BLWT, CFD and HAM modelling vs. the real world: Bridging the gaps with full-scale measurements

Journal of Wind Engineering

and Industrial Aerodynamics 91 (2003) 1651–1669

BLWT, CFD and HAM modelling vs.the real world: Bridging the gaps with

full-scale measurements

W.A. Dalgliesha,*, D. Surryb

a237 Valley Ridge Green NW, Calgary AB, Canada T3B 5L6bResearch Director of the Boundary Layer Wind Tunnel Laboratory, The University of Western Ontario,

Ont., Canada N6A 5B9

Abstract

The boundary layer wind tunnel (BLWT), which reproduces the increase of wind speed and

the propagation of mechanically induced turbulence upwards from the ground, owes its

development and validation to measurements of surface pressures, building motions, and

other wind effects on full-scale structures. Computational fluid dynamics models are now

considered useful adjuncts for some tasks in wind engineering, thanks to encouraging

comparisons with BLWT and full-scale results. More recently still, researchers are turning to

elaborate computer models to study the transfer of heat, air, and moisture (HAM) through the

building envelope. Full-scale experiments to validate HAM computer modelling will be more

complex and costly than those for wind alone, but such benchmark data are essential if we are

to have confidence in our predictions of the serviceability and durability of building envelopes

in the real world.

r 2003 Elsevier Ltd. All rights reserved.

Keywords: Boundary layer wind tunnel; BLWT; Heat, air and moisture transfer; HAM; Computational

fluid dynamics; CFD; Wind effects; Cladding pressures; Full-scale

1. Introduction

1.1. Field measurements—footholds in the real world

Engineering models, whether physical or mathematical, sometimes leave outimportant characteristics of ‘‘real world’’ phenomena. Prior to 1958, engineers relied

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*Corresponding author. Tel.: +403-286-2177.

E-mail address: [email protected] (W.A. Dalgliesh).

0167-6105/$ - see front matter r 2003 Elsevier Ltd. All rights reserved.

doi:10.1016/j.jweia.2003.09.015

on small-scale model tests in smooth-flow wind tunnels to assess wind loads onstructures, but such tests do not properly represent the variation of wind speed andturbulence near the ground. Jensen’s measurements on full-scale buildings in naturalwind differed from smooth-flow wind tunnel results by up to 50% [1].Davenport’s survey, ‘‘Wind Loads on Structures’’, revealed similar concerns about

the validity of wind tunnel testing for structures immersed in the earth’s boundarylayer [2]. In 1991 he paid tribute [3] to Martin Jensen for showing that wind tunneltests can be brought into line with wind effects on structures by applying ‘‘TheModel Law for Phenomena in the Natural Wind’’ [4]. By the mid-1970s, AlanDavenport had become a leader in boundary layer wind tunnel (BLWT) testing, inlarge part by his promotion and use of full-scale experiments to develop and confirmBLWT technology. Some of his earliest work when he arrived at Western involvedmeasurements of wind on local tall towers, and the measurement of forces on platesin the real wind [5]. This focus continued through the organisation of a conferencededicated to full-scale work held at Western in 1974 [6].Computational fluid dynamics (CFD) is another valuable modelling tool that is

gaining acceptance on the strength of comparisons with wind-tunnel results and full-scale experiments [7]. Full-scale experiments are costly, time-consuming, andfrequently require insights from modelling just to make sense of results, but withoutthem, how can we be sure that designs based on modelling are capable of predictingthe performance of real-world structures?Researchers use computer models to trace the movement of Heat, Air, and

Moisture (HAM) through the wall and roof assemblies. Essential input conditionsfor HAM models include wind-driven rain, which must be derived from full-scalemeasurements, BLWT tests, or CFD modelling. Confidence in HAM modellingseems to be growing to such an extent that users may be tempted to foregoconfirmation by full-scale measurements. Without sufficient full-scale validation,there is a risk of divergence between modellers’ predictions and the performance ofthe structures that they are supposed to represent. This paper first cites selectedaccomplishments of (wind tunnel and CFD)/full-scale comparisons, then introducesHAM modelling, and concludes with a call for field experiments to further developand validate HAM models as an aid to ‘‘moisture engineering’’ of the buildingenvelope.

1.2. Field measurements and model results: how close is close enough?

The instrumentation for, and monitoring of, natural wind effects on buildingsinvolve compromises between completeness of relevant data collection and feasibilityor cost. In the case of the office tower discussed in Sections 2.1 and 2.2, 32 surfacepressures were expressed by sets of mean and rms coefficients, sorted by winddirection. Variability of the pressure coefficients was largely due to uncertainty in thedirection and magnitude of the approaching wind. For the first few years ofmonitoring, two anemometers on a roof-mounted radio antenna provided the onlyon-site description of the wind, so there was actually no direct measurement of theflow immediately upwind at the instrumented levels. The variability reduced with

ARTICLE IN PRESSW.A. Dalgliesh, D. Surry / J. Wind Eng. Ind. Aerodyn. 91 (2003) 1651–16691652

continued monitoring, roughly in proportion to the number of measurements in eachwind direction ‘‘bin’’.Full-scale experiments are themselves fraught with difficulties, so care must be

taken not to assume truth just because measurements are taken at full-scale; forexample, there are few full-scale wind load measurements available near design windspeeds. Low wind speeds may include convective effects that blur the true designcase. Research efforts along the two avenues, i.e. model and full-scale, are mosteffective when done in close collaboration, with each compensating for weaknesses inthe other. Returning to the problem of adequate flow field description upwind of thereal building, a model study can ‘‘fill in the blanks’’, tied to the full-scale study byone representative velocity measurement in both studies. Then, when pressurecoefficients based on that velocity are compared, one can look for consistency notonly in mean values, but also in the spectral content and spatial variation of surfacepressures. How close is close enough to justify using model results in the design ofbuildings? A 15% difference between the wind tunnel and full-scale coefficientsmight be a reasonable target, but this is only a ball-park observation in the light ofthe examples discussed below.

2. BLWT model/full-scale comparisons

2.1. High-rise structure: Commerce Court Tower, Toronto—Cladding pressures

Commerce Court Tower, a slender glass-and-aluminium-clad office block36m� 70m� 239m (57 storeys, with roof antenna in Fig. 1) was designed for thewind effects predicted by model tests in the BLWT at The University of Western

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Fig. 1. Commerce Court, S40W.

W.A. Dalgliesh, D. Surry / J. Wind Eng. Ind. Aerodyn. 91 (2003) 1651–1669 1653

Ontario [8]. Architectural, structural, and wind tunnel consultants and the buildingowner, collaborated with the Division of Building Research, NRC Canada in theplanning, installation, and monitoring of wind effects from January 1973 to 1980.The first comparison with model predictions focussed on cladding pressures gatheredin the first year [9].Mean and rms pressure measurements at 8 surface taps on 4 floors, with a

common internal reference near mid-height, were calculated from 5-min samplesrecorded hourly at 2 per second. Fig. 2 compares model data (open circles) to full-scale estimates of pressure coefficients (with one standard deviation shown as shadedarea) for tap 312, near the NE corner of the 50th floor. As an example of thedependence on model results for interpretation of field data, the separation intointernal and external pressure coefficient (Cpe) relied on matching the externalpressure coefficient for one tap on the wake side of the building to the correspondingwind tunnel value. The tap for matching varied according to wind direction; theagreement of mean Cpe with the model result is about the same for all directions, andnot markedly different from the agreement of Crms; which requires no suchadjustment.Moreover, Cpe comparisons shown in Fig. 3 for tap 712 on the 25th floor use

matching at the same taps as in Fig. 2.

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Fig. 2. North wall 50th floor.

W.A. Dalgliesh, D. Surry / J. Wind Eng. Ind. Aerodyn. 91 (2003) 1651–16691654

2.2. High-rise structure: Commerce Court Tower, Toronto—Sway measurements

Wind-induced accelerations of lightly damped office buildings are of sufficientconcern to designers, not to mention occupants, that serviceability criteria appear inmany building codes. Subject to additional factors such as visual cues, tasks oractivities, and individual sensitivity, most people perceive building motion whenpeak acceleration amplitude reaches 0.5–1.5% of that due to gravity (i.e., 5–15mg)[10]. Accelerometers were added to the instrumentation of Commerce Court inMarch 1975, and from September 1978 to September 1980, about 3000 samples wereavailable Field data were compared with wind tunnel tests of an aeroelastic modelwith 7 mass levels (21 degrees of freedom) [11,12]. Due to torsion, nearby buildingsseem from upper floor windows to move, making occupants sensitive toaccelerations.Commerce Court’s elastic axis is south of its centre of mass, causing torsional

vibration that is inertially coupled with E–W translation. Fig. 4 shows, from left toright, standard deviations of E–W acceleration at the south and north ends, and N–Sacceleration. Peak accelerations were two to three times greater. The wind directionfor the bottom row is S 70W, and for the top row, S 60W. Note the increase inresponse for only a 10� change in direction. Square symbols on the solid line arefrom the wind tunnel study, and the dashed line shows the acceleration calculatedaccording to Commentary B to the Canadian National Building Code [10].

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Fig. 3. North wall 25th floor.


Commentary B recommends limiting the 1-in-10 year peak acceleration to 1–3% ofgravity, i.e. 10–30mg. For more information on motion perception, see Isyumov[13]. The ‘‘+’’ symbols come from the 1978–80 data set, while the ‘‘o’’ symbols arefrom earlier records, included because they give responses at higher wind pressures.

2.3. High-rise structure: Allied Bank Plaza Tower

In model/full-scale comparative measurements, it is rare to find examples thathave been derived from wind speeds of design strength. One notable early exceptionwas the set of acceleration measurements taken near the top of the 980 ft-high AlliedBank Plaza Tower in Houston Texas in 1983 [14]. The building had been completedin late 1982, and accelerometers were installed primarily to verify the calculatedvalues of natural frequencies and the assumed values of structural damping. Twoaccelerometers were used, one located to measure one of the sway modes; the otherpositioned off-centre to measure both the other sway mode and some torsion, thatcould be separated by filtering.When Hurricane Alicia started to threaten Houston in September 1983, the

manually operated system was activated by Bob Halvorson, who had to get tothe upper floors at the height of the storm. The peak accelerations recorded wereabout 45mg, corresponding to peak dynamic displacements approaching 2 ft.Halvorson has reported that the motion made walking difficult without somethingto hold on to.The analogue tape recorder acceleration traces and the predicted accelerations

were analysed by two groups at UWO, with a ‘‘Chinese wall’’ dividing them, beforethey were put together to compare reality with predictions. Thus the first groupdetermined the acceleration levels for successive 10-min pieces of the 80-min tape(it ran until the end of the tape, which was reached about an hour before the absolute

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Fig. 4. Acceleration (rms) in mg.


peak of the storm). The second group reconstructed the storm using the vortex-basedhurricane model, which has been widely used as the kernel of Monte Carlo hurricanesimulation predictions.This methodology had been the basis for assessing the statistical design speeds for

the building. The hurricane model utilised the central pressures, the size of the eye,the track and convection speed that were observed in the Houston area to estimatethe actual wind speed at the site as the storm progressed. Uncertainties in the stormparameters led to uncertainties in actual upper level wind speeds of75mph in windspeed, with the maximum in the storm being estimated as a 10-min average of98mph, and a 5� uncertainty in wind direction.Note that this predicted wind speed made Alicia a storm with a return period of

about 50 years; essentially a ‘‘design event’’. These predicted speeds were then usedwith the wind tunnel data to estimate the acceleration response of the building, usingtotal damping values that were verified from the response measurements to be about1.5% of critical.The predicted and actual accelerations were put together and formed Fig. 5. The

agreement is remarkable; particularly when the rounded shape might suggest someReynold’s number dependence. This has since been shown to be largely eliminatedby the combination of the strongly turbulent 3D shear flow in a built-upenvironment, together with the 3D low aspect ratio nature of most buildings. Notethat the significant uncertainties of the predicted bounds to the acceleration arisefrom the previously quoted wind speed uncertainties because the acceleration of atall building typically varies with speed to an exponent in excess of 3.

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Fig. 5. Peak resultant acceleration at 71st floor during storm and comparison with wind tunnel data.


Not only did these comparisons provide a good deal of credibility to wind tunneltechniques and assumptions, they did so in the most important wind speed range,under design conditions.

2.4. Low buildings: farm building research at Wrest Park, Silsoe (UK)—BLWT

Low buildings far outnumber the more exposed tall buildings, but the few that aredesigned for wind effects at all, usually rely on ‘‘generic’’ wind tunnel data.Individual projects rarely cost enough to justify wind tunnel tests for low buildings,which could then be monitored for validation purposes. Fortunately, there is anotheroption: sometimes field experiments of sufficient quality and duration are carried outto provide design information directly; later on, model studies can be donespecifically for confirmation of the field results, as well as for validation of themodelling procedures. Silsoe Research Institute gathered data from severalagricultural buildings from 1974 [15,16], and in 1991 collaborated with theUniversity of Western Ontario to compare mean pressure coefficients on fourpitched-roof buildings with their counterparts on 1:100 scale models tested in theBLWT [17].Building FB28, 12.9m� 24.1m in plan with 10� roof sloping to a height of 5.3m,

was tested with both curved and sharp eaves (A and B) and thus counts as two of thefour buildings. FB16, 6.7� 120m with 15� slope to 5.4m at the ridge, is on the WrestPark site at Bedford, about 300m east of FB28, for which the boundary layer profilehad been measured. FB19, 18.4m� 36.8m with 15� slope to 7.5m at the ridge, wasat a more open and flatter site in Eastern England, for which no specific data on theboundary layer was available.Model mean external pressure coefficients Cpe were in excellent agreement

for windward side walls of Buildings FB28A and FB28B, ranging between93% and 105% of full-scale values; the leeward model Cpe was on the low side,77% and 69% of A and B full-scale values. Cpe for the 10 windward roof tapsranged from 76% to 125%, and for the 10 leeward roof taps, from 64%to 108% of full-scale values. FB28A produced the best comparison of all fourmodels.Results were only fair for the two buildings without specific boundary profile

information from the field. Cpe for side-wall taps ranged from 83% to 126% of full-scale for the model of FB19, and from 62% to 116% for FB16. Cpe for FB19’s 16roof taps ranged from 34% to 157%, and for FB16’s 14 roof taps, from 60% to64%, of full-scale values. BLWT results might have agreed more closely, had theupwind boundary layer profiles for FB16 and FB19 been measured and matched.Perhaps a more likely contributor to poorer agreement is a Reynolds Number effectassociated with the formation of separation bubbles over the sharp eaves of FB16and FB19. No separation bubble was observed for FB28A. The results discussed sofar all refer to wind normal to the long wall. Results were also given for mid-windward wall, over windward eave, and mid-leeward wall for FB28A as a functionof wind direction, with agreement somewhat similar to that shown in Figs. 2 and 3for Commerce Court.


2.5. Texas Tech full-scale experiments

The design and construction of a well-instrumented full-scale low building atTexas Tech University, completed in 1989, and the subsequent 10-year CooperativeProgram in Wind Engineering with Colorado State University sponsored by NSF,has led to an explosion of model/full-scale comparisons that are too numerous to listhere. The test program is continuing and expanding under TTU leadership. KishorMehta, who initiated the design and construction of the 300 � 450 � 130 high, verylow-slope gable building, learned many lessons from earlier attempts at full-scaleinstrumentation of his own, from BRE’s Aylesbury house, and from the ongoingwork of the Silsoe group. The Cooperative Program, led by Mehta and Meroney,stimulated many model/full-scale comparisons of pressures, flows and pollutantdispersion as well as complementary studies in other areas of wind engineering.In very broad terms, some of the notable lessons regarding wind pressures learned

so far include the following:

(1) Vortex corner flows involve scales that are not successfully modelled in currentsimulations. Full-scale local rms and peak pressures near the upwind leading cornerin the vortex region are significantly larger in full-scale than model scale. While thesedifferences are unlikely to be significant for design of components with significanttributary areas, there are local components that may be affected. The implicationsof such scale dependency in other cases where vortex flows occur, such as nearcertain geometrical details on tall buildings, requires further research as to theimplications for cladding pressure measurements and specifications for those cases.

(2) Generally, the comparisons of local pressures on walls, and on the roof awayfrom the corner vortices, and of area loads, agree very well with modelsimulations, supporting current wind tunnel techniques.

(3) Even for a well-chosen site, the variability of observed wind characteristics fromthe same direction is quite large, presumably due to convective effects, and issustained over most of the range of observed wind speeds. Asking for designwind speeds to occur, of course, is overly optimistic, so that observations forwind speeds above mean 15-min speeds of 30mph at 33 ft are rare. There is still aneed for comparisons extending to design wind speeds, but this poses hugeproblems to accomplish artificially, and serendipity to accomplish naturally.

(4) Many other details of full-scale behaviour have been monitored and providegrist for ongoing detailed comparisons; look for recent publications from theTexas Tech Group (Director, Wind Science and Engineering Research Center:mailto:[email protected]).

3. CFD model comparisons with BLWT and full-scale results

3.1. CFD: Another tool for the researcher and for the designer

Several reviews of the progress and potential of CFD methods cover theirdevelopment over the past 20 years [7,18,19]. From those reviews, it appears that

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they can already fill a useful role, not only for research, but also for generatinginformation for the early stages of design. Benchmarking with BLWT data isindispensable for CFD applications in wind engineering, and where available, full-scale experiments play a useful role too. The most frequently used methods usecontrol-volume finite difference solutions of mean flows and pressures (e.g. ReynoldsAveraged Navier–Stokes equations supplemented by turbulence production anddissipation models). The most difficult regions for agreement with experimentalresults tend to be the extent of the recirculation zone behind the building, and theshear layers separating from sharp edges. Although turbulence is modelled, nopredictions are supplied of fluctuations in the flow, as important as they are indesign.

3.2. Low buildings: Venlo-type glasshouse-CFD model

Among the low buildings investigated by Silsoe Research Institute are commercialglasshouses [20,21]. Mean external pressures over seven windward spans of the saw-toothed roofs of two such structures have been used to evaluate various CFDmodelling methods [22,23]. As can be seen from Fig. 6 [23, Fig. 5], all CFD modelsdiverge from Hoxey’s experimental values to the greatest extent at each of the ridges,but elsewhere, pressure coefficients agree very well. The authors concluded that theirapproach to simulating pressure and airflow around a large multi-span roof wasacceptable for studying wind-driven ventilation.Measurements on the first seven spans of a 52 span glasshouse agreed even more

closely with the CFD model with the RNG (renormalisation group) turbulencemodel. Hoxey reported the standard deviation of the error in the coefficients as0.07cp: Differences scaled from some 40 measurements graphed in another figure(Fig. 6, [23]) ranged from 0.66 to 1.53, with an average of 0.87. This gives someperspective on how close simulation and experiment have to be to give an impressionof good agreement. In addition, we are reminded that experimental results aresubject to variation, and that care and diligence are required to provide such reliableresults as the ones from Silsoe Research Station.

4. Modelling of heat, air and moisture transfer

4.1. External climate data: wind-driven rain, crucial for moisture management of walls

The combination of positive wind pressure on the outside face of a buildingenvelope relative to the interior, and the presence of water on the exterior surfacepresents a severe test of many building cladding systems. Even very small circuitousopenings can allow water ingress, which is difficult to trace and to remedy. Variousdesign schemes like the pressure-equalised (or moderated) rainscreen have beendeveloped to manage this problem. However, there are a limited number of examplesof full-scale verification of how well such systems work. Full-scale verification isusually confined to major building systems subjected to artificial water and wind on


wall system mockups. Mockups provide an immediate pass/failure for newconstruction but are usually not well documented and related to expected full-scaleconditions and, of course, are limited in their ability to test the ageing of systems.Laboratory and in situ tests are hampered by the fact that any pressure boxtechnique inevitably applies uniform pressure, even if time varying, whereas inreality, wind-induced pressures vary in both time and space, especially in the regionsmost at risk. Spatial gradients can be particularly irksome if they exist acrossmultiple vent holes for the same ‘‘pressure-equalised’’ panel; they will tend to driveair and water in at the high pressure end. A solution to this suggested by Inculet [24]and others is to ensure that, near edges where spatial gradients are high, thecompartment is vented with a single vent placed at the location with the mostpositive mean pressure. Near edges, this is simply the furthest distance from thebuilding edge for the most relevant wind angles. The result is an outward-actingmean pressure differential opposing water infiltration.The worst spatial gradients have been found to be the regions near the side and top

edges of windward walls, where the flow curvature also leads to a significantamplification of wetting rates. Such information has been demonstrated in CFDsimulations by Choi [25] and physical model simulations of wind-driven rain impacton buildings carried out in a novel set of wind tunnel experiments by Inculet [26].These experiments have also been the basis for the development of CFD simulationsof wetting, carried out by Hangan [27]. The key idea here was to use the physicalsimulations as the basis for benchmarking the CFD modelling, which was then

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Fig. 6. Numerical predictions of the pressure coefficient (cp) compared with experimental values for the 7

span glasshouse.


applied to more complex real building shapes. An interesting outcome of both theexperimental and CFD work is the strong influence that relatively minorarchitectural embellishments can make on the pattern and intensity of wetting.Clearly, any water that can be induced to avoid the building is water that won’t leakinto the building.Development of CFD techniques benchmarked against physical modelling and

full-scale experimental results hold a lot of promise for improving the waterresistance of buildings. Nevertheless, full-scale experiments are needed because themodel experiments to date are really limited to identifying the intensity of the rain atfirst impact locations. It appears almost impossible in the wind tunnel to model thesubsequent migration of the water over the building face, although this mayeventually be possible with CFD. Recent observations at MIT [28] of water drops asthey hit a wet horizontal surface brings doubt as to whether all of the water in dropsthat strike a wet vertical face actually add to the water load at that point. Bushobserved that a large range of drop sizes appear to ‘‘bounce’’ on the surface,reducing their diameter by a factor of two on each bounce until they are smallenough to melt into the surface. If this also occurs on wet vertical surfaces(experiments are under way), then much of the impacting water may in fact bebroken up into smaller drops and swept around the building corner, leading to anoverestimate of wetting intensity by both the current physical models and the CFDmodels. More full-scale experiments in this difficult area are necessary.

4.2. ‘‘Moisture engineering’’—a new sub-discipline

The term, ‘‘engineering’’, in the sense of applying science and mathematics to theunderstanding and manipulation of material properties and energy sources instructures, processes, and systems, continues to find favour for naming newdisciplines. ‘‘Moisture Engineering’’ seems to have sprung to life in the last 4 years,judging from about 20 citations found in a search of the Internet.Anton Ten Wolde supplied a succinct rationale-cum-definition for this new field of

endeavour: ‘‘Many building failures are related to excessive moisture, and mold isincreasingly recognised as a source of indoor air pollution. This has prompted theemergence of ‘‘moisture engineering,’’ a formal methodology to moisture designanalysis that includes design moisture loads, computer analysis tools, and limit stateevaluation criteria’’ [29]. He pointed out that one would hardly make structuraldesign decisions without knowing what the loads were, yet until recently, moistureloads and criteria for design have gone largely undefined. Many researchersand specialists in the durability of building envelopes are responding to theseneeds [30–32].

4.3. Hygrothermal modelling—a computer analysis tool

For moisture engineering, the counterpart of finite element stress analysisprograms used in structural engineering would be the hygrothermal models, buttypically using control volume, finite difference solvers rather than finite element.


These programs tackle time-dependent solutions of heat, air, and moisture (as liquidand vapour) movements through one- or two- dimensional grids representing thevarious layers of a wall system. Some also track the movement of salts dissolved inthe vapour. Compared to CFD models of air movement, hygrothermal modelsrequire many more inputs in the form of material properties, as well as morecomplete climate data externally and internally. Material properties include heatcapacity, thermal conductivity, air and vapour permeability, liquid diffusivity,density, sorption isotherms, etc. Some of these properties vary with moisture contentor temperature.

4.4. Laboratory and field observations

Evaluation of the hygrothermal properties of building materials is a daunting task,considering the great variety of materials used, and the fact that many are complexaggregations of more than one substance. Properties are seldom isotropic, and oftenvary markedly depending on the source of the material, from batch to batch,manufacturer to manufacturer, and are installation sensitive. Nevertheless, greatstrides have been made in materials laboratories to measure and documentproperties for use in hygrothermal models. A database of nine hygrothermalproperties for about a dozen materials currently in use in North American wood-frame walls has recently been summarised by Kumaran et al. [33]. By doing hundredsof laboratory evaluations, the National Research Council (NRC) determined rangesfor the property values of these materials. In other work at the NRC, ‘‘mid-scale’’experiments were set up under carefully controlled conditions of temperature andhumidity for comparison with the results of hygrothermal simulations.Field experiments to monitor temperature and humidity through walls have been

done over the years, but it seems that the data gathered do not readily lendthemselves to validation of hygrothermal models. The ideal scheme from themodeller’s point of view would provide complete documentation of all relevantmaterial properties, time series of external and internal climate conditions, and ofcourse, temperature, moisture content, and air velocity at various locationsthroughout the wall. A frequent regret is that field records occasionally sufferextended stretches of data loss through malfunction of instruments. As a result, thereexist few published accounts of comparisons between model and full-scale results.

4.5. Applications of hygrothermal models

The prediction of long term performance of wall systems is perhaps the mostambitious application for hygrothermal models. Some relation is required betweenthe limit states that constitute various types of degradation or loss of service, and theoutputs of the hygrothermal simulation of performance. For wood-based products,damage functions are needed that relate various forms of biological attack to theavailability over time of heat, moisture, oxygen, nutrients, and so on. For metalcomponents, corrosion has similar needs (except for nutrients), but differentrelationships would apply. Mould growth, usually a precursor to wood rot, poses


health risks for occupants rather than an attack on the integrity of the materials inthe wall. The complex interrelationships between temperature and moisture contentfor determining growth potentials for mould in particular have been investigated bySedelbauer et al. [34].

4.5.1. Hygrothermal investigation of premature failure

In the mid-1980s, plywood sheathing in a significant number of manufacturedmodular homes in three northern States developed decay due to accumulation ofwinter condensation. Many occupants were experiencing respiratory illness thoughtto be related to extensive mould growth on interior walls. A one-dimensionalhygrothermal model called ‘‘MOIST’’ was used to simulate the performance of atypical house exposed to an average weather year for Madison WI [29]. The wallshad a low-permeance building paper (o one perm) acting as an outside weatherbarrier, while the interior wall was not tightly sealed. Taking air leakage intoaccount, 33 perms was assumed for the layers interior to the plywood sheathing.Three remedial measures were investigated, in addition to the base case

representing the houses as built. These were (1) replacement of the low-permweather barrier with a high-perm one; (2) increasing air-tightness to reduce the flowof warm air into the wall and (3) mechanical ventilation to reduce the indoorhumidity to 25–40% range in mid-winter. Without any of the remedies applied, thebase case simulation confirmed that both wood decay and mould growth should beexpected. According to the simulations, all three measures would have been requiredto eliminate both problems. The author pointed out that his objective was to showthe value of carrying out such analyses before trouble occurs. He concluded that thebase case result would have revealed the deficiencies, and the remedies might havebeen applied; however, it is not so clear whether the consequences of heavy mouldgrowth would have been appreciated on the basis of durability or servicerequirements in the 1970s and 1980s.

4.5.2. A systematic approach to moisture design

For a systematic approach to moisture design, a checklist of four steps isrecommended by Geving [35]. To illustrate, he carried out the analysis of whether ornot a vapour barrier should be required for a wood-frame house in Oslo, using twodifferent one-dimensional hygrothermal models, ‘‘1D-HAM’’, and ‘‘MATCH’’.Step one, problem definition, specified that the house would be relatively air-tight,

with a low-permeance weather barrier on the outside. This is similar to the case inSection 4.1, so the cards are stacked in favour of requiring a high-permeance vapourbarrier toward the interior. The performance condition selected was the avoidance ofmould growth, which should automatically guard against the later stage of wooddecay.Step two, simulation set-up and input parameters, settled on a statistical approach

using a commercial 1D simulation tool, 1D-HAM. Criteria for mould growth onpine or spruce were stated as functions of RH, temperature, and exposure time. Fordesign purposes, a probability of mould growth greater than 10% would be


considered failure. Input parameters, mostly from a database prepared by Kumaran[36], were given values for standard deviation.Step three, HAM simulation, involved 60 Monte Carlo runs, i.e. with input

parameters randomly varied following their specified means and standarddeviations and assuming normal distributions, to estimate the probability ofcriteria for mould growth being exceeded. The probability during the winter monthsof the weather year (1991) for Oslo turned out to exceed 70 percent (for about8 weeks).Step four, analysis and evaluation of performance, included a requirement to

check the accuracy and reliability of the simulation. In the absence of full-scale datafor comparison or validation, another simulation tool was used: MATCH. Only onerun was made, but the weekly estimates of RH all fell within the bounds of the 60Monte Carlo runs with 1D-HAM.

4.6. Comparison of four hygrothermal models

Mould growth was taken as the surrogate for long-term performance in acomparison of four different hygrothermal models [37]. Only two of the models weretwo-dimensional, but the comparison was set up to allow a fair comparison with thesimpler ones. The same climate files were adapted to suit each model, and one modeof liquid transfer (through capillaries) was deleted since one model failed to reachsolutions for east and south wall orientations when capillarity was included. Theauthors had intimate knowledge of their own simulation tool, hygIRC (two-dimensional), which they called ‘‘somewhat easy’’ to use. DIM3.1/DELPHIN4.4.10(two-dimensional), was ‘‘challenging’’, while WUFI2.2E and MOIST 3.0 (one-dimensional) were both ‘‘very easy’’ to use. The latter two are available for downloadfrom the Internet (WUFI 3.2: http://www.hoki.ibp.fhg.de/ibpe sof165 30ornlibp.html, MOIST 3.0, http://www.bfrl.nist.gov/863/moist.html).

4.6.1. Results of comparison

The most encouraging result was the similarity of temperature profiles on eitherside of the sheathing board. Humidity profiles, on the other hand, were in generalquite variable, with the best agreement coming between WUFI and hygIRC. Thedifferences in humidity profiles led to even greater contrasts in the final comparison,a calculation of the risk of mould growth during the three simulated years ofservice. WUFI and hygIRC are reasonably close for the outside of the sheathing(Fig. 7a). The mould index predicted by hygIRC for the inside surface only risesabove 4 (mould growth visible on >10% of surface) in the third year, whileWUFI rises steeply to 6 (whole surface covered) after the first winter and stays there(Fig. 7b).MOIST gives quite a different impression of the potential for mould growth, and

DIM/DELPHIN suggests no growth whatsoever (even when a microscope is used).The authors strongly recommended that further simulations be done with eachmodel to outline strengths and limitations.


http://www.hoki.ibp.fhg.de/ibpe_sof165_30ornlibp.html




http://www.bfrl.nist.gov/863/moist.html

5. Concluding remarks

The developments, first of BLWT technology, and second, CFD modelling forspecific tasks in wind engineering, show the way for the evolution and validation ofHAM computer models in the service of building envelope design for durability as afunction of climate inputs. The brief accounts of comparisons of BLWT and CFDmodel results with full-scale experiments illustrate only a few of the difficulties, andemphasise their reciprocal roles in the search for validation. Close collaborationbetween HAM modellers and field researchers is essential for obtaining maximumbenefit from the planning and execution of fieldwork.Moisture engineering, in the sense of using hygrothermal models to assess or

predict building envelope performance, urgently requires the parallel development ofreliable techniques for estimating both the mean and the standard deviation of theexternal moisture load, based on climate records. Fortunately, a start has alreadybeen made to model wind-driven rain in the BLWT, and there has recently been asurge of activity in field measurements, as well as in the critical analysis of rain gaugeaccuracy. CFD modelling of the mean wind flow around buildings seems to beaccurate enough to calculate hourly amounts of rain striking a given portion of the

ARTICLE IN PRESS

Fig. 7. Index (1–6) of mold growth: (a) outside and (b) inside sheathing board.


windward wall for simple building shapes, but further work is required to validateresults for complicated surface details and for groups of buildings.Explicit matching between field measurements of performance and the predictions

of hygrothermal models is hard to find in the literature, but comparisons arecertainly implied whenever a simulation tool is used to analyse a field failure.Admittedly, the requirements for matching hygrothermal simulations are moreonerous because of the need for material properties, and for a more complexpresentation of climate data. But that should not deter researchers from making useof every opportunity to check the output of simulations against field measurementseven if incomplete and sporadic in time.External climate inputs require the close collaboration of hygrothermal modellers

and those with expertise in wind-driven rain. Exciting work is underway in Europe,combining field and wind tunnel studies with CFD modelling. Here are two web sitesof interest:

1. http://perswww.kuleuven.ac.be/Bp0428890/frame e general.htm.2. http://www.bwk.tue.nl/fago/AIO/fabien/.

The conversion of horizontal rainfall measurements and wind velocity to rainimpinging on the wall represents a source of great variability, with manycomplicating factors, splash-back, absorption by porous surfaces, effects of eaves,mullions and other appendages, to name only a few.As of 1996, there were already nearly 40 different simulation tools [38], but very

few of those would be considered suitable for practitioners in the expanding field ofmoisture engineering. As well as seeking to build up a strong connection betweensimulation results and the real-world situations that they represent, we shouldapplaud the development of carefully documented versions of the more successfulmodels to edify and direct those responsible for design, and maintenance of buildingenvelopes.Finally, one of the key components of successful wall design for HAM

considerations will be quality control; the best predictive tools will fail inthe presence of detailing and installation deficiencies. This is the prime reasonwhy in-situ full-scale testing will remain important to successful methodologydevelopment.

Acknowledgements

The authors share a debt of gratitude with many others, to Alan Davenport, forencouragement, guidance, and opportunities to participate, in the paralleldevelopments of model and full-scale techniques for wind engineering: thank you,Alan. When it came to extending our review to the equally demanding and importantcomparisons with CFD and HAM tools, we would like to acknowledge in particular,Bas Baskaran and Kumar Kumaran for their enlightening conversations as well astheir own contributions to these new branches of engineering.


http://perswww.kuleuven.ac.be/~p0428890/frame_e_general.htm



http://www.bwk.tue.nl/fago/AIO/fabien/

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Documents

BLWT, CFD and HAM modelling vs. the real world: Bridging the gaps with full-scale measurements