Summary for CIFE Seed Proposals for Academic Year 2018-19 ... · The modeling will focus on the natural ventilation system in The Yang and Yamazaki Environment and Energy Building

Summary for CIFE Seed Proposals for Academic Year 2018-19

Proposal number:

Proposal title: Robust Design and Operation of Natural Ventilation Systems

Principal investigator(s)1 and department(s):

Catherine Gorlé, Martin Fischer, Civil and Environmental Engineering Department

Research staff: Chen Chen

Total funds requested: $83,856

Project URL for continuation proposals

https://cife.stanford.edu/Seed2017_VentilationSystems

Project objectives addressed by proposal2

Operable, Sustainable

Expected time horizon 2 to 5 years

Type of innovation Incremental

Abstract (up to 150 words)

The problem: Natural ventilation could save 10% to 30% of building energy consumption, but current design procedures cannot guarantee robust system performance. The primary challenge is that natural ventilation is strongly influenced by a building’s uncertain operating conditions, which translates into a higher risk of failing to meet design criteria. The proposed solution: We will develop efficient multi-fidelity modeling frameworks to predict natural ventilation system performance with quantified confidence intervals. Fast, robust models will support initial design choices; more expensive, detailed simulations will support fine-tuning the design. Variability in operating conditions will be accounted for using uncertainty quantification. Once operational, robust performance will be achieved by learning improved model settings and establishing robust control systems based on measurement data. The proposed research approach: Our modeling efforts will focus on the Y2E2 building to evaluate the predictive capabilities in an operational building. We will perform experiments and establish novel multi-fidelity modeling and inference methods to support robust natural ventilation system design and operation.

1 The PI(s) must be academic council member(s) at Stanford. 2 For this and the next points, delete the answers that don’t apply to your proposal.

C. Gorlé, M. Fischer Robust Design and Operation of Natural Ventilation Systems 2

Robust Design and Operation of Natural Ventilation Systems Engineering Problem 40% of the US energy consumption is in residential and commercial buildings [1]. Efficient natural ventilation strategies could save 10% to 30% of this energy consumption [2], but design procedures for optimal, robust natural ventilation systems are not well established. The primary challenge is that natural ventilation flow and heat transfer phenomena are strongly influenced by the building’s highly variable operating conditions. Ignoring or underestimating the effect of this variability in the design process translates into a risk of failing to meet thermal comfort and air quality criteria. Our project goal is to enable robust design and operation of natural ventilation systems by developing an efficient multi-fidelity modeling framework that can predict system performance with quantified confidence intervals. A schematic of the framework, which incorporates a reduced-order integral model and a detailed three-dimensional CFD model, is presented in Figure 1. The proposed multi-fidelity modeling strategy is essential to support important decisions in early design stages, such as the location and size of atria for buoyancy driven ventilation. A simple but robust model with a fast turnaround time can support these initial design choices, while more detailed and expensive simulations can verify the simple model’s assumptions and fine-tune the detailed design. The incorporation of uncertainty quantification (UQ) is essential to account for the inherent variability in the building’s operating conditions at all design stages. Once operational, the models provide an excellent starting point to establish robust performance strategies. Inverse analysis, using Bayesian model updating based on building measurement data as it becomes available, will inform a more accurate characterization of the uncertain model parameters. This will improve the model accuracy to support establishing smart operational control of the system. In summary, the proposed research will effectively mitigate the risk associated with naturally ventilated buildings by (1) incorporating uncertainty quantification in computational models during the design phase, and (2) using building measurements to tighten the confidence intervals of model predictions during the operational phase. This will result in smart systems that can compensate for variability in operating conditions over the building’s lifespan, and promote the more widespread implementation of natural ventilation.

Figure 1: Schematic of the proposed modeling framework


Theoretical and Practical Points of Departure Test Case The modeling will focus on the natural ventilation system in The Yang and Yamazaki Environment and Energy Building (Y2E2) building, shown in Fig. 2. The hallways, open areas, and lounges connected to 4 atria are cooled using a buoyancy-driven night flush. Cool air enters through mechanically operated windows on the 1st through 3rd floors and drives out warmer air through mechanically operated louvers at the top of each atrium. The resulting overnight cooling of the building’s thermal mass balances out the subsequent heating during the day. The selection of the Y2E2 building as our test case is motivated by its extensive measurement system, which provides the necessary data for validation of our models. Validation with an operational building is essential to evaluate the true predictive capabilities of the framework. Multi-Fidelity Modeling with UQ The potential of CFD to provide detailed predictions for natural ventilation flow and temperature fields is widely recognized, but has not resulted in the routine integration of CFD in the design process. Two review papers [3,4] indicate the principal reasons for this. First, CFD simulations are computationally expensive and require a detailed definition of the geometry and operating conditions. Hence, they are not ideally suited for providing an intuition of the likely effects of changes in the building design or operation, which is needed during the design process. Second, there is limited information on the sensitivity of CFD results to boundary conditions. Given the significant variability in a building’s operating conditions, this poses a major challenge. The proposed multi-fidelity model with UQ is formulated to alleviate these concerns. A simple integral model that solves the equations for the evolution of the volume-averaged indoor air and thermal mass temperatures will provide the required intuition in initial design stages, while a CFD model can verify the simple model’s assumptions and fine-tune the detailed design. The selection of these two models is motivated by considering the hierarchy of models available to evaluate the performance of a natural ventilation design, as shown in Fig. 3. When moving through this hierarchy one will obtain a more accurate prediction of the flow and heat transfer phenomena, thereby reducing uncertainties related to reduced order physics modeling. At the same time, the computational cost increases several orders of magnitude, and a higher level of detail in the input is required, resulting in a higher number of uncertain input parameters. This significantly increases the cost of quantifying the uncertainties related to the design or operating parameters. A combination of the extremes in this model hierarchy will maximize the benefits of a multi-fidelity simulation strategy: the integral model supports UQ for multiple input parameters; the CFD model can reduce uncertainties related to simplified physics models in the integral model.

Figure 2: Y2E2 building

Figure 3: Hierarchy of models for predicting the performance of natural ventilation systems


Previous results and ongoing research Over the past year, we published initial results on the predictive capability of the proposed multi-fidelity model with UQ for the night-flush ventilation in Y2E2 [5]. The objective was to investigate the potential of using information from CFD in the integral model UQ analysis. Five uncertain model parameters were accounted for: the heat transfer coefficients on the floors and the walls, which determine convective heat transfer between the air and the thermal mass; the discharge coefficient, which determines the natural ventilation flow rate; the internal heat sources due to the presence of people and equipment; and the thermal mass temperature at the start of the night-flush. The results showed that using CFD-based input distributions for the heat transfer and discharge coefficients can reduce the standard deviation of the predicted indoor temperature up to ∼40%. The result demonstrated that the proposed framework can effectively quantify and reduce uncertainty in natural ventilation predictions, but an important observation was that the models generally predicted a faster cooling rate than the experimental data. Potential reasons for this are inaccuracies in the assumptions for the uncertain parameter distributions, or spatial variability in the temperature field. The latter could imply that the local sensor measurement is not representative for the volume-averaged temperature calculated by the models. The CIFE research grant obtained last year is currently being used to further investigate this; in the following we summarize the results obtained to date that support the formulation of this year’s research objectives. First, we performed additional measurements to characterize the uncertainty in the initial thermal mass and office wall surface temperatures, since a sensitivity analysis had indicated the importance this uncertain parameter in both the integral and CFD model. Figure 4 presents measurements obtained during the first 4 hours of a night flush, demonstrating that the floor temperature varies slowly and is 2 to 3K below the initial indoor air temperature. This is on the lower end of the assumption made in the previous UQ study, where a difference of +/- 2K was assumed. The temperature of the gypsum wall between the hallway and an office varies with the indoor air temperature, which justifies the use of adiabatic boundary conditions on these walls. Second, we used Bayesian inference to more accurately characterize uncertainty in the internal load, which was also found to be an influential parameter. The analysis used data from January 2013, when the ventilation system was not active, to eliminate the effect of the natural ventilation heat flux. The design conditions used by ARUP are shown in Figure 5 and were used as the nominal condition. Uncertainty was introduced by multiplying this design load with an uncertain coefficient. The analysis returned a mean value of 0.4350 with a standard deviation of 0.0763 for the coefficient, indicating a lower load than assumed during the design. An important observation was that the errors between measured data and forward predictions using the inferred information were not independent. This indicates that the functional form assumed for the daily variation of

the design load is likely incorrect, thereby warranting the use of more advanced inverse methods that can infer the internal load without prior assumptions regarding its functional form. This will be the first objective of this proposal’s work plan. Subsequently, we investigated how this updated information regarding the initial thermal mass temperature and design Figure 5: Design values for the

daily internal load cycle.

Figure 4: Measured indoor air, side wall and floor temperature.


load affects the results of the integral model UQ analysis. Figure 6 shows the result, indicating that the confidence intervals decrease, but the trend of overpredicting the cooling rate is reinforced. This observation motivates a further investigation of spatial variability in the temperature field, which will be the second research objective in the work plan. Finally, ongoing work is establishing a CFD model that includes a dynamic thermal simulation, and velocity measurements to validate the flow rates and corresponding discharge coefficients calculated using the CFD. In combination with the work plan described next, the results of these simulations and experiments will support validation of the multi-fidelity framework.

Research Methods and Work Plan Based on the research results described above, we have formulated two research objectives (O1 and O2) that will advance our goal of developing a multi-fidelity framework modeling framework with UQ to support the design and operation of robust natural ventilation systems. In the following, we summarize the research methods and work plan to achieve each of these objectives. O1: Using Bayesian inference to quantify uncertainty in the internal load In the context of inverse problems involving parameter estimation, the Bayesian approach can be summarized as follows: model parameters are considered to be random variables with realizations and associated densities that incorporate known information or information obtained as measurements are acquired. The solution of the inverse problem is the posterior density that best reflects the distribution of parameter values based on the sampled observations [7]. In this context, Bayesian analysis is ideally suited for our purpose. The internal load can be represented as a random variable. During the design phase, when no measurements are available, the density of this variable is based on assumptions regarding the building operation. During the operational phase, when building measurements become available, the initial assumptions for the densities can be replaced by the posterior density obtained from the Bayesian analysis. As more measurement data becomes available over time, this analysis can be repeated to periodically update the densities. It is worth noting two fundamental differences with machine learning. First, Bayesian methods inherently assume the parameters to be random variables, thereby providing a natural way to account for the inherent variability in the internal load. Second, Bayesian methods can work with ‘little data’, i.e. the method will provide useful information even when only limited measurements under a subset of operating conditions are available. The initial results described above demonstrate the promising capabilities of the Bayesian approach, but also indicate a need for more advanced methods that do not rely on a fixed functional form for the daily time variation of the internal load. Recent developments using non-linear filtering for periodic, time-varying parameter estimations will provide this flexibility [8]. The technique treats the time-varying parameter as a piecewise function with unknown coefficients, estimated using the ensemble Kalman filter. This imposes periodic structure in the parameter, for example higher internal loads during daytime than nighttime, but allows the parameter estimate more flexibility in shape than prescribing the specific functional form shown in Figure 5. As in our previous study, the inverse analysis will use data from nights during which the ventilation system was not active to avoid interaction with uncertainties in the predicted natural ventilation

Figure 6: Previous (black) and updated (grey) UQ results compared to building measurements.


flow rates. The results of this analysis will be invaluable for two purposes. First, a more accurate characterization of the internal load will support more accurate validation of the multi-fidelity model for our test case. Second, the capability to update the parameter estimate as more data becomes available during the building’s operational phase will support using the model as the foundation for robust model predictive control systems. O2: Measuring spatial variation in the temperature in Atrium D and surrounding hallways All previous simulations have consistently predicted higher cooling rates than recorded by the building sensor network. Initial CFD results, shown in Figure 7 on the left, indicate this might be related to the location of the building sensors. The plot depicts contours of temperature on the first and second floor five minutes after the start of the night flush. The building sensors are positioned at Point 1, just around the corner of the atrium in the hallway. They are not directly exposed to the flow of outdoor air entering through the windows and exiting at the top of the atrium, and hence do not provide a representative measurement for the volume-averaged air temperature provided by the integral model. To further support this hypothesis, we performed some initial additional temperature measurements at Point 1 and Point 2. The results, shown in Figure 7 on the right, indeed confirm that the temperature at Point 2 is generally lower than at Point 1.

To enable a more accurate validation of the CFD and integral model results, the second research objective of this proposal is to perform additional measurements to map the spatial variation in the temperature throughout atrium D. We will deploy 6 anemometers on each floor. Their position will be determined based on the CFD temperature field; the objective is to place them in zones with different cooling rates, and to ensure that the average of all sensor measurements is representative of the volume-averaged temperature. The measurements will support the formulation of a functional relationship between the building sensor data and the volume-averaged temperature. This will support validation of the multi-fidelity modeling framework, but will also provide more accurate data for the inference procedure described above.

Expected Results: Findings, Contributions, and Impact on Practice The primary result of the proposed research will be a multi-fidelity computational framework to support robust design and operation of natural ventilation systems in buildings. To achieve this result, we have formulated two objectives: we will implement advanced inverse modeling techniques to estimate the variability in the internal load over time (O1), and we will characterize spatial variability in the temperature field in Y2E2 (O2). Both objectives support validation of the

Figure 7: CFD temperature contour plots on the first and second floor, 5 minutes after the start of the night flush (left); air temperature measurements at Point 1 and Point 2 on each floor (right).


multi-fidelity modeling strategy, to establish confidence in its predictive capabilities. In addition, O2 will provide the foundation for the development of robust control systems that leverage building measurements during the building’s operational phase. In this context, O1 will also demonstrate the potential of using CFD to decide on optimal building sensor positioning, such that the operational measurements will be representative of average conditions. Incorporating the results of the proposed project in design practice will be relatively straightforward, and will be promoted by dissemination of the results. Incremental implementations, such as the use of an uncertainty quantification algorithm in combination with an integral model are envisioned in the short term. The final multi-fidelity modeling framework is intended to provide engineers with the capability to obtain performance predictions with quantified confidence intervals at all design stages. We anticipate that such modeling frameworks will effectively mitigate the risk associated with naturally ventilated buildings and promote their widespread implementation, thereby significantly reducing building energy consumption.

Industry Involvement We are interested in collaboration with CIFE members to connect the multi-fidelity modeling framework to software strategies that combine computational building representations with a range of prediction tools. Building owners and design-build teams interested in high-fidelity predictions of building performance can help the research team connect the multi-fidelity modeling framework to current practice to speed up its widespread adoption. Given our access to the Y2E2 building and its measurement data base, the proposed research does not depend on data provided by CIFE members, but additional datasets could be helpful to validate our findings on a different building. Research Milestones and Risks The following milestones will be used to measure the progress of the proposed effort:

1. November 2018: Completed additional measurements to characterize spatial variability (O2). 2. February 2019: Submitted paper on comparison between these measurements and CFD. 3. September 2018: Completed implementation of, and analysis with, the advanced Bayesian

inference technique (O1). Submitted paper. The main risk in this project is related to the complexity of studying an operational building, for which the models inherently have a very large number of uncertain parameters. The challenge is to correctly isolate the most important physical processes, and ensure each individual one is modeled as accurately as possible. If this is not achieved, incorrect conclusions regarding the modeling of separate processes or estimation of uncertain parameters could be drawn. The proposed approach of performing inverse analysis and additional measurements to support UQ in the models is specifically intended to mitigate this risk.

Next Steps The proposed research provides an excellent starting point for additional research on robust design and operation of natural ventilation systems. The multi-fidelity modeling strategy should be extended to consider wind-driven ventilation in addition to the Y2E2’s buoyancy driven system. This entails a multi-scale modeling challenge, involving a more detailed analysis of the interaction between the building and the surrounding environment. The use of sensor measurements to implement smart control systems in operational buildings also provides exciting research opportunities. A proposal on multi-scale modeling is currently under review by the DOE, an additional proposal on smart control systems will be submitted to NSF.


References [1] U.S. Department of Energy Building Technologies Office. “Multi-Year Program Plan”, 2016. [2] A. Walker, “Natural Ventilation,” retrieved from http://www.wbdg.org/resources/

naturalventilation.php (2014). [3] P.F. Linden, “The fluid mechanics of natural ventilation,” Annual Review of Fluid Mechanics,

31, 201–38 (1999). [4] D. Etheridge, “A perspective on fifty years of natural ventilation research,” Building and

Environment, 91, 51-60 (2015). [5] G. Lamberti, C. Gorlé, “Uncertainty Quantification for modeling night-time ventilation in

Stanford's Y2E2 building,” Energy and Buildings, 168 (2018). [6] E. Hult, G. Iaccarino, and M. Fischer, “Simulation of night purge ventilation using CFD and

airflow network models,” Stanford Internal Report (2011). [7] R. C. Smith, “Uncertainty Quantification: Theory, Implementation, and Applications,” SIAM

series on Computational Science and Engineering, ISBN 978-1-611973-21-1 (2014). [8] A. Arnold, and A. L. Lloyd, “Nonlinear filtering for periodic, time-varying parameter

estimation,” arXiv:1710.07978 (2017).


Budget

Sponsor: SCIFESubmission Type: NewBudget Preparation Date: 4/25/2018Budget Start Date: 10/1/2018Project Name: CIFE SEED 2018Department: Civil EngineeringPrincipal Investigator: Gorle, CatherineAdministrator: Lee, Mike

Period 1 All PeriodsFrom 10/1/2018 10/1/2018

To 9/30/2019 9/30/2019Personnel Salaries

FacultyGorle, Catherine Summer 15.0% 6,742 6,742

Total Faculty Salaries 6,742 6,742

Graduate StudentsTBD Academic 50.0% 31,258 31,258

Summer 50.0% 10,419 10,419 Total Graduate Student Salaries 41,677 41,677

Total Salaries 48,419 48,419

BenefitsFaculty 2,124 2,124 Graduate 2,084 2,084

Total Benefits 4,208 4,208

Total Salaries and Benefits 52,627 52,627

Other Direct CostsTuition

TBD Academic 50.0% 21,172 21,172 Summer 50.0% 7,057 7,057

Total Tuition 28,229 28,229

Computer services 3,000 3,000

Total Other Direct Costs 31,229 31,229

Total Direct Costs 83,856 83,856

LessTuition (28,229) (28,229)

Modified Total Direct Costs 55,627 55,627

Total Amount Requested 83,856 83,856

Rates Used in Budget CalculationsBenefit Rates

Faculty: FY 1 31.50%; FY 2 31.50%; FY 3+ 31.50%;Graduate: FY 1 05.00%; FY 2 05.00%; FY 3+ 05.00%;

Indirect Cost Rates

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Summary for CIFE Seed Proposals for Academic Year 2018-19 ... · The modeling will focus on the natural ventilation system in The Yang and Yamazaki Environment and Energy Building