. This includes both the opaque and transparent wall elements as well as the roof.

Figure 2.1: Variations on the theme ‘adaptive’.

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2. CLIMATE ADAPTIVE BUILDING SHELLS

Adaptive behavior in internal partitions, floors and foundation is not taken into account. In commonarchitectural practice, the design of the roof frequently plays a subordinate role. Obviously promptedby the fact that for most buildings the roof lies outside the perceptible field of view. Nevertheless, theirsurface area is often substantial compared to façade surface, and in terms of energy exchange, neglect-ing the roof’s impact is unjustifiable. For these reasons, this study is not limited to adaptive façadesonly, but also includes adaptive roof concepts;

Essential in the definition of CABS is the word ‘effectively’. This term implies that performance aspectsshould be deliberately considered in the design of adaptive behavior and not be based on ‘coincidences’.Current façades tend to be developed in layers, resulting in a subdivision of the problems and also a sub-division of only partial solutions (Knaack et al., 2008). As Lichtenberg (2005) puts it, construction hasevolved in a way called ‘innovation by addition’; an approach that leaves room for substantial improve-ments. CABS attempt to reconcile the multiple performance aspects in a more holistic way. Resolvingoccurring problems via the layered approach is not what the concept of CABS aims for. Furthermore,the term ‘effectively’ also implies some kind of of automatic — but not necessarily automated — control.Too many projects have demonstrated that for example adding blinds that are irregularly controlled byoccupants will not fundamentally change the performance picture (Reinhart and Voss, 2003; Selkowitzet al., 2003; Mahdavi et al., 2008).

This chapter continues with a discussion of the performance of the building shell in general, and theperformance benefits of CABS in particular. Next, the characteristics of various CABS concepts are ana-lyzed, which leads to a methodology for classifying CABS, described in Section 2.4. Finally, the chapterconcludes with the results of a more systematic analysis of CABS, which reveals the general trends andpatterns of this broad concept.

2.2 CABS performance

Before the performance of CABS can be specified, it is important to first discuss the performance as-pects of the building shell in general. Hutcheon (1963) presented the following, widely accepted list ofeleven principle functional requirements to be considered in the design process of building envelopes:

Ï control heat flow;

Ï control air flow;

Ï control water vapor flow;

Ï control rain penetration;

Ï control light, solar and other radiation;

Ï control noise;

Ï control fire;

Ï provide strength and rigidity;

Ï be durable;

Ï be aesthetically pleasing;

Ï be economical.

In order to obtain adequate building designs, all these functional requirements need to be consideredand satisfied simultaneously. Because the requirements are strongly interrelated and sometimes evenconflicting, this is not always a simple task (Rivard et al., 1995). The relative importance of the variousfunctional requirements is different for every project and is dictated by the context of the project and

8

2.2. CABS performance

the client’s wishes. Nevertheless, all functional aspects are bounded by certain minimum requirements,typically prescribed in the form of building standards.

The performance of a technology refers to its technical effectiveness in a specific evaluation or a set ofapplications (Kolarevic and Malkawi, 2005). When this definition is projected to ‘building performance’,it implies consideration of both a certain environmental context, and the interests of the various stake-holders involved in the building process. The performance of contemporary building shells is best ex-emplified via the Triple Bottom Line philosophy (Figure 2.2). This business concept stresses that forevery successful technology a right balance needs to be achieved between performance attributes forpeople, planet and profit.

Figure 2.2: Triple Bottom Line principle (Elkington, 1998).

In the ideal case, the building shell is designed in such a way that performance of the whole building,in terms of environmental, economical and social attributes is maximized. The building’s environmenthowever changes over time, and this also applies for occupants’ preferences. By definition, changesthus also occur in building performance, in ways that tend to divert from what was intended. In orderto still be able to provide ‘satisfactory performance’ all year long, buildings need to have some way tocope with these changing conditions.

2.2.1 Robustness vs Flexibility

Robustness and flexibility are the two design strategies which refer to a system’s ability to handle change(Saleh et al., 2009). Robustness is often described as a system’s resistance or immunity to change. It isabout de-sensitizing a system’s performance or quality characteristics to changes in the system’s en-vironment, without eliminating the cause of these changes. Flexible systems on the other hand aredefined to be systems designed to maintain a high level of performance through real-time changes inconfiguration when operating conditions or functional requirements change in a predictable or unpre-dictable way∗ (Olewnik et al., 2004).

Once a building has been constructed, its thermophysical and optical properties are fixed, preventingchanges in the building shell’s state. Therefore these façades are rigid systems that will not be able togive ‘optimal performance’ except for a few time periods during the year. The only way in which con-ventional buildings can be flexibly adapted to changes in functional requirements is via adaptive reuse,or via renovation projects like lateral or vertical extensions, structural alterations and refurbishment(Brand, 1994). For coping with ‘real-time’ changes in its context, conventional buildings have to relyon the concept of robustness. Unfortunately, maintaining high performance in robust building designscomes at a cost. When buildings are left in free-running state, this will lead to uncomfortable or even

∗Application of flexible design does not remove the demand for robust control strategies.

9


unhabitable circumstances during the largest part of the year. The only way to mitigate these unwantedconditions is via energy intensive HVAC and lighting systems, which transform the indoor space in a‘manufactured’ rather than a ‘mediated’ environment (Addington, 2009). And in turn, this leads to higheconomical and ecological loads, thereby disturbing the balance in the Triple Bottom Line approach.

As opposed to the conventional building shell, CABS do have the ability to change their functions, fea-tures or behavior over time, and hence they can take advantage of the concept of flexibility. The fun-damental need for adaptive or flexible systems stems from the presence of multiple performance re-quirements, operating conditions or user’s preferences. Systems engineering has developed a thoroughfoundation with terminology that can be used for understanding the performance benefits of flexi-ble systems, including CABS. The qualities of CABS and the associated performance benefits of beingflexible can be described with three different terms: adaptability, multi-ability and evolvability; theseconcepts are discussed in more detail in the following subsections.

2.2.2 CABS as flexible systems

Adaptability

In systems engineering literature, adaptability is defined as the ability of a system to deliver intendedfunctionality under variable conditions through the design variables changing their physical values overtime (Ferguson et al., 2007). As a consequence, CABS can immediately act in response to changes in am-bient conditions. This offers huge energy saving potential compared to conventional buildings becausethe valuable energy resources in our environment can be actively exploited, but only at times whentheir effects are deemed beneficial. A second energy-related aspect of CABS’ adaptability is the factthat it allows for effectively taking advantage of the inertia of thermal energy stored in the building’sconstruction. In addition to providing energy savings, adaptability is also a useful means for improvingoccupants’ comfort. In pursuit of fully utilizing CABS’ energy saving potential, the operational controlstrategy has to take care that indoor environmental quality does not get compromised. In conventionalbuildings this can only be done by consuming energy in artificial lighting- and HVAC systems. However,because of their adaptivity, CABS can act as climate mediator, negotiating between comfort needs andthe ambient environment. Consequently, building services and the building shell can work in symbiosisto resolve the occurring trade-offs in the best possible way.

Multi-ability

The concept of multi-ability originates from the existence non-simultaneous performance require-ments, or the need to fulfill new roles over time. A key aspect of multi-ability is that the modifiablefunctions are part of the system’s initial functional requirements (Ferguson et al., 2007), and in thisway, it allows for a plurality of optimized states to address change. Multi-ability in CABS is best ex-plained via an example: Bloomframe® balcony can dynamically change its function between windowand balcony on-demand, depending on ambient conditions and users’ wishes (Figure 2.3). By doingthis, multi-ability facilitates resource efficiency, which adds to the list of benefits already mentioned inthe previous paragraph. A second application of multi-ability is spatial versatility. At the same point intime, the properties of the envelope can be different for various positions of the building shell. In thisway, the building shell can react to dissimilar conditions at the different orientations of the building, ordistinct comfort preferences requested by individual users in separate zones.

Evolvability

Future building requirements and circumstances are not always known in advance, or may even beunpredictable. Building shells that can evolve over time are a means of overcoming the uncertainty ofthese unforeseen events. Evolvability, sometimes called survivability (Siddiqi and de Weck, 2008) canbe employed to deal with changes in (i) system objectives, (ii) operating environment or (iii) the systemitself. In CABS, changing system objectives come into play when for example office buildings turn intoschools or apartments into shops. In case of these organizational function changes, existing building

10

2.2. CABS performance

Figure 2.3: Bloomframe® dynamic balcony (Bloomframe, 2010).

services often turn out to be inadequate, and need to be replaced or extended. An example of changingoperating environment is the climate change we are likely to face in the (near) future. Although inten-sity of the effects is still under debate, in traditional buildings it will probably lead to either increasedenergy consumption for cooling, or in case cooling capacity is limited or unavailable, to severe risks foroverheating. Not only requirements and ambient conditions, but also the building and its systems itselfnaturally evolve over time. Two typical examples are weathering of the building envelope and soiling ofducts and filters. Both effects may have negative impacts for the building’s energy balance: the first canlead to increased absorption of solar energy, and the latter is likely to reduce operational efficiency ofthe HVAC system. In all of the three instances mentioned, application of CABS increases the chancesthat the building can continue operation as intended by changing functions, features or behavior, with-out suffering from the potential negative impacts of unforeseen future conditions.

2.2.3 Performance benefits of CABS

Now the performance aspects related to CABS’ flexibility are highlighted, it is worthwhile to return againto the Triple Bottom Line principles. As the previous sections pointed out, CABS can offer positivecontributions to all the three aspects: people, planet and profit.

When CABS are operated effectively, their application holds the potential to result in energy savingscompared to conventional building shells. As this suppresses the need for import of fossil fuel basedenergy, it relieves the load on the earth’s environment (i.e. planet) and also directly influences the end-users’ energy bills (i.e. profit). In addition, a large number of CABS is equipped with building inte-grated renewable energy generation components. Environment and economy also benefit from CABS’material resource efficiency and the fact that CABS facilitate evolvability which will probably lead toincreased building lifetimes.

CABS’ performance benefits for people are typically expressed in terms of improved indoor environ-mental quality. This not only includes thermal comfort, but also occupants’ satisfaction with the lumi-nous environment (visual comfort), acoustics and air quality. Increasing evidence furthermore showsthat comfortable working environments are associated with improved health and productivity levels(Heerwagen, 2000; Haynes, 2008; Wargocki, 2009). Focusing on providing high comfort levels thus di-rectly influences the economic aspect as well.

It should however also be noted that CABS do not merely introduce positive contributions to the TripleBottom Line. Instead of only saving energy, several CABS actually consume electricity for facilitatingthe adaptive behavior. In addition, CABS typically require additional components and subsystems formaking the building shell adaptive. This requires the use of more raw material and also asks for higherinvestment costs. However, when operated in an intelligent way, these drawbacks of CABS are expectedto be off-set by the benefits. These, and several other challenges and barriers that need to be overcomefor successful application of CABS will be discussed further in Section 2.3.4.

Besides the direct performance attributes, CABS can also have impact on more indirect performanceaspects. These aspects are sometimes summarized as being cultural or socio-economic performance.Adaptivity in the building shells offers architects and designers an attractive additional design variable:

11


time. This can have dramatic impacts compared to the traditional aesthetics of a façade, which makesthat building equipped with CABS often results in exceptional or iconic architecture. Eventually, thismight also lead to what is called the ‘Bilbao effect’; after Frank Gehry’s Guggenheim museum in Bilbao,Spain (Jencks, 2005). This expressive landmark transformed a whole city from being in a ‘dilapidated’state to a town with inviting allure, mainly because of its architecture. In the first year after opening, thebuilding attracted 1,3 million extra visitors to Bilbao which brought the city huge financial growth andinternational prestige (Plaza, 2000).

2.3 CABS Analysis

Methodology

For the analysis of CABS, an inductive research approach was used in this thesis. This type of researchtries to retrieve the general characteristics of CABS by studying and observing a large number of indi-vidual examples. A structured scientific literature survey was conducted with the goal of exploring thefull diversity of climate adaptive building shells. For this purpose, numerous pairs of search terms wereused. The first keyword was selected from the terms in Figure 2.1, while the second keyword was alwayspicked from one of the following words: architecture, building, cladding, enclosure, envelope, exterior,façade, glazing, roof, shell, skin, wall or window.

After an initial round of investigations, the conclusion was drawn that CABS is still an immature field ofresearch. The number of existing products, built examples, design proposals and patents published inbooks, journal- and conference papers is limited, and extensive review papers have not yet been pub-lished. Furthermore, it was observed that these references tend to run in circles, with Jean Nouvel’sshutters and the switchable glazings as the most often cited examples. The application area of CABShowever ranges from built examples, successfully operating for many years, to the wildest utopian con-cepts. But because the outcomes of these developments emanate from creative processes, they are farfrom always published in scientific literature.

In order to still be able to obtain a representative number of CABS examples, the area of attention wasextended to other sources of information. The second round of the literature survey also includedboth ‘architecture and design’ magazines (e.g. Architectural Record, Architecture Week, Design Intel-ligence and Metropolis Magazine) and ‘architecture, design and sustainability’ weblogs like ArchDaily,DesignBoom, Dezeen, Greenlineblog, Inhabitat and Wired. Because of CABS’ dynamic nature, CABSalso ideally lend themselves for video clips. Therefore YouTube also turned out to be a valuable sourceof information.

The findings of this literature survey are reported in the following sub-sections. This starts with dis-cussing the four critical elements that constitute the adaptive behavior in CABS: climate, people,timescales and mechanisms. After that, the various concepts, inspirations and backgrounds of theadaptive principles are described. The existing topologies for controlling the adaptive mechanisms andbehavior is the subject of Section 2.3.3. Finally, the challenges and barriers that need to be overcome forgeneral adoption of CABS are highlighted. An approach to the more systematic classification of CABS,and the outcomes of this process are described in Section 2.4. The investigation of CABS also resultedin the CABS overview in the form of Appendix A. Cross-references to this overview are often given in themain text, and are indicated with superscripts, referring to the particular CABS-number.

2.3.1 Adaptive behavior

Climate

The outdoor climate forms a main constituent of CABS’ adaptive behavior, as its name already sug-gests. Climate (from the Greek word klima meaning inclination) describes the long term atmosphericconditions observed at a site. Weather is defined as the individual short term (e.g. hourly or daily)

12

2.3. CABS Analysis

observations of climatic features (Bougdah and Sharples, 2010). Climate has been regarded by archi-tectural practice both as the enemy or as a companion. The first design philosophy tries to eliminatethe impact of the climate on buildings. This is often manifested via highly conditioned buildings withsealed envelopes which makes the building insensitive to its environment, and uncouples the buildingshell from its role as environmental moderator (Addington, 2009). The latter works in close conjunctionwith its environment and takes full advantage of the positive influences found in nature. In this respect,a lot of things can still be learned from vernacular architecture. With its adaptive capabilities, CABSdecidedly belong to the second category.

The most important weather elements of interest in architecture and buildings, together with their cor-responding implications are:

Ï dry bulb air temperature - thermal comfort, heating and cooling;

Ï relative humidity - thermal comfort, condensation, mould growth;

Ï precipitation - drainage, loading, damage;

Ï wind speed and direction - energy, ventilation, comfort, loading;

Ï solar radiation - daylight availability and useful solar heat gains;

Ï cloud cover - diffuse daylight and radiation to the sky.

Variabilities in all of these elements are also actively exploited by CABS. Often these impacts are ‘hidden’in the control algorithms that drive the adaptive behavior. However, there are also some CABS thatdirectly react in response to the changes in for example ambient temperature23,27,28,77,92, intensity ofsolar radiation47,65, rain intensity64, wind speed72,88 or relative humidity84. Additionally, some CABSuse the position and movement of the sun through the sky to optimize the collection or rejection ofsolar energy29,35,49,86,90. Because the sun follows a prescribed path, no complex control algorithms orreactive behavior is necessary. Therefore, the ‘adaptive’ behavior in these CABS rather transforms intoa ‘choreography’ that can be determined in advance.

The perceived weather conditions on a building site are closely related to its geographical location.Numerous climate classifications are in use to map the global climate in different zones. In architecture,typically four different zones are distinguished: cool, moderate, hot-dry and warm-humid (Szokolay,2004; Cook, 1996). Because of their adaptability, most CABS can be applied universally. However, severalCABS are specifically tailored to a certain climate-zone; some CABS are suited for cool climates33,41

while others are more prosperous in warm and sunny weather40,43,81. The orientation of the buildingdetermines to a large extent the actual climatological impacts acting on the building. Some buildingswith CABS actively manipulate these influences by changing their orientation.

Weather forecasts are capable of predicting short-term weather conditions reasonably well. However,these forecasts are always subject to certain confidence bands and occasions of unexpected behavior.That is the reason why anticipating to future weather variables will always retain a certain sense of un-certainty. Even larger unpredictability is associated with expected long-term climate conditions. Thebuilt environment is not only one of the main instigators of the expected climate change; future build-ings also have to withstand the effects of this global warming (Roaf et al., 2005). Because CABS can drawupon the concept of evolvability, the detrimental effects for CABS are probably less severe.

People

The adaptive behavior of CABS is not limited to reaction of changes in exterior climate only. Tuning be-havior in response to variable requirements imposed by people is at least equally important. Providingcomfortable spaces is after all the reason why most buildings are being built. Some of the CABS exam-ined are able to respond to the mere presence of persons05,08. However, in terms of fulfilling comfortrequirements, it makes more sense to adapt in response to occupants’ performance preferences.

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The presence of people influences building performance in two different ways. In the first place it intro-duces contaminants, respiratory products and internal heat gains via metabolic activity and the use ofequipment. In the second place, occupancy dictates the need for comfort. Daylighting needs witnessesto be appreciated and indoor air quality is of secondary concern when nobody inhales the indoor air.Roughly spoken, buildings are uninhabited for about half of the time; dwellings during the day, schools,offices and factories at night. Arrival and departure, and hence occupancy is typically a stochastic pro-cess (Page et al., 2008), although periodic patterns can be discerned. Unexpected behavior and oc-casional events further complicate the prediction of occupancy. Consequently, occupancy and thuscomfort requirements are subject to both high variability and uncertainty which clearly supports thestrive for adaptive rather than rigid building shells.

It is important to take occupants’ requirements into account already in the design-stage of CABS, es-pecially because low-energy architecture is relatively sensitive to the impacts of tenants. Some authorsprefer to use the word ‘participant’ instead of ‘user’ to describe the way people should interact with ar-chitecture (Fox and Kemp, 2009). Occupants’ preferences are typically expressed in terms of setpointsand constraints, preferably in the form of some measurable quantities. Together this results in aggregatecomfort performance indicators (e.g. Predicted Mean Vote (PMV) or Daylight Glare Probability (DGP))or ‘comfort classes’. The actual perception of comfort is however a much more complex process. Ap-preciation of indoor environmental quality in the widest sense involves all our sensory perceptions;these processes are interrelated and take place across various physical domains. The fact that com-fort perception is not merely physically-based, but also ’invisible’ mental processes play a role, furthercomplicates the task of anticipating comfort perception.

Adaptation is not only observed in the building shell, but also plays a role in people’s perception ofthermal comfort. People respond to discomfort in one or both of two ways: either by trying to ad-just their environment to suit their requirements or by adjusting their requirements to match what thebuilding provides (Nicol and Humphreys, 2009). The fundamental assumption of this approach is ex-pressed by the ‘adaptive principle’: “If a change occurs such as to produce discomfort, people alwaysreact in ways which tend to restore their comfort” (Roaf et al., 2005). Three types of adaptive behaviorare distinguished: behavioral adaptation (adjustment), physiological adaptation (acclimatization) andpsychological adaptation (habituation) (Brager and de Dear, 1998). Because of this adaptivity, comfortrequirements can be less tight; which also increases the option space for saving energy while at thesame time supporting favorable comfort conditions.

Timescales

As demonstrated before, buildings are subject to various environmental impacts. All these influencestypically occur at a characteristic temporal resolution, ranging from the order of (sub)seconds to im-pacts that are only perceptible during the building’s whole lifetime. The adaptive actions observedin CABS are either continuous or discrete. Discrete adaptations are suddenly triggered after a certainthreshold-value or period of time has been exceeded, whereas the continuous type of adaptation typi-cally follows gradual transition paths. The following seven characteristic timescales were discerned inthe analysis of CABS.

seconds These short-term fluctuations typically have a stochastic nature. The swift variations in windspeed and direction for example, enable movement in the Wind shaped kinetic pavilion98. CABSthat behave in response to people’s vicinity, like Aperture08 and Living Glass54 are also capable ofchanging their configuration on this short time-horizon.

minutes Cloud cover and daylight availability have a characteristic time in the order of minutes. That’sthe reason why all CABS that aim to optimize daylight utilization and solar shading for reducingenergy demand and increasing visual comfort should be able to alter their degree of transparencyat a rate of change in the order of minutes. Additionally, changes in occupants’ thermal and visualcomfort preferences also typically vary at this temporal resolution.

14

2.3. CABS Analysis

hours The angular movement of the sun through the sky is in principle a continuous process. However,CABS that track the path of the sun35,38, typically adjust in the order of hours. Fluctuations inair temperature, both internal and external can appropriately be discretized in hourly values aswell. CABS that directly adapt in response to temperature stimuli27,28,40 are consequently alsoclassified in this category.

diurnal Presence of occupants in buildings normally follows diurnal patterns. In addition, these day-night cycles are also noticeable in meteorological boundary conditions like intensity of solar ra-diation, and ambient air temperature. Several CABS are specifically designed to take advantage ofthis fixed 24 hour pattern. Examples are: Blight16, Nocturnal ventilator61 and Solar barrel wall79.

seasonal Adapting to changing conditions occurring across the seasons is perhaps the most elegantapplication area of CABS. Winter, spring, summer and autumn all impose widely different bound-ary conditions, and buildings that can adapt to these changes are expected to provide substantialperformance benefits. Allwetterdach07 and Smart Shade77 are examples of CABS that fit in thiscategory.

annual Buildings are also subject to impacts varying from year to year. One can for example thinkof hot summer heat waves, occasional rainy periods or extremely cold winters. Anticipating tothese annual variations is not the major concern of any of the 100 CABS analyzed. In conven-tional buildings however, these unexpected environmental conditions almost always lead to in-creased energy consumption. Because CABS have the ability to adapt to these changing condi-tions, higher energy consumption is likely to be avoided.

decades Impacts that occur at these timescales are surrounded by uncertainty and can impossibly beincorporated in the building’s initial functional requirements. This type of changing long-termconditions can come from the outside (e.g. climate change, changing urban environment) orfrom the inside (e.g. organizational function changes of the building). But changes in the façadeitself are also possible. An example is the weathering or soiling of the envelope which may giverise to a change in the absorption coefficient of the outer surface (Heaberlin, 2004; Berdahl et al.,2008). Because CABS can draw upon the concept of ‘evolvability’, they have a higher change towithstand these long-term changes without performance losses.

Mechanisms

The mechanisms that foster adaptation in CABS can be categorized in two classes. The adaptive be-havior is either based on a change in properties or behavior at the micro-level, or at the macro-level;although combinations are also possible. The distinction between both mechanisms is raised by thespatial resolution at which the adaptive actions take place. Both concepts are described below in moredetail.

Micro-level Adaptive mechanisms on the micro-level can be further subdivided in two groups, fol-lowing the same approach as proposed by Addington and Schodek (2005):

1. If the mechanism affects the internal energy of the material by altering either the material’smolecular structure or microstructure then the input results in a property change of the material.Adaptation in CABS may alter either thermophysical41,78,89,95 or optical properties02,23,32,37,65,75.

2. If the adaptive mechanism changes the energy state† of the material composition, but does notalter the material, then the input results in an exchange of energy from one form to another. InCABS, this feature typically applies to photovoltaic cells which are used in no less than twenty ofthe CABS analyzed. Other examples of energy exchange at the micro-level include: electricity tolight16,21,42,47, and electricity to thermal radiation 01,33.

†An energy state is any identifiable collection of matter that can be described by a single temperature, pressure, density andinternal energy

15


The conditions which force the changing behavior may either be imposed via a direct energy input em-anating from the ambient environment, or a control signal from occupants preferences or supervisoryoperational strategy. Other features of CABS acting on the micro-level are the fact that the number ofcomponents is limited and no moving parts are involved.

Macro-level The second class of adaptive mechanisms consists of adaptations where movement canbe observed with the naked eye. This type of building shells is often referred to as ‘kinetic envelopes’,which implies that there is a certain kind of motion. Adaptation on the macro-level usually results in achange in the building shell’s configuration via moving parts, which can be (i) individual componentssupplemented to the building shell20,29,35,63, (ii) subsystems of the building shell itself08,22,73 as well as(iii) movement of the façade as a whole69,86,96. The types of motion observed vary widely and are typi-cally described by one of the following gerunds: folding, sliding, expanding, creasing, hinging, rolling,inflating, fanning, rotating, flowing, curling, etc.

The most frequently encountered driving principle for the mechanical transformation is still the elec-tromotor. However, the CABS overview also shows some examples of more innovative14,28,54 andsustainable27,40,76 actuation principles. Moreover, the CABS overview includes some examples wherenot only the building shell, but rather the whole building is dynamic25,45,98. Randl (2008) provides anextensive overview of the history of these rotating buildings.

2.3.2 Concepts

The study of CABS is not an isolated concept without background. In fact, it draws its inspiration frommany disciplines in- and outside the world of buildings and architecture. The following sections brieflyreview these concepts and put them in the right perspective in relation to CABS.

Polyvalent wall

“What is needed is an environmental diode, a progressive thermal and spectral switchingdevice, a dynamic interactive multi-capability processor acting as a building skin. The diodeis logically based on the remarkable physical properties of glass, but will have to incorporatea greater range of thermal and visual adaptive performance capabilities in one polyvalentproduct. This environmental diode, a polyvalent wall as the envelope of a building, willremove the distinction between solid and transparent”. ∼ Davies (1981)

Back in 1981, Mike Davies, a British architect working for Richard Rogers’ company proposed the ideafor ‘The polyvalent wall’ as depicted in Figure 2.4. This article is typically renowned for the integrationof several functions into one layer and can be seen as the keynote for research and imagination in inte-grated adaptive building envelopes. Although his imaginary concept for ‘a wall for all seasons’ was wayahead of his time it has served as source of inspiration for many.

Smart materials

Not all materials in the built environment are used as construction elements only. The performativ-ity of some new materials goes further than just being ordinary bricks, concrete, steel or glass. ‘Smartmaterials’ is a relatively new term for materials and products that have changeable properties and areable to reversibly change their thermophysical or optical properties in response to some physical orchemical influence (Ritter, 2007). Apart from only bothering about structural properties, durability andaesthetics, application of smart materials also demand to focus on what we want the material to do.Smart materials are sometimes used as bulk material, but more often finds its application in coatings;the boundary layer of a material which can drastically change total performance (Klooster, 2009). Thefive characteristics that distinguish a smart material from the more traditional materials used in archi-tecture are given in Table 2.1.

16

2.3. CABS Analysis

Figure 2.4: Schematic representation of the polyvalent wall (Davies, 1981).

Table 2.1: The five characteristics of smart materials (Addington and Schodek, 2005).

transiency the response is instigated by more than one environmental stateselectivity the response is discrete and predictableimmediacy the response happens in real-timeself-actuation the response is controlled inside the material or systemdirectness the response acts locally

Because of their dynamic and adaptive nature, smart materials are well-suited for application in CABS.The following ten different smart materials were encountered in the 100 examined CABS-concepts:electrochromic21,32,66, electroluminescent16,78, hygroactive84,97, photochromic65, piezoelectric14,43,87,thermochromic23,92, thermoelectric01 and thermostrictive27,28,40,61,77,97 materials, shape memory al-loys (sma)54,67,76,87 and phase changing materials (pcm)23,41,78,95.

Discussing the ins and outs of all these smart materials in detail lies outside the scope of this thesis.However, it can be observed that most of the smart materials have names that are combinations ofadjectives, mainly derived from the Greek language. The working mechanisms behind each type cantherefore easily be deduced from their respective names.

The concept of smart materials shows great promise, however, their application is still rare. Now theissue arises of how to bypass the ‘gadget-isation’ stage of smart materials (Kula and Ternaux, 2009).As with all other building materials, the following conditions absolutely need to be fulfilled: (i) thecost/benefit ratio needs to be realistic, (ii) the technology has to comply with the existing labor andassembly practices of the building industry, and (iii) the concepts need to be accepted from an aestheticpoint of view (Knaack et al., 2007).

Biomimicry

“Human subtlety will never devise an invention more beautiful, more simple or more directthan nature, because in her inventions, nothing is lacking and nothing is superfluous” ∼Leonardo da Vinci

Some buildings are designed to look like natural organisms (Aldersey-Williams, 2004). Most of timetime, this results in ‘funny’ or iconic buildings, but imitation of organisms’ appearance can also befunctional. Structural engineers have successfully studied phyllotaxis (i.e. the arrangement of leaves

17


in plants) for optimization of their constructions. But what if buildings not only look like their naturalcounterpart, but also behave like living organisms?

Biomimicry from the Greek words bios (life) and mimesis (imitation) is a design philosophy that seeksfor sustainable solutions by emulating nature’s time-tested ideas (Benyus, 1997). Citing John et al.(2005), “the natural world has an immense amount to tell us about how to achieve sustainability. Ituses energy far more efficiently and effectively and is capable of producing materials and structuresthat are far more benign than anything we have achieved in industry”.

Indeed, natural adaptation found in flora and fauna has proven to be a valuable source of inspirationfor the design of CABS (Gruber, 2008; Pedersen Zari, 2007):

Ï The hibiscus flower opens during the day but closes up at night;

Ï A chameleon can change the color of its skin under various environmental circumstances;

Ï Gas exchange through stomata is a function of relative humidity;

A distinct type of biomimicry related to CABS is the concept of tropisms. A tropism is defined as amovement found in nature fostering adaptability to change and occurs over single life-spans. Bothphototropism (i.e. changing in response to light) and heliotropism (i.e. changing in response to thesun), have been effectively transformed to buildings in CABS-concepts enabling to actively collect orreject solar energy (Vermillion, 2002).

A clear parallel exists between CABS and the evolution principles of Darwin: the capacity to survivedepends on the ability to adapt to a changing environment. However, the superposition of biologicalparadigms to architecture is not straightforward, and despite lots of efforts in this cross-disciplinaryresearch area the open question still remains: How to go beyond the metaphor?

CABS versus Passive design

Last decades, the design of low-energy buildings has diverged into two different directions: active tech-nologies and passive design strategies. The term ‘passive’ refers to buildings where the design of theconstruction and form of the building itself, as opposed to its servicing, is mainly responsible for cap-turing, storage and distribution of solar energy, normally with the aim of displacing other fossil fuels forspace heating (Porteous and MacGregor, 2005). Often, this results in very airtight buildings with a highinsulation standard and balanced ventilation with heat recovery. In warm climates on the other hand,passive cooling strategies try to utilize the building design in combination with natural energy availablein the environmental context with the aim of avoiding overheating without using electricity-poweredcooling systems.

In this context, the term ‘active’ is often considered as passive’s counterpart, as it represents the energyintensive HVAC-systems and artificial lighting the passive design tries to circumvent. By definition,CABS are also associated with ‘active technology’. This paradox may lead to the misleading interpreta-tion that CABS and passive design are conflicting concepts. In fact, both CABS and passive buildingsare designed with the same goals in mind; reducing energy demand while maintaining good comfortlevels.

Compared to CABS, passive design offers some distinct advantages. Passive solutions are often derivedform traditional technologies, and therefore they are reliable and easy to implement, which also makesthem relatively cost-effective. In addition, they have no moving parts, there is no need for complex con-trol systems, and no energy is dissipated for facilitating adaptive behavior. On the other hand, potentialdrawbacks are also associated with the passive approach. Passive houses are sometimes accused ofproviding poor indoor air quality and the sealed building envelopes have been linked to negative healthissues. In addition, there is the risk of not always meeting performance requirements, often expressedin terms of overheating. Because the passive approach heavily relies on the concept of robustness, pas-sive designs typically have difficulties to cope with unexpected conditions. Eventually, this has been the

18

2.3. CABS Analysis

motive underlying the search for means of making buildings responsive and dynamic (Wigginton andHarris, 2002).

To conclude, the concepts ‘active’ and ‘passive’ are not contradictory, and both strategies can mutuallyenhance each other. This symbiosis is encountered several times41,70,77,79 in the CABS overview. Passivedesign urges the need to carefully design and tailor the building to local conditions (e.g. site, climate,surroundings), this is also a very good point of departure for the design of CABS. Furthermore, it shouldbe noted that it is not fair to expect CABS to compensate for the deficiencies of poor building designs.

Intelligent buildings

Intelligent buildings are defined as buildings that should be sustainable, healthy, technologically aware,meet the needs of occupants and business and should be flexible and adaptable to deal with change(Clements-Croome, 2009). Because of this last argument, CABS can be part of an intelligent building.However, the realm of intelligent buildings is much wider than that of CABS as it also includes surveil-lance cameras, security and access control, elevators, fire safety alarms, etc.

Nonetheless, several authors have embraced the term ‘intelligent building envelope’ and alike as a sub-stitute for CABS (Aschehoug et al., 2005; Kuo et al., 2009; Ochoa and Capuleto, 2009; Wyckmans, 2005).The word ‘intelligent’ implies some human characteristics that give the building shell the ability tolearn, adjust and respond instinctively to its immediate environment (Wigginton and Harris, 2002). Toachieve this target, all components and subsystems constituting the building shell have to be coupledto a supervisory control system, which may be the building energy management system.

The third skin

People face the same difficulties as the building envelope in the sense they both have to deal with vari-able conditions in their environment (e.g. temperature differences and the impacts of wind, rain andsolar radiation). Human skin has developed several ingenious functionalities to deal with these intrica-cies. Depending on a specific situation, the skin is capable of autonomously changing its (thermoreg-ulatory) functions by e.g. releasing sweat, creating goosebumps, darkening color (tanning), shivering,changing porosity, controlling blood flow (vasoconstriction, vasodilation) or raising hairs (Wiggintonand Harris, 2002). Furthermore the skin also communicates information about physical and emotionalstates.

Our second skin is often conceived as the clothes we wear. By adjusting type and amount of clothing,we actively ‘assist’ our physiology to cope with the negative impacts coming from our environment inorder to sustain the feeling of comfort. Carrying this analogy further, it will probably be no surprise thatthe building shell can be regarded as the third skin (Drake, 2007).

The metaphor has been used in attempts to transform the building shell in a living membrane. Therebymimicking human’s dynamic exchange of energy and matter with the environment, and especially fo-cusing on the skin’s sensitivity, multi-functionality and ability of self-control. Despite various honorableefforts91,97, human skin’s outstanding adaptive qualities are however still unsurpassed.

Nanotechnology

Nanotechnology is a novel area in multidisciplinary materials science, which promises to open up newhorizons in the way our buildings are designed and constructed. The area of nanotechnology is con-cerned with the study, creation, application and manipulation of structures, molecular materials andsystems that are between 1 and 100 nanometer in size (Klooster, 2009). On this molecular scale, forcesand effects that do not play a role on the large scale will become dominant. Recent advances in scanningelectron microscopes have made it possible to see and manipulate matter at this small scale. Hence, weare now able to explore the possibilities of material concepts and composites that cannot be foundin nature. Some of the envisioned concepts include construction materials that are: self-diagnostic,able to communicate, can grow or evolve over time. Leydecker et al. (2008) presents a comprehensive

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overview of nano-applications in architecture and design. In general, the nano-scale products underdevelopment can be divided in two different groups.

The first type currently forms the majority of nano-applications in design and architecture and involvesenhancing the functionalities of (transparent) surfaces (Yeadon, 2008). Currently, several productswith protective nano-coatings are on the market. These coating are for example: anti-bacterial, anti-graffiti, fire-proof, scratch-proof, self-cleaning and abrasion resistant. But also some CABS are basedon materials with enhanced functionalities on the nano-scale; examples are CABS with phase changingmaterials95, and all switchable glazings32,65,92.

The second type of nano-materials aims at developing an entirely new class of materials and productsthat did not exist before. Nanotechnology offers the conceptual possibility to build materials ‘bottomup’. This opens up the possibility to assemble materials or structures on the molecular level, whichenables us to program the overall macro-scale properties of a material (Kula and Ternaux, 2009). Even-tually, nanotechnology may afford us to create revolutionary, ultra-performing materials which exhibitunprecedented behavior. Recent advances of this bottom-up approach of interest in architecture aresuper adhesives, carbon nanotubes and the extremely insulating aerogel. ‘Active window’02, a CABS-concept currently under development at the University of Kassel, Germany is also a technology thatfits into this category. This innovative light control systems integrates nanoscale structures in a macroobject; a window (Klooster, 2009).

2.3.3 Control

Adding adaptive features to the building envelope does not directly guarantee effective or successfuloperation. In order to achieve the desired goals, CABS have to respond to changing conditions in asound way. In addition, some kind of concerted activities have to take place; the various subsystemsin the façade have to cooperate, together and with other building services, to resolve conflicts, handletrade-offs and satisfy performance requirements synergetically and in the best possible way. Two typesof control concepts are distinguished: closed-loop and open-loop control (Figure 2.5). The subtle se-mantic difference between the words ‘automated’ and ‘automatic’ marks the distinction between thesecontrol concepts. As the following sub-sections show, both types of control can work automatically, butonly the first type is automated.

Sensor Processor Actuator

Controller

CABS

CABSdata controlsignal

actioninput input action

Figure 2.5: The two control concepts for CABS: (a) closed-loop control (b) open-loop control.

Closed-loop control

The structure of closed-loop controlled CABS consists of three basic elements: sensors, processors andactuators (Teuffel, 2004). The structure is illustrated in Figure 2.5(a), which also shows a fourth element;the controller which may or may not be present for operation of CABS.

A sensor is a technical component that can register specific physical or chemical ambient condi-tions (e.g. temperature, moisture, pressure, sound, brightness or acceleration). These parameters are

20

2.3. CABS Analysis

recorded by means of physical or chemical effects and invariably transformed into quantities (e.g. elec-trical signals or digital data) that can be interpreted by a processor (Klooster, 2009). Sensors are typicallyclassified according to the kind of stimulus they can detect.

The processor is the component that collects signals and data coming from all sensors and controllers.This computational logic is interpreted and subsequently results in a univocal control signal which ispassed on the the actuator(s).

The third component, the actuator, is a ‘device’ that converts input in the form of a control signal into amechanical, chemical or physical action (Addington and Schodek, 2005). In CABS this can both result ina property change on the micro-level or a kinetic action on the macro-level. Adaptive actions instigat-ing components to move are actuated via various mechanisms, including magnetic switches69, pneu-matic pistons04,05,36,57, shape memory alloys54,67, piezo-electrics devices14, hydraulic cylinders70,86 andelectromotorse.g. 07,17,93,94. Closed-loop controlled adaptations on the micro-level are typically actuatedvia electrical signals.

The distinguishing quality of closed-loop controlled CABS is the ability to take advantage of feedback.Feedback implies that effects of the current configuration (action) can be compared to the desired ef-fect, and if necessary the behavior of the building shell can be adjusted actively. Closed-loop adaptivebehavior can be controlled at two different levels:

Ï distributed, via embedded computation and local control rules in the CABS itself;

Ï driven by a central, supervisory control unit possibly linked to the building energy managementsystem (BEMS) or enhanced by interaction with users‡.

While the first concept can be vital for successful operation of CABS, the second option is the most so-phisticated and complex, but also offers the highest potential benefits. In recent years, a myriad of ad-vanced and intelligent control strategies have been developed for controlling building’s environmentalsystems (Dounis and Caraiscos, 2009). Examples are, multi-agent systems, predictive controllers, andreinforcement learning. Very likely, advances made in these research-areas can also be exploited forcontrolling adaptive behavior in CABS. One of the most promising concepts for CABS is model-basedpredictive control. Because of its ability to ‘think ahead’, intelligent strategies can be devised rather thanonly impulsively responding in real-time to occurring disturbances.

Open-loop control

Open-loop controlled CABS are characterized by the fact that the adaptive capacity is an intrinsic fea-ture of the sub-systems comprising the building shell. The adaptive behavior is automatically triggeredby a certain environmental stimulus which makes that CABS of this type are self-adjusting. This kind ofautonomous control is sometimes also called ‘direct control’ (Fox and Yeh, 1999) because environmen-tal impacts are directly transformed into actions without external decision making component. Onecould also state that the elements in intrinsic CABS-concepts are at the same time both sensor and ac-tuator. The adaptive behavior typically is reversible and repeatable and acts in a predictable fashionwhich makes the concepts reliable.

An advantage compared to CABS with closed-loop control is that these types of systems can immedi-ately influence a building’s climate without expending any fuel or electricity for its operation. In ad-dition, the ‘subtlety’ of the technology is a main asset: the number of components is limited since nocontrol units, processors or wires are necessary.

The main drawback of CABS with open-loop control is the fact that it can only adapt to variations thatwere expected in the design stage. The adaptive features are typically designed by tuning material prop-erties or other system variables to a certain range of conditions. As soon as real-occurring conditions

‡Manual control by occupants is often triggered to counteract discomfort and can be seen as a third entry to this list. Italso includes ‘sensors’, ‘actuators, ‘processors’ and ‘feedback’; not in the form of technical components but by exploiting humanintelligence.

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deviate from design conditions, the intended performance is not guaranteed anymore. A second disad-vantage is related to the self-controllability of open-loop systems. After the building is commissioned,there is no possibility for (manual) intervention. This may pose problems for the way conflicts andtrade-offs are handled and can also cause difficulties for user interaction and the cooperation with otherenvironmental systems in the building.

2.3.4 Barriers and challenges

The principle technologies that facilitate adaptive behavior in CABS are available for some years now,and prospects for the near future are auspicious. However, as e.g. Hoffman and Henn (2008) pointedout, it will require more that just technological development before adaptive and sustainable buildingtechnologies will become standard practice. The inherent economical, social and psychological chal-lenges and barriers that can hamper widespread application of CABS are the main subject of this sec-tion. Several of the hurdles that are listed here can be tackled by using building performance simulation(BPS). The application of BPS in relation to CABS is the main focus of Chapters 3 and 4.

Conflicts and trade-offs

The idea of CABS can not be seen as the panacea that cures all struggles of the building envelope withregards to people, planet and profit. Like every building shell, CABS have to cope with the competitiveor even conflicting requirements imposed by changing user requirements and environmental condi-tions. But fortunately, CABS have the capacity to behave in response to these changes during their op-eration. The open-loop CABS-concepts demand that the strategy for dealing with trade-offs is alreadydeliberately considered in the design phase. Whereas in the closed-loop type of CABS, the sensors andactuators can even effectively adapt their behavior in the operation phase and hence ensure that con-flicts are resolved in the best possible way.

In the analysis of CABS, seven different types of conflicts were distinguished. Each of them is highlightedbelow, and where appropriate illustrated with a typical example:

comfort – comfort Occupants’ comfort is a wide concept which has many facets that need to be con-sidered simultaneously. Opening the slats of Allwetterdach07 presumably improves indoor airquality and reduces the temperature in the zone. But opening the roof is also accompanied withincreased chances for draft problems, noise pollution, irritating smells and insects, privacy losses,or water damage. Before deciding upon opening, all these performance attributes should be care-fully weighed.

energy – energy Buildings are surrounded by various ‘energy fields’. The impacts of adaptive actionson each of these energy field can be different. Since the shades in Dr. Jokisch Building29 track thesun, generation of electricity is maximized. Because this adaptive action also blocks penetrationof valuable solar gains, it is perfectly conceivable that the surplus in electricity is offset by therequired additional energy for space heating and artificial lighting.

comfort – energy The traditional trade-off in operation of building systems is the balance betweencomfort and energy demand. High comfort levels can be achieved, but typically this also requireshigh energy consumption. When the roof of The Sliding House93 opens on a sunny day, energydemand can decrease because of passive solar gains and utilization of daylight. On the contrary,this also introduces a reasonable chance for discomfort in terms of overheating and glare.

among occupants Perception of comfort is influenced by many factors including age, gender, clothing,mood, position in the zone and type of activity. As a consequence, it is quite unlikely that optimalcomfort conditions for the ‘average person’ (whatever that may be) results in satisfaction of allcomfort preferences for all occupants. In the worst case, this individualism can lead to differentsets of requirements that may be misaligned. That’s the reason why CABS’ control systems haveto find the right trade-off between different individual comfort preferences on the one hand, andother performance aspects associated with these conditions on the other hand.

22

2.3. CABS Analysis

short term vs long term Successfully operating CABS guard the balance between short-term interestsand long-term behavior. Yielding to momentary temptations can disturb well-devised energybalances, while temporarily and slightly sacrificing performance may be easily surpassed by ben-efits on the long run. An illustrative example in this respect is the roof pond of the Hammondhouse70: Closing the roof’s lids during the day might relieve thermal comfort conditions in inter-mediate seasons. However, no heat is being accumulated in that way, which eventually may leadto disproportional additional heating energy consumption at night.

frequency of adaptation Although the exact relationships are not yet quantified, it is believed that acertain inverse relation exists between switching frequency and users’ acceptation. A buildingshell that keeps on oscillating between two states is bound to cause great nuisance. A good so-lution is to constrain the rate-of-change to a certain maximum, notwithstanding the fact that insome cases instantaneous action is absolutely necessary .

costs of adaptation The largest share of CABS consume some energy when they are adjusted from onestate to another. The amount of energy required to drive the actuating mechanism is sometimeslarger than the gains associated with the adaptive action. In these situations, it is advisable not tochange and keep the system in its current state.

In order to avoid a decrease in performance, the aforementioned trade-offs and conflicts need to be rec-onciled as good as possible, by making fair compromises in an intelligent way. The various performanceaspects spread across multiple physical domains with each domain having their own performance in-dicators. The optimization of a single factor is unlikely to coincide with the desired response in otherdomains (Addington and Schodek, 2005). Furthermore, the trade-offs that have to be made are difficultto compare in terms of a single metric. Therefore, this requires multi-attribute decision making, whichis not always straightforward and may even be counterintuitive.

While pursuing best possible overall building performance, special attention should be paid for attain-ing well-balanced trade-offs. The chosen control strategy should avoid that a slight improvement inone performance aspect (e.g. thermal comfort) evokes disproportional losses for other performanceaspects (e.g. energy consumption). As a general rule, all performance aspects should always be keptwithin certain acceptable limits or constraints.

Building process

The building industry has the reputation of being a conservative and inert sector. This conception grad-ually arose from the long experience with existing practices, which have proven to work out ‘reasonablywell’. However the high amounts of energy consumed for providing comfort to the built environmentare more or less taken for granted. Moreover, it seems that the peak of optimization has almost beenreached when the building sector keeps persisting in the customary way (Knaack et al., 2008). Thecurrent building sector can be characterized with the following phrases: driven by standardized proce-dures focusing on selection of components and materials, long lifespans which demand high reliability,strong predilection to historical values, prescriptive design methods instead of aiming at performance,dominated by small to medium-sized companies with fragmented responsibilities, thinking in terms ofcubic shapes with vertical walls and straight corners, and finally contracts with a partiality towards firstcosts instead of operational costs (Becker, 2008; Douglass, 2008; Sakamoto et al., 2008; Woudhuysenand Abley, 2004). Summarizing these characteristics, it may be stated that none of them is in favor ofintroducing new technologies like CABS. To enable adoption, the building sector needs to change in amore or less revolutionary way. A thorough understanding of the associated performance benefits ofCABS seems inevitable for this purpose.

Despite of this conservative approach towards innovation, new building designs every time start fromscratch as a one-off process (Woudhuysen and Abley, 2004). A quite remarkable approach, especiallywhen compared with other design-oriented practices like product manufacturing, automotive andaeronautical industry. Although it is considered as inescapable step towards really sustainable building

23


practices, iterative learning via post occupancy evaluations (POE) is not routinely applied (Meir et al.,2009). This leads to a situation where concepts can be overrated based on false assumptions, whereassuccessful designs can remain unnoticed. Consequently, the building sector hardly uses mechanismsby which new advances can be explored, tested and validated.

Some good steps towards this direction are the growing interest in design competitions and the role ofprototypes: These so-called “what-if” projects facilitate involvement, ’help to communicate the un-known’, and may make people think about the ways in which architecture can evolve in the future(Von Stamm, 2008). It follows from the CABS overview that these experimental projects form a largecontribution in the current state of CABS development.

When talking about CABS, people almost automatically start thinking of ‘new buildings’. In addition tonew buildings, CABS also offer a huge advantages in retrofitting projects. However, the constructionsector has to become more aware of this potential. Especially good perspectives exist for the adaptivetechnologies that are embedded in window frames. This makes them easily replaceable and relativelycost-competitive.

Maintenance issues

Repetitive motions are critical elements in CABS; actuators in kinetic CABS systems must be able tomove many times, and often in the same way from hour to hour, day after day. Without proper servic-ing, chances for dysfunctional behavior or breakdown drastically increase. This not only ruins perfor-mance but also reinforces the risk of high reparation costs. Regular maintenance programs can help tomitigate these problems. For successful operation of CABS, these maintenance issues should alreadybe considered during the early stages of the design process. This allows the designer to realize low-maintenance systems that can easily be cleaned, accessed and replaced. A second important aspectrelated to maintenance is the awareness that sufficient budget and resources should be allocated forthe life-cycle implications of maintenance issues. With continuous servicing, the expected lifetime ofthe building is likely to increase. This not only improves return on investment, but is also a significantindicator for sustainability. In spite of these benefits, the significance of maintenance issues is not al-ways acknowledged in design and operation of CABS. The following two examples demonstrate thatproblems with maintenance can even devastate CABS’ performance.

Arab Institute du Monde09 in Paris, designed by Jean Nouvel is a classical example of CABS. With itsphotoreactive shutters, the kinetic façade provides an exceptional means of controlling solar gains anddaylight. Not long after opening however, mechanical defects forced the kinetic apertures to cease mov-ing one after the other (Figure 2.6). And the overhead expenses for fixing these problems were notaccounted for in the budget. At the moment, the shutters still cover the building with an impressiveenclosure, but the intended dynamic features are now completely lost (Meagher et al., 2009).

Another example of a CABS that did not turn out to become a success story because of maintenanceissues is the Beadwall11. The styrofoam beads apparently built up static electricity in their transitthroughout the piping and window panes, and as a consequence, the beads tended to cling to the win-dow. The only solution to this problem was to periodically rinse the beads with glycerin. Besides beingtime-intensive, the glycerine deposited a waxy, unsightly film-coating to the window as an adverse sideeffect. That is the reason why maintenance issues eventually condemned this promising concept tooblivion (Chiras, 2001).

Human aspects

Providing better comfort conditions for occupants is one of CABS’ underlying principles. However,CABS also run the risk to bring about just the opposite effect when not operated properly. The inter-action with occupants should be carefully considered in the design of every building, but this is par-ticularly true for CABS. Since every individual is different, it is practically impossible to simultaneouslysatisfy comfort requirements of all occupants. It is essential that the occupants at least understand why

24

2.3. CABS Analysis

Figure 2.6: Mechanical failures at Arab Institute du Monde, Paris. (Left) broken driving rod, (right) disconnectedlinear actuator.

the façade is taking a specific action. But it would be even better if the façade’s control system offers theoccupants some opportunity for adjustment or manual override. The inability to overrule the system’sdecisions is the most frequently encountered complaint with intelligent building envelopes (Aschehouget al., 2005). Stevens (2001) even reports some cases of sabotage, where the occupants wilfully tried tobeat the behavior of active façades. The best way to avoid counterproductive operation is by payingspecial attention to the design of user interfaces and feedback mechanisms.

Although the importance of considering human factors in design and operation of active building tech-nologies is now widely recognized (Cole and Brown, 2009), there is still a lack of knowledge in this inter-disciplinary research area. This especially relates to the perception of comfort and human interactionwith environmental controls. Some of the open research question include: (i) How do effects of ‘groupdynamics’ affect individual user behavior, and (ii) what are the transient effects of ‘switching’ the build-ing envelope; acceptable rates-of-change remain a matter of conjecture. Fortunately, research effortsare currently underway which attempt to gain better understanding of this matter.

Added complexity

Knaack et al. (2007) reports that increasing complexity is the emerging trend in façade construction.Adding technical solutions to the building envelope has become the indicator of state-of-the-art. Thefight against climate change in the 21st century however is not about solving our problems by puttingmore and more machines into buildings (Roaf et al., 2005). Introducing adaptivity in the building shellundeniably increases complexity, especially when compared to e.g. an ordinary brick wall. CABS aretypical examples of complex systems, which means that the interaction among components of the sys-tem, and the interaction between the system and its environment, are of such a nature that the systemas a whole cannot be fully understood simply by analyzing its components. In essence, there is nothingwrong with complexity; in fact, truly smart architecture has to be complex§ (Senagala, 2005). However,we should avoid that things become complicated.

In integrated CABS-concepts, several performance requirements have to be satisfied simultaneouslyand only with a single configuration, while the system’s own state, its functional requirements and its

§Following the definition that a complex system is a system composed of interconnected parts that as a whole exhibit one ormore properties (behavior among the possible properties) not obvious from the properties of the individual parts.

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environment are constantly changing. The fact that (i) unexpected events can take place, (ii) decisionshave to be made in realtime, and (iii) future conditions are highly uncertain makes the process of controleven more complex. This asks for deliberate control strategies which take all the interrelated aspectsinto account. When one succeeds, it leads to an elegant solution for both energy and comfort. Thismulti-functionality of ‘integrating’ instead of ‘layering’ can thus have lots of positive aspects, but alsointroduces serious disadvantages in case of malfunctioning this is illustrated with the following twoexamples:

1. If the dynamics in adaptive frittng03 fails, not only a particular layer (the shading device), but thewhole glass façade has to be replaced;

2. If the pump in solaroof80 breaks down, than this does not only affect solar shading but is it alsoresponsible for performance losses in other physical domains (thermal insulation).

Increased complexity associated with CABS is sometimes also expressed in terms of ‘buildability’. Thisis directly related to the fact that CABS cannot fall back on many years of experience with proven tech-nology. CABS-projects often involve innovative technologies, resulting in challenging projects with rel-atively high risks. The construction companies have often not yet mastered the cutting-edge technologyor are completely unfamiliar with it. As a consequence, they are wary of taking risks because they fore-see chances for high investment and/or failure costs. Indeed, some kind of trial-and-error seems to beinevitable. However, taking risks also offers opportunities, and as soon as the field of CABS becomesmore mature, these initial struggles are likely to fade away. A more persistent hurdle that needs to beovercome is ‘overestimation of the unknown’. Some designers tend to overlook difficulties —whetherconsciously or not— while optimistically focusing on the advantageous aspects of technology. The po-tential drawbacks are often ignored in marketing discourses, where a more critical attitude would bemore realistic. Some questions that may arise from the CABS overview are: what happens to a liquidfaçade37,57 when it starts freezing, or can Living Glass54 withstand the forces of a heavy storm?

Another important topic in CABS design is the long-term behavior. New technologies can turn out tobe very attractive in experimental settings, when they are only tested for a short period, with expecteduser behavior and under controlled conditions. The question remains whether the concept proves to besuccessful on the long run, when it’s exposed to the intricacies of ‘the real world’. This actual behaviorcan only be assessed after the project has actually been built. Unfortunately, in some cases, things donot always turn out as expected. An iconic example in this respect is the green wall55 of the ParadisePark Children’s Centre is Islington, London (Figure 2.7). Once, this pleasing envelope was an award-winning design, but only three years after completion, it’s thriving glory is now completely perished(BBC, 2009).

Figure 2.7: Green wall of the Paradise Park Children’s Centre, just after completion (left), and three years later(right).

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2.4. CABS Classification

2.4 CABS Classification

2.4.1 Introduction

The CABS-examples we encountered thus far, all originate from individual design efforts, and hencetheir characteristics seem rather isolated and fragmented. Following the arbitrary order of the overviewin Appendix A makes it complicated to discern the common generalities and differences. This was themotive for developing an approach for classification of CABS in a more systematic way. Not with thegoal to obtain statistical ‘evidence’, but the approach should (i) reveal general trends and patterns, (ii)trace current practice and methods, and (iii) identify lacunae and potential problems. This classifica-tion places the variety of CABS in context with each other and thereby discovers the factors that havebeen critical to the early applications of CABS. Its results may further also identify valuable directionsfor future building projects and developments in CABS research.

2.4.2 Methodology

The methodology for classification builds upon the results obtained in the CABS analysis described inSection 2.3. The classification is accomplished in three different ways. Classification A starts by simplyobserving where adaption takes place and in what stage the concept is. In classification B the focus is onthe adaptive behavior and the way in which it is controlled. Finally, classification C is more physicallybased since it groups the CABS according to their relevant physical domains. Each classification step isdescribed below in more detail.

A - Application area of CABS

Adaptive surface Adding adaptive features to the building shell can affect the building’s appearancein various degrees. Figure 2.8 graphically shows the different possibilities. Adaptation can take place ina) opaque wall elements, b) windows or other (semi) transparent wall elements, c) the roof, d) insidethe surface of the façade as a whole, or e) the entire building in three dimensions.

a b c d e

Figure 2.8: Surfaces where adaptation takes place.

State of development Two global categories can be distinguished, CABS in the form of final ‘products’(to be) used in architecture, and CABS that are still in the research, design & development (RD&D)phase. A schematic representation of this classification is given in Figure 2.9. The dashed arrow in thisscheme indicates that concepts that are currently still in the RD&D phase may become more mature intime to eventually become part of real occupied buildings. In Appendix A, this subdivision of CABS isindicated with the respective corresponding color in the pictograms in the bottom-left corner.

The first category consists of buildings that are actually being occupied, or will be in the near future.This category can be further subdivided in two groups. The first are CABS that are representative for orespecially tailored to a specific building (red). In these cases, the architect, city and date of construc-tion are also indicated. The group ‘subsystems and components’ consists of CABS that have been usedin real buildings, but can be applied universally rather than being attributed to one particular build-ing (orange). Then there is a second category of CABS-concepts that did not yet find their way to themarket, but exist in the form of prototypes. Three groups were distinguished here, where (i) the work-ing mechanism is being demonstrated on the full-scale (blue), (ii) the prototype is being tested in a

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CABS

Architecture

Reduced‐scale prototypes

Subsystems and components

Real buildings

Full‐scale protoypes

Virtual prototypes

Research, design & development

Figure 2.9: Classification of CABS in stages of development.

reduced-scale proof-of-concept phase (green), or (iii) the CABS is only an imaginary concept or designthat did not yet left the drawing board (purple).

B - Adaptive behavior and control

Classification of adaptive behavior and control builds further upon the observations made in Sec-tion 2.3. The adaptive behavior can either take place on the micro-level (i.e. changes in material prop-erties) or on the macro-level which means that the building shell changes its configuration via movingparts. A second categorization is the way in which the the adaptive actions are controlled. Some CABSdirectly transform an input into a certain action (open-loop control), while others exploit the possibili-ties of feedback (closed-loop control). In Appendix A, the right classification for each CABS is indicatedin the top-right corner of the page.

C - Relevant physics

Building shells form the division between the ambient environment and indoor zones of a building.Consequently, the building shell is the interface where several physical interactions take place. Everyclimate adaptive building shell influences this multi-physical behavior in its own typical way, by forexample blocking, filtering, converting, collecting, storing or passing through the various energy fields.In order to characterize the differences and similarities in CABS, four domains are distinguished. Thedefinitions of these domains are given in Table 2.2.

Table 2.2: Definitions of the physical domains.

Thermal Adaptation causes changes in the energy balance of the building via conduction, con-vection, (long-wave) radiation and storage of thermal energy.

Optical The adaptive behavior influences occupants’ visual perception via changes in the(semi) transparent surfaces of the building shell.

Air-flow A flow of air across the boundary of the façade is present. Or the adaptive behavior isinfluenced by the direction and speed of the wind.

Electrical All processes where some kind of energy is converted into electricity in the perimeterzone of the building. On the other hand, this domain is also used for CABS whereelectricity consumption is an essential part of its working principle

The impacts of adaptive actions in CABS do not end at the borders of a single domain, in fact mostCABS influence performance in more than one domain. The interdependencies are visualized via thefour-ellipse Venn diagram in Figure 2.10. The relevant physical interactions for every CABS-exampleare indicated via one of the surfaces in this graph. The four different domains and all possible multi-physical overlaps together, results in a number of fifteen different possible combinations.

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Figure 2.10: Physical domains.

Boundaries of the study

A total number of 100 CABS-concepts was included in the analysis. The decision whether a buildingshell was included in the overview or not, was based on the definition of CABS in Section 2.1. As acorollary of this strict definition, a number of building shells with seemingly adaptive characteristicswere not included in the analysis. Some of these building shell concepts are given in Table 2.3, togetherwith their respective main reason for exclusion.

Table 2.3: Concepts not included in CABS analysis.

Concept Reference Reason for exclusion

Lotus effect coatings Leydecker et al. (2008) Does not affect building’s energy balanceIntumiscent coatings Bailey (2009) Not reversible nor repeatableBuilding Integrated PV Prasad and Snow (2005) Active but not adaptiveInsulative shutters Bokel and van der Voorden (2005) Only manual, no automatic controlRetractable stadium roofs¶ Ishii (2000) Providing comfort is not major concernMedia façades‖ Haeusler (2009) Does not effectively improve performance‘Wind motion façade’ Datta et al. (2009) Based on ‘climate’, but not adaptiveFaçade Vertigo∗∗ Cisar (2003) ‘Layered’ and not always ‘effective’

2.4.3 Results

A - Application area of CABS

The results of the classification according to adaptive surfaces and CABS’ state of development areshown in Table 2.4.This table reveals that the contribution of existing CABS-concepts in this analysis is approximately equalto the the number of CABS that are still in the RD&D phase. Moreover, CABS in the prototype-phasetend to focus more on a single surface of the building (either wall, window or roof). Whereas the CABSconcepts that are already found in architecture show a trend towards affecting the façade or total ap-pearance of the building as a whole. Another interesting conclusion is the fact that a significant numberof CABS use the roof of the building as the surface where adaptation takes place. The conception of ‘theroof as fifth façade’ is therefore legitimate.

¶Although they are not included in this study, these roofs offer lots of learning opportunities for CABS‖In this context, the term aims at projection surfaces, display façades and window matrices

∗∗Acts here as an exponent for all kinds of ‘multiple-skin’ façades

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Table 2.4: Adaptive surfaces and state of development.

Real buildings 1 8 17 10 36Subsystems and components 4 7 3 2 16Full-scale prototypes 4 4 5 1 14Reduced-scale prototypes 7 7 1 15Virtual prototypes 8 2 2 6 1 19

24 20 13 31 12

B - Adaptive behavior and control

This section shows the categorization of adaptive mechanisms and the way in which they are controlled.When both groups are combined, this leads to four alternative possibilities. The number of CABS thatfits in each of these categories in indicated in Table 2.5.

Table 2.5: Adaptive behavior and control.

Micro-level Macro-level

Closed-loop control 14 63 77Open-loop control 6 17 23

20 80

Architecture

38

5

5

4

closed-loop | macro-level open-loop | macro-level

closed-loop | micro-level open-loop | micro-level

Research, Design and Development

25

12

9

2

closed-loop | macro-level open-loop | macro-level

closed-loop | micro-level open-loop | micro-level

Figure 2.11: Adaptive behavior and control in current (left) and future CABS (right).

Both adaptive behavior and the way in which adaptation is controlled show a strong trend toward oneof two possible alternatives. The minority is formed by open-loop control or micro-level CABS in theproportion of roughly one to four. When the adaptive behavior is split according to CABS’ state of de-velopment, an interesting trend can be discovered. In the existing CABS-concepts it can be observedthat adaptations on the macro level, in combination with closed loop control is the prevailing methodof adaptation. In the majority of examples, this is manifested via sensor networks that drive servomo-

30


tors as actuator. Future CABS which are now still under development show a tendency toward otheradaptive mechanisms. As Figure 2.11 shows, there is a trend toward both more open-loop controlledCABS, and also CABS with adaptation on the micro-level.

C - Relevant physics

The dispersion of CABS across the physical domains is illustrated in Figure 2.12. These results show thathighest number of CABS are classified in surface E, i.e. a combination of thermal and optical effects.Followed by thermo-optical supplemented with airflow (surface H) and electrical (surface O).

Figure 2.12: Physical domains.

Furthermore, Figure 2.12 shows that the domain ‘Thermal’ is relevant in all CABS-examples. This is adirect consequence of the fact that the building’s thermal environment is constantly changing. Opticaleffects are important in 79 instances. In principle, this are all cases where controlled daylighting is partof the climate adaptive strategy. Airflow needs to be considered in one third of the CABS. And finally, theelectrical domain, mainly in the form of photovoltaics, is encountered in 26 of the 100 adaptive buildingshells.

Figure 2.13 shows the relevant physics for CABS, subdivided according to their state of development.In general, no large discrepancies are found between both charts. However, CABS in the RD&D phasemore often incorporate the electrical domain. In practice, this is expressed via the increasing trend toinclude distributed energy generation systems in the building shell.

Figure 2.13: Physical domains for current (left) and future CABS (right).

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CH

AP

TE

R

3BUILDING PERFORMANCE SIMULATION

3.1 Introduction

Building performance is a product of many interrelated factors, including the building’s shape and ori-entation, building shell design, occupant behavior, weather conditions, building services and controlactions. All these dynamic, non-linear and multi-physical interactions together make that predictingbuilding performance might not be a straightforward task. Equipping buildings with climate adaptivebuilding shells even amplifies the level of complexity. Nevertheless, Chapter 2 introduces the persistentdemand for methods that can quantify CABS’ performance aspects in the design stage. As Table 3.1shows, traditional design tools (e.g. analytic calculations, empirical relationships, selection charts andnomograms) are not capable of satisfying this demand.

Table 3.1: Comparison between conventional design tools and simulation tools (Hensen and Lamberts, 2010).

Traditional design methods Building Performance Simulation

Mono-disciplinary Multi-disciplinarySolution oriented Problem orientedNarrow scope Wide(r) scopeStatic DynamicExtreme conditions All conditions

Building performance simulation (BPS) on the other hand allows the designer to create a virtual modelof a building. By applying different sets of boundary conditions and operational logic, it is possible tostudy the dynamic interactions that affect building performance. These analyses can provide answersto “what-if” questions and can also promote further insight in the cause-and-effect relationships ofbuilding performance. Because of these qualities, BPS holds the potential to also become a valuabletool for performance prediction in the design stage of CABS.

Maver (1980) identified the four basic elements of computer-based building performance predictionas follows: (i) The designer generates a design hypothesis which is input into the computer (repre-sentation); (ii) the computer software calculates the behavior of the hypothesized design and outputsmeasures of cost and other performance indicators (measurements); (iii) the designer then examines

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3. BUILDING PERFORMANCE SIMULATION

this prediction and exercises his value judgement (evaluation); finally, (iv) the designer may decide onappropriate changes to the design (modification).

Three decades later, this four-step approach is still the predominant application of BPS in currentpractice. The potential of BPS is however much higher than only comparing the performance of pro-posed design alternatives. The goal of this chapter is to explore the possibilities of BPS in relation toCABS. Literature provides a brief number of examples were dynamic performance simulation studieshave already been applied in the design and/or development stage of CABS, including: Active BuildingEnvelope01 (Xu and Van Dessel, 2008), Beadwall11 (Engel, 1984), Breathing wall18 (Samuel et al., 2003),Switchable glazing32,65,92 (e.g. Lee and Tavil (2007); Jonsson and Roos (2010)), Heliotrop45 (Wiggintonand Harris, 2002), Green roofs55 (Sailor, 2009), Roof ponds70,74 (Yannas et al., 2006), SolVent82 (Lealet al., 2004) and wall integrated PCM95 (e.g. Heim (2010); Kuznik et al. (2010)). This limited set of exam-ples reveals that the application of BPS in CABS development is still marginal, but also gives reason fora more in-depth analysis of BPS’ potential.

The remainder of this chapter continues with the critical elements that have to be considered in per-formance prediction of CABS and how BPS can play a role in this process. It starts with identifying thequalities of BPS in general related to CABS but also shows the deficiencies and ways to work aroundthese problems. Subsequently, the specific capabilities and limitations of the state-of-the-art BPS toolsin relation to CABS are analyzed. This analysis covers (i) relevant physical domains and their integrationand (ii) the available options to model adaptive behavior. Finally, the chapter discusses some alterna-tive developments that may complement or enrich performance prediction of CABS, now and in thefuture.

3.2 Requirements for performance simulation of CABS

Chapter 2 identified four critical elements that constitute the adaptive behavior in CABS: climate, peo-ple, timescales and adaptive mechanisms. This section deals with the same requirements, now in rela-tion to BPS, and extends the discussion with control of adaptive behavior. By doing this, it indicates thequalities and points out why BPS can become a valuable instrument in design and operation of CABS,but also shows some deficiencies and points for improvement.

Climate

One of the prerequisites for successful modeling and simulation of CABS is using an appropriate setof weather data for the problem at hand. In most BPS applications, typical meteorological year (TMY)weather-files are used for supplying these boundary conditions. These files contain full-year time-seriesof mean hourly values for all relevant weather variables at a specific location. The original data is de-rived from long-term weather observations collected from nearby weather stations, typically at airports(Barnaby and Crawley, 2010).

Traditional applications of BPS using TMY-files are typically employed for the purpose of comparativestudies, and therefore the need for absolute accuracy is limited. In addition, the potential effects offalse predictions are compensated by energy consumption in oversized HVAC systems. This contrastswith CABS, whose actual operation is closely related to real occurring weather conditions at a site. Be-cause actual weather conditions are likely to deviate from the original design conditions, the risk existsthat buildings with CABS are not able to achieve desired performance levels. The appropriateness ofapplying TMY-files for performance prediction of CABS is debatable because of the following reasons:

Ï Most buildings are not situated in direct vicinity to the respective meteorological station. How-ever in some areas, a distance of say 100km may introduce structural discrepancies in observedweather conditions (Cook, 1996). Moreover, longitude and latitude, and hence solar geometry arenot the same, which particularly affects performance of sun tracking CABS-concepts.

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3.2. Requirements for performance simulation of CABS

Ï CABS’ adaptive actions occur at various timescales. In order to accurately predict the perfor-mance of CABS there is a need for high-resolution weather data, where the length of one time-stepis of the same order or smaller than the time constant of the relationships being studied. TMY-files consist of averaged or integrated hourly approximations of real-weather conditions whichcan lead to inadequate results. Other implications of the different timescales in performance pre-diction of CABS are further discussed in the Section ‘Timescales’.

Ï Data for TMY-files is typically collected in open field conditions (e.g. airports) whereas buildingsusually are situated in (dense) urban contexts. Consequently, this urban environment creates amicro-climate around the building with implications for e.g. temperature, solar radiation andwind speed and direction that are not represented in TMY-files. Especially open-loop CABS di-rectly adapt in response to the site-specific environmental conditions. This introduces errors inperformance predictions due to discrepancies between boundary conditions in the model andthe real world situation. One way to solve this problem is to perform on-site measurementsthat are directly supplied to the BPS tool. In cases where this detailed information is not avail-able, it is possible to take advantage of model-based approaches. One possible technique usessimplified analytical models based on empirical relationships to create local weather data (Erelland Williamson, 2006), other approaches directly modify existing weather files, based on first-principle models (Bueno Unzeta et al., 2009) or detailed CFD-studies (Yi and Malkawi, 2008).

Ï The weather data constituting TMY-files is intended to be ‘as average as possible’. Real-worldCABS however, also have to deliver adequate performance under non-typical conditions. Threesolutions exist to work around this problem: (i) ‘manually’ alter the values in the weather files torepresent (more) extreme conditions, (ii) use design-day weather files to assess performance un-der the worst-case scenario’s or (iii) run simulations with multiple-year weather files to increasethe number of different situations.

Ï Although it might seem trivial, it is important that all essential weather variables are included inthe analysis of CABS. However, not all standard weather files do include all the relevant aspects.Typical omissions are precipitation data (e.g. important in Phantom house64, but also for theopportunity to use natural ventilation), effective sky temperature for modeling long-wave radiantheat exchange (e.g. important in roofponds70,74 and solaroof80) and snow cover (e.g. importantfor daylight reflections) (Hensen, 1999).

People

In building energy simulations, the impacts of people are typically expressed via deterministic esti-mated occupancy schedules combined with average metabolic rates and typical use of equipment. Thisapproach is a rather simplistic representation of reality, because actual occupancy and metabolism aresubject to both fluctuations and change (Mahdavi et al., 2008). Recent studies have demonstrated thevalidity of simple but effective Markov-chain based, stochastic models for occupant presence in offices(Page et al., 2008) and synthetically derived probabilistic occupancy and electricity load profiles in thedomestic sector (Richardson et al., 2008; Widén and Wäckelgård, 2010). In addition, efforts have beengoing on to include the more sophisticated occupancy models into BPS tools (Bourgeois et al., 2006;Hoes et al., 2009).

Presence of people also introduces the need for providing comfort. In current use of BPS, comfort con-siderations are mainly used for determination of heating and cooling system’s capacity and energy de-mand. Combinations of setpoints and system layouts result in a set of state variables (e.g. air temper-ature, surface temperatures, luminance etc.) which are then transformed into aggregate thermal andvisual comfort indicators (e.g. predicted percentage of dissatisfied (PPD) or useful daylight illuminance(UDI)) that have to stay within certain bandwidths. Although not yet routinely applied, it is well con-ceivable that these state variables and/or comfort indicators can actually be used to drive the adaptivebehavior in building simulations.

35


In buildings with CABS, people may also manually influence adaptive behavior in order to (i) achievedesired indoor environmental conditions, or (ii) counteract discomfort. Prediction of these actions re-quires deep fundamental knowledge of comfort perception and associated behavioral responses whichcan only be obtained via extensive field surveys (Haldi and Robinson, 2008). More additional work isrequired for gaining better understanding of individual users’ adaptive actions and group dynamics inrelation to CABS. But in this respect, the capabilities of current BPS tools will likely not be a restrictingfactor for modeling this behavior.

Timescales

The traditional application of BPS computes full-year datasets containing 8760 hourly values per statevariable. This approach has turned out to provide sufficient accuracy for thermal building energy sim-ulation and will probably also provide adequate results for CABS’ hourly and diurnal cycles. However,as Section 2.3.1 points out, adaptation in CABS can also take place at considerable shorter and longertimescales. Successful simulation of CABS has to acknowledge these time-cycles by taking them intoaccount in the models.

Performance simulations at short time-steps do in essence not pose new problems, as long as the ap-propriate sub-hourly boundary conditions (people and weather) are available. Most BPS tools tendto neglect the short-term dynamics of daylight, however the stochastic Skartveit-Olseth model imple-mented by Walkenhorst et al. (2000) provides a proper solution.

In order to make fair comparisons of CABS that behave in response to seasonal variations, it is requiredto perform simulations on an annual basis. In case of integrated high-resolution simulation approaches(e.g. using CFD or ray-tracing), this may raise problems with regards to computation speed and canresult in excessive run-times. Three solutions have been proposed to by-pass this problem: (i) sacri-ficing level-of-detail (with or without loss of accuracy) for increasing computation speed (Reinhart andWalkenhorst, 2001; Zuo and Chen, 2010); (ii) using the full-fledged simulation only for a certain numberof extreme conditions, not for whole-year simulations; and (iii) via external coupling of simulators, i.e.only occasionally invoking the detailed simulator (e.g. every nth time-step) during the simulation (Zhaiet al., 2002).

The last group of timescales encountered in the analysis of CABS are the long-term effects. The existingworking methods in BPS can handle these implications fairly well. It typically requires simulation-runsof multiple scenario’s with dissimilar boundary conditions. For assessing the impacts of future climatechange on CABS, it is possible to take advantage of the work by e.g. Guan (2009) and Jentsch et al.(2008). Furthermore, the long-term effects that are subject to unexpected behavior are the place whereuncertainty analysis can play an important role.

Adaptive mechanisms

In traditional BPS applications, the design of the building shell is represented by (i) size, geometry andorientation of the various faces and (ii) the associated material properties, via either selecting layersof materials from an existing database, or actually entering the appropriate thermophysical and op-tical properties. As opposed to the dynamics of boundary conditions, these material properties arehard-coded as constants that are fixed throughout the simulation in the majority of BPS applications.Adaptivity in material properties has not been part of the initial fundamentals in the development ofmost BPS tools which makes that the available adaptive mechanisms for micro-level CABS are limitedin off-the-shelf BPS tools. Additionally, the number of available actuating mechanisms that can aptlymodel macro-level CABS is also restricted. Two notable exceptions are formed by shading devices andwindow openings which in several tools can be modeled and controlled in a dynamic way. In general,the lack of capabilities to model these adaptive mechanisms may turn out to be the major obstacle forsuccessful performance prediction of CABS. For this reason, Section 3.6 presents an in-depth review ofthe adaptive mechanisms available in state-of-the-art BPS tools.

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3.3. Capabilities of BPS tools

Control of adaptation

As Section 2.3.3 already points out, there are two different concepts for controlling adaptive behaviorin CABS; open-loop and closed-loop control. In practice however, this control can range from straight-forward boolean logic to highly complex multiple-input multiple-output strategies possibly in combi-nation with user interactions or weather forecasts. In terms of modeling this adaptive behavior in BPS,control algorithms of CABS must always based on the values and interpretation of certain quantifiableconditions in the building’s internal or external environment. One of the common outputs of dynamicbuilding simulations are time-series of all the building’s state-variables. Together with weather files andoccupancy, this allows us to ‘sense’ all possible relevant variables and accordingly decide upon the rightaction. The specific response to a certain environmental state is characteristic for each CABS, and herethe type of control (i.e. open-loop or closed-loop) plays a leading role.

BPS tools are typically developed with design applications in mind instead of the purpose of control.This imposes some restrictions on the possibilities to represent adaptive behavior in CABS during run-time. The situation where state-variables are substitute for actual sensor-output is for example a ratheridealized representation of reality. Unfortunately, BPS tools are not (yet) equipped with features thatmodel sensors in a more realistic way. In order to increase credibility, future BPS tools should also in-clude e.g. delays, hysteresis, probability of detection, range, coverage and the actual position of thesensor.

3.3 Capabilities of BPS tools

Introduction

The previous section presented the requirements for performance prediction of CABS in a general tool-independent way. In practice, a plethora of different BPS tools is being used for performance predictionin building design and operation with each tool having its own characteristics and possibilities. Usersof BPS ought so select the tool that best suits their needs and manage the job with the capabilitiesthat tool offers. The rational decision process may be influenced by various other factors, including:previous experience with the tool, purchasing costs or availability within the organization, requireddomain knowledge, support facilities and the presence of a convenient user-interface.

The U.S. department of energy (DOE) maintains a database with information about building softwaretools for evaluating energy efficiency, renewable energy and sustainability in buildings (DOE, 2010). Atthe moment of writing this thesis, this Building Energy Software Tools Directory contains 382 entries.The nature of the tools listed in the directory varies widely. Usually, BPS tools are classified in terms ofaspects, resolution and scope:

Aspects By definition, all simulation tools make use of simplified models to represent reality. Most BPStools are primarily oriented and specialized towards one single aspect of the building’s perfor-mance while neglecting the impacts in other physical domains. Examples include Radiance andDIALux for (day)lighting design; COMIS and CONTAM for airflow simulation; and PVsyst andPV*SOL for photovoltaic systems. On the other end of the spectrum are the integrated whole-building performance simulation tools. These types of tools predict all aspects of building’s per-formance via a comprehensive integrated modeling approach. The physical impacts in buildingsspan across multiple physical domains, which often necessitates that the multi-physical behavioris analyzed simultaneously. The subject of how to deal with performance prediction of CABS inmultiple physical domains is discussed in more detail in Section 3.5

Resolution The physical interactions in the built environment take place at various levels of detail,both in terms of space and time. In the current classification of BPS tools, usually five differentspatial levels are distinguished. From macro to micro, the tools are able to predict performancevia (1) urban-level simulations, (2) whole building performance simulation, (3) component-based simulations (e.g. fenestration and HVAC-systems), (4) simulation of local effects (e.g. ther-mal bridges) and (5) simulation of small-scale effects (e.g. heat, air and moisture transfer inside

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building slabs). Buildings are thus subject to impacts at multiple spatial resolutions but these im-pacts also take place at various timescales. The BPS tools can assume (1) steady-state behavior,(ii) integrated (annual) evaluations or (iii) explicitly model dynamic behavior at successive time-steps. When choosing a simulation tool for a given task at hand, one should always check that thespatial and temporal resolution of the tool matches with the typical characteristics of the problemto be investigated.

Scope The scope of a BPS tool is closely related to the objective of the simulation task (e.g. feasibility,design, analysis, visualization, optimization). In current practice, the majority of simulation toolsis used in the buildings’ detailed design stage. These tools have facilities for code compliancechecking, and are suitable for comparing alternative design variants or even for optimizing thebuilding design. Several attempts have been made to increase the value of BPS by making it anactive design tool that can be used in an interactive way. Specialized conceptual design toolshave been developed that can deal with the abstract information available in the building’s initialdesign stages. In addition, some research projects are currently going on where BPS is used in thepost-construction phase, e.g. for commissioning, fault detection & diagnosis (FDD) and model-based control (Claridge, 2010; Henze and Neumann, 2010). Since the scope of the simulation canhave such a large variance, it also influences the tool’s capabilities to a large degree.

Every BPS tool can thus be characterized by the place it takes in the “three-dimensional space” con-strained by aspects, resolution and scope. All of these criteria however do not give explicit indicationsabout a tool’s capability to predict the performance of adaptive behavior in CABS. The next sectionprovides the methodology that was used to clarify these abilities.

Methodology

In a recently published report, Crawley et al. (2008) contrasted the capabilities of twenty differentwhole-building energy performance simulation tools. The publication contains comparative informa-tion about many aspects of BPS but does not disclose much about capabilities for simulation of CABS.Therefore, this chapter continues with analyzing the capabilities of the various BPS tools with respectto CABS. The information that was used in this analysis comes from various different sources. Theprocess started with reviewing the user manuals, software-tutorials and ‘help’-facilities of commercialand academic BPS tools. These documents however typically deal with the basics of BPS and con-sequently do not provide many clues for simulation of CABS. More advanced uses of BPS are found inresearch settings. Results of these efforts are usually published in scientific journals and dissertations orpresented at conferences like the International Building Performance Simulation Association (IBPSA)conferences. Furthermore, users of BPS tools form a worldwide community where ideas are sharedand information is interchanged via mailing lists. The online archives of these mailing lists togetherform a wealth of information, including questions and answers to the most sophisticated modelingapproaches and non-standard uses of the various tools.

It would be impracticable and precipitate to assess the simulation possibilities for all CABS-concepts(> 100) in all available BPS tools (> 380). Appendix A shows that the CABS-concepts can be classifiedaccording to their relevant physical domain(s). The application areas of the BPS tools tends to followthe same logic, as indicated in Figure 3.1. Section 3.4 presents the classification of BPS tools, and in thisway simplifies the analysis because in general, tools in each physical domain share the same qualitiesand characteristics. Appendix A further shows that effects of CABS typically span across more than onephysical domain. Therefore Section 3.5 pays attention to the strategies for performance evaluation ofthis multi-physical behavior in an integrated way. Subsequently, the analysis of adaptive features in thevarious tools is summarized in Section 3.6.

3.4 Physical domains

The work by Crawley et al. (2008) focuses on the capabilities of BPS tools for predicting energy perfor-mance of buildings, a process sometimes also called building energy simulation (BES). In this thesis,

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3.4. Physical domains

Figure 3.1: Overview of BPS tools and their physical domains.

the scope is wider as it also includes simulation tools for performance prediction of daylighting/visualcomfort, indoor air quality and issues related to electricity. Here, the tools are classified following thesame logic as the classification of CABS in Table 2.2. However, during this analysis it was discoveredthat a significant number of tools specifically deals with the interplay between the opaque and trans-parent elements of the building shell. For this reason, the analysis in this section is extended with a fifthcategory — Thermal and Optical — which is equivalent to surface E in Figure 3.1.

A - Thermal

Prediction of thermal effects in buildings is what has usually been regarded as the typical applicationof BPS. Some authors prefer to use ‘BES’, but here the word ‘Thermal’ is retained in order to avoid con-fusion with other energy related aspects of CABS. These tools simultaneously evaluate the transientenergy flowpaths of conduction, convection and radiation across the building envelope and also takecasual gains and HVAC systems into account. Thermal BPS tools do include the effects of visible short-wave solar radiation, but often do not consider the effects of visual perception. The same is true forinfiltration and ventilation; the air volumes associated with these processes and the impacts it has onthe buildings energy balance are considered. But because buildings are typically divided into ‘zones’that are assumed to be well-mixed, it is not possible to assess the impacts of airflow inside the zones.

Typical outputs of thermal BPS tools are e.g. air temperature, surface temperatures, thermal comfortindicators, and heating and cooling energy demand. These results are used to prove compliance withcomfort standards and energy regulations and for comparison of proposed design alternatives. Someof the most popular tools are: ApacheSim/IES-ve, DesignBuilder, DOE-2, EnergyPlus, eQUEST, ESP-r,IDA-ICE, TAS, TRACE, TRNSYS type 56 and VABI 114. But as will be demonstrated in the followingsubsections, the capabilities of some of these tools extend further than the thermal domain only.

B - Optical

For performance prediction of CABS with transparent parts, it is important that the collection, admis-sion and distribution of daylight and the interplay with artificial lighting are considered simultaneously.Too low daylight levels result in (i) gloomy spaces, (ii) high electricity demand and internal heat produc-tion via artificial lighting and (iii) limited views to the outside. Too high levels on the other hand canresult in comfort problems associated with glare and increased cooling loads caused by excessive solarheat gains. Both intensity and spatial distribution of daylight is extremely dynamic throughout the year,which has led to the conception that well-daylit spaces can only be designed by using computationalsimulation (Rogers, 2007). In addition, daylight simulation promotes further insight in operational per-

39


formance conflicts, and may help to devise intelligent control strategies for adaptive behavior that canhelp to achieve balanced trade-offs.

Daylight simulation engines first combine a sky model with a scene (i.e three-dimensional geometricmodel with optical material descriptions for all surfaces) and then calculate illuminances and/or lu-minances profiles at sensor-points within the scene (Reinhart, 2010). The development of simulationengines has diverged into two directions: Ray-tracing and Radiosity. Table 3.2 summarizes the charac-teristics and qualities of both approaches.

Table 3.2: Comparison between classical ray-tracing and classical radiosity methods (Geebelen, 2003).

Ray-tracing Radiosity

View-dependent View-independentHandles specular behavior best Handles diffuse behavior bestHandles any geometry Performs best with facetted shapesCan handle transparency Performs best with opaque surfaceDoes not compute overall light distribution Does compute overall light distributionDifficulties with indirect lighting Indirect lighting is treated correctly

Daylight simulations have traditionally been used for performance prediction under static conditions— an approach that only gives information about daylight at a single point in time. The most widelyused tool is Radiance (Reinhart and Fitz, 2006), not only for performance studies, but also for the pur-pose of visualization. In recent years, there is a shift towards dynamic daylight simulations, based onthe Daylight Coefficient (DC) method which is a favorable development for aiding design of CABS . Thisapproach results in annual illuminance profiles that can be used to predict daylight contributions andelectric lighting energy use, and can be combined with a thermal simulation program to predict overallenergy use for heating, lighting and cooling (Reinhart, 2010). When used in this way, dynamic daylightsimulations offer good perspectives for performance evaluation of CABS. Several climate-based day-lighting metrics that satisfy both visual comfort and energy demand are currently being tested. Theseperformance indicators consider the quantity and character of daily and seasonal variations of daylightfor a given building site. However, there is still a need for establishing the proper benchmarks (Mardal-jevic et al., 2009).

C - Airflow

Airflow in buildings serves two purposes: it helps in maintaining acceptable indoor air quality and alsoinfluences the energy balance to effect heating and cooling. For typical engineering approximationsand initial design studies, it is often sufficient to use scheduled bulk flows. Some applications of CABShowever demand for a more accurate prediction of airflow that accounts for flow-effects across theenvelope driven by temperature differences and wind characteristics. Several modeling approachesexist for this purpose, at various complexities and levels of detail (Chen, 2009).

The most rigorous approach is to use computational fluid dynamics (CFD) to numerically solve the gov-erning Navier-Stokes equations. This results in detailed information about pressure, contaminant con-centration, airspeed and temperature at every point in space. CFD-software like Flovent, Fluent/Airpakand Phoenics has been extensively used in many building applications such as (natural) ventilation,thermal comfort, indoor air quality, and fire and smoke safety studies (Lu et al., 2009). Application ofCFD however introduces some drawbacks. Since no generally accepted modeling guidelines are avail-able users have to take care of cost- and time-intensive validation and verification studies especiallywith regards to turbulence modeling, wall-functions and grid sensitivity. In addition, significant do-main expertise is required in order to manage uncertainties in the simulation process. Finally, CFD iscomputationally very demanding which may prevent dynamic performance prediction of airflows inbuildings that is required to predict performance of CABS.

40

3.4. Physical domains

A second approach that offers less detailed information but that is potentially more valuable for CABS-design are airflow networks (AFN). This method calculates airflow and contaminant transport betweenthe zones of a building and between the building and outside air on the basis of pressure differences.Since AFN is based on some simplifications (e.g. neglecting momentum effects and assuming uniformair temperature in a zone) the method is fast enough to be used for whole year ventilation performanceprediction. AFN tools may be either stand-alone applications (e.g. COMIS and CONTAM) or coupledto thermal solvers as is the case in EnergyPlus, ESP-r, IES-ve and TRNSYS. Unfortunately, AFN does notcalculate air velocity inside zones which is an important parameter for evaluating local thermal comfort(van Treeck, 2010).

A third approach makes use of semi-empirical relations and results of analytical models. The methodestablishes case-specific bulk ventilation airflow rates through different types of openings as a functionof temperature and wind conditions. The methods can be used in a stand-alone manner, but are alsovaluable for specifying boundary conditions in thermal BPS tools, e.g. via the use of equations-type inTRNSYS. Although this method incorporates many assumptions, it is an improvement with respect toscheduled bulk-airflow, and is also effective for modeling infiltration, and allows for easy integration ofadaptive features.

D - Electric

Performance simulation of electrical systems in the built environment requires consideration of gener-ation, distribution, storage and consumption of electricity. Distributed electricity generation via solarcells is encountered numerous times in the CABS overview. Various software packages exist for plan-ning and development of such photovoltaic systems. These tools can be used for e.g. pre-feasibilityassessment, system sizing, simulation of operation and economic assessment studies. The capabili-ties and typical application areas of the various available tools are summarized by Turcotte et al. (2001)and Šúri (2005). Unfortunately, these tools are only used for ‘stand-alone’ configurations, and do notcapture the mutual interactions with the building’s energy balance.

Most of the BPS tools do also claim to support facilities for simulation of building-related electrical sys-tems. Yet, in most cases this capability is only restricted to scheduled building power loads (Crawleyet al., 2008). However, some tools offer the possibility to perform predictions of building integratedphotovoltaic (BIPV) system performance at various levels of detail. Successful integration of embeddedenergy systems in the built environment requires careful matching of both supply and demand. Inte-grated power flow modeling in BPS can capture the transient electrical effects of decentralized energysystems like BIPV, (ducted) wind turbines, micro-cogeneration, fuel cells, batteries, artificial lighting,etc. In this respect, the models in TRNSYS and ESP-r are the best equipped to serve this purpose. The li-braries in TRNSYS offer various possibilities to simulate and couple electrical components to the build-ing model. In the case of ESP-r, the power flow modeling facilities are fully integrated with the buildingmodel and other subsystems (Kelly, 1998).

E - Thermal and optical

Most BES tools emanate from attempts to help engineers to realistically size HVAC equipment in build-ings. This has led to some simplifications which in turn make that these tools experience difficultiesto assist designers in the actual building design process. In response to this, several tools have beendeveloped in recent years which are specifically attuned to allow for more insights in the intricacies ofthe design of building shells. Such tools are able to simultaneously address thermal and visual comfortrequirements together with the associated energy demand for heating, cooling and lighting. These toolsalso mark the shift towards increased use of BPS for assistance in making informed design decisions inearlier stages of the design process. In this section, three of such BPS tools are discussed. Each of themclaims to support facilities for simulation of ‘advanced’, ‘intelligent’ or ‘climate-responsive’ buildingshell concepts. Here their applicability with respect to ‘climate adaptive’ building shells is highlighted.

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The MIT Design Advisor (DesignAdvisor, 2008) is a fast and simple design-tool that is especially in-tended for improving energy performance in the first hours of the building design process. This web-based tool is specifically developed for architects and building designers, and allows comparing de-sign alternatives side-by-side (Urban and Glicksman, 2006). MIT Design Advisor was introduced byLehar and Glicksman (2003) as “a simulation tool for the optimization of advanced façades". The web-interface enables users to choose from eight different pre-defined façade typologies. Among these aretwo types of double-skin façades with internal airflows and movable shading devices. It is however notpossible to influence control of adaptive behavior or dynamic properties of these building shells whichmakes the tool inappropriate as a design aid for CABS.

NewFacades is a simulation tool “that helps pass from ideas to significant concepts in the design ofintelligent façades” (Ochoa and Capuleto, 2009). It basically is a shell around EnergyPlus that automat-ically generates and compares the performance of design alternatives. In this way, the tool explicitlymodels the thermal and visual comfort levels and energy demand of various building shell concepts onthe basis of generic, high-level input data. The ‘intelligent’ features that are considered are howeverrestricted to lightshelfs, traditional shading devices and controlled night ventilation.

The main objective of DAYLIGHT 1-2-3 is “to help design professionals with interest in — but no ex-pert knowledge of — daylighting to develop climate-responsive daylighting design concepts”. Integratedthermal and visual performance predictions are performed via a customized version of ESP-r linkedto Radiance-based Dynamic Daylight Simulations (DDS) and the sub-hourly occupancy-based controlmodel (Reinhart et al., 2007). The approach that DAYLIGHT 1-2-3 uses seems to hold great promisefor CABS as it satisfies most of the requirements for performance simulation of CABS presented in Sec-tion 3.2. The tool relates an intuitive user interface via quick but reliable simulation runs to a com-prehensive set of output result. Currently, the only way to introduce adaptivity to the building shellmodel is via predefined sets of window-blind combinations that are controlled automatically or via lat-est generation occupancy control models (SHOCC) (Bourgeois et al., 2006). Nevertheless, it would berecommendable to investigate the possibilities of expanding the tool with more adaptive mechanisms.

3.5 Coupling of physical domains

In order to assess building performance as a whole, impacts form the various physical domains pre-sented in the previous section have to be interpreted simultaneously. This issue is no novelty that comeswith introduction of CABS, but needs to be considered in in every building design. Several methodsfor multiple domain performance simulation have been discussed elsewhere (Citherlet, 2001; Hensenet al., 2004; Malkawi, 2004; Wang and Beausoleil-Morrison, 2009). This section summarizes these con-cepts and places them in the context of CABS.

Combination of stand-alone tools

The relevant physics in CABS happens to take place in four different domains. Since each domain de-mands for specific algorithms to predict performance, this has led to the development of monolithicdomain-specific simulation tools. With a combination of stand-alone tools, CABS full performance canbe assessed in parallel, with the reservation that each domain is treated in an isolated way. This ap-proach may be most pragmatic in some cases but also introduces several drawbacks. Because there isno coupling between the domains, the interrelated aspects are not captured which prevents a simulta-neous, integrated performance assessment. Since there is no communication between tools, the iso-lated tools are not aware of each other’s adaptive behavior. An illustrative example showing this short-coming is a stand-alone daylight simulation which has no means for changing degree of transparencyin response to e.g. air temperature.

The stand-alone approach will further also give rise to data redundancy and to potential inconsistenciesbetween the different building models that are developed separately. This last problem can be obviatedby using data model interoperation in which multiple tools either (i) share or (ii) exchange (parts of) thebuilding model. Examples of the first type are the Integrated Data Model (ModelIT) in IES-ve and the

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3.6. Adaptive features

Uniform Environment in VABI. The second type works with neutral file formats like IFC or XML. In someapplications of CABS there is no reason for objection of this simple combination of separated tools, butin other cases integrated performance assessments are inevitable. Chapter 4 presents a methodologyfor selecting the appropriate modeling and simulation strategy.

Integrated performance assessment

"The aim of an integrated approach is to preserve the integrity of the entire building system by simul-taneously processing all energy transport paths to a level of detail commensurate with the objectives ofthe problem to hand and the uncertainties inherent in the describing data" (Clarke, 2001). Integratedsimulations allow for a holistic performance assessment via the use of a single simulation tool withonly one user interface and one building model. This conflation of domains also makes sure that eachdomain is ‘informed’ about the state of adaptive behavior in response to other domains’ stimuli — anapproach that is in favor of reliable performance prediction of CABS.

Three frequently used integrated BPS tools are EnergyPlus, ESP-r and TRNSYS. As we will see in the nextsection, each of them offers different possibilities for modeling and simulation of CABS. A disadvantageis that the user is restricted to the (adaptive) features the particular tool offers. In addition some sub-domains are still missing or inadequately represented in the tools (Clarke, 2009).

Co-simulation

No single BPS tool appears to be able to model all relevant physical phenomena that determine perfor-mance of CABS. In addition, the available adaptive mechanisms are limited and dispersed across thevarious tools. Consequently, users of BPS will often be confronted with the problem that there is no toolavailable that satisfies their simulation requirement. Co-simulation enables coupling of BPS tools withcomplementary capabilities and may thus be the solution to this problem. In this way, added value canbe generated by exploiting adaptive features in one tool together with the reuse of powerful modelingcapabilities of another tool. Co-simulation further allows for generalization of fragmented developmentand accelerated infusion of innovations: once new adaptive mechanisms are implemented in one tool,they can be made available in other BPS tools as well.

Depending on the type of problem, the workflow in co-simulation can be either sequential or bi-directional (Trcka, 2008). In the first approach, the different simulators are invoked successively, whilein the latter approach simulators run in parallel and data flows between simulators during run-time.The latter approach requires setting-up the connections and communication protocols between thetools; a task that might be too arduous for performance prediction of just a single building design.

An interesting development that might offer opportunities for design and operation of CABS is theBuilding Controls Virtual Test Bed (BCVTB) (Figure 3.2). This “modular, extensible, open-source soft-ware platform allows designers, engineers and researchers of building energy and control systems to in-terface different simulation programs with each other and, in the future, with Building Automation Sys-tems (BAS)” (Wetter and Haves, 2008). Instead of linking two programs to each other, this co-simulationapproach uses a middleware-tool that manages communication, data exchange and integrated outputfacilities for a number of different simulators. One of the envisioned application areas of BCVTB is theinvestigation and implementation of intelligent local-loop and supervisory control strategies for activefaçades.

3.6 Adaptive features

Most state-of-the-art BPS tools have a history of gradual development and extension of capabilities. Atthe time these tools were created, software-developers had no incentive to implement adaptive behav-ior into building shell models. This inheritance is now the reason that the state-of-the-art in BPS lacks

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HVAC & controlsModelica

airflowFluent

wireless networksPtolemy II

lightingRadiance

building energyEnergyPlus

real-time datawww+xml

controlsSimulink

controls & data analysisMATLAB

building energyTRNSYS

implementedfundedin proposalin discussion

hardware in the loop

building automationBACnet

building energyESP-r

BCVTB

Figure 3.2: Building Controls Virtual Test Bed (Wetter, 2009b).

abundant capabilities for performance prediction of CABS. Moreover, attempts to introduce adaptiv-ity in these tools are confronted with severe restrictions pertaining to the basics of energy modeling inbuildings:

Because of their computational efficiency and accuracy, Response Factors and Conduction TransferFunctions are the most widely used methods for load calculations and building energy analysis (Craw-ley et al., 2008; Spitler, 2010). The coefficients in these equations are constants that are calculated onlyonce, prior to the actual simulation. As a consequence, thermophysical properties of opaque construc-tions are coerced to be fixed, preventing the simulation of adaptive behavior (Pedersen, 2007). An al-ternative approach that allows for more flexibility uses numerical methods for calculation of transientheat transfer in buildings. This approach also has difficulties with simulation of adaptive behavior sincethe way the equations are solved makes that attempts to reassign state variable’s values at run-time is“incongruous” (Clarke, 2001). The state variables are the output of the numerical solution processes,and therefore reassigning particular values will violate energy balances. Instead, it is necessary to adjustthe source terms of equations in question to effect the required adaptive behavior. Nakhi (1995) argueshowever that introduction of discontinuity in material properties is not straightforward because it mayaffect the accuracy and numerical stability of the overall solution.

A further complexity is that building energy analyses have to respect the time-history effects of thermalstorage. Simply hopping from one state to another in separate building models without state-rewindingis therefore unacceptable∗. Instead, adaptive behavior in BPS has to be considered “on-the-fly”, whichintroduces high burden on the tool’s control algorithms.

Despite of these barriers, several possibilities to simulate CABS have found their way in the state-of-the-art BPS tools. In this study, two different types of adaptive features are distinguished, prompted by thenature of the BPS tools. The first group consists of tools that only offer selection of predefined functionsand in this way adaptivity always serves a specific application. The second group allows for more libertyin the simulation process and can therefore be used to model ‘general purpose’ adaptive behavior. Theresults of the analysis for both categories is described in the following subsections.

3.6.1 Application-oriented

Most BPS tools under study are ‘closed environments’ where building models are created by selectingpredefined functions via e.g. clicking buttons, ticking boxes and filling in mandatory fields. In addition,

∗In CABS with distinct winter-summer behavior it can be a feasible approach to perform semiannual simulations with twobuilding models.

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the majority of these off-the-shelf BPS tools are non-transparent and the source-code is often inacces-sible and unmodifiable. This makes it difficult to manually tweak or extend the tools’ capabilities whichis also the reason that the tools lack active developer communities. As a consequence, users of thesetools have to manage their task merely with the capabilities the tool is offering. Unfortunately, thesecommercial tools are attuned to ‘straightforward’ building designs. Making profit is the core activity ofthe software developers, and capabilities are usually only extended in response to their clients’ wishes.During the course of the research, the list of available adaptive features in these tools expanded with thelaunch of new software-versions. This might give an indication that there is an increasing demand forperformance prediction of CABS. The analysis resulted in five different application-oriented adaptivefeatures; each of them is discussed below.

Electrochromic glazing is a mature product for some years now, and many research papers have beenwritten about its application in buildings and architecture. As a consequence, switchable glazing tech-nologies are embedded in several simulation tools, for example BuildingDesignAdvisor, DesignBuilder,EnergyPlus, ESP-r and eQUEST offer the possibility to switch the window state during runtime. The dif-ferences between the various models are the number of possible window states (e.g. on/off or gradualtransitions) and the state variables that can be used for control of adaptation.

Simulation of thermochromic windows is slightly more complicated because of its open-loop character;adaptation is directly triggered by window temperature rather than via a decision making componentbased on more general simulation variables. Nevertheless, a thermochromic window capability wasfound to be available in EnergyPlus. The input of this model consists of sets of window properties atvarious temperatures. During the simulation, the thermochromic layer temperature of the previoustime-step is passed on to the control algorithm which then selects the window properties that bestmatches with the given temperature.

IES-ve (ApacheSim) offers the possibility to give dynamic shading devices additional thermal resistanceproperties. This makes it possible to simulate the performance of insulating solar shading systems,including Beadwall. Unfortunately, the thermal models in IES-ve do not explicitly account for daylightlevels, which prevents insights in the trade-offs between thermal and visual comfort.

Phase change materials models are present in EnergyPlus, ESP-r and TRNSYS. These models influ-ence heat transfer in constructions via either the ‘effective heat capacity’ or the ’additional heatsource’/‘enthalpy’ method. Implementation of PCM forced developers of EnergyPlus to abandon theConduction Transfer Functions approach and introduce a numerical finite difference algorithm. Thisnew algorithm also has a provision for including a temperature coefficient that makes thermal conduc-tivity variable during the simulation.

Both EnergyPlus and its user-friendly interface DesignBuilder offer the possibility to simulate greenroofs. The models account for: (i) long wave and short wave radiative exchange within the plant canopy,(ii) plant canopy effects on convective heat transfer, (ii) evapotranspiration from the soil and plants,and (iv) heat conduction and storage in the soil layer. Implementation of moisture dependent thermalproperties is planned for the next version of EnergyPlus.

3.6.2 General purpose

The application-oriented adaptive features presented in the previous subsection only allow for re-stricted flexibility and by definition lag behind the latest state of adaptive building shell technologies.Fortunately, some BPS tools offer the freedom to simulate more ‘general purpose’ adaptive features,which also gives users the ability to respond to emerging technologies. Using these features demandsfor advanced use of BPS and also asks for creativity on the side of the user of the tool. In this light it isalso possible to make use of ‘workarounds’ — features of a BPS tool that can aptly simulate certain adap-tive behavior, but that initially were not intended for that specific purpose. General purpose featuresare expected to have highest potential in the RD&D stage of CABS, it is however questionable to whatextent these modeling approaches will turn out to be valuable in more mainstream design processes.

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Airflow network (AFN) tools are based on wind pressure coefficients (cp ) and in this way are inherentlyadaptive to prevailing wind conditions in a passive way. All AFN tools under study also have flow co-efficients and/or opening areas that are controllable during runtime. This introduces the possibility toactively bring about the desired adaptive behavior for airflows.

Apart from retractable Venetian blinds, none of the examined daylight simulation tools offers the capa-bility to change behavior dynamically. However, daylight simulations do not suffer from ‘history effects’.Therefore, adaptive behavior can be accomplished artificially after computation of raw luminance andilluminance data for all possible states of the building shell. This data can subsequently be coupled toa control algorithm that selects right state and corresponding solar gains. Matrices with this data canfurthermore be transformed into the desired dynamic daylight performance metrics.

Because of their particular background and ’open’ make-up, the BPS tools TRNSYS and ESP-r both offera number of attractive capabilities for modeling and simulation of adaptive behavior. A comprehensiveoverview of both tools’ adaptive features is the subject of the following subsections.

TRNSYS

The approach that TRNSYS takes towards managing complexity in the built environment is typified bybreaking the problems down into a series of smaller components. One of these components is a multi-zone building model — in TRNSYS called TYPE 56 — that can be connected to a vast number of othercomponents, including: weather data, HVAC systems, occupancy schedules, controllers, output func-tions, storage tanks, renewable (solar) energy systems, etc. This typical configuration allows the userto set-up and manipulate the connection rules and order between the building and all other subsys-tems/components in the simulation environment.

TRNSYS TYPE 56 offers the possibility to change the thermal and optical window properties duringruntime with a function called variable window ID. Additionally, it is also possible to control the ratio ofwindow/frame area which influences the degree of transparent façade elements. All the other adaptivemechanisms in TRNSYS can be found in the connections with other components in the SimulationStudio. Using equations enables the application of boolean logic and algebraic manipulations to anyvariable in the simulation. This flow of information can then be used to drive a control algorithm thatis able to dynamically ‘switch on’, ‘switch off’ or modulate e.g. overhangs and wingwalls (TYPE 34),shading masks (TYPE 64), attached sunspaces (with or without movable thermal insulation) (TYPE 37),windows with variable insulation properties (TYPE 35) and photovoltaic cells (TYPES 94, 180 and 194).In addition, it is also allowed to adjust the connections with weather files and radiation processors. Inthis way it is possible to mimic the effects of changing orientations or to reduce the impacts of solargains on the construction as a way of modeling external shading devices/objects.

The standard TRNSYS distribution already comes with an extensive number of components. Yet, one ofthe distinct benefits of TRNSYS’ modular nature is the fact that it allows users to add content by intro-ducing new components (McDowell et al., 2004). With some coding efforts it is possible to encapsulatethe desired CABS behavior in a new TRNSYS TYPE which can then be linked to the building model. Un-fortunately, coupling these new TYPES with the building model works in a rather indirect way via the’slab-on-grade approach’. In TRNSYS it is not possible to replace building shell constructions, insteaddevelopers must impose the desired behavior via manipulation of inside surface layer temperatures ofboundary zones and the respective heat transfer coefficients. Kuznik et al. (2010) recently demonstratedthis approach for CABS via the implementation a new PCM wallboard TYPE (Figure 3.3).

Another attractive functionality of TRNSYS is the opportunity to call external programs at every iterationstep during the simulation. This approach extends the capabilities of TRNSYS with airflow calculationsof COMIS, CONTAM, and Fluent; spreadsheet functions and Visual Basic macro’s of Microsoft Excel,M-files and S-functions of Matlab/Simulink† and the algebraic equation solving methods of EES. Capa-

†With the launch of TRNSYS 17 in early 2010, it is now also possible to embed TRNSYS TYPES as .mdl models in Simulink(Riederer et al., 2009). With this approach, it is possible to exploit Matlab’s full functionality together with the distinct qualities of

46


Figure 3.3: Coupling of new PCM model (TYPE 260) with TRNSYS multizone building model (TYPE 56) (Kuzniket al., 2010).

bilities can be further expanded by reading in (measured) data from generic external data files. This canserve various purposes, including the import of high-resolution casual gains and illuminance data.

ESP-r

ESP-r is an integrated research-oriented BPS tool with an active developers community and a sourcecode that is accessible and modifiable. The tool is used for prediction of thermal, visual, indoor air, elec-trical and acoustic performance of buildings. Users can extend these features in the source code as de-sired and in this way introduce adaptive behavior. In the course of years, several functionalities that canbe used to model adaptive behavior in the building shell have already been implemented. The use ofthese capabilities however remained limited, possibly because the features are (i) not well-documentedor (ii) concealed somewhere in the distributed menu-structure of ESP-r. This section summarizes six ofsuch adaptive features.

One of the control laws in ESP-r is called thermophysical property substitution mode. Instead of beingused for controlling operation of the HVAC-system, this control strategy can replace the thermophysicalproperties (ρ, cp , λ) of a construction during the course of the simulation. In essence, this control workslike any other control algorithm in ESP-r, in the way that actions are triggered based on ‘tests’ appliedto sensed variables during run-time. Unfortunately, this feature does not allow for full flexibility since itonly affects opaque wall elements and the only ‘sensor variable’ is indoor air temperature.

The previous feature dealt with opaque construction elements only, however ESP-r also has a similarfunctionality available for modeling dynamic behavior of windows; transparent multi-layer construc-tion control. This functionality can for example be used for performance prediction of switchable glaz-ing technologies. Currently it is possible to replace window properties (.tmc-files) based on time, tem-perature, solar radiation level or lux level. Restrictions are that no more than two states are supportedwithout the possibility for gradual transitions. Recently, the capabilities of ESP-r have been furtherextended with the implementation of a facility for modeling and controlling complex fenestration con-structions (CFC) (Lomanowski and Wright, 2009).

In ESP-r the special materials facility was introduced to model ‘active building elements’ (Evans andKelly, 1996). This universal functionality may be applied to any node within a multi-layer construction.

TRNSYS types.

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The special material subroutines can actively modify the matrix coefficients of these specific nodes atevery time-step. By doing this, it directly changes basic thermophysical or optical properties and/or theassociated energy flows based on the respective physical relationships. Currently, the following specialmaterials are implemented: Building integrated photovoltaics‡, ducted wind turbines, solar thermalcollectors, thermochromic glazing, evaporating surfaces and phase change materials. It is possible toadd new user-defined special materials, however this may require laborious programming work.

A further capability of ESP-r is to impose measured data on a time-step basis. This information canindeed be obtained via experiments, to realistically represent actual behavior. But this functionalityalso allows to manually bring about certain predetermined transient behavior, and in this way modelsome of CABS’ features. Data files of this type can be associated to various attributes of the simulation,including: elements of climate data, casual gains, infiltration, air velocities, setpoints and control states.

ESP-r offers the peculiar possibility to use roaming files. This facility is used to change the location ofa ’building’ as a function of time, and was intended to be used for cruise ships. Because this roamingfile not only includes coordinates but also orientation of the zone, it is very well fit to simulate rotatingbuildings.

Nakhi (1995) introduced variable thermophysical properties in ESP-r to more realistically model heattransfer in building slabs under a wide range of operating conditions. It takes into account the tem-perature and/or moisture content dependency of a material’s thermophysical properties. This is im-plemented via transient thermophysical material properties (ρ, cp , λ) that are linear or polynomialfunctions of layer temperature or moisture content. In this way it is possible to model the continuousor discontinuous properties of smart materials and other micro-level CABS.

3.7 Discussion

Together with mere advancements in BPS, alternative developments are going on that may enrich orcomplement the performance assessment capabilities for CABS. This section first discusses the need toassess the more generic environmental performance of buildings and how this applies to CABS. Then,ways to deal more explicitly with the risks and uncertainties associated with CABS’ performance predic-tions are presented. Finally, this section provides some perspectives about BPS’ future directions andwhat this implies for CABS.

Environmental impact

Driven by an increasingly stronger demand for sustainable development in the built environment thereis a growing interest for methods able to evaluate a building’s overall environmental impact. This has ledto many country-specific environmental assessment tools (including e.g. LEED and BREAAM) whoseoutcomes are typically expressed in mandatory performance thresholds or voluntary ambition targets.These kinds of labels or certificates have proven to be effective for communication purposes and alsofor promoting the commercial value of being eco-friendly (Bougdah and Sharples, 2010). The varioustools have in common that they unite the broad range of environmental aspects of buildings into onesingle metric by using weighting functions. The assessment tools do evaluate energy consumption inthe whole building’s life-cycle, and also include issues like transportation, land use, materials, pollutionand water consumption (Gowri, 2004).

The prediction of energy-related aspects varies among the tools; some use simplified (steady-state) cal-culations while others are based on the results of dynamic performance simulations. But in general,all rating tools share the characteristic that Energy Performance of the building is eventually cast intoone single value. The appropriateness of this approach in assessing environmental impact with regards

‡The efficiency of the solar cells is a function of layer temperature. In turn, power output is subtracted from the nodalabsorption of the incident beam which reflects the fact that part of absorbed solar radiation is converted to electrical powerrather than being manifested as a heat gain

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3.7. Discussion

to CABS is disputable because of the following reasons. CABS are inherently dynamic and its perfor-mance is closely related to the specific application (e.g. climate, surroundings, orientation, buildingfunction) and control strategies. Consequently, it is complicated to generalize CABS performance as-pects in terms of universal points or credits. In addition, CABS’ specific merits might get obscured in theweighting process with the other sustainable assets. Some authors further argue that the value of envi-ronmental assessment tools in the design process of buildings is limited (Ding, 2008; Lstiburek, 2008).Despite of its good intentions, the tools are used as ‘checklists’ for endorsement of already completeddesigns rather than being used for supporting informed decisions in the actual design process.

Analogue to the green building rating schemes, there is also an increasing interest to establish systemsfor energy labeling or energy rating of windows. These ratings are typically used for communication andmarketing purposes and should be understandable for consumers. The current window energy ratingsystems (WERS) are based on the following static fenestration properties: U-value, solar heat gain co-efficient, visible transmittance and air leakage (Urbikain and Sala, 2009) which makes them unsuitableto capture the dynamics of CABS. Recently, Papaefthimiou et al. (2009) proposed a new environmen-tal rating scheme for switchable windows. This assessment indicator includes dynamic energy savingpotential calculated with optimal control strategies in a customized BPS tool.

Uncertainty and risk

For prediction of building performance, several assumptions have to be made regarding e.g. real occur-ring weather-data, occupants’ behavior, and control actions. The innovative materials and emergingtechnologies abound in CABS further increases the level of uncertainty inherent in the problem defi-nition process and associated with the selection of the thermophysical properties of materials (Clarke,2001). While being aware of these uncertainties, it is a rather awkward situation that BPS typically com-putes results for only one scenario, that is at best an educated, but always arbitrary guess. Consequently,design decisions informed by BPS do not acknowledge the probability distribution of possible outcomeswhich is closely correlated to an increased risk, with associated consequences, of not meeting perfor-mance targets. Traditionally serviced buildings use oversized HVAC-systems as a means of overcomingthe effects of uncertainty. In buildings with CABS, a more risk-conscious design approach seems a nec-essary step forward (Hu, 2009).

Various types of uncertainty analyses (UA) are available to elucidate the effects of (i) physical, (ii) designand (iii) scenario uncertainties on building performance (Hopfe, 2009). Currently, UA-methods still ex-ist is the form of research prototypes that require laborious pre-processing and post-processing efforts.It is now up to the software developers to merge these methods in the tools’ existing workflows to letbecome UA a valuable tool for guiding both buildings’ and CABS’ innovation processes.

Look at the future

The analysis in this chapter mainly focused on (commercial) off-the-shelf software tools, because itis believed that these tools have the highest potential to be valuable for architecture, engineering andconstruction (AEC) professionals in the design process of CABS. However, the availability of adaptivefeatures in these tools appears to be limited, which confines the option space for simulation of CABS.In academia, alternative developments are going on that will allow us to simulate buildings with muchgreater flexibility and extendibility. By exploiting advances in computer science and software engineer-ing these tools are at the source of what might become the next generation of BPS tools. This newapproach combines a higher abstraction level with object-oriented modeling and in this way decou-ples the mathematical models from the solver in the simulation program. This results in BPS tools withgeneral-purpose symbolic programming languages (e.g. Modelica) that discard the fixed input-outputrelationships, and hence gives users the liberty to tune or extend models in an intuitive way.

Wetter (2009a) identifies four applications of a new Modelica-based modeling and simulation environ-ment for building energy and control systems. The simulation tool should be able to:

Ï offer a platform to stimulate innovations in energy efficient building systems;

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Ï enable virtual rapid prototyping to select most promising alternatives for further refinement andproduct development;

Ï permit researchers to quickly add models for performance assessment of emerging technologies;

Ï allow for extraction of design models to be embedded within building control systems for model-based controls, fault detection and diagnostics.

All the envisioned applications seem both relevant and promising for performance prediction of CABSas well. A concurrent research project at the University of Ljubljana already demonstrated the prelim-inary application of Modelica for performance simulation of building shells with dynamic properties(Zupancic and Sodja, 2008). Before this approach can become more mature, some important steps arerequired: the essential validation studies still have to be undertaken, and more attention has to be paidto the design of convenient user interfaces.

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CH

AP

TE

R

4MODELING AND SIMULATION OF CLIMATE ADAPTIVE

BUILDING SHELLS

Thus far, this thesis presented the properties and characteristics of CABS in Chapter 2, which has alsoled to the CABS overview in Appendix A. An analysis of the capabilities of state-of-the-art building per-formance simulation tools with respect to CABS is given in Chapter 3. Successful simulation of buildingperformance is a challenging task and has been phrased as the art of performing the right type of virtualexperiment with the right model and tool (Augenbroe, 2010). In this chapter, the two lines converge asit presents a strategy for effective modeling and simulation of CABS. A graphical representation of thedeveloped strategy is given in Figure 4.1. The remainder of this chapter follows the logic of this graph.

BPS-tools Overview

Conceptual Models

Real World System

Simulation Model

CABS Overview

Verif

icat

ion

User

Simulation requirement

Model

abstr

actio

n

Val

idat

ion

ExecutionSi

mula

tion

strat

egy

Relevant physics

Tool Selection

Figure 4.1: Strategy for modeling and simulation of CABS.

4.1 Model abstraction

Simulation projects usually originate from queries about ‘real world systems’, which for CABS in mostcases is a design for a new building or building shell concept. Before the performance of these objectscan be predicted, the systems first need to be translated into conceptual models. A conceptual model is

51

4. MODELING AND SIMULATION OF CLIMATE ADAPTIVE BUILDING SHELLS

defined as “a non-software specific description of a computer simulation model, describing the objec-tives, inputs, outputs, content, assumptions and simplifications of the model” (Robinson, 2008). Effec-tive conceptual models of CABS contain the critical entities and relationships at the right level of detailto predict actual behavior with sufficient accuracy.

In order to substantiate the choices made during the model abstraction process, it is essential to havesufficient domain knowledge and insights in occurring physical processes. Therefore, the first step inmodel abstraction is knowledge acquisition (Kotiadis and Robinson, 2008). Conceptual models are ei-ther (i) physically-based or (ii) data-driven. Models of the first type are based on first-principles andtheoretical considerations like mass and energy balances, differential equations and conservation laws.The second type is also known as the black-box or input-output approach and constructs models en-tirely based on mapping and fitting input-output relationships based on observations from experimentsor data. Models of this kind do not necessarily reflect the structure of the actual system nor containphysically meaningful parameters (Henze and Neumann, 2010). Nowadays, semi-physical or grey-boxmodels are gaining popularity as a means of overcoming the deficiencies of one of both individual ap-proaches (Lu et al., 2009).

Modeling and simulation by definition implies ‘approximation’, introduced in the abstraction processby means of assumptions and simplifications. Assumptions are ways of of incorporating uncertaintiesand beliefs about the real world into the model (Robinson, 2008). In this way, it deals with (quasi-)randomness and unknowns about the system and despite of this tries to obtain reliable outcomes. Hereit should be noted that it is not a model itself, but the model’s results that should be close to reality(Chwif et al., 2000). Simplification is concerned with deliberately reducing the complexity of a modelwith the aim of increasing the model’s utility without significantly affecting its accuracy and validity(Barlow, 2009). This involves reducing either (i) the scope (i.e. boundaries of the study or number ofcomponents), (ii) the resolution (i.e. level of detail in the components) or (iii) the interconnectionsamong components of the model.

In general, the model abstraction process establishes which aspects of the real-world system must bemodeled, and for those to be included, how accurate the model must be. Consequently, conceptualmodels of CABS can be developed at various levels of detail and complexities. Finding the right balancebetween model complexity, efficiency and resulting accuracy should be a major consideration in everymodel development process (Lu et al., 2009). Increasing the complexity of conceptual models adverselyaffects ease-of-use, computational performance, flexibility and cost (Henriksen, 2008), and therefore itis not always the most sophisticated modeling approach that offers the highest value. Depending onavailable system knowledge, increasing complexity beyond a certain level further also reduces the util-ity of a model in terms of accuracy due to higher uncertainties involved (Trcka and Hensen, 2010). Thefinal structure and contents of the model is always determined in consultation with the simulation re-quirement, as discussed in Section 4.2. Once implemented, the validity of this model is either approvedor the model needs to be modified on the basis of the verification and validation process which is de-scribed in Section 4.5. Nevertheless, all conceptual models of CABS should consider the following twoelements: adaptive mechanisms and control. Both elements are discussed in more detail first.

Adaptive mechanisms

A description of how adaptive mechanisms are represented has to be part of every CABS-model, sinceadaptivity is what discriminates CABS from the conventional building envelope. Conceptual modelingof micro-level CABS is relatively straightforward: it basically involves a direct change in thermophysicalor optical material properties. Therefore these CABS-concepts can rely on existing BPS models, but nowwith flexible, rather than the traditional rigid material properties. Macro-level CABS on the other handmay not only affect material properties but also cause changes in the configuration or compositionof the building shell. Consequently, an additional interpretation step is required that translates theseadaptive mechanisms to an abstraction level that is manageable for the available BPS tools.

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4.2. Simulation requirement

Control

Another important issue in conceptual modeling of CABS is the identification of the way in which adap-tive behavior is controlled. As Section 2.3.3 already points out, two different types for control of adapta-tion in CABS exist: open-loop and closed-loop control. In the first type, adaptive behavior is an intrinsiccharacteristic of the design of the building shell and these specific input-response relations have to beincluded as such in the conceptual model. Closed-loop control does give designers and developers thefreedom to devise appropriate control strategies that satisfy their requirements. It is important to takethese control concepts into account in the model because the way in which the CABS is controlled willpotentially also have implications for the software-side of the simulation process. For this purpose, thefollowing three types of closed-loop control are distinguished:

Schedule based: The adaptation follows a scheduled or scripted sequence that is defined in advance.Strictly speaking, building shells controlled in this way are dynamic, but do not belong to adaptivebuilding shells, because they do not have the ability to react in response to varying conditions.The adaptation rate is in principle free to choose, and can follow for example diurnal cycles orseasonal patterns. The switching frequency is however restricted by the length of the time-stepused in the simulations.

External variable based: Adaptive actions of this type are directly a function of one of the inputs to thesimulation model. Most likely, this is a variable in the weather-file, another possibility is to controladaptivity as a function of occupancy level. The control of CABS in this way is independent of theoutput of the simulations. In case of coupled simulations, there is no need for communication ofadaptive states, as long as the boundary conditions supplied to the models are the same.

Internal variable based: The last, and most complex type of control is based on ‘internal’ variables,which means that control stimuli are calculated during runtime. This typically is the case whenadaptive behavior is controlled in response to comfort considerations. The complicating factoris that the control stimulus and adaptive behavior are mutually influencing each other. This pre-vents the use of stand-alone simulation approaches and requires bi-directional workflow in thecase of co-simulation.

In addition to the three mentioned approaches, all types of hybrid control schemes are possible as well.One can think of CABS-examples where the type of control strategy differs in various time-slots. Otherexamples of hybrid control are e.g. time-scheduled envelopes with the possibility for manual overridewhen certain comfort limits are exceeded, which is in principle a combination of schedule based andinternal variable based control.

4.2 Simulation requirement

Selection of the ‘right’ conceptual model depends on what goal the user wants to accomplish with thesimulation process, and the type of information he or she wants to obtain. As this is an iterative process,the simulation objectives should be considered throughout the whole model development cycle. Per-formance simulations of CABS exist in many forms and can amongst others be used for gaining moreinsight in cause-and-effect relationships, demonstrating performance benefits to convince investors,investigating the effects of control strategies, the purpose of model-based control etc. Each particularapplication demands for consideration of different conceptual models of the same system at variouslevels of explicitness and accuracy. Consequently, it is wise to focus on those parts of CABS that are rel-evant to the problem situation and the modeling objectives, and to develop a model that only addressesthese objectives, rather than a general model of the system that includes all that is known about the realworld system. As will be shown next, the simulation requirement consists of two parts: the performanceindicators of interest at the right resolution.

53


Performance indicators

Chapter 1 demonstrated that performance of CABS is a very broad concept. Some of these performanceaspects may be conceived as being isolated, whereas others demand for an integrated evaluation. Thelevel of building performance is usually expressed via performance indicators (PIs) which adequatelyrepresent satisfaction with a particular performance requirement of a system (Augenbroe, 2005). ThesePIs must be quantifiable, well understood and amenable to computational analysis (Becker, 2008), andin this way they allow for comparing and contrasting various design options and the effects of oper-ational strategies. PIs are coupled to objectives or performance requirements of the building via e.g.target values, minimum requirements and acceptable discomfort levels.

Every physical domain in CABS typically uses its own set of PIs. However, some PIs can be combined toresult for example the building’s overall energy performance. The trade-offs between multiple perfor-mance aspects can be evaluated by using multi-criteria decision analyses. Sometimes the user is onlyinterested in the performance of a single component or sub-domain of the overall system. In this waythe system boundaries of the conceptual model can be confined, which reduces the overall scope – andthus complexity – of the problem.

Resolution

The type of performance indicator under examination is closely related to the minimum required reso-lution of the simulation problem, and this in turn defines the eligible BPS tools. CABS’ performance isby definition a dynamic phenomenon, and in order to represent the system dynamics in simulations,the continuous problem has to be discretized in time-steps. The length of time-steps, or temporal reso-lution, needs to be matched with the characteristic time-length of the simulation problem to capture allrelevant processes. New for CABS is the increased relevance of transient comfort aspects, and thereforeit is important to focus on the rate-of-change of PIs as well.

The way in which PIs are quantified also relates to the spatial resolution of the simulation problem. Invarious CABS-concept, the adaptation and corresponding performance depends on location-specificphysical conditions in immediate vicinity to the respective sensor, occupant or active material. In thesecases it might be insufficient to assume uniform or homogeneous conditions in the whole zone, espe-cially when large gradients occur. Additionally, assessment of some comfort PIs further demands forhigh-detailed analysis; examples are effects of cold downdraught and the risk of glare.

The spatial and temporal resolution together determine the required minimum level-of-detail – andthus complexity – of the simulation model. Djunaedy (2005) distinguishes the minimum model-resolution required to calculate particular performance indicators for simulations in the thermal andairflow domains. Analogously, Reinhart et al. (2006) and Mardaljevic et al. (2009) provide guidelines forassessing the performance of daylight systems. All the authors stress that conceptual models should bebased on the nature of the problem; not led by the capabilities a specific (familiar) BPS tool offers.

4.3 Relevant physics

Figure 4.1 shows that the ‘relevant physics’ for a simulation problem is a product of the model abstrac-tion process and the simulation requirement, and in this way is application-specific. In addition themodels are different and characteristic for every particular CABS-concept. This section presents an at-tempt to clarify the relevant physics for a simulation problem by means of a graphical representation.Graphs of this kind were assigned to all 100 CABS concepts in Appendix A. The methodology for identi-fication of relevant physics consists of the following four steps:

1. Identification of all physical domains that play a role in performance assessment of the CABS-concept. Classification in domains is identical to the rules for classifying CABS, presented in Ta-ble 2.2: (A) thermal, (B) optical, (C) air-flow and (D) electrical.

54

4.3. Relevant physics

2. Determination of the occurring adaptive mechanisms by assigning adaptivity to the appropriatephysical domain(s). This determines which adaptive mechanisms should be considered in themodel and is indicated with filled circles in the graphs.

3. The way in which adaptivity is controlled is indicated in red and may be either specific for thegiven situation (open-loop) or ‘free to choose’ (closed-loop). For open-loop CABS, the red arrowrepresents the way in which the environment influences adaptive behavior. In schedule based andexternal variable based control, it is not the simulation outcome, but the boundary conditionsthat force the building shell to change. The last type is based on internal variables, here indicatedvia red circles to represent the physical domain(s) in which the stimulus is imposed.

4. Lastly, the appropriate physical relationships and directions of interaction are established. Theseinteractions are prescribed either by the intrinsic physics of the problem and/or the way in whichadaptivity is controlled. Arrows indicate the nature of the relations and can be one-way or two-way. The first type is only concerned with unidirectional dependencies and thus can be modeledin a sequential way. In the second case, the effects are mutually interrelated and therefore needto be considered simultaneously.

In order to make the concept more clear, two examples are given that show how these graphs can beused for transforming relevant physics into an appropriate simulation strategy. Figure 4.2 shows thegraphs for thermochromic and photochromic windows respectively. In both CABS-concepts, only ther-mal and optical effects play a role, and they further share the characteristic that adaptivity is manifestedvia changes in the optical properties of the window, as indicated with the grey circles. The open-loopstimulus that controls this behavior is however different in both cases; for thermochromic windows thistakes place in domain A and for photochromic windows in domain B. As the black arrows indicate, thisdifference also has implications for the physical interactions to be considered in the model. Simulationsin the thermal domain require input from the optical domain in both instances, as is inherent for everyCABS with transparent parts. In thermochromic windows, adaptation occurs as a function of windowlayer temperature. When the user is only interested in the visual comfort of this switchable window, heor she is compelled to simultaneously consider the thermal domain in order to include the changingwindow state. For photochromic windows on the other hand, visual comfort performance can be beassessed via single domain lighting simulation tools since thermal interactions are not relevant.

A BA B

(a) (b)

Figure 4.2: Relevant physics for (a) thermochromic and (b) photochromic windows. Where A indicates thermaland B the optical domain; the red arrows indicate the open-loop stimulus and the black arrows indicatethe corresponding physical interactions.

The graphs for electrically heated glass are given in Figure 4.3. It shows that airflow is not relevant inthis case and that adaptivity takes place in the electrical domain. Moreover, no arrow is pointing to do-main B which implies that queries about visual comfort or daylighting performance can be answeredin a stand-alone manner with rigid material properties. As this is a closed-loop controlled CABS, adap-tive behavior can be controlled in many different ways and therefore it is insufficient to use only onegraph to represent relevant physics. Figure 4.3 (a) shows the relevant physics for schedule based and

55


external variable based control, where adaptation happens without regard to simulation outcomes. inthese cases, only the minimal physical interactions are required in the model. Figure 4.3 (b) and (c)both represent internal variable based control, as a function of inputs from the thermal and electricaldomain respectively. The graphs reveal that in case adaptivity is controlled in the thermal domain (e.g.as a function indoor air temperature), an additional coupling from the thermal to electrical domain ismandatory.

(a) (b)

AB

D

B

D

AA B

(c)

D

Figure 4.3: Relevant physics for Electrically heated glass. Where A indicates the thermal, B the optical and D theelectrical domain; the red lines represent the type of control, and the black arrows indicate the corre-sponding physical interactions.

4.4 Simulation strategy

Conceptual models are defined to be non-software specific representations of a real-world system. How-ever, once the relevant physics of the problem is established, the building-specific conceptual modelneeds to be implemented in one of the available BPS tools. Selection of appropriate candidate tools hasto consider four requirements: 1) the adaptive features in the conceptual model; 2) the desired (mix of)performance indicators at the appropriate level-of-detail; 3) the way adaptive behavior is controlled;and 4) the applicable physical interactions. Chapter 3 presented the foundations for this tool selectionprocess, including the available adaptive mechanisms in the state-of-the-art BPS tools.

Like every other BPS study, the simulation strategy for performance evaluation of CABS further includesspecification of scene and scenario and selection of appropriate boundary conditions. Depending onthe type of simulation this includes development of a 3D building model and selection of a locationand its surroundings, a climate file, occupancy profiles, internal gains etc. The user should further con-sider the required simulation period and an appropriate length of time-steps. In addition, the outputvariables and the way how the results are going to be presented should be decided.

Users may be confronted with the situation that no tool is eligible for implementing the desired be-havior. In these cases, the user should first reconsider the conceptual model and/or the simulationrequirement. This perhaps requires more rigorous assumptions or a less detailed analysis, which may,or may not violate the utility of the simulation process. In case these changes are unacceptable, userscan endeavor to extend the capabilities of the current range of BPS tools. It should however be notedthat this is a cost- and time-intensive process — an effort that might be unjustifiable in a ‘routine’ designprocess.

4.5 Verification and Validation

Performance simulation of CABS is often concerned with innovative technologies which in general lackwell-accepted modeling strategies. The assumption that a model is reliable can however not be madepurely on the basis of good intentions by the developers (Sokolowski and Banks, 2009). For this reason,verification and validation (V&V) studies should be part of every CABS model development process.

56

4.5. Verification and Validation

The role of verification and validation in relation to the other components of modeling and simulationof CABS is illustrated with the dashed lines in Figure 4.1. Verification is concerned with transformationalaccuracy, i.e. that of transforming the model’s requirements into a conceptual model and the concep-tual model into an executable simulation model. In this way, verification confirms or rejects that themodel implemented as software, is consistent and complete with respect to the conceptual model. Val-idation is concerned with representational accuracy, i.e. that of representing the real-world system inthe conceptual model and the results produced by the executable simulation model (Sokolowski andBanks, 2009). Validation of CABS-models is always to be regarded within the specific domain of ap-plicability, and hence accuracy and error tolerances should be consistent with the specific simulationrequirement (Balci, 1998).

There is no general recommended process or standard procedure available for selecting the right typeof verification or validation technique (Sargent, 2005). In verification, general software engineeringmethods that compare the implemented model with its design specification are often applicable. Dur-ing the development of CABS models, it is not the intention to prove validity of the general simulationalgorithms of the BPS tools, since these codes have gone through rigorous tests via inter-program com-parative (e.g. Judkoff and Neymark (1995); Reinhart and Herkel (2000)) and empirical validation studies(e.g. Mardaljevic (1995); Reinhart and Walkenhorst (2001); Judkoff (2008)). Instead, V&V studies shouldbe used to confirm that the assumptions and simplifications about adaptive behavior are justified.

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CH

AP

TE

R

5MODELING SMART ENERGY GLASS

The present chapter is the first of three chapters that deals with the case study of Smart Energy Glass.This chapter starts with an introduction of the concept and then provides a brief description of under-lying physics. Subsequently, the conceptual model development process is discussed according to thestrategy for modeling and simulation of CABS as presented in Chapter 4. The chapter concludes with adescription of the simulation strategy and shows how the conceptual model was implemented in soft-ware tools. Validation of this model is the subject of Chapter 6 and is followed by an application studyof the model for performance simulation of Smart Energy Glass in Chapter7.

5.1 Introduction

Smart Energy Glass (SEG) is an innovative window technology, currently under development by Peer+;a spin-off company from the faculty of chemical engineering at Eindhoven University of Technology(Peerplus, 2009). The working mechanism behind SEG is based on a polymer coating sandwiched be-tween two layers of glass. These layers together form the external pane of an insulated glazing unit(IGU). By changing an external voltage applied to the coating, it is possible to control the optical prop-erties of the window within less than a second. SEG can be switched into three different states: a brightstate, a dark state and a translucent state (Figure 5.1).

The polymer coating in SEG also acts as planar waveguide in the same way as a luminescent solar con-centrator (LSC) (Goetzberger and Greube, 1977). The dye molecules absorb part of the incoming sun-light and re-emit photons in approximately random direction at a longer wavelength. Via this mech-anism part of the incoming light is captured and redirected to the edges of the window where photo-voltaic cells are situated to convert the collected radiation into electricity. The generated electricity isused for switching the state of the window, the surplus can be fed back to the grid. Switching the win-dow can exploit the qualities of multi-ability since the translucent state then offers ‘privacy’ rather than‘transparency’. A more important aspect of changing the window state is that the flow of solar gains andadmission of daylight can be actively modulated. In this way, the window can provide passive heating inthe winter while reducing cooling load and overheating risk in the summer. Exploiting this adaptabilityfurther allows for maximizing the positive effects of daylight in terms of energy and comfort, but alsoacts as active instrument to counteract the likely occurrence of glare.

SEG is a unique concept that facilitates energy saving while generating energy by combining displaytechnology and photovoltaic energy conversion into one device. The window tint can be produced invarious different colors, and the electrical connectors are invisible. In addition, no external wiring is

59

5. MODELING SMART ENERGY GLASS

R.C.G.M. Loonen | CABS – What can we simulate? | MSc Thesis | TU/e | 2010

Smart Energy Glass

An innovative coating allows the window to switch its optical properties in three different states: dark, bright and privacy.

Part of the sunlight is captured and converted into electricity in PV-cells mounted at the edges of the window.

When equipped with batteries, the system becomes autonomous and can be integrated in the building envelope without the need for external wires.

Peer+ Smart Energy Glass, Website: http://www peerplus.nl

Kemper, A. (2008) ‘Schone energie dankzij superslimme ruiten’Volkskrant 14 oktober 2008

MICRO

Peer+ | Eindhoven, The Netherlands

75closed-loop

[www.peerplus.nl]

Figure 5.1: Smart Energy Glass info sheet in the CABS overview (Appendix A).

required when electricity is stored in batteries, which makes SEG very attractive for renovation projects(Benson and Branz, 1995). SEG is however not the first technology that aims at adding adaptivity and/orelectricity generation to the list of window functionalities. Table 5.1 contrasts the capabilities of SEGwith a selection of competitive technologies.

Table 5.1: Comparison between various types of advanced window systems.

Energy

generation

Views

unobstructed

Switchable

properties

Unobtrusive

appearance

Smart Energy Glass (Peerplus, 2009)

Semi-transparent PV (Wong et al., 2008)

LSC (van Sark et al., 2008)

Switchable glazing (Granqvist, 1991)

Photovoltaic shading (Janak, 2009)

Sphelar® (Biancardo et al., 2007)

The concept of SEG is currently not a market-ready commercial product, but is still under continu-ous development. Research and development activities currently focus on optimizing absorption andemission spectra, thermal performance of the window, optical losses, electrical circuits, stability andlongevity of the dye, etc. The work presented in this thesis complements and interacts with these activ-ities by providing computational support for assisting the innovation processes.

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5.2. Real world system

5.2 Real world system

The characteristics of SEG are already briefly introduced in Section 5.1. To predict the performanceof the system, this information first needs to be abstracted into an appropriate conceptual model andafterwards into a simulation model. To allow for substantiated assumptions and simplifications in thesemodels, it is important to further elucidate the physical processes occurring in SEG.

In its most elementary form, SEG can be described as a LSC with tunable optical properties (Debije,2010). The working mechanism of LSC’s basically consists of two parts: (a) collection and concentrationof photons and (b) conversion of these photons into electrons. Figure 5.2 gives a schematic overview ofworking mechanisms of SEG.

PV c

ell

PV c

ell

(i)

(ii)

(iii)

(iv)

(v)

(vi)

Figure 5.2: Schematic overview of the physical phenomena taking place in SEG.

A stream of photons coming from the sun hits the window pane, where the light is either reflected (i)or refracted (ii) into the material. In the bright state, most photons that enter the window do not getabsorbed and thus again hit the interface. Depending on angle of incidence and refractive index, thisphoton flux may either be internally reflected or leave the window as transmitted radiation (iii). Thedefining characteristic of SEG is the luminescent dye that has been dispersed with the goal of absorbingand re-emitting (iv) a controllable part of incoming radiation. The direction of re-emission is approxi-mately isotropic, and consequently, a large fraction∗ of the re-emitted photons is trapped in the waveg-uide via internal reflection (v). Successive reflections carry the luminescent photons to the edges of theplate where they are absorbed, captured and converted to electricity in PV cells (vi). The bandgap ofthese solar cells is matched with the spectral emission wavelength of the dye with the aim of obtaininghighest possible efficiency.

LSC’s are able to collect both direct and diffuse radiation without any moving objects and are particu-larly interesting because they take advantage of the geometric concentration factor between front sur-face and edge surface of the window. The electrical performance of LSC’s is however subject to severalloss mechanisms and fundamental efficiency limits, some are indicated in Figure 5.3. The majority oflosses in SEG occur because: (i) not all absorbed light is re-emitted (quantum efficiency of the dye);(ii) absorbed light is re-emitted at longer wavelengths and thus possesses less energy (Stokes shift); (iii)imperfections in substrate layers scatters part of the light; (iv) not all luminescent light is trapped in theconcentrator (loss cone)(A); and (v) not all trapped luminescent light reaches the edges due to (self)absorption in the dye (B) or bulk molecules (C).

Switching window state upon application of external voltage affects the global alignment of moleculesin the dye, and this in turn changes the relative probability that each of the events in Figures 5.2 and 5.3

∗Approximately 75 % for a plate with a refractive index of 1.5 (Goldschmidt, 2009)

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PV c

ell

PV c

ell

(B)

(A)(C)

Figure 5.3: Schematic overview of the loss mechanisms in SEG.

takes place. As this probability distribution influences transmission, reflection and absorption charac-teristics of the dye, it ultimately results in changes in optical properties of the window. Figure 5.4 showsfor example the spectral transmission curves of a SEG-sample in the dark and bright state determinedby performing measurements with an integrating sphere and spectrophotometer. Like every other ‘op-tical device’, SEG satisfies the condition that the sum of reflectance, transmittance and absorptanceequals one for every wavelength.

0

1

200 400 600 800 1000 1200 1400 1600 1800 2000

Wavelength (nm)

Tran

smis

sion

(-)

Bright state

Dark state

Figure 5.4: Spectral transmission curves of a SEG sample.

Classification

Based on the foregoing discussion of governing physics in SEG, it is now possible to classify the conceptaccording to the categorization of Chapter 2. Adaptive behavior is instigated by changing the appliedexternal voltage to the SEG layer. The concept thus includes a decision making component and there-fore the control in this type of CABS is closed-loop based. Switching the window in either the bright,dark or translucent state causes the molecules of the polymer dye to rearrange. This adaptation takesplace at molecular scale and hence SEG’s adaptivity takes place at the micro-level.

5.3 Conceptual model

The relevant physics that determines the performance of SEG is identified to take place in three differentdomains: thermal, optical and electrical. As a consequence, the conceptual model also consists of

62

5.3. Conceptual model

three parts. The development of conceptual models for each component is discussed in the followingsubsections.

5.3.1 Optical Model

The goal of the optical model is to predict visual performance and associated energy demand of switch-ing SEG’s window state by considering both daylight and artificial lighting. Visual comfort comprisesmany factors; on the one hand, the daylight system needs to ensure adequate illuminance levelswhereas on the other hand excessive brightness or glare needs to be avoided.

Adaptivity in SEG is manifested via changes in the optical properties of the window assembly (Fig-ure 5.1). These optical properties of SEG-samples were determined in experiments with an integratingsphere and spectrophotometer. Values for reflectance and transmittance were measured according tothe protocols in ISO 9050. This data was then transformed into spectrally averaged properties with theaid of the software tool OPTICS, developed by Lawrence Berkeley National Laboratory (LBNL, 2010).The software reads in measured data as ‘laminate interlayer construction’, where the SEG-layer is sand-wiched between two layers of glass with known properties (Figure 5.5).

OPTICSalgorithms+

Reference laminates substrate

Interlayer

Interlayer

Figure 5.5: Schematic representation of the ‘laminate interlayer construction’ method in OPTICS.

The algorithms in the tool (Rubin et al., 1998) then extract the optical properties of this interlayer toyield the properties of the SEG coating only. Subsequently, OPTICS serves as ‘virtual glass laboratory’since it allows this coating to be applied to an extensive number of glazing materials available in the in-ternational glazing database (IGDB). After constructing an appropriate fenestration system, the opticalproperties of the entire SEG window can be analyzed. These calculations include the photopic responsecurve that accounts for the relative spectral sensitivity of the human eye according to ISO/CIE 10527.One of the outputs of OPTICS is the visible transmittance (Tvis) of the entire window system, which isdifferent for each window state.

5.3.2 Thermal Model

Although SEG fulfills the function of window in the building shell, it in fact is a LSC with controllable op-tical properties and corresponding physics. Nevertheless, thermal behavior of SEG is approximated inthis study as a conventional fenestration product, and modeled accordingly. The objective of this ther-mal model is to facilitate insights in the effects of SEG on thermal comfort perception and to demon-strate its saving potential for heating and cooling energy demand.

Heat transfer phenomena in windows are well understood and usually characterized by using validatedmodeling methods as documented in e.g. ISO 15099. The influence of windows on the building’s over-all energy balance depends on the location of the window, its size and construction (including cavity,frame, spacers etc.) as well as the optical properties of the glazing. The momentary energy flow throughwindows can be split in the following two contributions, and is frequently calculated with Equation (5.1)(ASHRAE, 2001).

Temperature driven heat transfer When a temperature difference between the inside and outside ispresent, heat is gained or lost through the glazing via the combined effects of conduction, con-

63


vection, and radiation. These effects are quantified in terms of U [Wm−2K−1]; the U -value or totalheat transfer coefficient of a fenestration assembly.

Solar gain Regardless of the temperature difference across the glazing, heat can be gained through fen-estration products by direct or indirect solar radiation. The amount of heat gain through productsis measured in terms of the solar heat gain coefficient (SHGC ) [−] of the glazing.

q =U · A ·∆T +SHGC · A · Itot (5.1)

Where q is instantaneous energy flow [W ], A is window area [m2] ∆T is difference in air temperaturebetween inside and outside [C] and Itot is incident total irradiance [Wm−2].

Although U -value and SHGC are effective parameters for comparing window technologies, their valuein modeling thermal window behavior is limited since both terms disguise the contributions of partic-ular heat transfer mechanisms. The window properties used to model the thermal behavior of SEG are:convective heat transfer coefficients, thickness and conductivity of individual window panes and gaps,emissivity at front and back surface, and transmittance, reflectance and absorptance as a function ofangle of incidence. Numerical values for these thermal properties are again established on the basis ofthe experimental data as described in Section 5.3.1. The datasets for the different window states werefirst extended with properties for the infrared wavelength area and then read in by the software-toolWINDOW5 (LBNL, 2010). This program then calculates properties of interest for SEG integrated in anarbitrary window system in compliance with ISO 15099.

5.3.3 Electrical Model

The aforementioned optical and thermal models of SEG are relatively straightforward and conform tothe customary practice in building performance assessments. The electrical model aims to predict theannual electrical energy yield of SEG and therefore requires a more in-depth analysis of the photon-to-electron conversion process. These phenomena have been modeled for various purposes in the past,however no study has been reported in BPS literature. Figure 5.6 presents an illustrative impression thatshows the complexity of photon trajectories inside LSC’s.

Figure 5.6: Visualization of three-dimensional photon trajectories in a LSC (Schüler et al., 2007).

The photon-to-electron conversion in these devices basically comprises a chain of gain-factors andefficiencies for each (loss) mechanism in the process. But as Goldschmidt (2009) argues, these effi-ciencies are neither easy to calculate, nor directly accessible by measurement. For this reason, severalapproaches for modeling the behavior in LSC’s have been proposed in literature. In general, two differ-ent modeling methods can be distinguished: the first type is based on explicit thermodynamic energy

64


balances (Chatten et al., 2003; van Sark et al., 2008) while the second type employs (Monte-Carlo) ray-tracing techniques (Bendig et al., 2008; Schüler et al., 2007). Both approaches have in common thatlarge numbers of parameters are required which introduces a large variance in possible outcomes andthus a high level of uncertainty. These simulations have been used to investigate the relative contribu-tions and to assess which mechanisms are important in the collection and conversion processes. Thefocus in these studies was on optimization of product design in the research stages, with less atten-tion for the actual output in large-scale settings under real conditions. Consequently, the resolution ofthese LSC models does not accord with the simulation requirement in this study. Therefore the decisionwas made to develop a data-driven model, not based on first-principles, but on empirical knowledgeobtained after conducting experiments.

Electrical performance of LSC’s is typically characterized in laboratory settings under standard test con-ditions (STC). These controlled conditions assume constant irradiance of 1000W/m2 (AM 1.5 spectrum)at normal angle of incidence and a module temperature of 25C. Outcomes of these experiments arethen converted in maximum efficiencies and used for comparisons of peak performance rather thanfocusing on annual energy yield. When installed in buildings, solar radiation incident on LSC’s virtuallynever impinges at normal angle of incidence nor at 1000W/m2. To illustrate this, Figure 5.7 shows theangles of incidence of direct solar radiation on a vertical plane facing the four cardinal directions in theNetherlands.

0

100

200

300

400

500

600

700

800

0-10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90

Angle of incidence (°)

Hou

rs p

er y

ear

NorthSouthWestEast

Figure 5.7: Direct sunlight incident on vertical façades with different orientations in the Netherlands.

This deviation from optimal environmental conditions is expected to have considerable influence onthe annual electrical output of SEG. The experiments that feed the data-driven model were designed toemphasize this year-round behavior. Based on examined LSC literature, the hypothesis was made thatthe electrical output can be described as a function of intensity of incident solar radiation (Iin), angle ofincidence of solar radiation (θ), window state, and geometric gain (G). Where the latter factor is specificfor a given situation and defined as the ratio between front surface (Af) and edge surface (Ae) of thewindow (Batchelder et al., 1979). A description of the experimental setup and interpretation of the datais presented in the following subsections.

Experimental setup

Figure 5.8 shows two pictures of the experimental setup that was used to supply the data-driven model.The tests were carried out on a sample of SEG with an area of 5×5cm2 and a thickness of 6mm, with-

65


Figure 5.8: Overview of the experimental setup (left) and close-up of the sample (right).

out photovoltaic cells. The sample was illuminated by a 300W solar simulator equipped with filters toapproximate the spectral make-up of the sun (AM 1.5 spectrum). The output emission coming fromthe edge of the sample was determined by placing one end of the sample adjacent to the entry portof an integrating sphere that was equipped with a spectral light measurement system (SLMS) array todetermine the spectral radiant power.

Figure 5.9: Schematic representation of the experimen-tal setup showing angle of incidence (red) andthe direction of output (blue).

Figure 5.10: Projected area of the sample as afunction of angle of incidence.

Variation of incident angle was accomplished by rotating the sample’s supporting mechanism in the di-rection of the white arrow in Figure 5.8. This set-up is further illustrated in the schematic representationin Figure 5.9. The graph shows that the angle of incidence was adjusted in the xz-plane while outputwas measured in the y-direction only. This configuration ensures that merely the effects of waveguidingare considered, not disturbed by effects of direct radiation. Moreover, all other ambient light sourceswere switched off. The output emission was measured in consecutive steps, for one edge at a time whileblack tape was applied to the other edges of the sample in order to prevent light from scattering in atother sides than the window face only†.

The spectral radiant power from the edge of the sample was recorded in eight discrete steps of 10 de-grees, ranging from normal (i.e. 0) to 70. Higher incidence angles were not attainable due to thepractical limitation that the rotation angle would become too steep for the sample to remain fixed.Furthermore, the intensity of incoming light was modulated by changing the applied current to thelight source. Three different configurations were investigated, corresponding to a solar irradiance of700W/m2, 920W/m2 and 1170W/m2 respectively.

†The black tape is not shown in Figure 5.8

66


Results

Figure 5.11 shows the spectral radiant power leaving the exit aperture for the wavelength-interval from400 to 815nm when the sample was illuminated with an irradiance of 920W/m2. The output in thegraph shows a peak around 700nm which coincides with the emission spectrum of the dye. The figurealso shows that output at the edge decreases as θ increases, and that the level of decay increases at moreoblique angles of incidence. Part of this effect is explained by the projected area — and thus incidentradiation received by the sample — that drops with the cosine of angle of incidence (Figure 5.10). Inaddition, a larger part of incoming light gets reflected at the interface at higher incident angles. Thesignificance of these phenomena is however more comprehensible when compared on the basis of effi-ciency, as is demonstrated later. Figure 5.11 further shows that the tail end of the emission spectrum ismissing in the analysis. This is due to the limited detection range of the spectrophotometer used in theexperiments. Consequently, this shortcoming introduces an underprediction in measured output ofapproximately 5%. Further inaccuracies penetrate into the measured output because (i) spectral make-up of the light source is only an approximation of a single sky configuration, and (ii) adjusting the angleis done manually, in a coarse way.

0,00

0,02

0,04

0,06

0,08

0,10

0,12

0,14

400 450 500 550 600 650 700 750 800Wavelength (nm)

Spec

tral

rad

iant

pow

er (

mW

/nm

)

0° 10° 20° 30° 40° 50° 60° 70°

Figure 5.11: Spectral radiant power as a function of wavelength and angle of incidence.

The radiant output power as a function of incidence angle for the three levels of irradiance is givenin Figure 5.12. Radiant power is obtained by integrating the spectral output power of Figure 5.11 overthe wavelength-interval from 400 to 815nm. On the basis of these results, it is possible to calculate theoptical collection efficiency (ηopt) of the SEG-sample per angle of incidence according to Equation (5.2).

ηopt(θ) = photons out

photons in=

∫ 850

400Eout(λ,θ)dλ · Ae∫ 2500

280Ein(λ)dλ · Af,proj

= Iout(θ) · Ae

Iin · Af cos(θ)(5.2)

Where Eout is spectral intensity of the output at one edge [W m−2 nm−1 ], Ae the area of one edge of thesample [m2], λ wavelength [nm], θ the angle of incidence [], Ein spectral intensity of the light source[W m−2 nm−1 ], Af the front surface area of the sample [m2], Iout radiant intensity emitted at one edge[Wm−2] and Iin irradiance of incoming radiation [Wm−2].

Figure 5.13 shows angular efficiency of SEG displayed as normalized optical collection efficiency whichis defined as the optical collection efficiency at particular angles (ηopt(θ)) divided by the optical col-

67


0

5

10

15

20

25

0° 10° 20° 30° 40° 50° 60° 70° 80° 90°

Angle of incidence

Rad

iant

pow

er (

mW

)700 W/m² 920 W/m² 1170 W/m²

Figure 5.12: Radiant power as a function of angle of inci-dence.

0,0

0,2

0,4

0,6

0,8

1,0

0° 10° 20° 30° 40° 50° 60° 70° 80° 90°

Angle of incidence

Nor

mal

ized

opt

ical

col

lect

ion

effic

ienc

y

700 W/m² 920 W/m² 1170 W/m² Curve fit

Figure 5.13: Normalized optical collection efficiency, orKθ , as a function of angle of incidence.

lection efficiency at normal angle of incidence (ηopt,⊥). The overlapping triangles in Figure 5.13 indi-cate that the optical collection efficiency of SEG is independent of incident radiation intensity, whichis inconsistent with the results for quantum dot LSC’s in Gallagher et al. (2007). Since the results inFigure 5.13 compare efficiency relative to normal efficiency, it provides insights in relative losses andtherefore acts as correction factor that accounts for the angular performance of LSC’s. Correction fac-tors of the same kind are frequently employed for modeling solar thermal collectors where they aredescribed as incidence angle modifiers (IAM) or (Kθ) (Duffie and Beckman, 2006). In this thesis, thesame terminology is adopted.

An empirical relation for this IAM was obtained by fitting a curve through the measured datapoints.The shape of this curve shows similarities with the specular reflection component of clear float glass.Therefore, inspiration for candidate curve-fits was found in functions that describe angular resolvedproperties of windows (Rubin et al., 1998; Karlsson et al., 2001). In principle, it would have also beenpossible to model this behavior based on first-principles by using Fresnel equations and Beer-Lambert’slaw. The following reasons justify the use of an empirical rather than the theoretical approach: It is un-known how luminescence effects influence specular behavior of the sample. Spectral refractive index,spectral extinction coefficient and thickness of all substrate layers needs to be characterized accuratelyin the theoretical approach (Singh and Garg, 2010). Considering the required level of detail, an empir-ical approach is computationally the most effective. The tangent function in Equation (5.3) was foundto be a simple yet effective method for representing the desired behavior.

Kθ = 1− tanx(θ

2

)(5.3)

By definition, these functions return 1 when θ is zero and 0 when radiation hits the sample in paralleldirection. The curvature of the function is tuned by selecting the most appropriate value for x. Thebest curve fit was obtained by calculating root mean square errors (RMSE) as a function of x (Equa-tion (5.4)), and subsequently selecting the value for x that minimizes this function. The best matchingtangent function was found to have a value of x = 3,5 and is plotted with the black line in Figure 5.13

RMSE(x) =√√√√1

8

7∑n=0

(yexp(10n)− yfit(10n))2 (5.4)

When the results for this IAM correlation are now combined with the expression for calculation of op-tical collection efficiency (Equation (5.2)), this results in the semi-empirical relationship for calculating

68

5.4. Simulation model

optical output as a function of angle of incidence in Equation (5.5). Assuming that Kθ is a character-istic property of SEG, no angular dependent measurements are required for characterizing the opticaloutput of SEG. The shape effects are included via the geometric gain factor G = Af

Ae; the efficiency factor

ηopt,⊥ accounts for the effects of different window states.

Iopt = Itot ·G · cos(θ) ·ηopt(θ) = Itot ·G · cos(θ) ·Kθ ·ηopt,⊥ (5.5)

Thus far, the development of the electrical model has only dealt with the optical collection of outputemitted at the edges of the window relative to the incoming photon flux at the front surface. The nextstep is then to include the photovoltaic conversion process in the model. Photovoltaic conversion effi-ciency (ηpv) includes both coupling losses at the PV interface and the quantum efficiency of the PV cells(Currie et al., 2008), and is best calculated by a dedicated photovoltaic model. The electrical output of aSEG window (Pel) is obtained by multiplication of the optical output power (Popt) with the photovoltaicconversion efficiency (Equation (5.6)):

Pel = Popt ·ηpv = 4· Itot · Af · cos(θ) ·Kθ ·ηopt,⊥ ·ηpv (5.6)

Where the factor four in Equation (5.6) accounts for the difference between single-edge collection tofull-perimeter conversion efficiencies of square windows. The product Itot · Af · cos(θ) represents theamount of incident solar radiation. The variable properties of changing SEG’s window state are includedin the model via ηopt,⊥; a factor that is characteristic for the specific geometry and window state. When(measured) electrical output of SEG is compared to incoming radiation, the expression of Equation(5.6) can be further simplified by using the total power conversion efficiency (ηpc) of the window whichis defined as: 4·Kθ ·ηopt,⊥ ·ηpv, and results in the expression of Equation (5.7):

Pel = Itot · Af ·ηpc (5.7)

5.4 Simulation model

5.4.1 Simulation strategy

The main objective of the simulation process is to quantify the overall performance of SEG. This allowsthe user to gain insight in the trade-offs between thermal and visual comfort on the one hand andassociated energy demand on the other hand. This simulation requirement necessitates that thermal,optical and electrical models are considered simultaneously, and therefore confirms that a combinationof stand-alone tools is inappropriate for the purpose. The BPS tools overview of Table 3.1 containstwo programs that can cope with integrated simulations of the thermal, optical and electrical domains:ESP-r and EnergyPlus. The possibility to change the window properties in three states during runtimeis identified as a pre-requisite for successful simulation of SEG. Chapter 3 draws the conclusion thatTRNSYS appears to be the single tool that is able to aptly handle this adaptive behavior. Unfortunately,TRNSYS does not offer the possibility to predict the building’s daylighting performance (Table 5.2).

Table 5.2: Capabilities of tools to simulate SEG.

Adaptation Thermal Optical Electrical

ESP-r

EnergyPlus

TRNSYS

As a consequence, integrated simulation approaches are also inadequate to fulfill the simulation ob-jective, and the only remaining simulation strategy is co-simulation. The decision was made to use

69


TRNSYS for performance prediction in the thermal and electrical domain, and to couple this modelwith the results of dynamic daylight simulations in DAYSIM. Daylight simulations for all window statesare first conducted in a preprocessing stage. This data is then supplied to TRNSYS that ‘selects’ the rightdata during runt-time corresponding to the controlled window state. Data is flowing across the threedomains at every time-step and therefore data exchange is dynamic. The simulators in DAYSIM andTRNSYS are however invoked consecutively and therefore the workflow is sequential (Figure 5.14). Zhaiet al. (2002) have identified this simulation approach as virtual dynamic coupling (or bin coupling),originally developed as a way to reduce computational costs of detailed thermal-airflow simulations. Aschematic representation of this simulation strategy for SEG is given in Figure 5.15, and is discussed inmore detail in the following sections.

B11 B12 .... B1n

B21 B22 .... B2n

A1 A2 .... An

D1 D2 .... Dn

Workflow

DAYSIM TRNSYS

Figure 5.14: Workflow of the simulation process, where A,B and D indicate the thermal, optical and electrical do-main respectively.

5.4.2 DAYSIM

DAYSIM is a dynamic daylight simulation tool that uses the general accepted Radiance algorithms incombination with a daylight coefficient method and Perez sky model (Perez et al., 1990) to calculateannual time-series of light distribution in a room. The simulation process starts with specification of(i) scene and (ii) scenario for every window state. This includes modeling of external obstructions,room geometry, surface reflectance and daylight openings. According to the conceptual model, thecharacteristics of SEG can be modeled by using — and changing — the visible transmittance (Tvis) of thefenestration system in each window state. Thin glass surfaces like SEG are best represented by using theglass primitive (Larson and Shakespeare, 1998), which computes multiple internal reflections withouttracing rays; thus saving rendering time without compromising accuracy. In Radiance/DAYSIM, glassis characterized by transmissivity (tn) at normal incidence. Therefore it is necessary to convert themeasured visible transmittance derived by OPTICS to the theoretical value for transmissivity (Larsonand Shakespeare, 1998).

In DAYSIM, the scenario is specified by selecting an appropriate weather file. These files containhourly values of solar irradiance and therefore do not acknowledge the fickle supply of daylight (Sec-tion 3.2). Shorter time-step data, upt to one minute can be obtained by using the model-based stochas-tic Skartveit-Olseth approach implemented by Walkenhorst et al. (2000).

After the building scene and scenario is modeled, the simulation process continues with full-year day-light simulations for all window states‡. Raw luminance and illuminance data are computed at spec-ified sensor points and stored in data-files on a time-step basis. Analysis of these results also yields

‡Although SEG can be switched in three states, the decision was made to only include the bright and dark state in the initialmodel while excluding the translucent state. This simplification was made for the following three reasons: (i) from the energypoint of view, the effects of translucency are inferior to switching between the dark to bright state, (ii) the translucent state isthe least mature technology with a currently only a mediocre haze factor, and (iii) the goniophotometer equipment requiredto accurately characterize the optical scattering properties was not at our disposal. Switching the window in three states washowever part of the initial requirements of the model. The simulation model thus enjoys the benefit of high flexibility and can beeasily extended with additional window state(s).

70

5.4. Simulation model

the amount of supplementary artificial lighting needed to sustain sufficient illuminance levels and theadditional internal heat gains associated with this contribution. Another distinct quality of DAYSIM isits occupancy model that is based on Lightswitch-2002 (Reinhart, 2004). Since this model uses behav-ioral patters, it exhibits a relatively high level of detail and is consequently favored above the simpletime-schedules in TRNSYS.

TYPE 56

TYPE 56

Control

Control

time

state

ill, lum

time

state

ill, lum

Ti

Ti

Window state

Window state

Internal heat gain & electricity consumption


Daylight data

Daylight data

DAYSIM

TYPE 56

PV‐model

PV‐model

Electricity generation


Ti

Window state



His

tory

His

tory

Sim

ulat

ion

time

One

tim

e-st

ep

Figure 5.15: Simulation strategy for the SEG model.

5.4.3 TRNSYS

Thermal

The influence that SEG exerts on thermal performance of a zone or building is evaluated by using theTRNSYS multizone building model (TYPE 56). This dynamic model explicitly calculates many possibleoutputs including, air temperature, surface temperatures, heating and cooling power demand and ther-mal comfort indicators. In the simulations, only one building model is used that dynamically changesits window properties during run-time with a function called variable window ID. Environmental con-ditions are ensured to be identical to those subject to the daylight model by selecting the same weatherfile. Internal heat gains of the building depend on the state of the window since the amount of artifi-cial lighting changes with daylight availability. Together with occupants’ presence, this data is importedfrom the data files precalculated by DAYSIM.

71


Electrical

The electrical model calculates electrical output of SEG according to Equation (5.6). The amount ofradiation incident on the window is calculated by the weather data processor (TYPE 15). This flux ofenergy is then multiplied in equation-TYPES by the incidence angle modifier (Kθ) and the optical col-lection efficiency (ηopt,⊥) whose value is adaptive and depends on the window state as imposed by thecontrol component. The remaining photovoltaic conversion process is calculated by using TRNSYSTYPE 180; the photovoltaic array with data file. This type is specifically developed for characterizingphotovoltaic output and is based the equivalent one-diode model (Wenham et al., 2007).

The parameters of this model include short-circuit current (Isc), open-circuit voltage (Voc), and voltage(Vmpp), current (Impp), and power (Pmax) at the maximum power point (MPP), all measured at standardtest conditions (STC). In addition, the short circuit current temperature coefficient (µI,sc) and open-circuit voltage temperature coefficient (µV,oc) are required. This data is typically available in the productinformation provided by the manufacturer of the PV cells.

Control

The control component plays a central role in SEG’s simulation model as indicated in Figure 5.15. Thecrude output files from DAYSIM are first rearranged in a spreadsheet tool and then imported in TRN-SYS via free format data readers in expert mode (TYPE 9e). Every time-step, this data is accessed bythe control component which decides upon the right adaptive actions on the basis of an imposed con-trol strategy. This control logic is implemented via equations containing conditional statements thatcompare model output (e.g. temperature, window luminance, workplane illuminance) to target valuesand return window ID for the next time-step as output. This window ID is passed on to the thermaland electrical model and used in the respective calculations for the next time-step. TRNSYS assemblesand manipulates the controlled state variables and stores them in output files. Once the simulationis finished, these results are used to calculate the desired performance indicators of SEG for the givenoperational strategy.

72

CH

AP

TE

R

6SMART ENERGY GLASS MODEL VALIDATION

Performance predictions in support of decision making is identified as a controversial use of simulation(Sokolowski and Banks, 2009). This is because validity of predictions and associated decisions entirelyrests on the assumption that the simulation model is valid too. In this chapter, the validity of the SmartEnergy Glass (SEG) model is tested on the basis of an empirical validation study. It is not the intentionto question or demonstrate the validity of the underlying algorithms of the software tools. Instead, thespecific assumptions regarding the SEG model will be evaluated.

6.1 Introduction

Up till now, SEG has only been tested as small samples under the controlled conditions of a laboratoryenvironment. Doing experiments on a larger scale, in windows exposed to atmospheric conditions, ishowever indispensable for gaining more insight in thermal, optical and electrical performance, and thepotential advantages of SEG. Furthermore, this approach can also reveal potential practical problems(e.g. scaling effects, homogeneity problems, performance degradation) related to the integration ofSEG in a real façade, that will not show up in laboratory settings. To be able to as well evaluate theperformance of SEG under conditions∗ outside those experienced during the measurements, one cantake advantage of the qualities of BPS. These models are however based on a number assumptions,simplifications and uncertainties. In order to use the model with sufficient confidence, one needs toassure that the model results are reliable. One way to check the validity of BPS-models is by comparingsimulated results with the outcomes of an experiment; a process called empirical validation (Judkoffand Neymark, 1995).

Although it is the first time SEG is being tested under ‘real-world’ conditions, it is no novelty that theeffects of switchable glazing are investigated under atmospheric conditions. Several research projectsreported on the use of experiments for determining the performance of electrochromic glazing. Thesetests were carried out in different settings, ranging from (i) reduced-scale test boxes with small windowsamples (Piccolo et al., 2009; Piccolo, 2010), and (ii) highly standardized and instrumented test-cells forthermal and solar building research (PASSYS) (Aleo et al., 2001; Assimakopoulos et al., 2007), to (iii) full-scale windows in office buildings with occupants as subjects (Platzer, 2003; Lee et al., 2006; Zinzi, 2006).A selection of methodological aspects in these studies serves as reference for setting up the experimentsin this project.

∗Including environmental boundary conditions, orientation, occupant behavior, design variables, material properties, etc.

73

6. SMART ENERGY GLASS MODEL VALIDATION

6.2 Methodology

Empirical validation studies in the light of BPS essentially consist of the following three steps (Judkoff,2008; Strachan, 2008):

1. Monitoring the boundary conditions and the performance of a test object in an adequate experi-mental setup;

2. Creating a simulation model of the experimental setup and test environment, and undertakingsimulations using the measured climatic data;

3. Comparing predicted performance with measured performance and deciding upon validity ofthe model.

If the third step turns out to be successful (i.e. the results are within certain bounds), it proves thatthe simulation program can correctly model the component’s characteristics when that component issubject to arbitrary dynamic outdoor conditions lying within the tested range. In addition, the valida-tion process then also gives sufficient confidence that the model reliably predicts performance in newsituations, provided that the extrapolated conditions are selected with care. Empirical validation how-ever encompasses more than simply comparing predictions with empirical data for the binary decisionwhether the model is right or wrong. The procedure also provides an opportunity for new insightsand/or enhanced understanding of the system, and this new knowledge can be used for ameliorationof the initial model and advancements of the product design (Palomo Del Barrio and Guyon, 2004).

The following two subsections describe the methodology that was applied in the first two steps of theempirical validation study. Subsequently, Section 6.3 discusses the comparisons between measure-ments an simulations. The focus in this validation study is on the daylight model and electrical model.Rigorous studies have already demonstrated that solar energy gains through glazing systems are ac-curately modeled in TRNSYS (Loutzenhiser et al., 2006; Judkoff, 2008). Replicating these experimentsfor SEG would be a cost- and time intensive undertaking that requires an extensive set of measure-ments and a large degree of control on disturbances. Because thermal behavior of SEG resembles thatof conventional windows reasonably well, it is assumed that the decision to rely TRNSYS’ algorithms forthermal performance predictions of SEG is justified.

6.2.1 Experimental setup

All measurements took place at the test façade facility of the Vertigo building on the campus of Eind-hoven University of Technology in the Netherlands (Figure 6.1). The orientation of this façade is west,no significant obstructions that block direct solar radiation are present.

Figure 6.1: Overview of the test-façade with the SEG-prototype installed.

74

6.2. Methodology

The performance of SEG is tested on the basis of a working prototype. In this custom-made window(Figure 6.4(a)), the SEG-coating is sandwiched between two layers of 3mm clear Guardian glass to formthe internal pane of the double glazing unit. The external pane is formed by a 6mm clear glazing withUV-filter. This UV-blocking layer was selected to protect the SEG-coating and to increase its durability.In SEG’s envisioned final product design, the UV-protection layer will be mixed in as part of the glasssandwich. Consequently, the SEG layer can move to the external pane which directly has beneficialeffects for the solar heat gains and internal window layer temperature in summertime.

72 cm

40 cm

35 c

m

54 c

m

108 cm

Figure 6.2: Dimensions of the test-box, the window is ori-ented to the west.

1

54

8

2

3 6

7

Figure 6.3: Segments of PV cells for electrical mea-surements, view from the inside.

Given the size of the available SEG-prototype (35 x 40cm2), the decision was made to install a test-box,to mimic the effects of a zone behind the façade. The dimensions of the fibreboard box are given inFigure 6.2 and correspond to a typical full-scale equivalent office room, scaled with a factor 1 : 5. Inorder to asses SEG’s daylighting performance correctly, it is important that the internal surfaces of thebox (walls, floor, ceiling) are painted in representative colors (Bodart and Deneyer, 2006). The floor ofthe test-box was painted grey, all other interior surfaces were colored with matt white paint, pursuant tothe monitoring protocol developed in IEA SHC Task 21 / IEA ECBCS Annex 29 (Velds and Christoffersen,2001).

Light distribution in the test-box was measured via an array of photocell illuminance sensors, all cali-brated in advance with a reference cell in integrating sphere measurements. The arrangement of thesesensors inside the box is given in Figure 6.4 (c).

(a) (b) (c)

Figure 6.4: Photo’s of the experimental setup, showing (a) fabrication of the prototype, (b) the window installed inthe façade, and (c) the test-box with illuminance sensors.

Electrical performance of SEG was evaluated by monitoring the output voltage of the PV cells. For thispurpose, the electrical circuit of PV cells was subdivided in eight segments, as illustrated in Figure 6.3. To

75


demonstrate the energy generating potential, electrical wires were uncoupled from the window frame(Figure 6.4 (b)) and each circuit was connected to a fixed load with a resistance of 980Ω. The voltageacross each resistance was measured and recorded in time-steps of one minute.

The last essential part of the experiments is the monitoring of occurring operational conditions. Thestate of the window (dark, bright or translucent) was manually controlled via a remote control system.Actual switching was accomplished via a Moeller Xcomfort wireless control switch. This signal was alsosent to the data logger and continuously monitored during the entire measurement period. Environ-mental boundary conditions were monitored every two minutes with a Vaisala WXT510 weather stationthat measures wind speed and direction, liquid precipitation, barometric pressure, air temperature andrelative humidity. Direct and diffuse solar irradiance was additionally measured via a cm10 pyranome-ter.

6.2.2 Simulations

Building performance simulations were carried out with the integrated thermal-optical-electrical sim-ulation models as detailed in Chapter 5. The model was executed in TRNSYS v16 in combination withdaylight results from DAYSIM v2.1. In both tools, building models were created following the dimen-sions of the test-box given in Figure 6.2, without any external obstructions.

The internal surfaces of the test-box are treated like perfect Lambertian surfaces with reflectance ρ = 0.3for the floor and ρ = 0.8 for walls and ceiling. SEG is modeled as windows and the respective materialproperties for the bright and dark state are given in Table B.1. Simulations in DAYSIM were carriedout with a time-step of two minutes, while in TRNSYS a time-step of ten minutes was selected. Theappropriate measured boundary conditions were supplied to the models, and representative days wereselected for comparisons of the results.

6.3 Comparing results

“The ultimate or ‘absolute’ validation standard would be a comparison of simulation results with a per-fectly performed empirical experiment, the inputs for which are perfectly specified to the simulation-ists” (Neymark et al., 2002). Obviously, this ideal situation does not exist in practice. Multiple potentialerror sources are simultaneously operative which makes interpretation of the results of validation ex-ercises a challenging task. A mismatch between measured and simulated output does not necessarilyimply that the assumptions of the simulation model is wrong.

The following subsections present the comparison of results and judgement about the models’ validity.This starts with the daylight model in Section 6.3.1, and then discusses the validation outcomes of theelectrical model in Section 6.3.2.

6.3.1 Daylight model

Figure 6.5 shows the comparison between measurements and simulation results for the three sensorpositions closest to the window. The results present interior illuminance values on a sunny day whenthe window was switched in the bright state.

The comparisons in Figure 6.5 show generally good agreement between results of measurements andsimulations, with a maximum overprediction at the peak of approximately 15 %†. The results can beused to test the assumption that SEG is reliably modeled by using DAYSIM’s glass primitive and visibletransmittance (Tvis) of the fenestration system. The daylight coefficient approach in DAYSIM is basedon the principle of superposition which makes that Tvis correlates to the magnitude of illuminance

†An error of 15 % may seem unduly high. It should however be noted that the human eye is a logarithmic sensor, which makesthat the accuracy of the simulation model corresponds to the sensitivity of illuminance our eyes can detect (Reinhart, 2010).

76

6.3. Comparing results

0

4000

8000

12000

16000

20000

0 4 8 12 16 20 24

Time (h)

Illu

min

ance

(lu

x)

Sensor A

Sensor B

Sensor C

Figure 6.5: Comparison between measurements and simulations on a sunny day, with: the measured illuminance(solid lines) and simulated illuminance (dashed lines) for three different sensor points.

levels. The observed errors in the results compare well to the results in e.g. Reinhart and Walkenhorst(2001) and Reinhart and Breton (2009), and are only slightly higher than the confidence bounds as spec-ified by Mardaljevic (2000).

The differences in measured illuminance among the sensor points A, B and C are prompted by theirrespective positions in the test-box. The window is mounted in the west façade, which makes Sensor Bthe first to receive direct radiation. Sensor C receives direct radiation later in the afternoon, and at thisparticular day, sensor A does not receive any direct radiation at all. The results of the simulations tendto predict the same pattern, however, the starting time of the sudden increase for sensors B and C showsa considerable offset. Figure 6.6 introduces the most important error sources in daylight performancepredictions.

Figure 6.6: Main causes of error in daylighting performance assessment (Thanachareonkit, 2008).

Discrepancies between measurements and predictions stem from the compound error of multiple errorsources and cannot be traced back to model photometry only. The observed time shift in Figure 6.5depends on the moment when the sensor starts to observe direct radiation. Therefore, the discrepancy

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is believed to be attributable to model geometry rather than model photometry. Indeed, this seems aplausible explanation, since window sills and mullions, and the shading obstructions of the test setupwere not included in the building model. This simplification was corrected in the results of Figure 6.7where the building geometry was set up in more detail and compared to the measurements and theresults of the original model.

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Figure 6.7: Comparison between measurements and simulations with the original and detailed model for sensorposition A.

When shading obstructions are included in the model, the overall residuals are reduced. Although thetime shift is only of minor importance for visual comfort levels, it does represent a significant contri-bution of daily solar gains. Therefore it is concluded that modeling building geometry with sufficientaccuracy deserves special attention in the simulation process.

Currently, no general accepted standard or reference values exists for evaluation of empirical validationstudies related to daylight models (Reinhart and Breton, 2009). Errors in the presented results do how-ever compare well to other prominent validation studies. Consequently, the conclusion is made thatthe simulation approach in DAYSIM is confidentially used for assessing SEG’s daylighting performancein the bright state, provided that the model geometry is included at an appropriate level of detail. Theoutcomes of SEG in the dark state showed error bounds comparable to the bright state, which is a di-rect consequence of the superposition principle in DAYSIM’s daylight coefficient method. In addition,DAYSIM can also be used with confidence for performance prediction of SEG in in the translucent pri-vacy state. Reinhart and Andersen (2006) found that DAYSIM “is fully capable of simulating the shorttime step dynamics of indoor illuminances due to daylight for a design that features translucent pan-els”. For this purpose, the bidirectional light distribution function of SEG first needs to be establishedin transmission (BTDF) and reflection (BRDF) measurements.

6.3.2 Electrical model

Figure 6.8 shows the comparison between results from measurements and simulations for a day withfull overcast sky. It can be observed that the measured electrical output in all segments (see Figure 6.3) isproportional to the intensity of incoming solar radiation and that the results of simulation tend to followthe same pattern. The measured output features a high degree of variability that resembles the erraticsupply of sunlight. In the simulations however, a timestep of 10 minutes was used which makes thatthe output is averaged and the variability is attenuated. Figure 6.8 further shows that the PV cells still

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6.3. Comparing results

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Figure 6.8: Comparison between measurements and simulations on a day with overcast sky, with: the measuredelectrical output (PV1 to PV8) in [V]; measured global solar irradiance on the façade in [W/m2]; andsimulated electrical output in [V].

produce electricity around sunset whereas the model predicts zero output. This discrepancy is probablycaused by the fact that twilight and reflected radiation from surrounding objects to the window is notexplicitly included in the model. Another difference is introduced by the rectangular dimensions of theSEG-prototype (35 x 40cm2), where the model assumes square window shapes.

The results of measurements for a sunny day are given in Figure 6.9. Output on a sunny day apparentlydiffers one order of magnitude compared to cloudy days. The results in Figure 6.9 also show that elec-trical output on a sunny day is not the same for all edges of the window, but instead shows substantialdirectional effects. The model as developed in Chapter 5 is based on the waveguiding effect only, andtherefore assumes equal electrical output at all four sides of the window. This assumption introducessignificant underprediction of electrical output that is not justifiably neglected and therefore deems theinitial model unacceptable.

To overcome this deficiency, the electrical model was extended with a component that accounts for theradiation that falls directly onto the PV cells. The modified expression for electrical output is given inEquation (6.1)

P?el = Pel + Pdirect

= 4· Itot · Af · cos(θ) ·Kθ ·ηopt,⊥ ·ηpv +4∑

i=1(Idir,i − Irefl,i ) · Apv,i ·ηpv (6.1)

Where Pel is equal to Equation (5.6), Idir is direct beam irradiance hitting the PV cell [Wm−2], Irefl isreflected radiation at the interface [Wm−2], Apv,i is area of the PV cell [m2] and i represents the positionof the PV cell in the frame.

Since this new component only considers direct beam radiation, obstruction elements are used in themodel to determine at what time of the day the additional radiation strikes the PV cells. The expressionincludes a summation for the four edges, because the position that the PV cell occupies in the window

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Figure 6.9: Measured electrical output voltage on a sunny day in [V ] for the four edges of the window, together withmeasured solar irradiance on the façade in [Wm−2] (green line).

frame — and thus its orientation and shading — determines the amount of received radiation. Thiseffect is modeled in TRNSYS by obstructing the respective views to the sky via TYPE 34 shading compo-nents (overhang and wingwall shading). Finally, the reflection component is introduced in the modelbecause a part of the radiation that would have hit the bare PV cells is lost in advance due to specularreflection at the external SEG window pane.

Figure 6.10 again shows the results for the same sunny day, but now in comparison with the simulationresults of the modified electrical model. For pragmatic reasons, it was not possible to assess these out-comes on the basis of absolute comparisons: In the experiments, the PV cells were connected to fixedelectrical resistances that choose an arbitrary working point on the IV-curve of the PV cell, dependingon available photocurrent. Because of the high variability in supply that is introduced by direct radia-tion, the PV cells often operate in suboptimal working points. The modeled PV cells on the other handare equipped with MPP-trackers, and therefore they do always operate in maximum power point (MPP).This disparity between experiment and model prohibits fair comparisons on the basis of output voltageonly, and also prevents useful conversion from one quantity to another via Ohm’s law. Nevertheless,the results still do contain valuable information that is relevant for assessing validity of the modifiedelectrical model.

The PV cells facing downwards (top) and facing the north (left) do not receive any direct beam radiationand therefore the simulation results for these PV arrays, as displayed in Figure 6.10, are identical andsimilar to results of the initial model. It can be observed that the shapes of the simulated curves com-pare reasonably well to those measured. In addition, the relative magnitude of increments in outputbetween the segments is also represented in the model. Given the relatively large amount of sources ofuncertainty (e.g. temperature dependency, reflected radiation from the environment, losses in electri-cal circuits), the model predicts the actual electrical output with fairly good similarity. Therefore, theconclusion is made that the model is valid within the boundaries and purposes of this study.

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6.4. Conclusion

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Figure 6.10: Comparison between measurements and simulations on a sunny day, with: the measured electricaloutput voltage for the four edges in [V] (solid lines) and corresponding simulated electrical outputpower in [W] (dashed lines).

6.4 Conclusion

This chapter presented the findings of the empirical validation study that was carried out to check thevalidity of the optical and electrical SEG models. Errors predicted by the daylight model were foundto be commensurable with the results of other validation studies, and consequently deemed accept-able. The electrical model however, needed to be extended with an additional component that modelsthe radiation directly falling onto the PV cells, in order to yield accurate results. On the basis of thisstudy, it can be concluded that the results of the integrated SEG model are accurate enough to be usedfor comparisons of alternative design variants and for investigations into the effects of different con-trol strategies. The results of the simulation study that was carried out with this validated model arepresented in Chapter 7.

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7PERFORMANCE SIMULATION OF SMART ENERGY GLASS

In the third step of the case study, performance simulations of smart energy glass (SEG) are carriedout. The results of this study are primarily used for quantifying this integrated performance and forpointing to future directions of product development. In the second place, the simulation process isalso useful for clarification of practical points of attention associated with performance simulation ofCABS in general. This chapter first introduces the methodology for this application study in Section 7.1.Subsequently, the results of three examined cases are presented. Finally, this chapter concludes with adiscussion that places the specifics of the simulation results in a broader perspective.

7.1 Methodology

The performance of SEG is assessed on the basis of the simulation strategy as developed in Section 5.4.1and illustrated in Figure 5.15. The modified expression for electrical output (Equation (6.1)) was usedto predict electrical output. Three different cases are examined in this chapter: “Energy efficient office”,“Advanced renovation” and “SEG in a continental climate”. This section discusses the common charac-teristics for these simulations, and introduces the performance indicators used in the analysis. Specificassumptions and motivations for each case are presented together with the results in Sections 7.2 to 7.4respectively.

7.1.1 Model setup

“Smart energy glass” is a coating that can be applied to almost any glazing system. The technology is stillunder development and the performance of the dye continues to be improved. Developing the speci-fications of SEG has to satisfy multiple and competitive requirements, including: aesthetics, efficiency,bandwidth, UV-stability and homogeneity. In this evaluation, the properties of SEG were selected tobe a fair projection of what is possible in the foreseeable future. The switchable SEG-layer is assumedto form the external pane of the double glazing system∗. Thermal window performance is further en-hanced with a low emissivity (low-E) coating and argon filled cavity to meet current building energystandards.

Earlier simulation studies with electrochromic windows indicated that selection of the reference caseis of essential importance for assessing feasibility of the entire concept (Lee and Tavil, 2007). Compar-isons to invalid reference values can disguise the true potential of the technology and is on the other

∗This contrasts with the composition of the SEG prototype used in the validation study of Chapter 6, where SEG formed theinternal window pane because of UV-stability reasons.

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7. PERFORMANCE SIMULATION OF SMART ENERGY GLASS

hand misused to manipulate stakeholders’ opinions. In this study, the SenterNovem reference officebuilding (SenterNovem, 2009) is used for baseline comparisons, because this reference case is specifi-cally developed for obtaining rational indications of the energy saving potential of innovative buildingtechnologies.

The analysis in this study is restricted to a two-person south-facing perimeter office zone, situated at anintermediate floor of the reference building. The office space is assumed to be surrounded by identicaloffice spaces. These adiabatic boundary conditions were selected to ascertain that observed perfor-mance differences are effectively attributable to SEG. Storage of thermal energy in internal partitions istaken into account, and typical office equipment amounts to a heat load of 10W/m2. The dimensionsand geometry of the zone are given in Figure 7.1. The window-to-wall ratio of the south façade equals35 %. The opaque façade elements are well-insulated with an Rc-value of 3.5m2K/W.

3,6 m

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Figure 7.1: Schematic representation of the office room used in performance simulations.

Occupancy in the office room follows DAYSIM’s probability-based five day workweek-schedule with in-termediate and lunch breaks. Artificial lighting (15W/m2 installed power) switches according to thissame schedule and is continuously dimmable up to an indoor illuminance of 500lx on the basis of theLIGHTSWITCH-2002 algorithms (Reinhart, 2004). Lighting is controlled based on a work plane photo-sensor at a distance of 1.8m from the envelope. Thermal conditions in the zone are controlled on thebasis of indoor air temperature, with setpoints for heating (20C) and cooling (24C) between 8 a.m.and 17 p.m., and a heating setpoint of 16C outside working hours.

Identical weather files in EPW-format are supplied to both DAYSIM and TRNSYS. Stochastically gener-ated short time-step (five minute) solar irradiance data files (Walkenhorst et al., 2000) are created oncefor every case, and are used for predicting daylighting performance.

The number of possible strategies for controlling SEG’s adaptive behavior is virtually infinite. The aimof this chapter is to explore their potential and provide some first insights in the cause-and-effect rela-tionships of various options. It was not intended to seek for the best possible strategy nor to accountfor the practical implementation aspects of these algorithms. Table 7.1 provides an overview of controlstrategies that were studied.

7.1.2 Performance indicators

The underlying motive behind this application study is to gain insight in the interplay between thermaland visual comfort and associated impacts on energy demand for heating, cooling and lighting. Forthis purpose, it is important to establish the performance indicators (PIs) that form the basis for com-parisons beforehand. Values for annual heating, cooling and lighting energy demand are obtained after

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7.1. Methodology

Table 7.1: Overview of the six investigated control strategies.

A Reference caseB SEG always switched in the bright stateC SEG always switched in the dark stateD SEG switched to the dark state when indoor air temperature ≥ 21CE SEG switched to the dark state when daylight illuminance on work plane (Eh) ≥ 700lxF SEG switched to the dark state when window luminance (Lv) ≥ 1500cd/m2

summation of the momentary power that is required to satisfy setpoints on a time-step basis. These val-ues are converted to the same base unit (kWh) because this allows for (i) calculation of overall annualenergy demand and (ii) insights in the relative importance of individual contributions.

Apart from integrated energy consumption, peak power demand is also considered as important PI.Peak capacities not only determine investment costs, but also influence operating system efficiencies,and dimensions of equipment and distribution systems. Both overall annual energy demand and peakpower should be aimed to be as low as possible. This goals however needs to be achieved withoutmaking compromises on performance in terms of the comfort requirements as discussed below.

Minimum comfort levels with SEG are guaranteed by proper sizing and control of HVAC and artificiallighting equipment. Too cold thermal environments are of minor concern in well-insulated office build-ings, because internal heat gains contribute to quick warm-up times. The same is true for minimumilluminance levels, because lighting systems are dimensioned to deliver adequate visual performanceunder dark outside conditions. The crux of controlling SEG’s performance is to counteract discomfortdue to an oversupply of solar gains without the penalty of expending too much energy.

In this thesis, thermal comfort is assessed on the basis of overheating risk. This is accomplished bycounting the number of hours that indoor air temperature exceeds 25C. Allowing discomfort duringmaximum 5 % of working hours is usually seen as realistic and economic target value in the trade-offbetween energy and comfort. As a result, this amounts to an allowed number of 100 overheating hours.

Visual comfort is evaluated by considering the risk of glare, which is defined as “the sensation pro-duced by luminance within the visual field that is sufficiently greater than the luminance to which theeyes are adapted to cause annoyance, discomfort or loss in visual performance and visibility” (IESNA,2000). Multiple assessment methods and performance indices are in use to evaluate the occurrenceof glare (Bellia et al., 2008). The current DAYSIM distribution does not come with a module for directassessment of the risk of glare†. The tool is however capable of calculating surface luminance values,and these can be transformed into the desired performance indicator. Occurrence of glare is found tobe affected by many interrelated factors, including: absolute luminance of the glare source and sur-rounding surfaces, transient adaptation of the eye, surface reflectances, type of work (screen, paper),position in the room, etc. In this study, the risk of discomfort caused by glare is assessed by countingthe number of times when the ratio between window luminance (Lv) and paper task luminance (Lh) ishigher than 10:1 (IESNA, 2000). Occurrence of glare is further extended with the additional clause thatLv ≥ 2000cd/m2, in order to exclude from analysis cases with high contrast but low brightness. Levelsof maximum allowed visual discomfort typically exist in terms of design recommendations rather thanfixed standards. All these guidelines agree that occurrence of glare should be ‘as low as possible’; thetrade-offs in relation to other PIs should rationalize for each situation whether this strive is justifiable.

†An extension with dynamic daylight glare probability (DGP) functions based on glare rating classification and frequencydistributions (Wienold, 2009) is planned for the next version of DAYSIM.

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7.2 Case 1 - Energy efficient office

Product development of SEG initially focuses on commercial buildings, although applications in e.g.residential buildings and automotive settings are also possible. With their large casual gains and highcomfort standards, commercial buildings are seen as the primary segment for early market introduc-tion. In this first case, the performance of SEG is investigated under the climatic conditions of theNetherlands. Dutch offices typically feature equivalent heating and cooling demand, therefore energysavings by controlling admission of daylight are expected in both summer and winter. This potential isfurther illustrated in Figure 7.2 that shows a temporal map of annual distribution of solar irradiance inAmsterdam. This graph shows high variability in supply of sunlight, hence it is expected that adaptivityin window transparency can be valuable throughout the whole year.

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Figure 7.2: Temporal map of direct horizontal solar irradiance in the Netherlands (NASA, 2010).

In each of three application studies, the building with SEG is compared to a reference. Here, the refer-ence case (case A) assumes equal window areas as is the case for SEG (Figure 7.1), but is now equippedwith solar control glazing. The presence of a metallic reflective coating in these windows offers a mod-erate but fixed reflectance in the visible, and high reflectance in the near infrared wavelength area.Application of such windows reduces cooling load because solar heat gains are blocked while views tothe outside are preserved. In addition, shading systems can be omitted in buildings with solar controlglazing. A comparison of fenestration product properties between the reference situation and SEG isgiven in Table B.2; the simulation results are presented in Figure 7.3.

The overall annual energy demand for the reference case is only 1236kWh or 64kWh/m2. These val-ues are comparable to target values for low-energy office buildings in most West European countries(Jensen et al., 2009). The energy efficient reference case is realized with an acceptable number of over-heating hours, the occurrence of glare on the other hand appears to be unacceptable. In practice thiswould unavoidably lead to application of screens or other types of brightness control. Consequently,the results of this study contradict with advertisements for solar control glazing arguing that these de-vices are not necessary. Beside higher investment and maintenance costs, this would also influenceviews to the outside and daylight utilization and thus move away from the optimal results as presentedin Figure 7.3(A).

Considering SEG as design alternative can provide an adequate solution for fulfilling both thermal andvisual comfort requirements. Figure 7.3 however also shows the natural consequence that applicationof SEG has not so much to gain in terms of annual energy demand. If continuously in the bright state

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7.3. Case 2 - Advanced renovation

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Figure 7.3: Comparison between energy and comfort performance of the reference case (A) and SEG (case B to F)for a new office building in the Netherlands. With on the left axis: heating, cooling and lighting energyconsumption [kWh], and on the right axis: risk of overheating [h] and glare [times].

(case B), energy consumption gets reduced, but glare is still a problem. When always switched to thedark state (case C), the occurrence of glare drops drastically, but at the same time this also introducesan undesirable higher energy demand for artificial lighting. Implementation of an appropriate controlstrategy (e.g. case D to F) is the key that allows for profiting from the benefits of both the dark and thebright state.

As expected, occurrence of glare is counteracted when SEG is switched to the dark state at times whensupply of solar gains is high. At the same time, this relieves cooling load since the transmitted part of so-lar radiation is reduced. However, increasing visual comfort in terms of glare is nearly always adverselycompromised by an increase in electricity demand because daylight illumination is firmly reduced aswell. Case D demonstrates that it may even be detrimental to switch to the dark state when the indoorair temperature is high. This is because the control strategy sometimes leads to situations with simul-taneous demand for cooling and high demand of artificial lighting, where the latter then directly addsto the cooling load of the zone. Better results are obtained when window state is controlled based onstimuli from the luminous environment (case E and F). In these cases, daylight is only allowed whendesired and blocked when unwanted.

It can be observed that electricity generation potential of SEG is not included in the results. This isbecause the produced amount of electricity is only marginal compared to the scale of hundreds of kWhin Figure 7.3. Currently, the focus is on making adaptivity of the window self-sufficient, which appearsto be a realistic ambition. Treating SEG as decentral power supply source — to feed back to the grid orto be consumed locally — will be considered in future generations of SEG. The model as developed inthe previous chapters provides the basis for computational analyses of this potential.

7.3 Case 2 - Advanced renovation

With an annual construction renewal rate of only 1-2 %, it is obvious that energy targets in the built en-vironment cannot be met by considering new buildings only (WBCSD, 2009). Façades have been identi-fied to offer greatest potential for pursuing sustainable renovation projects in the existing building stock(Kuhn, 2010). In this light, the prospects of SEG are especially promising because it is a self-sufficientdevice that can be installed according to the plug-and-play principle. This allows for introduction of the

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positive aspects of adaptivity to the building shell, in a cost-effective way, without the need for majoroverhauls.

Appraisals pertaining to the renovation potential of SEG requires comparisons to be made against a dif-ferent reference situation. The performance assessment in this second case assumes the same buildinggeometry and orientation as Case 1, but other building characteristics are taken to be representative forthe 1970s. For the window, this is manifested via conventional double glazing with the properties givenin Table B.3. Insulation levels of opaque construction elements were also revised to have a Rc-value of0.67m2K/W, a typical value for office buildings constructed around 1975 (Petersdorff et al., 2006).

Solar shading and brightness control in the reference case is achieved via manually controlled internalvenetian blinds. Operation of blinds in the simulations is controlled in DAYSIM on the basis of theActive users profile. This stochastic algorithm assumes that blind settings are rearranged on a regularbasis with the aim of maximizing daylight availability while also trying to exclude glare (Reinhart, 2004).

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Figure 7.4: Comparison between energy and comfort performance of the reference (A) and SEG (case B to F) inan advanced renovation case. With on the left axis: heating, cooling and lighting energy consumption[kWh], and on the right axis: risk of overheating [h] and glare [times].

The simulation results in Figure 7.4 suggest that cooling energy demand after window replacementwith SEG is cut by more than a factor two. In addition, installed cooling power capacity was found to besafely reduced with more than 30 % (from 1.0kW to 0.67kW) when SEG is installed, while still achievingsufficient comfort levels.

Figure 7.4 further shows that heating energy demand for the basecase compares well to that for SEG,even though the window U -value of SEG is much lower as a result of the presence of a low-E coating.Closer inspection at the energy balance reveals that the transmission losses after renovation are indeedlower, but this difference is almost compensated by the decrease in valuable passive solar gains. Thelower visible transmission values of SEG also give rise to a relatively large electricity demand for light-ing‡.

The combined effects of high heating and lighting energy demand make savings in overall energy de-mand in this particular case marginal. The actual renovation potential of SEG however is likely to bemore differentiated, because of the following reasons:

‡Luminous efficacy of the light sources is assumed identical in both cases. A more conservative reference situation wouldresult in higher energy saving potential.

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7.4. Case 3 - SEG in a continental climate

Ï Any attempt to model occupant control of shading devices adds to the uncertainty in outcomes ofthe simulation. The reference situation in this case assumes the optimistic setting of active blindcontrol. Blinds are opened and closed as to maximize daylight utilization and thus represent thebest case scenario for manual blind control. In practice, a much more passive operation of blinds,and thus degradation in performance is likely to be expected (Daum and Morel, 2009). SEG onthe other hand is easily integrated in the building automation system (BAS), which makes thatdifference are likely to become more clear.

Ï All cases assume continuous dimming control, where daylight levels are almost perfectly supple-mented by artificial lighting. In both new and old buildings, this might be a too idealistic repre-sentation of reality. When internal heat gains are considered to be more independent of ingressof daylight, the energy saving potential of SEG’s adaptivity is supposed to be higher.

Ï More aspects than only operational energy demand and comfort levels play a role in the decisionfor renovation or preservation. A distinct advantage of SEG is the fact that shading devices areno longer required and therefore the access to outdoor is views preserved and maintenance costsreduced. In addition, renovation with SEG prevents early demolition, and thus increases econo-mized exploitation of the building and extends resource efficiency of the building’s life cycle.

7.4 Case 3 - SEG in a continental climate

It is not only control strategy and building design parameters, but also prevailing weather conditionsthat determines to a large extent the performance of switchable glazings (Jonsson and Roos, 2010). Inboth aforementioned cases, buildings with SEG were exposed to weather conditions of the Netherlands.The Dutch maritime climate is characterized by its moderately cool summers and comparatively warmwinters, and consequently has a narrow annual range of temperatures. The assumption is that SEG’sperformance becomes more pronounced in regions with more divergent winter–summer climatic fea-tures. Testing of this hypothesis by replication in other climates is relatively straightforward in BPS —as opposed to real-world experiments — and happens by simply selecting a different climate file. Inthis third case, the continental climate of Chicago, Illinois (O’Hare International Airport) was selected,because it is known for its large annual range of temperatures.

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Figure 7.5: Comparison between energy and comfort performance of the reference case (Case A) and SEG (Case Bto F) in a continental climate. With on the left axis: heating, cooling and lighting energy consumption[kWh], and on the right axis: risk of overheating [h] and glare [times].

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With the exception of climate file, all other building model parameters are taken identical to the modernoffice space of case 1 in Section 7.2. In this case the zone is subject to more extreme summer and winterconditions, therefore, the capacity of heating and cooling system was adjusted accordingly in order tomeet thermal comfort standards. The results of simulations are presented in Figure 7.5.

The results in Figure 7.5 show the same tendency that energy efficiency of the reference case can hardlybe surpassed by SEG. The results again do show that in this particular application, SEG can especiallyscore on prevention of glare. As opposed to the mild Dutch climate, conditions in Chicago are moresevere, thus thermal energy considerations become dominant, while lighting energy is less important.In addition, the effects of switching window state on the risk of overheating become perceptible becauseSEG’s impacts on admission of solar gains is more pronounced. Controlling window state on the basisof luminance seems the most promising, as it allows to take advantage of low glare occurrence whileat the same time avoiding excessive electricity consumption. More work is required to evaluate if moreadvanced control strategies can further enhance performance of SEG

7.5 Discussion

The work presented in this chapter uncovered the potential of SEG by investigation of three differentcases. In this way, it provides a good point of departure for further product development of SEG. In-dications of performance benefits are however better substantiated once a more elaborate parametricstudy is conducted. A sensitivity analysis can be helpful to elucidate which factors are most influential,and thus have the highest priority to be considered for further development.

The results of this study might not be as promising as one would expect on the basis of recent studieswith other switchable glazings (Jonsson and Roos, 2010; Mardaljevic and Nabil, 2008; Piccolo, 2010).The deviations may be explained by the following reasons:

Ï The bandwidth for switching SEG is relative narrow compared to other switchable windows§. Inaddition, SEG only switches in either one of three states, without the possibility for gradual tran-sitions in between.

Ï Most comparative studies are based on thermal or daylight performance only, whereas this studyconsiders the integral performance aspects simultaneously. Consequently, performance trade-offs are included in the analysis and this removes the unconscious bias towards solutions that areout of balance (i.e. too much in favor of either thermal or visual performance).

Ï Switching the optical properties of SEG primarily takes place in the visible wavelength area.Blocking solar gains is therefore followed by a proportional increase in lighting energy use. Thenet result is that solar gains are exchanged for internal gains, and consequently part of the energysaving potential is counterbalanced.

The specifications of SEG in this thesis have been characterized as being Version 0.1 and more workis still required towards optimization of the final product design. SEG introduces the possibility of se-lecting and tuning window properties for the dark, bright and translucent state. These properties arehowever subject to some fundamental restrictions as illustrated in Figure 7.6. A high visible transmit-tance in the bright state (1) is attainable, but this also involves a relatively high level of transparency inthe dark state. On the other hand, a larger switching range (2) is also possible, but this then puts a con-straint on the maximum level of transparency in the bright state. The simulation model as developed inthis project can be used to investigate which optical properties and control strategy yield the optimumbalance for heating, cooling and lighting energy demand. At the moment, this is still an open question.Moreover, expectations are that eventually a catalog of SEGs with different properties will coexist, eachtuned to specific climatic conditions and building designs. Again, building performance simulationscan be a helpful tool to accomplish this goal.

§In comparative studies, Tvis was smaller than 0.1 in the dark state and larger than 0.55 to 0.8 in the bright state.

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7.5. Discussion

Dye intensity

Visi

ble

tran

smitt

ance

Bright state

Dark state

1

2

Option space

Visi

ble

tran

smitt

ance

Bright state

Dark state

1

2

Figure 7.6: Conceptual representation that shows the restrictions for optimization of SEG’s optical properties.

The optical properties of SEG in this study only switch in the visible wavelength area. The luminescentdye technology however makes extension of the switching range to other parts of the spectrum viable.The ultimate solution would be a window that is capable of switching visible and infrared transmit-tance independently. Such an ideal window has been identified as the holy grail of façade technology(Selkowitz and Lee, 1998). Research efforts that aspire this aim are underway, but currently the lumi-nescent materials active in the infrared wavelength area still suffer from low stability, low quantumefficiency, and a relatively narrow absorption spectrum (Goldschmidt, 2009).

Thus far, this chapter assessed the potential of SEG on the basis of annual energy consumption andthermal and visual comfort requirements. The remainder of this chapter complements this analysis byevaluating the performance of SEG on the basis of two alternative performance indicators.

7.5.1 Dynamic daylight performance

Controlled ingress of daylight in buildings is generally known for its energy saving potential and con-tribution to sustainability in the built environment. Nowadays, architects are becoming increasinglyaware of the great importance and qualities of daylighting in perception of comfort and well-being ofoccupants. Daylight not only acts as major determinant for the perceived quality of the space, it is alsofavored because of its role in supporting levels of motivation, mood, alertness and views to the outside(Altomonte, 2008). Together with the emerging use of dynamic daylight simulations, there is a strongdemand for new evaluation methods that do appreciate the temporal dynamics of daylight illumination(Reinhart et al., 2006; Mardaljevic et al., 2009).

One of the climate based dynamic daylight performance evaluation metrics that was developed forthis purpose is the useful daylight illuminance (U D I ) (Nabil and Mardaljevic, 2005). This performancemetric subdivides internal daylight illuminance levels in three bins according to their utility. Daylightwith illuminance values below 100lx creates gloomy atmospheres and is considered to be too low tobe valuable as addition to artificial light. Illuminance values of 2000lx are seen as the upper limit forcomfortable, or tolerable conditions in daylit spaces. As a result, values above this level are undesirableand considered as a measure for the propensity of visual discomfort. The intermediate bin is composedof values between 100 and 2000 lx, and thus ranges from (minimum) desirable to (maximum) tolerabledaylight levels. Consequently, values within this bin are regarded to constitute useful illuminance levels.

Table 7.2 shows U D I values for SEG under Dutch climate with clear double glazing and internal blindsas reference case. Evidently, designers should aspire to realize buildings with the largest share of il-luminance values in the intermediate bin (U D I100-2000). No consensus has yet been reached about

91


Table 7.2: Useful daylight illuminance for the reference renovation case (case A) and SEG with different operationalstrategies (cases B to F).

A B C D E F

U D I<100 [%] 14 55 77 69 60 56U D I100-2000 [%] 70 41 23 28 40 44U D I>2000 [%] 16 4 0 3 0 0

acceptable or target values that satisfy both thermal and visual criteria (Mardaljevic et al., 2009). Ta-ble 7.2 reveals that application of SEG can avoid an oversupply of daylight, but on the other hand theU D I<100 value is comparatively high even when continuously switched in the bright state. These resultsreinforce the notion that it is worthwhile to explore possibilities to broaden the radius of action of SEG(i.e. difference between bright and dark state) to be able to modulate daylight in a variety of conditionsthat is as wide as possible. U D I can thus be a useful indicator to find the best operating point on thecurves of Figure 7.6. The results in Table 7.2 only consider one sensor point in space. U D I however, iswell-suited to provide insights in the spatial distribution of daylight. It would be interesting to also seein visualization plots how SEG with different operational logic influences spatial distribution of daylightin the zone.

7.5.2 Radiant asymmetry

Acceptable thermal comfort conditions are not only determined by the average thermal environment;people are also sensitive to the effects of local disturbances. Experimental studies have demonstratedthat asymmetric radiant fields result in ‘uneven body temperature’ and consequently lead to a higherprobability of complaints about thermal comfort (Fanger et al., 1985). In Figure 7.7, the percentage ofpeople dissatisfied (PPD) as a function of the radiant temperature asymmetry caused by a warm ceiling,a cool wall, a cool ceiling or by a warm wall, is shown.

Figure 7.7: Percentage of people expressing discomfort due to asymmetric radiation to a warm ceiling, cool wall,cool ceiling or warm wall or as a function of the radiant temperature asymmetry (Fanger et al., 1985).

Especially in the dark state, SEG absorbs a relatively large amount of incident solar radiation and for thatreason creates an increase in window temperature. As a result, application of SEG can be a potentialsource of discomfort. The risk of radiant asymmetry due to SEG as warm wall was assessed on the basisof results from the simulation model. Figure 7.8 shows the simulated surface temperature of the internalSEG window pane, in comparison with ambient air temperature and the mean surface temperature ofthe other zone surfaces. The temperature difference between wall and window reaches a maximum of9C on the hottest day with the Dutch climate file.

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7.5. Discussion

0

5

10

15

20

25

30

35

40

0 1 2 3 4 5 6

Time (days)

Tem

pera

ure

(°C

)

Surface: window

Surface: wall

Air: ambient

∆Tmax = 9 °C

Figure 7.8: Surface temperature of wall and window, and ambient air temperature during six days in summer.

Radiant temperature asymmetry, instead of surface temperature, is the determinant of local thermaldiscomfort and is defined as the difference between the plane radiant temperature

(Tpr

)on two oppo-

site sides of a small black element. Consequently, it is not only temperature of the window but also theview factor from the occupant to the window that is important in assessing local discomfort. As Fig-ure 7.9 illustrates, this view factor depends on window size, room geometry and occupants’ location inthe room.

Figure 7.9: Schematic diagram illustrating how window geometry and observer position influences the view factorfor long-wave radiant heat transfer (Huizenga et al., 2006).

According to ASHRAE (2001), radiant temperature asymmetry(∆Tpr

)for the configuration of Figure 7.9

can be calculated with the expression of Equation (7.1):

∆Tpr = Tpr, left −Tpr, right = (Twindow ·Focc→window +Twall ·Focc→wall)−Twall ·1 (7.1)

ISO 7730 specifies an allowable threshold value of 23C for radiant temperature asymmetry, which cor-responds to a PPD of 5 %. Assuming that the view factor to the window (Focc→window) in spaces withmoderate window-to-wall ratio’s go up to approximately 0.5, this results in a maximum allowable win-dow temperature of 65C. On the basis of the simulations it can be safely concluded that for the in-vestigated configuration there is no cause for concerns regarding discomfort due to the high windowpane temperature. On the contrary, cold window surfaces likely offer a much higher risk of discomfortbecause the allowable temperature difference is much lower.

93

CH

AP

TE

R

8CONCLUSIONS

8.1 Concluding remarks

The main objective of this study was to evaluate the role that building performance simulation (BPS)can play in design of buildings with climate adaptive building shells (CABS). To clarify the character-istic properties that distinguish CABS, a total number of one hundred different application exampleswas examined. These projects feature a large diversity in concepts and also show a growing interest inclimate adaptive architecture to promote building performance in terms of people, planet and profit.It was found that it is not only adaptivity, but also multi-ability and evolvability that are responsible forthe potential success of CABS.

Before becoming mainstream, first some barriers that are rooted in the complex nature of CABS needto be bypassed. More than traditional building envelopes, design of CABS needs to address (i) mainte-nance issues, (ii) the human aspects associated with dynamic behavior of the envelope, and (iii) how thenew technology merges with the existing practices of the conservative building industry. By definition,CABS also have to deal with trade-offs in performance because requirements imposed by changinguser requirements and environmental conditions are competitive or even conflicting. Caused by theimpact of these factors together, it turns out that successful design and operation of CABS might not bestraightforward.

Nevertheless, energy policies increasingly advocate the necessity for more sustainability, and ways tosuppress greenhouse gas emissions. The building sector is often regarded as the low-hanging fruiton the tree (Ürge Vorsatz and Novikova, 2008; UNEP, 2009) because the cumulative mitigation poten-tial is large, and changes are readily achievable as well as highly visible to the community. CABS holdthe promise to play a major role in this pursuit, and can contribute without having the need to makecompromises in terms of comfort, provided they are designed in an effective way. Essential is that adap-tivity should be based on informed design decisions rather than being adventitious properties as is thecase in most of the 100 CABS in the overview. To achieve this, both the opportunities and risks of makingfaçades adaptive should be made transparent to remove controversy and rationalize uncertainties al-ready in the design stage. This was also recognized by Nobel prize winner and U.S. Secretary of Energy,Steven Chu who has phrased it in the following way (Turner et al., 2009):

"Low-hanging fruit will remain on the tree as long as those making decisions about buildingconstruction, renovation, and operation are unaware of its value or do not have an easy wayto harvest it."

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8. CONCLUSIONS

This study demonstrates that BPS can turn out to become the powerful toolbox that is required as cata-lyst for this job. BPS is able to predict building performance by coupling the theory of building physicswith behavioral models and climate data. The system dynamics, ranging from short-term impacts toseasonal cycles can be analyzed, and BPS also makes it possible to explore the integrated effects ofvarious operational control strategies. Because of these properties, BPS allows for gaining increasedunderstanding of the interdependencies between energy and comfort and dynamics of the systems inmultiple physical domains. Consequently, BPS can be a valuable tool exploiting the latent potential ofCABS. What may be most promising is that current BPS tools are still amenable to continuous develop-ments. Enhanced domain integration and incessant extension of capabilities will certainly improve theutility of model outcomes. Software developments also promote more flexibility which enables mod-elers to analyze systems that are ahead of technology rather than only having the opportunity to usepredefined functionalities. The ever increasing computational power in addition favors shorter simula-tion periods which opens the door for consideration of more design and control alternatives.

The assertion that BPS can be a valuable instrument in designing buildings with CABS was confirmed inthe case study of Smart Energy Glass (SEG). BPS not only displayed its traditional capabilities as a designaid, but also proved to be useful as active tool in product design and development. This study is uniquein its attempt to bridge the gap between the fundamental research in luminescent solar concentratorsand its practical application in architecture. A consequence of this novelty was the fact that outcomesof an empirical validation study were necessary input to ensure reliability of the simulation model. Itshould however be noted that such test facilities are often not available, and efforts often not justifiablein routine design processes.

On the basis of the simulations it is not yet possible to give conclusive answers about SEG’s energysaving potential. The concept however is promising because energy savings can be achieved while oc-currence of discomfort is reduced. The same results do also suggest that there is still room for improve-ment. The focus of attention for developing the concept of SEG should be in the laboratory as well as inthe actual large-area integration in the building shell. The next section shows that BPS can continue toplay a role in these ongoing developments.

8.2 Recommendations for future work

While the CABS overview examined the entire spectrum of CABS, the case study zoomed in to the de-tails of only one concept. A corollary is that less attention was paid to other types of systems, especiallythose operative in the airflow domain. Performance of CABS is not only application-specific but alsocontext-dependent, which makes that extrapolating the potential of SEG in universally applicable pro-jections for CABS is senseless. More work is required to develop understanding of effects of other kindsof adaptive behavior on building performance and to identify the potential role of e.g. airflow simu-lation tools. The strategy for modeling and simulation of CABS in Chapter 4 provides a good startingpoint for research in this direction.

Currently, adaptive features in BPS are primarily available in the research-grade simulation tools only.These tools require a thorough understanding of domain knowledge and often lack convenient user in-terfaces. It is questionable to what extent these tools will effectively assist in consultancy and designof CABS in practice. More efforts should be directed to valorization of CABS models and tools by mak-ing them easily accessible to a wider community, to support early design decisions without losing thepowerful modeling capabilities.

After having been used in the design stages, BPS models can also be used in the post-constructionphase. One of the envisioned applications is to couple BPS with the building automation system forsimulation-assisted predictive control. Although practical application examples of simulation-assistedcontrol are still limited, the viability of the concept has been demonstrated successfully (Clarke et al.,2002; Henze et al., 2005; Mahdavi, 2008). Exploring the role that BPS can play in operation of adaptivebehavior of CABS is a challenge for future research.

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8.2. Recommendations for future work

The fragmented developments in CABS technology are typically driven by advances in science and tech-nology (i.e. technology push). Unknown is however what kind of adaptive behavior of the façade reallyyields the most favorable building performance (i.e. demand pull). The true value of making buildingenvelopes adaptive is most likely only partially accessible with current CABS concepts. Smart use ofBPS can help to divert from such sub-optimal solutions. A so-called inverse modeling approach canexplore the full option space to derive the optimal dynamic properties of a building shell. These resultscan then be used to outline future R&D paths that should lead towards this goal.

Apart from its role in developing CABS concepts in general, BPS can also continue to play a role inresearch, design and development processes of SEG. No two building projects are alike and thereforeevery (new) building needs to be designed in harmony with its local context and specific functionalrequirements. The technology behind SEG makes it possible — within certain limits — to freely ‘choose’the switchable window properties (U-value, SHGC and Tvis for the dark, bright and translucent state)for a given situation. This makes it possible to develop, tune and optimize multiple SEG-coatings, eachfor a different application area. It would be very interesting to employ BPS to answer the followingquestions: What are ideal specifications of SEG in a range of different applications∗?; What are theassociated trade-offs and performance benefits for each of these cases in terms of energy consumptionand thermal and visual comfort?; Will it be sufficient to focus on the development of a single, general-purpose window, or should Peer+ invest in tailoring the properties of SEG for each particular situation?

Unlike façades in the scenery of western movies, CABS do not exist in isolation. The performance eval-uations in this study however only considered fictive heating and cooling loads, and did not explicitlymodel interactions with other building components. The assumption is that good opportunities forSEG lie in the interaction with slow-responding low-exergy building systems like aquifer thermal stor-age and concrete core conditioning. These kinds of systems typically offer a tempered, steady interiorclimate, but cannot respond fast to changing conditions, which may lead to uncomfortable conditionsor needless waste of energy. Because SEG has the ability to quickly react to changes in performancerequirements and environmental boundary conditions, it is potentially possible to mitigate these haz-ardous effects. To test this hypothesis, further use of the BPS model is inevitable.

∗Including window-to-wall ratio, orientation, type of building, insulation standard, renovation vs. new building, climate, etc.

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IX

AOVERVIEW OF 100 CLIMATE ADAPTIVE BUILDING SHELLS


Climate Adaptive Building ShellsWhat can we simulate?

R.C.G.M. Loonen

100Overview of

Building ShellsClimate Adaptive

Please refer to the separate booklet for the actual overview of 100 climate adaptive building shells.

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BSMART ENERGY GLASS PRODUCT INFORMATION

The product data of Smart Energy Glass is withheld from public disclosure because it is consideredconfidential business information. The data is available upon request.

Table B.1: Material properties of the SEG prototype in the experimental setup of the validation study in Chapter 6

Bright state Dark state

Tvis [-]SHGC [-]U -value [Wm−2K−1]

Table B.2: Material properties of the SEG window and the reference window for Case 1 — Energy efficient office

Reference case 1 SEG bright state SEG dark state

Tvis [-] 0.52SHGC [-] 0.29U -value [Wm−2K−1] 1.31

Table B.3: Material properties of the SEG window and the reference window for Case 2 — Advanced renovation



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B. SMART ENERGY GLASS PRODUCT INFORMATION

Table B.4: Material properties of the SEG window and the reference window for Case 3 — SEG in a continentalclimate



114


Climate Adaptive Building ShellsWhat can we simulate?

R.C.G.M. Loonen

100Overview of

Building ShellsClimate Adaptive


Overview of 100

Climate Adaptive Building Shells

R.C.G.M. Loonen

0570677

Building Services

Architecture, Building & Planning

Eindhoven University of Technology

CABS – What can we simulate?

Student

id

Master

Faculty

Advisors

prof. dr. ir. J.L.M. Hensen

dr. dipl.-ing. M. Trčka

D. Cóstola, MSc

21 June 2010

part of MSc. Thesis


Preface

This booklet presents the overview of 100 climate adaptive buildings shells (CABS) that was developed as part of the MSc project “CABS – What can we simulate?”. The overview is preferably read in conjunction with the main body of the thesis, however, it was drafted in such a way that it also invites to be examined in a stand-alone manner.

The concept of CABS is very broad and described by a multitude of different terms. Many building envelopes do exhibit some form of dynamism; in this overview only those concepts were included that fulfill the following definition:

A climate adaptive building shell has the ability to repeatedly and reversibly change its functions, features or behavior over time in response to changing performance requirements and variable boundary conditions. By doing this, the building shell effectively seeks to improve overall building performance in terms of primary energy consumption while maintaining acceptable thermal and visual comfort conditions.

Beside being used as reference book for the thesis, the material presented in this booklet serves more purposes. By synopsizing case studies, prototypes and research projects it offers a technology scan that shows the state-of-the-art in adaptive façade technology. This information can be used by professionals engaged in development of building envelopes, and also provides a point of inspiration for designers exploring possibilities to enrich the domain of adaptive building shell technology.

The overview of 100 CABS is the result of an extensive literature survey, collected from different kinds of information sources. It attempts to provide a comprehensive database that covers the whole spectrum of CABS, ranging from built examples that successfully operate for many years, to the wildest utopian concepts. The field of CABS is relatively young and in a continuous state of flux. Therefore the expectation is that this compilation will soon be superseded by even more appealing concepts and better performing technologies.

Before starting the actual overview, the next page first provides a short introduction that (i) helps to understand the logic of classification, and (ii) shows a legend to the symbols and abbreviations used in the overview.


Introduction

In two or three sentences, this section gives a short description of the working principles of the concept.

The functions and performance aspects of the concept are indicated and substantiated with claims by the author or developer.

For more details and additional information, the interested reader is encouraged to investigate the references in the bottom right corner

This area provides references to the original information sources and gives recommendations for further reading

Where possible, two or more independent references are provided in order to keep clear of too biased opinions

Architect / developer| place | year

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00Superscripts in the main text refer to these CABS numbers

Adaptive surface, indicated via thick lines

State of development, indicated via colors

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This area features some pictures and schematic drawings that demonstrate the working mechanisms and appearance of the concept

Name of the concept, listed in alphabetical order

The relevant physics of Chapter 4 is presented in this section

Control of adaptivity Adaptive features


Active Building Envelope

In ABE systems, photovoltaic (PV) cells are used to transform solar energy directly into electrical energy.

This electrical energy is subsequently used to power thermoelectric (TE) heatpumps. Both TE and PV systems are integrated in one enclosure surface.

Depending on the direction of electrical current applied to the TE system, ABE systems can either operate in a heating or cooling mode.

Khire, R., Messac, A. and van Dessel, S. (2006) ‘Design of thermoelectric heat pump unit for active building envelope systems’, International Journal of heat and mass transfer 48(19-20): 4028-4040

Xu X. and van Dessel, S. (2008) ‘Evaluation of an Active Building Envelope window-system’, Building and environment 43(11): 1785-1791

Rensselaer Polytechnic Institute | Troy, New York

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[Khire et al., 2006][RPI, Steven Van Dessel]


Active window / micromirror

Large-areas of micromirror arrays (150 x 400 μm2) enable translucent protection from solar glare and sunshading, controllable both in terms of time and activation area.

Each mirror can be moved within a prescribed angular range, by application of small voltages between the mirror layer and a planar ITO electrode.

In contrast to macroscopic solutions, such micromirror-based solutions are nearly invisible, weatherproof and maintenance free.

Klooster, T. (2010) Smart Surfaces – and their application in architecture and design, Berlin: Birkhäuser

Viereck, V., Li, Q., Jäkel, A. and Hillmer, H. (2009) ‘Large-area applications of optical MEMS: micromirror arrays guide daylight, optimize indoor illumination’, Photonik International 2009(2): 48-49

Viereck, V., Ackermann, J., Li, Q., Jäkel, A., Schmidt, J. and Himmler, H. (2008) ‘Sun glasses for Buildings on Micro Mirror Arrays, in IEEE Proceedings of Networked Sensing Systems 2008. pp: 135-139

University of Kassel | Germany

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[Viereck et al., 2009] [Viereck et al., 2009][Viereck et al., 2009]

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Adaptive Fritting

Integrated glass units with movable fritted patterns that allow for a broad range of possible transparencies.

Utilizes a graphic pattern surface treatment, inspired by nature, in order to control heat gain and modulate light, while allowing sufficient transparency for viewing.

Winner of the Wyss Prize for Bioinspired Adaptive Architecture 2009.

Brownell, B. (2010) Transmaterial 3: A catalog of materials that redefine our physical environment, Princeton Architectural Press

Hoberman, C. (2009) Adaptive fritting http://hoberman.com/portfolio/gsd.php?projectname=Adaptive%20Fritting

Hoberman Associates | New York

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[Hoberman, 2009][Hoberman, 2009][Hoberman, 2009]

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AeroDimm

The biomimetic surface is inspired by the color change of cephalopod skin. Elastic membranes change volume between two layers of the façade and in

this way control levels of daylight and solar gains. Pneumatic pressure is the driving force behind the adaptive mechanisms, and

the ventilation tubes are integrated into the external layer.

Gruber, P. (2008), ‘The signs of life in architecture’, Bioinspiration & Biomimetics 3(2): 1-9

Stefan Pfaffstaller | Vienna, Austria | 2004

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[Gruber, 2008]

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Aegis Hyposurface

The system consists of transformable triangulated metal plates, driven by a bed of 896 pneumatic pistons.

Deforms in real-time, based on various environmental stimuli, including the sounds and movement of people, weather and electronic information.

The piece marks the transition from autoplastic (determinate) to alloplastic(interactive, indeterminate) space.

Brownell, B. (2006) Transmaterial: A catalog of materials that redefine our physical environment, Princeton Architectural Press

Ataman, O. and Rogers, J. (2005) ‘Toward New Wall Systems: Lighter, Stronger, Versatile’, in Proceedings of the 10th Iberoamerican Congress of Digital Graphics, Santiago de Chile, Chile. pp: 248-253

Mark Goulthorpe | dECOi Architects | 1999-2001

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[http://hyposurface,org]

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Aldar Central Market

The grid system provides panels of controlled permeability that vary smoothly between a covered state and a largely open state.

In its covered configuration, the shading roof appears similar to a traditional coffered Islamic roof.

When retracted, the roof becomes a slender lattice that complements the designs by the Foster team for fixed shading.

Aldar Central Market, Abu Dhabi, Foster+Partners Projects. http://www.fosterandpartners.com/Projects/1431/Default.aspx

Aldar Central Market, Hoberman Associates Works. Website: http://hoberman.com/portfolio/aldarcentralmarket.php

Grid System, Adaptive building initiative. Website: http://www.adaptivebuildings.com/cladding-shading-systems.html

Foster and Partners | Abu Dhabi | 2006-2010

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[http://hoberman.com]

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Allwetterdach

When the slats are open, heat is prevented from building up in glazed rooms and conservatories because warm air can escape via the chimney effect.

In winter, when the roof is closed, the infrared radiation (IR) can pass through in only one direction. The warmth thus remains under the roof.

Smout, L. (2008) ‘Slat roof’, European Patent Application EP1992756, http://www.freepatentsonline.com/EP1992756.html

Allwetterdach Flyer. http://allwetterdach.com/downloads/EscoProspektDT2009.pdf

ESCO | Germany

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[www.allwetterdach.com]

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Aperture

This façade installation consists of a matrix of iris diaphragms. Like the human eye's iris and irises in camera objectives, they react to light,

widening and contracting with corresponding increases and decreases in intensity of incoming light.

The degree of opening is controlled via Light Dependent Resistors (LDR) that actuate servo-motors.

Frédéric Eyl and Gunnar Green | University of the Arts Berlin, Germany | 2005

Klooster, T. (2010) Smart Surfaces – and their application in architecture and design,Berlin: Birkhäuser

Eyl, F.and Green, G. (2005) ‘Aperture façade installation’, Website: http://www.fredericeyl.de/aperture/

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[Eyl and Green, 2005]

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Arabe Intitute du Monde

Arabe Institute du Monde is perhaps the most widely known example of a building with a climate adaptive building shell.

The automatically controlled shutters are a technical interpretation of the traditional Arabic sun screens in integrated pane systems.

The mechanical irises open and close in response to different light conditions over the course of the day.

Coelho, M. and Maes, P. (2009) ‘Shutters: A Permeable Surface for Environmental Control and Communication’, in Proceedings of the Third International Conference on Tangible and Embedded Interaction, Cambridge, UK

Arab World Institute, Jean Nouvel, Projects, Paris. Website: http://www.jeannouvel.com

Jean Nouvel | Paris, France | 1987

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[http://edwardlifson.blogspot.com/][http://wikimedia.org] [http://mimoa.eu]


Balloon Sun Shading

The entire system is a combination of deflated (façade) and inflated (balloons) construction elements.

Inflating the balloons creates an integrated active mechanism for daylight regulation and solar protection.

An additional positive feature is that the shading system also makes profit of the acoustic qualities of the balloons.

Knaack, U. and, Bilow, M. (2009) ‘Innovationen im Fassadenbau’, Bauportal121(5):254-258

Knaack, U., Klein, T. and, Bilow, M (2008) Imagine 02 – Deflatables, Rotterdam: 010 Publishers

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Marcel Bilow, Tillman Klein | TU Delft, The Netherlands | 2006

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[Knaack et al, 2008]

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Beadwall

A form of movable insulation and shading that uses tiny polystyrene beads blown into the space between two window panes.

Beadwall windows are a great application for greenhouses with the ability to drain and let the sun in during the day and then fill up to maintain temperature at night.

Baer, S. (2009) ‘Some passive solar buildings with a focus on projects in New Mexico’, Presentation for the Albuquerque chapter of AIA, January 15, 2009

‘BeadWall’, Website: http://www.commonwealthsolar.com/cwbead.htm

Zomeworks | New Mexico, USA | 1980

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[Bear, 2009][Bear, 2009] [www.commonwealthsolar.com]

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Bengt Sjostrom Starlight theatre

The faceted roof consists of six triangular, stainless-steel-clad panels. Under the folded, origami-like roof, an intimate social setting is created with

a porous boundary to the landscape. The roof can provide weather protection, if necessary. But when the roof is

fully open, the panels form a six-point star through which the audience views the starlit sky.

Starlight Theatre, Uni-systems, featured projects, Website: http://www.uni-systems.com/Projects/FeaturedProjectDetails.aspx?ProjectID=3&PN=Starlight%20Theater

Bengt Sjostrom Starlight Theatre / Studio Gang Architects, ArchDaily 05 August 2009. http://www.archdaily.com/28649/bengt-sjostrom-starlight-theatre-studio-gang-architects/

Fox, M. and Kemp, M. (2009) Interactive Architecture, New York: Princeton Architectural Press

Studio Gang Architects | Rockford, Illinois | 2003

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[http:/archdaily.com][http:/archdaily.com][http:/rockvalleycolege.edu] [Fox and Kemp, 2009]


Bi-directional thermodiodes

Like electric diodes, thermodiodes have a favorable direction of heat flow. This forward direction can reversibly be changed via rotatable joints.

The tested thermal conductivity of the thermodiode panels in forward mode is 3–5 times higher than that in backward mode.

Three different system types have been studied: tilted rectangular loops (see figures), bayonet-shaped diodes and loop-and-tank diodes.

Chun, W., Chen, K. and Kim, H. (2002) ‘Performance Study of a Bi-Directional Thermodiode Designed for Energy-Efficient Buildings’, Journal of Solar Engineering 124(3): 291-299

Chun, W., Ko, Y., Lee, H., Han, H., Kim, J. and Chen, K. (2009) ‘Effects of working fluids on the performance of a bi-directional thermodiode for solar energy utilization in buildings’, Solar Energy 83(3): 409-419

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Cheju National University| Republic of Korea

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[Chun, 2009]

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Bionic Breathing Skin

The envelope transforms into a distributed ventilation system via contractions and expansions of an elastic membrane.

The skin performs a process of inhaling and exhaling via lung like chambers inspired by the respiratory surface of sea sponges.

The constantly changing rate of deformation of the functional units is controlled via piezo-electric actuators.

Badarnah, L. and Knaack, U. (2007) ‘Bio-Inspired Ventilating System for Building Envelopes’, in Proceedings of the International Conference of the 21st Century: Building Stock Activation, Tokyo, Japan. pp: 431-438

Badarnah, L. and Knaack, U. (2007) ’Bionic breathing skin for buildings’, in Proceedings of the International Conference SB07: Sustainable Construction, Materials and Practices.

Lidia Badarnah | TU Delft, The Netherlands | 2007

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[Badarnah and Knaack, 2007]

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BioTower

BioTower adds value to the building with its leaf-cluster systems for air filtration, sound baffling and heat control.

The cladding consists of a series of branch panels with an origami-like folded paper skin modeled from the observation of leaves.

The outer biomechanical sensor-node pods embed biological filters, and passive cooling system embodied in digital leaf panels.

Jodidio, P. (2009) Green Architecture Now!, Köln, Germany: Taschen

Dollens, D. (2009) ‘D-BA2: Digital Botanic Architecture’, Website: http://www.tumbletruss.com/TrussImages/DBA2-150.pdf

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Dennis Dollens,

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[Dollens, 2009]

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Blight

Rather than only blocking the sun’s rays, the design re-envisions blinds as sun-soaking solar panels that store energy during the day and illuminate the interior at night.

With the revolving blades, the course of the sun is followed in order to catch a maximum of energy and convert the energy in PV cells.

The electricity is stored in a battery, and re-emitted at night via electroluminescent foil.

Core 77 Greener Gadgets Design Competition (2009) ‘BLIGHT’, Website: http://www.core77.com/greenergadgets/entry.php?projectid=41

Cruden, D. (2009) Rethinking Design – environmental/ecological design: materials design interior. http://tur-www1.massey.ac.nz/~interior/blog/wp-content/uploads/2009/03/302_09_ecology-booklet.pdf

Vincent Gerkens | Belgium | 2009

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[www.core77.com]

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Bloomframe Balcony

An innovative window frame that can be transformed into a balcony-on-demand: users can add light, air and space to a space with a single push on a button.

The dynamic balcony offers an opportunity to add outdoor space to compact apartments, offices or hotels in inner city areas.

Winner of the prestigious Red Dot design award 2008 and Wallpaper design award 2009.


Hurks geveltechniek (2008), Bloomframe brochure, http://website.hurks-gdtechniek.nl/beheer/uploads/docs/bloomframe_folder_english.pdf

Hurks Geveltechniek | The Netherlands | 2007-2010

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[Hurks geveltechniek, 2008]

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Breathing wall

Breathing wall or “dynamic insulation” panels form the basis for a distributed ventilation air supply system where the wall functions as (i) a supply source, (ii) heat exchanger and (iii) filter for airborne pollutants.

The building fabric is used as cross-flow heat exchanger through which the movement of air from outside to inside is controlled. As a consequence, the U-value is a function of the air-flow. The amount of airflow can be both passive, or actively controlled.

Imbabi, M. (2006) ’Modular breathing panels for energy efficient, healthy building construction‘, Renewable Energy 31(5): 729-738

Taylor, B., Cawthorne, D. and Imabi, S. (1996) ‘Analytical investigation of the steady-state behaviour of dynamic and diffusive building envelopes’, Building and Environment 31(6): 519-525

The Environmental Building Partnership Ltd | United Kingdom

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[Imbabi, 2006][www.environmental-building.com] [www.environmental-building.com][www.environmental-building.com]

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Bremtex

The two storey tall aluminum shutters transform this circular building into a real eye-catcher.

While individually adapting in response the sun, the fins take care of an optimized balance between daylight utilization and solar gains.

The whole system is controlled via a coupling to the overall building energy management system.

Bremtex, VOW architecten, projecten. Website: http://www.vowarchitecten.nl/architecten/projecten/project03/project03-buiten.htm

Schoepenzonwering, architectenweb. http://www.architectenweb.nl/aweb/producten/product_detail.asp?productID=3231

VOW architecten | Oisterwijk, The Netherlands | 1996

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[www.vowarchitecten.nl] [www.vowarchitecten.nl] [www.architectenweb.nl]

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Burke Brise soleil

The brise soleil covering a museum pavilion is made up of 72 steel fins, ranging in length from 8 to 32 meter, which makes that its span is comparable to that of a Boeing 747-400.

The “wings” open Tuesday–Sunday at 10 a.m. with the Museum, close/reopen at noon, and close again with the Museum at 5 p.m.

Whenever wind speeds exceed 37 km/h for more than 3 seconds, the wings close automatically.

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Santiago Calatrava | Milwaukee, Wisconsin | 2001

Milwaukee Art Museum, Santiago Calatrava, Recent projects. Website: http://www.calatrava.com

Ataman, O., Rogers, J. and Ilesanmi, A. (2006) ‘Redefining the wall: Architecture, Materials and Macroelectronics’, International Journal of architectural computing 4(4): 125 - 136

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[www.333cn.com][www.wrightinwisconsin.org] [www.calatrava.com]


Chanel Ginza

LED elements are combined with the steel mesh and sandwiched between layers of switchable PRIVA-LITE glass.

This innovative system allows the building to take on different appearances during the course of the day.

During the day, the electrooptic glass is switched to the transparent or trannsluscent state, but at night, the façade becomes a projection screen for the 700.000 LEDs.

Ritter, A. (2007) Smart Materials – in architecture, interior architecture and design. Basel: Birkhäuser

Block, R. and Constantinou, M. (2009) ‘Media façades – Interactive brand communication in the urban space’

http://www.arclighting.de/weblog_01/wp-content/uploads/2009/05/04_03_media-facades_text_version.pdf

Peter Marino Associates | Tokyo, Japan | 2004

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[www.quantumglass.com]


City of justice

Hexagonal shading units will occupy the central circular atrium as well as the eight peripheral atria of the judicial complex.

When extended, the system will cover the entire triangulated roof grid. When retracted, their profile will 'disappear' into the structural profile of the roof.

A custom algorithm combining historic solar gain data with real-time light-level sensing will control the shading units.

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Foster + Partners | Madrid, Spain | 2006-2011

City of Justice, Madrid, Foster+Partners Projects. Website: http://www.fosterandpartners.com/Projects/1453/Default.aspx

Audencia Provincial, Hoberman Associates Works. Website: http://www.hoberman.com/portfolio/audenciaprovincial.php

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[www.hoberman.com]


Climate Adaptive Skin

All functions for heating, cooling, solar control, energy storage, ventilation and electricity generation are integrated in a single skin.

The system is universally applicable on every building because it is able to operate independently of building services.

Maintenance and servicing is kept as low as possible because the number of moving parts is limited.

Hasselaar, B. and Looman, R. (2007) ‘The climate adaptive skin, the integral solution to the conflict of comfort and energy performance, in Proceedings of the CIB World Congress, Cape Town, South Africa. pp: 111 - 1125

Hasselaar, B., van de Spoel, W., Bokel, R. and Cauberg, H. (2008) ‘Simulation of innovative climate control strategies using passive technologies’, in Proceedings of The 1st International Conference On Industrialised, Integrated, Intelligent Construction, Loughborough, UK. pp: 239 - 249

Bas Hasselaar | TU Delft, The Netherlands | 2005-2009

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[Hasselaar et al. 2007-2008]


Curtain Wall House

The pliable fabric used in the walls’ construction makes it possible for them to fully retract, channeling more light and air into the core of the house. In winter, the curtains close to retain heat.

In cold weather, a set of glazed doors can completely enclose the house for insulation and privacy.

The building shell is described as the literal interpretation of a curtain wall façade.

Quinn, B. (2006) ‘Textiles in architecture’, Architectural Design 76(6): 22-26

Curtain Wall House, Works Shigeru Ban Architects, Website: http://www.shigerubanarchitects.com/SBA_WORKS/SBA_HOUSES/SBA_HOUSES_15/SBA_Houses_15.html

Shigeru Ban Architects | Tokyo, Japan | 1995

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[www.architettura21.eu][www.architetture21.eu] [http://skellis.net][http://www.3dallusions.com]


Da Vinci Rotating Tower

The floor segments rotate independently and individually; one rotation can take place in 90 minutes.

The rotating tower is the first skyscraper to be entirely assembled from factory-produced pre-fabricated parts.

The technology that will enable the floors to rotate autonomously is triggered according to tenants' wishes via voice commands.

Fox, M. and Kemp, M. (2009) Interactive Architecture, New York: Princeton Architectural Press

Fisher, D. (2010) ‘Dynamic Architecture’, Website: http://www.dynamicarchitecture.net

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David Fisher | Dubai | 2010-2011

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[Fisher, 2010]


DBU conference pavilion

Rotatable blinds and a translucent insulation material together form this functional roof that is covered by a single ETFE layer.

Adaptivity of the roof allows for maximum flexibility of interior and furnishing in the windowless conference rooms below.

Because thermal insulation and the blinds are sandwiched between polymer layers, these systems are protected from rain and maintenance is minimized.

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Herzog + Partner | Osnabrück, Germany | 2002

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Jeska, S. (2007) Transparent plastics: design and technology, Basel: Birkhäuser

Cremers, J. (2007) Multifunctional Membrane Systems for Intelligent Buildings, Intelligent Glass Solutions 1(1): 29-32

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[www.dbu.de][www.hightexworld.com] [www.hightexworld.com]


Deployable External Insulation

Deployable External Insulation offers the possibility of actively modulating building fabric thermal performance.

The movement of insulative shutters is passively controlled via phase change wax piston actuators.

One piston (the ‘cold’ piston) opens the shutter when the temperature rises above a minimum. A second piston (the ‘hot’ piston) closes the shutter when the temperature exceeds the maximum threshold.

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Stephen Gage, Bartlett School of Architeture | London, UK | 2008

Deployable External Insulation Project Website: http://www.deployable.org.uk

Borden, I. (2009) ‘Sustainability and architectural design’, Pallette, UCL’s journal of sustainable cities, Summer 2009,

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[www.deployable.org.uk]


“Documentation Centre”

The kinetic external cladding acts as shading device and produces electricity via thin film solar cells.

The degree of shading is automatically controlled via a thermal expansion material (linear actuator) with PTC thermistors.

The expansion material is wrapped with insulation in order to ensure that light is the only controlling stimulus.


Axel Ritter | Proposal for Former Concentration Camp | Hinzert, Germany | 2004

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[Ritter, 2007]


Dr. Jockisch Building

The mobile photovoltaic sunshading system covers 3/8 of the façade and follows the sun while it floats around the building.

Motorized wheels support the structure of 182 PV-panels on its way around the building.

A sloped ‘solar shield’ mounted on the roof provides additional electricity generation.

Inglin, C. (2007) ‘Sunny side up, with BIPV’, Malaysian International Building Exposition (MALBEX) ; http://www.mbipv.net.my/dload/SunnySideUp(PhoenixSolar-ChristopheInglin)-070919.pdf

HBH architekten (2009) HBH Broschüre mit ausgewählten Projekten Stand 04/2009,http://www.architekten-hbh.de/fileadmin/img/Download/Buerobroschuere_04-2009_web.pdf

Architecten HBH | Landshut, Germany | 2000

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[HBH, 2009] [Inglin, 2007] [Inglin, 2007][www.architekten-hbh.de] [www.architekten-hbh.de]


Eclipsis shutter shade

Eclipsis shutter shade is made from stainless steel and a translucent polycarbonate panel filled with aerogel.

The shutter shades can slide along the north and south façades, providing protection from direct sunlight while simultaneously allowing for indirect, natural lighting, views to the exterior and privacy to those inside.

The home’s smart system gathers information from its rooftop weather station to automatically control the screens.

Solar Decathlon, Virgina Tech. Website: http://www.solardecathlon.org/2009/team_virginia_tech.cfm

Lumenhaus homepage, a brighter way, everyday. http://www.solar.arch.vt.edu/

Virginia Tech’s entry to US DOE Solar Decathlon competition | 2009

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[www.solardecathlon.org][www.flickr.com/afagen][http://lumenhaus.blogspot.com]


Edge Monkey

These imaginary devices are perhaps the building’s environmental control system of the future.

Their function would be to patrol building façades by regulating energy consumption and indoor comfort conditions.

Basic duties of these autonomous agents include closing unattended windows, checking thermostats, activating movable insulation panels and adjusting blinds.

Sullivan, C. (2006) Robo Buildings: Pursuing the interactive building envelope. Architectural Record April 2006:149-156

Lim, C (2006) Devices: a manual of architectural + spatial machines, Architectural Press

Stephen A. Gage and Will Thorne | 2005

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[Sullivan, 2006]


Electrochromic glazing

An electrochromic coating enables the glass to switch its optical properties and hence color, while maintaining transparency.

The optical properties can be controlled on-demand or via intelligent supervisory control systems.

Because of the adaptability, thermal and visual comfort can be improved while energy consumption is minimized.

Lee, E., Selkowitz, S., Clear, R., DiBartolomeo, D., Klems, J., Fernandes, L., Ward, G., Inkarojrit, V. and Yazdanian, M. (2006) ‘Advancement of Electrochromic Windows’, California Energy Commission, PIER. Publication number CEC-500-2006-052.

Quantum Glass Brochure – inspiring solutions by Saint-Gobain. http://www.quantumglass.com/download-pdf.php?type=quantumglass

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[www.quantumglass.com][www.quantumglass.com] [www.sage-ec.com] [www.sage-ec.com]


Electrically heated glass

The window can perform three functions: heating, anti-condensation and snow melting.

Active insulating glazing with low-emissivity coating and embedded electrodes also improves the thermal distribution in the room.

This concept offers highest potential in regions with extreme climates, like Scandinavia, Siberia and Russia.

Kurnitski, J., Jokisalo, J., Palone, J., Jokiranta, K., Seppänen, O. (2004), ‘Efficiency of electrically heated windows’, Energy and Buildings 36(10):1003-1010

Quantum Glass Brochure – inspiring solutions by Saint-Gobain. http://www.quantumglass.com/download-pdf.php?type=quantumglass

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[www.prelco.ca] [www.quantumglass.com][www.quantumglass.com]


ETFE roofs

ETFE (ethylene tetrafluoroethylene) is increasingly used in buildings because of its lightweight properties, high daylight transmittance and the potential for energy savings.

Compared to conventional glazed roofs, ETFE cushions can provide thermal insulation with reduced initial costs and less structural supports.

Application of a reflective frit to an inflatable intermediate cushion can dynamically alter the roof’s optical properties.

LeCuyer, A. (2008) ETFE – Technology and Design, Basel: Birkhäuser

Poirazis, H., Kragh, M. and Hogg, C. (2009) ‘Energy modelling of ETFE in building applications’, in Proceedings of Building Simulation 2009, Glasgow, Scotland. pp: 696-703

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[www.drmm.co.uk] [LeCuyer, 2008][LeCuyer, 2008]


EWE Arena

The integrated photovoltaic shading system creates a temporary double skin façade while floating around the building.

The kinetic PV-wall provides shading when and where desired, and at the same time maximizes electrical output.

Daylight and view to the outside are unobstructed in the rooms that are not exposed to direct sunlight.

Weller, B. and Jakubetz, S. (2008) ‘Fassaden als Kraftwerke’, Deutsches Ingenieurblatt, 2008(4): 24-28

Lüling, C. (2009) Energizing Architecture, Berlin: Jovis

Müller, H. (2007), ‘Building Integrated Photovoltaics’, in Proceedings of PREA Workshops, Stellenbosch, Dar es Salaam, Kampala,

ASP Architekten | Oldenburg, Germany | 2005

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[www.panoramio.com] [www.asp-stuttgart.de] [www.asp-stuttgart.de]


Flare façade

Acting like a ‘living skin’, flare façade allows a building to express, communicate and interact with its environment.

The system consists of a number of tiltable metal flake bodies supplemented by individually controllable pneumatic cylinders.

By reflecting ambient or direct sunlight, the individual flakes of the FLARE system act like pixels formed by natural light.


Flare – kinetic ambient reflection membrane, Website: http://www.flare-facade.com

Tscherteu, G. (ed) (2008) Media Façades Catalogue, German Centre for Architecture, http://www.mediaarchitecture.org/wp-content/uploads/2008/11/media_facades_exhibition_companion.pdf

WhiteVoid interactive art and design | Berlin, Germany

MACRO

36closed-loop

[www.flare-facade.com]


Fluidized Glass Façade

The concept consists of a four layer glazing element with two fluid filled chambers and one gas filled chamber in the middle.

Colored water in the external cavity is used for control of solar gains; water in the inner cavity serves as transparent large area heating or cooling element.

Heating, cooling and shading levels are regulated by controlling fluid flow levels and water color.

Platzer, W. (2005) ‘Steps towards a vision – a multifunctional fluidized glass façade’, Intelligent glass solutions2: 54-56

Platzer, W., Hube, W., Adelmann, W., Crisinell, M. and Vollmar, T. (2005) ‘Development of a glass façade with internal fluid flow’, in Proceedings of the International Conference Glass Processing Days 2005. pp 303-307

Fraunhofer Institute for Solar Energy | Germany | 2005

MACRO

37closed-loop

[Platzer et al. 2005]


Gemini Haus

About 40 m² of window-area is always orientated towards the sun for maximum passive solar heating.

The solar curtain at the front (output 4.0 kWp) rotates uni-axially around the vertical axis of the building and helps to avoid overheating.

The PV modules integrated in the façade receive the sunlight via hologram foils, thus enhancing the electrical output.

ArchBüro Kaltenegger, Projekte, Wohnbau. Website: http://www.erwin-kaltenegger.at/projekte/wb_ef.php?was=090915_1547

‘Mit dem Energieüberschuss die Hypothek abzahlen’, Frankfurter AllgemeineZetitung, 16 July 2002, page T6

ArchBüro Kaltenegger | Weiz, Austria | 2001

MACRO

38closed-loop

[www.erwin-kaltenegger.at]


Gerontology Centre

The first building in the world that uses a single layer of transparent film, which spans the complete height of the façade.

This was the only way to create the double curvature spiral form that covers a multi-story void.

During the night, vents in the façade open up which enables effective stack-ventilation of the heat that is built up during the day.

Siegert Architekten | Bad Tölz, Germany | 2003

MACRO

39closed-loop


Cremers, J. (2007) Multifunctional Membrane Systems for Intelligent Buildings, Intelligent Glass Solutions 1(1): 29-32

[www.kockmembranen.de] [www.hightexworld.com] [www.grp.hwz.uni-muenchen.de]


Girasol

Girasol (Italian for sunflower) is a controllable external shading system consisting of glass louvers with photovoltaic cells integrated into the glass.

The innovative, autonomous passive sun-tracking system does nor consume any electricity.

Two absorber evaporator tubes cause different expansion or contraction of a working fluid. This difference in pressure in turn actuates the thermohydraulic drive mechanism.

Janak, M. and Kainberger, R. (2009) ‘Integrated building energy and lighting simulation in the framework of EU PV-LIGHT project’, in Proceedings of Building Simulation 2009, Glasgow, Scotland. pp: 1671-1677

Starlinger, M. (2007) ‘Ensuring that louvered shading systems always follow the sun – Applied bionics in the building skin’ Architektur und Sonnenschutz 2007(3)

MACRO

COLT international Ltd. | United Kingdom

40open-loop

[www.coltinfo.co.uk]


GlassX crystal

GlassX crystal integrates four functionalities into a single unit: transparent insulation, overheating protection, energy conversion and thermal storage.

The key component is the slim transparent PCM heat storage module which has a storage capacity equivalent to about 20 cm concrete.

Solar heat is stored in the PCM by means of a melting process. At night, the stored heat is delivered to the interior during re-crystallization.


GlassX – Speichern, Wärmen, Kuhlen, Website: http://www.glassx.ch

GlassX AG | Zürich, Switzerland

MICRO

41open-loop

[www.glassx.ch]


GreenPIX

The world’s largest display area (2200 m2 - 2292 LEDs) combines sustainability with digital media technology.

The system is also called ‘Zero Energy Media Wall’ since no external power is required to fuel the diodes.

In addition, the photovoltaic panes provide a means for both natural ventilation and solar shading.

Sinclair Eakin, J. (2007) ‘A glam in the eye – China makes room for an energy-efficient media wall’, I.D. –The International Design Magazine 54(1): 47-48


GreenPix, Press release http://www.greenpix.org/press/PDF/GreenPix_press-kit_EN.pdf

Simone Giostra & Partners | Beijing, China | 2008

MICRO

42closed-loop

[www.greenpix.org]


GROW

GROW is a hybrid energy delivery device that provides power via the sun and wind.

The solar leaves act as micro-power stations by employing thin film photovoltaics with piezoelectric generators and screen printed conductive ink encapsulated in ETFE fluoropolymer laminates.


SMIT - Sustainably Minded Interactive Technology, Webstite: http://www.S-M-I-T.com

Cochran, T. (2008) ‘Sustainable Minded Interactive Technology (SMIT) Design’, in Proceedings of Sustainable design: at the crossroads of art, policy and science, Boston, MA

SMIT | New York | 2005-2010

MACRO

43open-loop

[www.S-M-I-T.com]


Habitat 2020

This concept is exploring the possibility of using sensitive textile skins on buildings to create energy independent structures.

Electronics and bio-chemical functionalities are being introduced in the building shell.

The membrane is used as transporter for collecting and channeling air, water and light, from the outside feeding into the inside space.

Off the grid: Sustainable Habitat 2020. Website: http://www.design.philips.com/probes/projects/sustainable_habitat_2020/index.page

Kim, H. and Stephens, B. (2009) ‘A conceptual tool for critical interaction design for sustainability’, In Proceeding of International Conference on Designing Pleasurable Products and Interfaces, Compiegne, France.

Philips Design Probe | 2009

MACRO

44closed-loop

[www.design.philips.com]


Heliotrop

The building has been named after heliotropism which refers to plants that grow in response to the stimulus of the sun.

Via a powerful engine, the building is able to vary its orientation in response to the sun.

One-half of the building is highly glazed, the other side is well insulated. A central control system decides which side will be exposed to the sun.

Wigginton, M. and Harris, J. (2002) Intelligent Skins, Oxford: Butterword-Heinemann

‘Heliotrope – in the spotllights of success’, Renewable Energy Journal 2000(10), http://ec.europa.eu/energy/res/publications/doc1/rej_10.pdf

Heliotrop, Robert Disch, Projects, Website: http://www.rolfdisch.de/project.asp?id=45&sid=-1402147115

Rolf Disch | Freiburg, Germany | 1993

MACRO

45closed-loop

[www.rolfdisch.de]


Homo Lumens MMVII

The environment-responsive kinetic glass façade consists of an array of transparent lamellas and panels of six LEDs embedded in the window sills.

Each lamella responds to temperature and wind signals from roof-mounted weather sensors.

The whole façade moves slowly when the weather is quiet, and faster when it is windy. Color patterns and shades of each hue are programmed to change subtly with time and temperature.

Homo Lumens MMVII blogspot, http://lanchid19.blogspot.com/

Dumiak, M. (2008) ‘Homo Lumens moves, inviting many interpretations’, Architectural Record may 2008

MACRO

László Benczúr, Péter Sugár, László Kara | Budapest, Hungary | 2007

46closed-loop

[http://lanchid19.blogspot.com]


Hotel habitat

Because the envelope acts as ‘thermometer’, it gives a special aesthetic appearance to the building.

Each node of the steel mesh consists of one photovoltaic cell, a battery, CPU and a RGB LED which creates an artificial light cloud around the hotel.

The light mesh has sensors that will read the daylight sun amplitude and then at night each node will give off color according to how much that node had absorbed.

Hotel Habitat, Ruiz-Geli, Website: http://www.ruiz-geli.com/download/project.pdf

Interactive Architecture (2006) ‘LED light mesh facade – James Clar & Cloud9’, http://www.interactivearchitecture.org/led-light-mesh-facade-cloud9-james-clar.html

MICRO

Ruiz-Geli – Cloud9 | Barcelona, Spain | 2006-2010

47closed-loop

[www.ruiz-geli.com]


iHomeLab

Every other vertical louver can be forced to curve, and in that way control the level of daylight and solar gains entering the building.

During its operation, the lamellae very much act like the gills of a fish. At night, the membrane changes color when and where people approach the

building.

Boog, P. (2009) ‘Es bewegt sich was!’ Fassade / Façade 2009(2): 39 - 42

Coltpost (2009) Colt Group Limited. http://www.coltgroup.com/view.aspx?/about-colt/colt_post_2009.pdf

Lischer Partner Architekten| Lucerne, Switzerland | 2008

48MACRO

[http://www.lischer-partner.ch] [http://www.lischer-partner.ch] [Boog, 2009]

closed-loop


IC solar façade system

Solar concentrating tracking technology is transferred to a day-lighting system within a ‘double-skin’ façade.

The lens directly concentrates the light (>400:1) onto a high-efficiency multi-junction PV cell that recently demonstrated an efficiency of 39.4%.

Absorbed power that is not converted to electricity is captured via a coolant flow and is used for domestic hot water generation and space heating.

Dyson, A., Jensen, M. and Stark, P. (2007), ‘Integrated Concentrating (IC) Solar Façade System’, DOE Solar Energy Technologies Program Review Meeting

RPI, Center for Architecture, Science and Ecology, Website: http://www.case.rpi.edu/projects/ICsolar.html

Rensselaer Polytechnic Institute | Troy, New York | 2007-2010

MACRO

49closed-loop

[www.case.rpi.edu]


Kameleon Concept

The concept consists of a rotatable aluminium box girder with four different surfaces and replaceable coffers.

Depending on the prevailing weather, the façade can perform different functions: e.g., energy generation, heat storage, air purification, advertising and rainwater drainage.

The system is designed to create maximum value, 24 hours a day.

Sublean | Sliedrecht, The Netherlands | 2009

Beerda, E (2010) ‘Kameleontisch oppervlak werkt verduurzamend’ Cobouw 15 January 2010, pp: 3/4

Submission to the competition: Het ei van Columbus, ‘Kameleon concept’, Website: http://www.ei-van-columbus.nl/inzendingen/inzending/419

MACRO

50closed-loop

[www.sublean.nl]


Kinetic light

Behind a screen of perforated aluminum, 120 floodlights fade from blue to yellow, illuminating the front of the building.

The overall image is directed by a weather station on top of the building: The ambient temperature determines the amount of yellow on the blue wall; The yellow patches move in line with the direction of the wind and rain; LED lines in the upper area of the façade visualize noise in the street in real-time.

Christian Moeller, works, ‘Kinetic Light Scuplture’, Website: http://www.christian-moeller.com/display.php?project_id=30

Interactive Architecture (2005) ‘Kinetic Light Sculpture of the Zeilgalerie’, Website: http://www.interactivearchitecture.org/kinetic-light-sculpture-of-the-zeilgalerie.html

Christian Möller and Rüdiger Kramm | Frankfurt, Germany | 1992

MICRO

51closed-loop

[www.christian-moeller.com]


Kinetic roof Medina house

Because the sun changes position, the roof keeps moving like a living structure.

Winter day: the kinetic structure opens to the sun and its warm light. Winter night: the roof closes to keep the heat inside. Summer day: the roof “denies” the sun while facilitating cross ventilation. Summer night: the kinetic roof opens for night ventilation.

Abdel Kawi, A. (2001) ‘Medina Third Annual Design Competition: Youth ideas competition, Smart Village, Egypt’, Medina 19: 50 - 63

Magnoli, C., Bonanni, L. Khalaf, R. and Fox, M. (2001) ‘Designing a DNA for responsive architecture: a new built environment for social sustainability’, MIT Media Lab – Development by design Workshop

MACRO

MIT | Entry for Medina 3rd annual design competition | 2001

52closed-loop

[Magnoli et al., 2001]


Le mur neutralisant

Le mur neutralisant is often considered as the revolutionary predecessor of the double skin façade.

It consists of two glass panels within which tempered air is flowing at a constant temperature of 18 degrees Celsius.

This air layer neutralizes the effects of the cold downdraught in winter or keeps solar heat outside in summer, thereby enhancing thermal comfort conditions throughout the whole year.

Banham, R. (1984) The architecture of the well-tempered environment, University of Chicago Press

Knaack, U., Klein, T., Bilow, M. and Auer, T. (2007) Façades - Principles of construction, Basel, Boston, Berlin: Birkhäuser

Le Corbusier | 1929

MACRO

53closed-loop

[Knaack et al., 2007] [www.quintademosteiro.com] [www.quintademosteiro.com]


Living glass

Elastic shape memory alloy (SMA) wires control the level of carbon dioxide in a room.

High CO2 concentrations force SMA wires to contract and pull open slits etched in the window to allow fresh air to flow in.

Inspired by the gill, the respiratory organ that controls the absorption of oxygen and the secretion of carbon dioxide in most aquatic organisms.

Manfra, L. (2006) ‘Envisioning architecture that performs like natural organisms’, Metropolis 25(5): 52-54

Living glass: What if architecture responded to you? http://www.thelivingnewyork.com/lg.htm

The Living | New York | 2005

MACRO

54closed-loop

[www.thelivingnewyork.com]


Living walls and roofs

These self-sufficient vertical or horizontal surfaces restore the level of flora and fauna in the urban context.

The systems add mass and thermal insulation to the building shell and are also useful for controlling rainwater runoff.

The plants can help to reduce overall temperature of building surfaces via evaporative cooling which reduces energy consumption

Blanc, P. (2008) The vertical garden: from nature to the city , New York: Norton

Leenhardt, J. and Lambertini, A. (2007) Vertical gardens : bringing the city to life, London: Thames & Hudson

MACRO

55open-loop

[www.massstudies.com] [http://rpbw.r.ui-pro.com] [www.verticalgardenpatrickblanc.com]


L’hemisferic

A large shelter over a spherical IMAX theatre that opens up in the same way as a retractable eyelid.

The aluminum awnings can fold up individually or collectively over the full breadth (90 meter) of the building.

The system is actuated via telescoping hydraulic cylinders which allow fresh breezes to enter the inside.

MACRO

Santiago Calatrava | Valencia, Spain | 1998

City of sciences and arts in Valencia, Santiago Calatrava, Recent projects, Website: http://www.calatrava.com

Ataman, O., Rogers, J. and Ilesanmi, A. (2006) ‘Redefining the wall: Architecture, Materials and Macroelectronics’, International Journal of architectural computing 4(4): 125 - 136

56closed-loop

[www.britannica.com] [www.cac.es] [Calatrava Sketchbook][www.cac.es]


Liquid façade

Water is used for its thermal mass and energy storing capabilities and the system also has a load bearing capacity.

Insulation values can be controlled pneumatically by changing the position of the water-filled chamber.

In future generations the water can be used for collection of thermal energy to supply heating and cooling to the building systems.

MACRO

Ulrich Knaack | TU Delft, The Netherlands | 2006

57

Knaack, U., Klein, T. and, Bilow, M (2008) Imagine 01 – Façades, Rotterdam: 010 Publishers

closed-loop

[Knaack et al., 2008]


Metal shutter houses

The building can literally become a uniform minimal closed cube, or it can open completely.

Perforated metal shutters on the outside can modulate incoming light and acts as privacy screen.

Additionally, sliding glazed doors on the inside can be opened to further blur the boundary between inside and outside.

Metal Shutter Houses, Shigeru Ban West Chelsea, Website: http://www.metalshutterhouses.com/

‘Eyes wide open - Shigeru Ban’s "metal shutter houses" under construction in Manhattan’, World Architecture News: editorial 28 October 2007

MACRO

Shigeru Ban Architects | New York, USA | 2007-2010

58closed-loop

[www.metalshutterhouses.com]


Microblind

An array of overhanging pre-stressed ‘invisible’ microblinds with a size of 100 micrometer covers the window.

Curling of microblind-electrodes is electrostatically activated. Arrays can be deposited on the flat glass by magnetron sputtering like regular

low-E coatings, and then patterned by laser. The microblinds have several advantages including switching speed

(milliseconds), UV durability, customized appearance and transmission.

Lamontagne, B. and Py, C. (2006) Microblinds and a method of fabrication thereof, US Patent Application 20060196613

Lamontagne, B. (2009) Next generation of smart windows based on micro-blinds (MEMS) and Silicon photonics at IMS-NRC: from bio-sensing to space applications, Lecture in Mininova, HelsinkyUniversity of Technology

MICRO

Boris Lamontagne and Christophe Py | National Research Council of Canada

59closed-loop

[Lamontagne and Py, 2006]


Nano Vent Skin

This conceptual project tries to make (existing) objects greener with a skin made out of micro windturbines.

Kinetic energy of the wind is converted into electricity and at the same time, CO2 is absorbed via organisms inside the turbines.

The outer skin of the structure absorbs sunlight through an organic photovoltaic skin.

Otegui, A (2008) Nano Vent Skin weblog. http://nanoventskin.blogspot.com/

May Young, N. (2008) ‘Living skin could be the future in sustainable design’, World Architecture News: editorial June 2008

Augustin Otegui | 2008

MACRO

60open-loop

[Otegui, 2008]


Nocturnal ventilator

The system stores thermally induced movement of a wax piston over a day’s temperature change in a spring mechanism.

The spring is then released during the night via an ingenious mechanism, and this creates a breeze that lasts for hours.

Arrays of ventilators can be retrofitted to existing dwellings, and provide energy-free localized ventilation.

Gage, S. (2008) ‘The wonder of trivial machines’, System Design and behavioral science 26(3):771-778

Lim, C (2006) Devices: a manual of architectural + spatial machines, Architectural Press

MACRO

Nick Browne | Bartlett School of Architecture | London, UK | 2005

61open-loop

[Lim, 2006][Gage, 2008]


Paper art museum

The façade of the museum consists of translucent glass-fiber-reinforced panels that can be pivoted over their whole length.

By opening the stacking shutters and awnings (shitomido), a spatial continuity between the interior and exterior is achieved.

This enables outside air to freely flow into the building while at the same time protecting the building from overheating via solar shading.


Paper art museum, Works Shigeru Ban Architects, Website: http://www.shigerubanarchitects.com/SBA_WORKS/SBA_OTHERS/SBA_OTHERS_9/SBA_Others_9.html

MACRO

Shigeru Ban Architects| Shizuoka, Japan| 2002

62closed-loop

[www.shigerubanarchitects.com]


Pfalzkeller

Calatrava designed a symmetrical system, covering the glazed roof of a largely underground building.

Two arched tubes position the articulated slats on each side of the roof, and in this way dynamically control daylight.

The system was the first application of a movable roof cover based on slats in combination with a mechanical hoist system.

Pfalzkeller Emergency Service Center, Santiago Calatrava, Past projects, Website: http://www.calatrava.com

‘Santia Calatrava St Gallen’, Kinetic Architecture blog. http://kineticarchitecture.blogspot.com/2008/05/santiago-calatrava-stgallen_27.html

Santiago Calatrava | St. Gallen, Switzerland | 1998

MACRO

63closed-loop

[www.calatrava.com]


Phantom house

The roof consists of a deployable shelter that can serve two functions: 1) collecting rainwater and 2) providing sunshading.

Water sensors on the roof double the areas able to catch rainwater when necessary and store the water for use in toilets and the garden.

The house has a dynamic overhang which allows valuable solar gains in the winter but avoids overheating in summer.

Jodidio, P. (2009) Green Architecture Now!, Köln, Germany: Taschen

Diller, E., Scofidio, R., Renfro, C., Baccarini, C., Donnelly, R. and Gesauldi, F. (2007) ‘An eco-house for the future’, The New York Times Magazine, May 20, 2007

MACRO

Diller Scofidio + Renfro | 2007

64closed-loop

[Diller et al., 2007]


Photochromics

These materials reversibly change their color when exited by the effect of light (electromagnetic energy).

A common application is its use in self-adjusting sunglasses, but architectural implementation is also pursued for a long time.

Relatively high costs, sensitivity to heat and poor long-term behaviour have prevented large scale use of these systems.

MICRO


Smith, G. (1966) ‘Chameleon in the sun: photochromic glass’, IEEE Specctrum 3(12): 39-47

Fernández, J. (2007) ‘Materials for Aesthetic, Energy-Efficient, and Self-Diagnostic Buildings’, Science 315(5820): 1807 - 1810

65open-loop

[www.x-celoptical.com][www.photochromicwindows.com][www.photochromicwindows.com]


Pixelskin01

The system consists of a combination of electrochromic glass and ultra-bright electroluminescent tubes controlled by a distributed network of microcomputers and sensor consoles.

The architectural surface is designed to switch between a conventional window and a display/advertising/communication screen.

Pyroelectric sensors on each disk are used for controlling transparency.

MICRO


Anshuman, S. (2006) ‘Pixelskin01’, Website: http://www.orangevoid.org.uk

Sachin Anshuman | Orangevoid, United Kingdom | 2006

66closed-loop

[Anshuman, 2006]


Pixelskin02

A matrix of interconnected pixel tiles that are controlled interactively via embedded controllers.

The system utilizes two-state material SMA wires for actuating each pixel in one of 255 potential states.

The surface can also be used to generate dynamic low resolution images.


Anshuman, S. (2006) ‘Pixelskin02’, Website: http://www.orangevoid.org.uk

Sachin Anshuman | Orangevoid, United Kingdom | 2006

MACRO

67closed-loop

[Anshuman, 2006]


PV honeycomb glass façade

Display skin and structural design are integrated in Honeycomb system. The energy generated by photovoltaic panels will be used to illuminate high-

efficiency LED lights at night. The system mimics nature: honeycomb structure has durability, like

Radiolaria, and optical efficiency, like fly’s eyes.

Photovoltaic Honeycomb Glass Screen Façade (2006) ‘Light Objects, A design competition about sustainability’, Website: http://www.core77.com/lightobjects/img/1474/default.asp

bright LED (2007) ‘a design competition organized by designboom’, Website: http://www.designboom.com/contest/view.php?contest_pk=19&item_pk=13925&p=1

MICRO

Daisuke Nagatomo and Minnie Jan | Japan | 2006

68closed-loop

[www.core77.com]


Responsive kinetic façade

A composition of hinged glass panes and spandrel panels laminated with solar cells.

The flexible skin is capable of changing its shape, controlled via magnetic switches that are powered via the PV cells.

The building skin is considered as a pump: depending on the season, air is supplied to or extracted from the room.

SOM + SCI-Arc on CF:Responsive Kinetic Façade, CORE.FORM-ULA 15 April 2009. Website: http://www.core.form-ula.com/2009/04/15/som-sci-arc-on-cfresponsive-kinetic-facade/

MACRO

SOM and Sci-Arc | 2008

69closed-loop

[www.core.form-ula.com]


Roof pond Hammond house

Large water storing bags are covered by insulating hinged lids which are opened and closed by a hydraulic ram.

When the panels are raised on sunny winter days, water bags are exposed to the sun, while the panels act as reflectors to enhance absorption of solar radiation.

In summer, the panels are raised at night. Absorbed warmth from the inside is released to the cool night sky, via radiation and convection.


Anderson, B. and Michal, C. (1978) ‘Passive Solar Design’, Annual Review of Energy 3(1):57-100

MACRO

Jonathan Hammond | Winters, California | 1975

70

[Bear, 2009] [Bear, 2009]

closed-loop


Sagami bay house

This modern dwelling is designed according to the high transparency guidelines of traditional Japanese architecture.

The architects brought even more light into the main house by conceiving the roof as a fifth façade of glass.

The roof creates a theatre of shifting light inside, adding another dimension to the variety of light admitted by the perimeter of glass.

MACRO

House in Japan, Tokyo, Japan, Foster+Partners Projects. Website: http://www.fosterandpartners.com/Projects/0411/Default.aspx

Giovannini, J. (2008) ‘A Japanese Modernism – Fusing tradition and progression in a house on the volcanic coastline of Sagami Bay’ Architectural Digest March 2008

Foster + Partners | Tokyo, Japan | 1987- 1992

71closed-loop

[www.fosterandpartners.com]


Self-regulating ventilation

Via a combination of pivoting and bending mechanisms, the ventilation grille installed in the window frame becomes ‘self-regulating’.

These vents can achieve stable ventilation levels in spite of the highly irregular variations in natural driven conditions.

The system also allows for users intervention via a manually controlled ventilation flap.

Breesch H., Descheermaeker K., Janssens A., Willems L. (2004) Evaluation of natural ventilation systems in a landscaped office, Proceedings of PLEA2004, Eindhoven: The Netherlands; pp:1-6

Axley, J., Emmerich, S. and Walton, G. (2002) Modeling the Performance of a Naturally Ventilated Commercial Building With a Multizone Coupled Thermal/Airflow Simulation Tool. ASHRAE Transactions 108(2)

MACRO

72open-loop

[www.renson.be]


Showroom Kiefer technic

The shades of this dynamic façade can be adapted individually to changing conditions and needs.

The façade changes continuously; each day and each hour shows a new “face” - the façade is turned into a dynamic sculpture.

A video clip featuring a choreography dedicated to this ‘dancing façade’already attracted more than 160.000 viewers on YouTube.

Showroom Kiefer technic, Giselbrecht + Partner projekte, Website: http://www.giselbrecht.at/sect01_projekte/gewerbe_industriebauten/kiefer/frames_unten.html

e-architect, Kiefer Technic Showroom: Architecture Information, Website:http://www.e-architect.co.uk/austria/kiefer_technic_showroom.htm

Ernst Giselbrecht + Partner | Bad Gleichenberg, Austria | 2007

MACRO

73closed-loop

[www.giselbrecht.at]


Skytherm solar roof pond

The system consists of a large mass of water, packed in containers and placed on top of a metallic corrugated ceiling deck.

The flat roof also holds an insulation panel that moves on rails to cover and uncover the water to the atmosphere.

Depending on season and time of the day/night, the roof pond system can act as source of heating and cooling to the zone below.

Gut, P. and Ackerknecht, D. (1993) Climate Responsive Building, St. Gallen: SKAT

Givoni, B. (1979) ‘Passive cooling of buildings by using natural energies’ Energy and Buildings 2(4): 279-285

MACRO

Harald Hay | Atascadero, California | 1973

74closed-loop

Heating cycle Cooling cycle

[Gut and Ackerknecht, 1993]


Smart Energy Glass

An innovative coating allows the window to switch its optical properties in three different states: dark, bright and privacy.

Part of the sunlight is captured and converted into electricity in PV-cells mounted at the edges of the window.

When equipped with batteries, the system becomes autonomous and can be integrated in the building envelope without the need for external wires.

Peer+ Smart Energy Glass, Website: http://www.peerplus.nl

Kemper, A. (2008) ‘Schone energie dankzij superslimme ruiten’Volkskrant 14 oktober 2008

MICRO

Peer+ | Eindhoven, The Netherlands

75closed-loop

[www.peerplus.nl]


SmartScreen

The shading device is automatically activated by changes in local interior air temperature.

The R-Phase SMA (shape memory alloy) smart material simultaneously acts as both a sensor and a motor.

Adaptation is triggered automatically and consequently does not consume any fuel or electricity.

Decker, M. and Yeadon, P (2008) SmartScreen: Controlling solar heat gain with shape memory systems, Boston Society of Architects Design Research Grants

Morell, D. (2007) The role of memory in architecture and materials, MetropolisMag.com

MACRO

Martina Decker and Peter Yeadon | New York

76open-loop

[Decker and Yeadon, 2008]


Smart shade

A passive solar shading device that automatically adjusts to changing light and heat conditions.

Smart shade employs the thermodynamics of zinc and steel to control the amount of sunlight passing into a building's interior.

Expansion and contraction of these sandwiched materials in response to temperature cause the blinds to curl up in winter (allowing more sunlight in) and curve down in summer (allowing less).

Manfra, L. (2006) ‘Envisioning architecture that performs like natural organisms’, Metropolis 25(5): 52-54

Application design – Interaction design, Robotecture lecture, http://robotecture.com/spaceelevatorbase/lectures/applicationdesign.pdf

Lance Hosey | ATMO / Atelier Modern | Washington DC | 2005

MACRO

77open-loop

[Manfra, 2006]


SmartWrapTM

Rolled and printed onto fabrics and plastics to fulfill the following functions: shelter, climate control, lighting, information display and power supply.

The construction material features organic photovoltaics, thin film batteries and OLED’s, all inspired by the printing industry.

The inner skin supports thermal storage and insulation via pockets filled with phase change material and aerogel.


SmartWrapTM: The Building Envelope of the future, http://kierantimberlake.com/research/smartwrap_research_8.html#

Kieran Timberlake Associates | Philadelphia, US | 2003

MICRO

78open-loop

[http://kierantimberlake.com]


Solar barrel wall

During the day, thermal energy from the sun is collected and stored in a stack of 200 liter water-filled steel oil drums.

At night, the external lids are closed while the internal lid is opened, so the stored energy is discharged to the room.

The ends of the barrels are painted black, and the shutter is made highly-reflective; both effects together improve storage of thermal energy.

Knaack, U., Klein, T. Bilow, M. and Auer, T. (2007) Façades – Principles of construction, Basel: Birkhäuser


MACRO

Steve Baer | Corrales, New Mexico | 1971

79

[Bear, 2009] [Bear, 2009][Knaack, 2007]

closed-loop


SolaRoof

Bubbles in the transparent building cavity can provide insulation or shade as required.

A liquid film instead of bubbles acts as solar energy harvester or liquid cooling film.

The generation and movement of soap bubbles and the rainbow color reflection of soap bubbles provides a fascinating and charming atmosphere.

Han, H.J., Jeon, Y.I., Lim, S.H., Kim, W.W. and Chen, K. (2010) ‘New developments in illumination, heating and cooling technologies for energy-efficient buildings’, Energy 35(6): 2647-2653

SolarBubbleBuild SolaRoof technology, Website: http://www.solarbubblebuild.com/

MACRO

Harvey Rayner | Suffolk, UK | 2005

80

[www.solarbubblebuild.com]

closed-loop


Solar Ivy

A system of solar powered panels disguised to look like ivy leaves and made out of 100% recyclable polymer solar cells.

Solar Ivy's unique visual appeal and flexibility brings a technology traditionally restricted to the roof to almost any architectural surface.

It has the ability to provide varying degrees of opacity to modulate heat gain, actuated by the vagaries of the wind.


Solar Ivy, Website: http://www.solarivy.com

Muis, R. and Griffioen, R. (2009) ‘Gevels begroeid met zonnecellen’ Architectenweek Magazine 29: 84

S-M-I-T | New York | 2005 - 2009

MACRO

81

[www.solarivy.com]

open-loop


SolVent window system

The system consists of an innovative reversible frame, incorporating two glazing assemblies: a clear glazing to provide a weatherproof seal, and an absorptive glazing to provide solar control. The two glazing assemblies and the ventilated channel between them rotate together through 180° to enable the transformation from winter mode to summer mode.

In winter, the glazing system allows passive solar space heating. In summer, penetration of unwanted radiation is reduced, thus reducing

overheating and visual glare. External shading devices are made unnecessary.

Erell, E., Etzion, Y., Carlstrom, N., Sandberg, M., Molina, J., Maestre, I., Maldonado, E., Leal, V. and Gutschker, O. (2004) ‘SolVent: development of a reversible solar-screen glazing system, Energy and Buildings 36(5): 467-480

Leal, V., Erell, E., Maldonado, E. and Etzion, Y. (2004) Modelling the SolVent ventilated window for whole building simulation, Building Services Engineering Research and Technology 25(3): 183-195

MACRO

82

[Leal et al, 2004][Erell et al, 2004]

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[Erell et al, 2004]


Sonomorph

Sonomorph is a kinetic building system that changes its behavior in response to various acoustic stimuli.

It consist of cellular components with aluminum outer panels and glass-reinforced plastic inner panels.

By opening or closing these panels, the façade can change from a sound reflective to a sound absorptive state.


Bifokal Projects, Sonomorph: interactive absorbant. http://bifokal.no/project/architecture/

MACRO

Natasa Sljivancanin | 2007

83

[http://bifokal.no]

closed-loop


Stomata Inspired Skin

A functional biomimetic translation of stomata, the openings that control the gas exchange in the leaves of plants.

At high relative humidity, the blue Cithosan polymer swells and separates the two layers of fabric, which increases airflow through the façade.

The selfregulating smart material provides distributed ventilation and enhances the quality of the internal climate.

Gruber, P. (2008), ‘The signs of life in architecture’, Bioinspiration & Biomimetics 3(2): 1-9

Michael Murauer | Austria, Vienna | 2004

MACRO

84

[Gruber, 2008]

open-loop


Sunbender

The key to the Sunbender’s performance is the adjustable reflector that can be mounted to every existing skylight.

In the lowered summer position, it shades the skylight and greatly reduces heat gain, while still allowing diffuse light to enter.

Sunbender allows collection of desirable heat during cold weather months,the curved shape of the reflector concentrates the reflected sunlight.

Zomeworks Passive Energy Products, Sunbender Brochure http://zomeworks.com/files/sunbenderTM/Sunbender%20Brochure%202009.pdf


MACRO

ZomeWorks Passive Energy Products | New Mexico, USA

85

[http://zomeworks.com]

closed-loop


Sündreyer

The round buildings and turnable roofs ensure optimal utilization of solar radiation.

The heliotropic horizontal sun-tracking roof produces a 25% surplus of electrical energy compared to a static configuration.

A central electro-hydraulic unit drives the system via small wheels on rails.

Sündreyer GmbH, Website: http://www.suendreyer.de

‘Sündreyer – Drehbares dach’, Kinetic Architecture blog. Website: http://kineticarchitecture.blogspot.com/2008/05/sundreyer-drehbares-dach_06.html

SÜNDREYER GmbH | Treia, Germany | 2005 - 2010

MACRO

86closed-loop

[www.suendreyer.de]


Super Cilia Skin

Super Cilia Skin (SCS) is basically a surface for displaying haptic and visual information but is also envisioned as dynamic cladding material.

Electromagnetic energy generators make it possible to harness energy from the wind blowing over the building’s exterior.

SCS can be both a billboard-like ambient display surface and a source for alternative energy that is also visually appealing.

Raffle, H., Joachim, M.W. and Tichenor, J. (2003) ‘Super Cilia Skin: An Interactive Membrane’. In Proceedings of the Conference on Human Factors in Computing Systems (CHI'03), Ft. Lauderdale, FL, USA.

Raffle, H., Tichenor, J. and Ishii, H. (2004) ‘Super Cilia skin, A Textural Interface’, Textile, The journal of cloth and culture 2(3): 238-247

MIT Media Lab | Massachusetts, USA | 2003

MACRO

87open-loop

[Raffle et al., 2003-2004]


Swindow

The basic configuration consists of a horizontally pivoted window that is hinged just above mid-height.

The window starts moving when the wind blows, hence, the naturalventilation rate is a function of wind speed and direction.

Shock absorbers and a balancing weight ensure calm operation and a steady ventilation flow rate.

Aschehoug, Ø. and Andresen, I. (2008) ‘State-of-the-art review, IEA-ECBCS Annex 44: Integrating environmentally responsive elements in buildings‘

Lee, SW., Kato, S., Takahashi, T., Chang H, and Murakami, S. (2000) ‘Field Test on Natural Ventilation Performance of Swinging Windows in a Building’, in Proceedings of the Meeting of the Society of Heating, Air-Conditioning and Sanitary Engineering, pp 261-264

MACRO

Tateyama aluminium inc. | Japan | 2007

88open-loop

[Annex 44][Annex 44] [www.nav-window21.net][http://buildingsash.net]


Switchable insulation

The system is based on the controllable release and re-absorption of hydrogen in metal hydride, which changes thermal conductivity.

Thermal conductivity can reversibly be changed by a factor 40, after applying a small amount of electricity.

Passive solar heat is transferred to the inside, but only at times when this is desired and advantageous.

Horn, R., Neusinger, R., Meister, M., Hetfleisch, J., Caps, R. and Fricke, J. (2000) ‘Switchable thermal insulation: results of computer simulations for optimization in buildings’, High Temperatures – High pressures, 32(6) 669-675

ZAE Bayern projects, ‘Switchable Insulation for Utilizing Solar Energy’, Website: http://www.zae.uni-wuerzburg.de/english/division-2/projects/archive/si.html

ZEA Bayern | 1999-2001

89MICRO

closed-loop

[Horn et al., 2000][www.zae.uni-wuerzburg.de]


The B&W house

The Roof is self-orientating along the day, following the sun. Moving the PV roof gives 10.3% extra power compared to a horizontal fixed roof.

The movable façades are clad with semi-transparent PV-cells. These façade panels are also oriented towards the sun in order to maximize the capture of solar energy.

Solar Decathlon, Team Spain, Universidad Politécnica de Madrid. Website: http://www.solardecathlon.org/2009/team_spain.cfm

UPM Solar Decathlon 2009, Website: http://www.solardecathlon.upm.es/

Universidad Politécnica de Madrid| US DOE Solar Decathlon competition | 2009

MACRO

90closed-loop

[Universidad Politécnica de Madrid]


The esplanade

Movable triangular sunshades admit diffuse daylight while at the same time excluding direct sunlight.

The shapes and sizes of each of the 7139 shutters were determined on the basis of solar optimization studies.

Provides the building with climate control mechanism, much as an animal can raise the hair on its back.

Aldersey-Williams, H. (2004) Towards biomimetic architecture, Nature / materials 3(5):277-279

‘The realisation of complex roof geometries’, Roof & Façade, Asia, March 2004.

DP Architects and Michael Wilford & Partners | Singapore | 2002

MACRO

91closed-loop

[http://app.mica.gov.sg/][http://mikebm.wordpress.com] [http://keropokman.blogspot.com]


Thermochromics

Thermal energy (heat) to the material alters its molecular structure. Which in turn changes spectral reflectivity of the thermochromic material.

The ‘smart material’ found its way in architecture via applications in switchable windows, concrete and wallpaper.

Thermochromic windows become opaque in warm environment. Unfortunately, the system does not allow for manual override.

Arutjunjan, R. (2003) ‘Thermochromic Glazing for “Zero Net Energy” House’, in Proceedings of the International Conference Glass Processing Days 2003. pp 299-301

Nitz, P. and Hartwig, H. (2005) ’Solar control with thermotropic layers’, Solar Energy 79(6): 573-282

MICRO

92open-loop

[Nitz and Hartwig, 2005][Nitz and Hartwig, 2005][www.shiyuan.co.uk]


The Sliding House

A 20 ton mobile roof-and-wall enclosure traverses the site on recessed railway tracks, and in this way gives the building envelope its adaptive properties.

Sliding house offers radically variable spaces, and also provides different levels of shelter, insulation and ingress of daylight throughput the day.

Winner of: RIBA 2009 award, Grand design award 2009 and WAF09 award.

May Young, N. (2008) ‘Extraordinary design concept veiled within a seemingly ordinary exterior’, World Architecture News: editorial March 2008

Russel, R. (2010) ‘Sliding house’, Website: http://www.therussellhouse.org/

dRMM projects, ‘Sliding house’, Website: http://www.drmm.co.uk/dRMM/projects/by%20name/sliding%20house/sliding%20house.pdf

dRMM architects | Suffolk, United Kingdom | 2008

MACRO

93closed-loop

[Russel, 2010]


“Turning the place over”

This piece of public artwork is a slowly rotating circle of the façade itself, offering recurrent glimpses of the interior during its constant cycle during daylight hours.

The oval section cuts back into the building and veers out over the pavement as it goes round.

The revolving façade rests on a specially designed giant rotator, usually used in the shipping and nuclear industries.

‘Future of cities: When does public art become ‘plop’ art’, Guardian, 1 October 2008. http://www.guardian.co.uk/society/2008/oct/01/regeneration.public.art

Croft, C. (2007) ‘Sculptor Richard Wilson and Price & Myers turn a Liverpool office façade inside out’, Building Design Magazine – Envelope, July 2007

Richard Wilson | Liverpool, United Kingdom | 2007

MACRO

94closed-loop

[Croft, 2007]


Wall integrated PCM

Encapsulated latent heat storage material is incorporated in the construction elements of the building.

Heat or cold is stored and automatically released when indoor or outdoor temperature rises or falls beyond the melting point.

Phase change materials also have the ability to dampen temperature fluctuations without consuming energy.

Baetens, R., Jelle, B. and Gustavsen, A. (2010) ‘Phase change materials for building applications: A state-of-the-art review’, Energy and Buildings 42(9): 1361-1368

Tyagi, V and Buddhi, D. (2007) ‘PCM storage in buildings: A state of art’, Renewable and Sustainable Energy Reviews 11(6): 1146-1166

MICRO

95open-loop

[www.basf.com]


Wall street ferry terminal

The fully retractable wall diminishes the threshold to the city and its living waterfront.

In fair weather, the building opens to the south, erasing the line between inside and out.

The terminal can also provide shelter, a place not only for the traveler, but for the local community as well.

Pier 11 Wall Street Ferry Terminal and Transit Hub, SMH+, Website: http://www.smharch.com/project_template.php?id=29&bgId=&category=planning

Turner, J. and Hejduk, J. (2000) Between spaces: Smith-Miller + Hawkinson Architecture, New York: Princeton Architectural Press

Smith-Miller + Hawkinson Architects | New York | 1995

MACRO

96closed-loop

[www.smharch.com]


Weather Sensitive Envelope

A polyreactive mechanomembrane, bionically inspired by the various functions of the human skin.

This technology demonstrator has the ability to change color, exploit evaporative cooling and “raise hairs” in response to changes in temperature and humidity of the surrounding air.

The concept works via a complex system of thermobimetals, water absorbing ceramics, color filters, springs and elastics bands all attached to a flexible textile membrane.


Currey, M. (2007) ‘Performance enhancers’, Metropolis 26(9): 172-175

Axel Ritter | 1997

MACRO

97open-loop

[Ritter, 2007]


Wind Shaped Kinetic Pavilion

Each of its six main segments twists freely around its central support frame in response to fluctuations in the wind.

The rotation of the light-weight slices generates enough electricity as it moves to light the pavilion at night.

The view from inside literally changes at the whims of the weather.

Jantzen (2010) Portfolio Michael Jantzen, Website: http://www.michaeljantzen.com/portfolio/

Dove, C. (2007) ‘Wind shaped pavilion, a mind-blowing experience’, Modern design, architecture and art, 2007(6): 33

Michael Jantzen | 2007

MACRO

98open-loop

[Jantzen, 2010]


Zollverein school

The wall construction only consists of a 30 cm single leaf concrete layer with embedded heating ducts, acting as ‘active thermal insulation’.

Symbiosis is achieved because excess mine-water at 30°C that is pumped from a nearby colliery to prevent collapse of the mine, is fed to the radiant tube heat exchanger to modulate heat transfer across the envelope.

This circulation of warm water does not only serve as heating supply in winter, but it also renders conventional thermal insulation superfluous.

Knaack, U., Klein, T. Bilow, M. and Auer, T. (2007) Façades – Principles of construction,Basel: Birkhäuser

Risen, J. (2008) ‘Zollverein school building: active thermal insulation’, Greenlineblog. Website: http://greenlineblog.com/2008/02/zollverein-school-building-active-thermal-insulation/

MACRO

Sanaa Architects | Essen, Germany | 2007

99closed-loop

[Risen, 2008][Risen, 2008] [www.thomasmayerarchive.de]


Zon-Wel SmartFaçade

Zon-Wel concept improves daylight entrance with window integrated retroreflective lamellae, saves energy with high insulating vacuum panels and generates energy with integrated photovoltaic modules.

The 'smartbox‘, part of Zon-Wel, is a small, façade integrated, unit that incorporates all climate control functions like cooling, heating and ventilation with heat and moisture recovery in the building envelope.

de Boer, B et al. (2008) ‘Zon-Wel Dynamische zonnegevel, openbaar eindrapport’

Jelgersma, M. (2007) ‘Halvering energiegebruik - Smartbox is opgenomen in gevel en regelt klimaat van achterliggende ruimte’, Bouwen op innovatie, december 2007

SenterNovem EET project | The Netherlands | 2007

MACRO

100closed-loop

[www.smartfacade.nl]


Documents

Loonen_2010