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Indoor and Built Environment

DOI: 10.1177/1420326X06067336

2006; 15; 305Indoor and Built Environment Zhiqiang Zhai

Application of Computational Fluid Dynamics in Building Design: Aspects and Trends

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Key Words

Building modelling · Computational fluid dynamics ·

Building design · Energy efficiency · Building

systems · Development trend

Abstract

Computational fluid dynamics (CFD), as the most

sophisticated airflow modelling method, can simultan-

eously predict airflow, heat transfer and contaminant

transportation in and around buildings. This paper

introduces the roles of CFD in building design, demon-

strating its typical application in designing a thermally-

conformable, healthy and energy-efficient building.

The paper discusses the primary challenges of using

CFD in the building modelling and design practice. Fur-thermore, it analyses the developing trends in applying

CFD to building design, by thoroughly reviewing the lit-

eratures in all the proceedings of the International Con-

ference on Building Simulation, one of the most

influential symposiums in the building simulation field.

Introduction

Building, as one of the largest industries, has signific-

ant impacts on the environment and natural resources. In

the United States, buildings account for one-third of theprimary energy usage and two-thirds of all the electricity

consumption [1]. The construction and operation of

buildings generate tremendous pollution that directly

and indirectly cause urban air quality problems and

climate change. Poor design of buildings and systems not

only wastes resources and energy and causes adverse

impacts to the environment, but also creates uncomfort-

able and unhealthy indoor environments. Reports of

symptoms and other health complaints due to poor

indoor environments have been increasing in the last

decade. It was estimated that potential annual savingsand productivity gains could be $15 to $40 billion from

reduced sick building syndrome symptoms, and $20 to

$200 billion from direct improvements in worker per-

formance that are unrelated to health [2].

In the past few years, CFD has been playing an

increasingly important role in building design, following

its continuing development for over a quarter of a

century. The information provided by CFD can be used

to analyse the impact of building exhausts to the environ-

Zhiqiang ZhaiDepartment of Civil, Environmental and Architectural EngineeringUniversity of Colorado at Boulder, UCB 428, ECOT 441Boulder, CO 80309-0428, USATel. 303-492-4699, Fax 303-492-7317, E-mail [email protected]

© 2006 SAGE PublicationsDOI: 10.1177/1420326X06067336Accessible online at http://ibe.sagepub.comFigures 1 to 8 appear in colour online

Review Paper

Indoor Built Environ 2006 15;4:305–313 Accepted: November 20, 2005

Application of ComputationalFluid Dynamics in BuildingDesign: Aspects and TrendsZhiqiang Zhai

Department of Civil, Environmental and Architectural Engineering, University of Colorado

at Boulder


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ment, to predict smoke and fire risks in buildings, to

quantify indoor environment quality, and to design

natural ventilation systems, etc. This paper summarises

the most important aspects in which CFD can assist in

achieving a comfortable, healthy, and energy-efficient

building design. The areas range from building site plan-

ning to individual room layout design, from activeheating, ventilating and air-conditioning (HVAC) system

design to passive ventilation study, and from regular

indoor air quality assessment to critical fire smoke and

contaminant control. By using the work of the author as

examples, this paper demonstrates the typical CFD appli-

cation processes and discusses the primary application

challenges encountered. It further analyses the potential

trends in applying CFD for building design by reviewing

all the CFD-related papers in the proceedings of the

International Conference on Building Simulation for the

years 1985–2003. This series of conferences is among the

most important in the building industry with its focus on

computer simulation of buildings.

Assessment Index for Building Performance

CFD, by numerically solving the governing equations

for fluid flow, provides spatial- and temporal-distributed

information of airflow, pressure, temperature, turbulence

intensity, moisture and contaminant concentration. These

details can be used to evaluate the levels of thermal

comfort, indoor air quality (IAQ) and building systemenergy efficiency, which are interesting to architects, build-

ing HVAC designers, building consultants and researchers.

Air velocity, temperature and humidity ratio are the

most important parameters for the determination of the

predicted percentage dissatisfied (PPD) distribution in a

building. PPD is a major index for building thermal

comfort judgement. It can be calculated via [3]:

PPD

10095Exp(0.03353PMV40.2179PMV2) [%]. (1)

The PMV (predicted mean vote) in the equation is deter-

mined by:

PMV [0.303Exp(0.036M)0.028]L (2)

where M is the body metabolism (W·m2) and L is the

thermal load on the body (W·m2). M and L are the func-

tions of air velocity, temperature, humidity ratio and

enclosure temperature.

In addition, CFD results can be used to calculate the

distribution of percentage dissatisfied (PD) people due to

draft [4], another major thermal comfort index, through

the equation:

PD(34T)(U0.05)0.62(3.140.37U Tu) [%] (3)

where T is the local air temperature (°C), U is the local

air speed (m·s1), and Tu is the turbulence intensity (%).

If the turbulence kinetic energy k (m2·s2) is simulated

with a turbulence model, the turbulence intensity can be

estimated as

Tu100(2k)0.5/U [%]. (4)

As for the indoor air quality, CFD can directly predict

the concentration distributions of different contaminants

in a space with appropriate boundary conditions. These

concentration distributions can be further used to deter-

mine the ventilation effectiveness, :

C

C

e

C

C

s

s (5)

where Ce, Cs and C are the contaminant concentration

(ppm) of exhaust air, supply air and room air, respec-

tively.

Thermal comfort and indoor air quality status of a

building are influenced dominantly by installation loca-

tions, operating conditions and control strategies of the

HVAC systems used. CFD can examine the effectivenessand efficiency of various HVAC systems by easily chang-

ing diffuser types and locations, supply air conditions and

system control schedules. Furthermore, CFD can help

develop passive heating/cooling/ventilation strategies

(e.g. natural ventilation) by modelling and optimising

building site-plans and indoor layouts.

The following sections demonstrate some typical

aspects in which CFD can contribute to building and

system design, by using the projects investigated by the

author and other collaborators in architecture and engin-

eering.

Applications of CFD for Building Design

Application-1: Site Planning

Site planning is the first stage of building design. CFD

can help optimise building sites by predicting the distri-

butions of air velocity, temperature, moisture, turbulence

intensity and contaminant concentration around build-

306 Indoor Built Environ 2006;15:305–313 Zhiqiang Zhai


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ings. Good site planning can effectively protect building

groups from adverse impacts of surrounding pollution. It

can also improve outdoor pedestrian comfort and

increase energy efficiency of buildings by allowing

passive HVAC strategies, such as using natural ventila-

tion for summer and using wind break for winter.

Figure 1 presents such an example of using CFD for asite planning in Beijing, China. The initial plan intro-

duces highly imbalanced airflow through the four high-

rise buildings at the right hand of the figure, which may

cause the pedestrian discomfort due to the large wind

speed among some of the high-rise buildings. The non-

uniform airflow pattern also reduces the chance of using

natural ventilation for the buildings that confront less

wind. The new design revised the building shape and ori-

entation to allow natural wind movement to smoothly

cross each building so that there is a comfortable outdoor

environment and the occupants have the opportunity to

use a natural ventilation strategy. Chen et al. [5] indi-

cated that natural ventilation can save about 40% of the

total cooling energy required by buildings in Beijing.

Applying CFD for building site planning has become

fairly convenient as most current commercial CFD pro-

grams can import AutoCAD files of building site models

into the computational domain of a CFD simulation. The

major remaining challenge is probably the long comput-

ing time due to the large number of mesh grids required

to cover a building site with reasonable resolution. Thecomputing cost may become more significant when

dynamic wind conditions need to be modelled. Multi-grid

and locally-refined grid technologies may, to some

extent, accelerate the simulation; however, substantial

computing time is still needed even with a multi-

processor parallel computer.

Application-2: Natural Ventilation Study

Natural ventilation is one of the most fundamental

ways to reduce energy usage in buildings. In principle,

CFD can simultaneously model indoor and outdoor air-

flows to achieve an optimal natural ventilation strategy.

However, because of the scale difference between a

typical room (1m) and a site plan (100m), a large

number of numerical grids must be used to meet the res-

olution requirement. This imposes an undue expense to

designers by challenging current computer memory and

speed. Therefore, a practical approach is to decouple the

outdoor and indoor airflow simulation. Outdoor airflow

around buildings is first predicted, which provides airflow

and pressure information at the openings of buildings.

With these boundary conditions, indoor airflow for each

space can be simulated independently and natural venti-lation rate can be determined. Designers can then change

building indoor layouts and window sizes and locations

to maximise natural ventilation rate.

The decoupled simulation method is based on the

assumption that indoor airflow and building openings

have little impact on outdoor airflow and pressure distri-

butions; indoor and outdoor flow fields can therefore be

studied separately. The study [6] verified that room parti-

tions and windows do not contribute to a major dif-

ference in outdoor flow patterns and pressure fields. This

decoupled method logically well matches the generalarchitectural design procedure: from site plan to unit

design. The decoupled method first studies the outdoor

airflow around solid building site models during the site

plan stage when most details about building units are not

determined yet; then it moves into building interior

layout and opening design when the site plan is generally

finalised. As a result, refining the microscopic unit design

during the second stage of building design does not

require the recalculation of the macroscopic site plan.

307Indoor Built Environ 2006;15:305–313CFD in Building Design

Fig. 1. CFD for site planning.

(a) Initial plan

(b) Final plan




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Hence, the method greatly reduces the unnecessary com-

puting time for dynamic design modifications, which

allows designers to more easily refine the site plan and

apartment and window layouts separately.

As examples, Figure 2 shows the simulation results of

(a) buoyancy-driven natural ventilation in a high-rise

building with atrium and chimney, and (b) wind-drivennatural ventilation in a typical one-floor apartment.

These results provide designers with a straightforward

understanding of the performance of natural ventilation

design, and thus allow them to refine the plans and reach

an optimal solution.

One of the major challenges of using CFD for natural

ventilation study is the method to extract airflow con-

ditions at building openings from outdoor simulations

and specify them for indoor simulations. Because of the

high sensitivity of CFD results to boundary conditions,

small changes in airflow conditions at openings may

result in a significant shift of indoor airflow patterns. In

addition, simplification methods for indoor heat sources

(e.g. occupant, equipment, etc.) also challenge indoor

environment modelling.

Application-3: HVAC System Design

CFD is a powerful tool to evaluate indoor air quality

and thermal comfort provided by diverse HVAC

systems, leading to an effective and efficient system

design. It is superior to the conventional design approach

that typically relies on the use of charts provided by dif-

fuser manufacturers and jet formulae that were

developed from laboratory data. The use of such empiri-

cal data can result in great uncertainties when they are

applied to large spaces (such as atria, concert halls and

sports facilities) or applications that are dissimilar from

those upon which the laboratory data were developed.

When an innovative HVAC system is used, there are

inevitably no data or formulae available for the engin-

eering design.

Figure 3 illustrates the modelling of an office with new

displacement ventilation systems. Displacement ventila-

tion is an advanced indoor ventilation approach. Unlike

the conventional mixing ventilation, displacement venti-

lation provides a cleaner indoor environment with less

energy consumption. A typical displacement ventilation

system supplies fresh air at or near floor level at a verylow velocity and a temperature slightly below room tem-

perature. Exhausts are located at or near the ceiling. The

supply air spreads across the floor and rises as it is heated

by sources such as people and equipment, removing

indoor heat and contaminants directly from the occupied

zone to the upper zone without mixing. Since only the

occupied zone must be maintained at the room set-point

temperature while the upper zone may be warmer, the

supply air flow rate can be significantly reduced due to

the vertical temperature gradient, resulting in the

reduced fan energy. The CFD results help to understandthe physics of the displacement ventilation (such as the

large re-circulation at the lower part of the room). They

also quantify the vertical temperature stratification that is

necessary for building energy calculation. Moreover, the

supply air conditions can be optimised in CFD to reach

the best comfort for occupants.

CFD can also be directly used to guide design process

and optimise ventilation system design. Figures 4 and 5

demonstrate such an example that uses CFD to design


z

Fig. 2. CFD for natural ventilation study.

(a) Buoyancy-driven natural ventilation

(b) Wind-driven natural ventilation




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HVAC systems for the world’s first large-scale indoor

auto-racing facility. The facility is primarily a single space

building with a floor area of over 0.2106 m2 and a

ceiling height of 46m. It is designed to accommodate up

to 120,000 spectators – 60,000 in the grandstands and

60,000 in the infield, as well as a maximum of 45 racing

cars running simultaneously on the track at an average

speed of 217km·h1 (135mph). Such a large-scale and

complicated building with a variety of indoor com-

ponents strongly challenges the experience and capability

of ventilation system designers, even with the aid of CFD

modelling tools. CFD simulation has been used to

improve the initial HVAC system design, step by step, to

an optimal design. Figures 4 and 5 compare the air tem-

perature and lead concentration in the mid-section of the

facility under the steady design conditions by using dif-ferent HVAC systems. The study concluded that a com-

bination of an underneath displacement ventilation

system and a conventional overhead duct system as well

as a partial air curtain system between the occupied zone

and the racing zone is the most effective solution for this

complex to obtain a comfortable and healthy indoor

environment with less energy consumption [7].

CFD results are much more informative and accurate

than those that could be obtained via empirical-formu-

lae-based hand calculation for HVAC design. However,

CFD for HVAC system design still presents various chal-

lenges, especially in the simplification of sophisticated

building system components, such as diffusers [8], fans,

evaporators and diverse heat and contaminant sources

(e.g. moving cars, breathing occupants).

Application-4: Pollution Dispersion and Control

Wide use of CFD has demonstrated its capability in

modelling the transportation of contaminants, with its


6 2

8

53 (2)

7

8

7

1

3 (1)

4

2.43 m

4.16 m

3.65 m

xz

Fig. 3. Simulation of displacement ventilation in an office: (a)CFD model; (b) velocity and temperature distribution in themiddle plane of the room; (c) velocity and temperature distributionin the plane across an occupant.

(a)

(b)

(c)

Fig. 4. Air temperature distribution in the middle section of anindoor auto-racing complex (°C).

(a) Base case

(b) Optimal case

inlet-1, outlet-2, person-3, table-4, window-5, fluorescent lamps-6,cabinet-7, computer-8




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low costs, high efficiency and flexibility. It is particularly

useful for predictive studies in extreme conditions, for

instance, extreme-hot or toxic scenarios, and it can be

easily employed to investigate the impact of a particular

flow parameter, such as wind speed or air temperature,on the dispersion of a certain contaminant. Both indoor

and outdoor contaminant dispersions can be simulated,

while indoor scenarios are more complicated and haz-

ardous because people spend over 90% of their time

indoors and more factors can affect the dispersion of

indoor contaminants. The geometry and structure of a

building, as well as the HVAC system used in the build-

ing, have a dominant influence on the dispersion of

indoor contaminants. Partitions, furniture and passage-

ways between indoor spaces can also distort the airflow

and the contaminant distributions. Importance of indoorcontaminant study is also reflected by the fact that the

indoor pollution is controllable by using good sensor and

response systems. CFD prediction can be used to locate

the best sensor positions in a building, to indicate the safe

paths for evacuating occupants, and to develop the

effective emergency response strategies to isolate and

clean the contaminated air.

Figure 6 presents a realistic office complex as an

example, in which CFD has been used to predict the dis-

persion of contaminants from different locations in the

offices. The study showed that the contaminant disper-

sion is very fast and strongly depends on the indoor

airflow pattern. It also indicated that early warning from

the sensors is possible if they are placed properly. The

investigation proposed and tested several response strat-

egies by supplying or exhausting emergency air through

three ceiling-mounted air devices. It found that the con-

taminant dispersion can be effectively controlled by

simply pressurising or vacuuming the indoor spaces.

Figure 7 illustrates another example of using CFD todesign exhaust hoods for chemical and biological laborato-

ries. The simulated results showed that, without particular

design cares, hoods with standard/enhanced ventilation

rate may still leak toxic materials from operating zone to

occupied zone due to the local turbulent vortices at hood

openings. Hence, central air system and hood air system

should be designed as a comprehensive system.

In general, the study of indoor contamination is not an

easy task. Zhai [9] previously discussed the primary


Fig. 5. Lead concentration distribution in the middle section of anindoor auto-racing complex (gLead per kgAir).

EASE 1 EASE 2 EASE 3

S7

S3

S2

S1

C1

S1S8

Office 2

C2

Corridor

O2

S6

S5

S4

C3 from

diffuser S10

O1

C1–C3 represent three different types of airborne contaminants from three

locations – under a desk in office 1 (C1), in the corridor (C2) and from the supply

air in office 1 (C3). O1 and O2 are two occupants’ nose locations (0.9 m above the

floor) and S1–S10 are ten different sensor locations to be tested in office 1.

EASE1–3 stand for three emergency air supply and exhaust (EASE) outlets.

Fig. 6. Simulation of indoor contaminant dispersion and control: (a)CFD model; (b) concentration contour of C1 at occupant head levelat t5min after contaminant release (without emergency response);(c) concentration contour of C1 at occupant head level at t5minafter contaminant release (with air pressurising for corridor and office2 and vacuuming for office 1 starting from t2min).

(a)

(b)

(c)

(a) Base case

(b) Optimal case




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Trend-1: Integration of CFD with other Building

Modelling Programs

Building is such a sophisticated product that building

components can rarely be designed and evaluated sepa-

rately. For instance, building energy consumption is

determined by many factors. Besides the traditional

factors such as building size and materials and schedules,room air distribution and lighting design may result in

significant heating/cooling load change. The integration

of energy simulation and CFD can eliminate many

assumptions involved in separate applications [17]. Many

efforts (e.g. [18–22]) have been made to develop an

integrated building design tool by coupling airflow,

energy, lighting, acoustics, materials, environment and

life cycle analysis programs. Such an integrated design

tool can improve the prediction accuracy of building per-

formance, as well as providing possibilities of developing

a general graphic user interface. In order to facilitate the

data exchange between different simulation engines, a

new file format – Industry Foundation Classes (IFC) –

has been developed and used [23–24]). Ultimately, an

integrated and virtual simulation environment for build-

ing design is highly anticipated [25].

Trend-2: Simplification and Intelligence of CFD Tools

Since the majority of CFD users in the building indus-

try are building designers and engineers who have

limited knowledge of fluid flow they would undoubtedly

welcome an intelligent CFD program. A good graphical

user interface (GUI) can dramatically reduce the inputeffort and errors, which will attract more designers to

apply CFD programs for their designs. Simple and intelli-

gent interfaces [26] can minimise the need to understand

the underlying flow physics and numerical methods but

still obtain meaningful results, in which automatic gener-

ation and adjustment of CFD grids are the first necessi-

ties.

In addition, the development of the Internet enables

remote collaborative partnerships and the imminent

opportunities for “e-simulation”. The Web facilitates

new forms of data sharing and distributed engineeringthrough web hosted services, as well as new forms of

teaching and training solutions, as presented by Lam

[27].

Trend-3: Improvement of CFD Computing Cost

Due to demanding numerical iteration and necessary

spatial resolution requirement, most CFD simulations

require considerable computing time ranging from hours

to days. It is unacceptable for most design tasks, which

need rapid evaluation of alternative plans. Extensive

studies have been conducted to develop approaches that

can accelerate CFD computation, such as locally-refined

grid, multi-grid and adaptive grid techniques [28]. In

addition, the trade-off between speed and accuracy has

been noticed since most building designers have lessaccuracy requirements than aerospace engineers, espe-

cially at the early stage of building design when most

design details have not been determined and architec-

tural plans may change rapidly. Hence, various simple

airflow models, such as coarse grid CFD [29] and zonal

air-flow models [30], may be more suitable for these pur-

poses. Reliable and simple turbulence models (e.g. con-

stant viscosity model) for indoor and outdoor airflows

should be developed.

Trend-4: Development of Critical Modelling Methods

Although most building phenomena can be simulated

by current CFD programs, advances in building continue

to impose new challenges to CFD, for example, to model

innovative diffusers [31], new window designs [32], and

advanced heating/cooling systems [33]. The problem may

become more complicated when involved with mass

transfer, phase changes and multi-phase interactions,

such as air condensation and combustion. Special

methods and models need to be developed [34–36].

Moreover, advanced CFD techniques such as Large

Eddy Simulation (LES) may be needed to study critical

problems in buildings which can not be solved withregular CFD approaches. Such examples are (1) natural

ventilation studies that are heavily dependent on instan-

taneous airflows [37], and (2) particle transport studies

and their interactions with human bodies [38].

Summary

This paper has introduced the applications of CFD for

building design. CFD can provide important information

to assist in the design of energy-efficient, user-comfort-able and environmentally friendly buildings. The paper

discusses the typical aspects that CFD can contribute to a

successful building design, along with brief comments on

the application challenges. By reviewing the papers pre-

sented on one premier building simulation conference in

the past 20 years, the paper analyses the potential trends

of using CFD for building design in the next few years.





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References

1 U.S. Energy Information Administration:State Energy Data Report-95. Tables 3–7.1995.

2 Fisk WJ: Health and productivity gains frombetter indoor environments and their relation-ship with building energy efficiency: Ann Rev

Energy Environ 2000;25:537–566.3 ASHRAE: ASHRAE Handbook Fundamen-tals – SI. ASHRAE, Atlanta, 1997.

4 Fanger PO, Melikov AK, Hanzawa H, Ring J:Turbulence and draft: ASHRAE J1989;31(7):18–23.

5 Chen Q, Allocca C, Hamilton S, Huang J,Jiang Y, Kobayashi N, Zhai Z: Buildingnatural ventilation design and studies. ProcConf Thermal Environ Residential Buildings,Beijing, China, 2000;1–6.

6 Zhai Z, Hamilton SD, Huang J, Allocca C,Kobayashi N, Chen Q: Integration of indoorand outdoor airflow study for natural ventila-tion design using CFD. Proc 21st AIVC AnnConf Innovations Vent Technol, The Hague,The Netherlands, 2000;P13:1–13.

7 Zhai Z, Chen Q, Scanlon PW: Design of ventila-tion system for an indoor auto racing complex:ASHRAE Trans 2002;108(1):989–998.

8 Srebric J, Chen Q: Simplified numericalmodels for complex air supply diffusers: Intl JHVAC&R Res 2002;8(3):277–294.

9 Zhai Z, Srebric J, Chen Q: Application of CFD to predict and control chemical and bio-logical agent dispersion in buildings: Intl JVent 2003;2(3):251–264.

10 Stankovic S, Setrakian A: Thermal and CFDmodelling vs. wind tunnel in natural ventila-tion studies. Proc Building Simulation ’93,Adelaide, Australia, 1993;457–462.

11 Satwiko P, Donn M: With what confidencecan an architect use a CFD codes package as a

building environmental design tools? ProcBuilding Simulation ’95, Madison, USA,1995;604–610.

12 Depecker P, Virgone J, Rusaouen G: Numeri-cal modelling of air flows in buildings anddesign of a data base of experiments. ProcBuilding Simulation ’95, Madison, USA,1995;97–104.

13 Setrakian A, McLean D: Building simulationsusing thermal and CFD models. Proc BuildingSimulation ’91, Nice, France, 1991;235–240.

14 Cook MJ, Lomas KJ: Guidance on the use of computation fluid dynamics for modelingbuoyancy driven flows. Proc Building Simula-tion ’97, Prague, Czech Republic, 1997;140–147.

15 Bartholomew D, Hand J, Irving S, Lomas K,McElroy L, Parand F, Robinson D, StrachanP: An application manual for building energy

and environmental modelling. Proc BuildingSimulation ’97, Prague, Czech Republic, 1997;205–211.

16 Hand JW, Crawley DB: Forget the tool whentraining new simulation users. Proc BuildingSimulation ’97, Prague, Czech Republic,

1997;186–192.17 Zhai Z, Chen Q, Klems JH, Haves P: Strat-egies for coupling energy simulation and com-putational fluid dynamics programs. ProcBuilding Simulation ’01, Rio de Janeiro,Brazil, 2001;59–66.

18 Murakami S, Kato S, Kim T: Indoor climatedesign based on feedback control of HVACcoupled simulation of convection radiation,and HVAC control for attaining given opera-tive temperature. Proc Building Simulation’99, Kyoto, Japan, 1999;P02:1–8.

19 Maliska CR: Issues on the integration of CFDto building simulation tools. Proc BuildingSimulation ’01, Rio de Janeiro, Brazil,2001;29–40.

20 Beausoleil-Morrison I: The adaptive couplingof computational fluid dynamics with whole-building thermal simulation. Proc BuildingSimulation ’01, Rio de Janeiro, Brazil,2001;1259–1266.

21 Beausoleil-Morrison I, Clarke JA, Denev J,Macdonald IA, Melikov A, Stankov P:Further developments in the conflation of CFD and building simulation. Proc BuildingSimulation ’01, Rio de Janeiro, Brazil,2001;1267–1274.

22 Laine T, Reinikainen E, Liljeström K, KarolaA: Integrated LCA-tool for ecological design.Proc Building Simulation ’01, Rio de Janeiro,Brazil, 2001;739–746.

23 Karola A, Lahtela H, Hänninen R, HitchcockR, Chen Q, Dajka S, Hagström K: BSPRO

com-server – interoperability between soft-ware tools using industry foundation classes.Proc Building Simulation ’01, Rio de Janeiro,Brazil, 2001;747–754.

24 Nytsch-Geusen C, Klempin C, Voigt JN,Radler J: Integration of CAAD, thermalbuilding simulation and CFD by using the IFCdata exchange format. Proc Building Simula-tion ’03, Eindhoven, the Netherlands,2003;967–974.

25 Ono K, Morikawa Y: Development of virtualsimulation system for housing environmentusing rapid prototype method. Proc BuildingSimulation ’01, Rio de Janeiro, Brazil,2001;899–906.

26 Broderick CR, Chen Q: A simple interface toCFD codes for building environment simula-tions. Proc Building Simulation ’01, Rio deJaneiro, Brazil, 2001;577–584.

27 Lam KP, Mahdavi A, Brahme R, Kang Z, IlalME, Wong NH, Gupta S, Au KS: Distributedweb-based building performance computing: aSingapore-US collaborative effort. ProcBuilding Simulation ’01, Rio de Janeiro,Brazil, 2001;807–814.

28 Imano M, Kamata M, Kurabuchi T, HayamaH, Kishita M: Study on adaptive mesh genera-tion method in CFD calculation with conju-gate heat transfer model. Proc BuildingSimulation ’99, Kyoto, Japan, 1999;D02:1–6.

29 Liu C: DNS/LES Perspective. Preface forTAICDL Proc, Third AFOSR Intl Conf Direct Numerical Simulation and Large EddySimulation (TAICDL), Arlington, TX, 2001;1–2.

30 Wurtz E, Deque F, Mora L: SIM_ZONAL: Asoftware to evaluate the risk of discomfort:coupling with an energy engine, comparisonwith CFD codes and experimental measure-ments. Proc Building Simulation ’03, Eind-hoven, the Netherlands, 2003;1423–1428.

31 Xu HT, Niu JL: A new method of CFD simu-lation of airflow characteristics of swirlingfloor diffusers. Proc Building Simulation ’03,Eindhoven, the Netherlands, 2003;1429–1434.

32 Leal V, Maldonado E, Erell E, Etzion Y:Modelling a reversible ventilated window forsimulation within ESP-R – The SOLVENTCase. Proc Building Simulation ’03, Eind-hoven, the Netherlands, 2003;713–720.

33 Zmrhal V, Hensen J, Drkal F: Modelling andsimulation of a room with a radiant coolingceiling. Proc Building Simulation ’03, Eind-hoven, the Netherlands, 2003;1491–1496.

34 Liu J, Yoshihiro A, Hiroshi Y: Experimentaland CFD studies on surface condensation.Proc Building Simulation ’03, Eindhoven, theNetherlands, 2003;585–590.

35 Sinai Y, Stopford P, Edwards M, Watkins S:CFD modeling of fire suppression by waterspray: sensitivity and validation for a pool firein a room. Proc Building Simulation ’03, Eind-hoven, the Netherlands, 2003;1217–1220.

36 Zukowski M: A numerical analysis of heatand mass transfer in a room with the air-underfloor heating. Proc Building Simulation’03, Eindhoven, the Netherlands, 2003;1497–1504.

37 Jiang Y, Chen Q: Study of natural ventilationin buildings by large eddy simulation: J WindEngineering Indust Aerodynamics 2001;89(13):1155–1178.

38 Jiang Y, Chen Q: Study of particle dispersionin buildings with large eddy simulation:Indoor Air ’02 2002;5:530–535.


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