commemorating 20 years of indoor air

8/15/2019 Commemorating 20 years of Indoor Air

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Commemorating 20 years of Indoor Air

CFD and ventilation research

Introduction

Building ventilation, together with source control andair cleaning, controls indoor air quality. Mechanicalfans and/or natural forces drive ventilation. A majordesign objective is to ensure that ventilation air is

delivered from a limited number of supply openings toevery (occupied) part of the room in an effective andefficient manner. This has become a challenging engi-neering task as buildings become larger, higher, anddeeper, and the penetration depth of a ventilationsupply air jet is limited. Air distribution is affected bysupply air jets, supply and exhaust location, thermallyinduced airflows, as well as room geometry andobstacles.

Air distribution is all about airflow and transport of heat and airborne pollutants. Air distribution isgoverned by the conservation principle. It is mathe-matically described by a set of partial differential

equations, known as the Navier–Stokes equations.These can be solved analytically only for simple andideal conditions. For complex geometry and/or com-plex boundary conditions, numerical methods may beused to solve these equations or solve their modelingversions, given the initial and boundary conditions, i.e.

computational fluid dynamics (CFD). There have beenat least three distinctive or overlapping stages in thesolutions of the governing equations, similar to otherfields involving fluid mechanics and heat transfer.

• The stage of analytical closed-form or approximatesolutions. Examples include Koestel (1955), Raja-ratnam (1976), etc.

• The stage of empirical relationships obtained frommeasurements in small- and full-scale tests. Exam-ples include Straub et al. (1956) and Mu ¨ llejans(1966). Both examples were fully cited in ASHRAE(2009) and Awbi (2003), respectively.

Abstract There has been a rapid growth of scientific literature on the applicationof computational fluid dynamics (CFD) in the research of ventilation andindoor air science. With a 1000–10,000 times increase in computer hardwarecapability in the past 20 years, CFD has become an integral part of scientificresearch and engineering development of complex air distribution and ventila-tion systems in buildings. This review discusses the major and specific challengesof CFD in terms of turbulence modelling, numerical approximation, andboundary conditions relevant to building ventilation. We emphasize the growingneed for CFD verification and validation, suggest ongoing needs for analyticaland experimental methods to support the numerical solutions, and discuss thegrowing capacity of CFD in opening up new research areas. We suggest that

CFD has not become a replacement for experiment and theoretical analysis inventilation research, rather it has become an increasingly important partner.

Y. Li1, P. V. Nielsen2

1Department of Mechanical Engineering, The University

of Hong Kong, Pokfulam Road, Hong Kong SAR, China,2Department of Civil Engineering, Aalborg University,

Aalborg, Denmark

Key words: Computational fluid dynamics; Building

ventilation; Experiment; Theory; Analysis; Validation.

Y. Li

Department of Mechanical Engineering

The University of Hong Kong

Pokfulam Road, Hong Kong SARChina

Tel.: +852 28592625

Fax: +852 2858 5415

e-mail: [email protected]

Received for review 29 October 2010. Accepted for

publication 5 May 2011.

Practical ImplicationsWe believe that an effective scientific approach for ventilation studies is still to combine experiments, theory, andCFD. We argue that CFD verification and validation are becoming more crucial than ever as more complex venti-lation problems are solved. It is anticipated that ventilation problems at the city scale will be tackled by CFD in the

next 10 years.

Indoor Air 2011; 21: 442–453wileyonlinelibrary.com/journal/inaPrinted in Singapore. All rights reserved

2011 John Wiley & Sons A/S

INDOOR AIRdoi:10.1111/j.1600-0668.2011.00723.x

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• The stage of numerical solutions.

During the 1950s to the 1970s, the aerospaceindustry and weather/climate prediction were theprimary drivers in early CFD development, but anengineering application such as building ventilation hasbeen a main driver in recent CFD development overthe last 30 years. Nielsen (1973) was probably the firstto apply CFD in ventilation studies. Since 1970s, therehave been considerable developments in both CFD(faster computers, faster numerical methods, andimproved turbulence modeling) and ventilation (venti-lation methods and ventilation control). Indeed, CFDhas now been used in nearly all disciplines whereinsights into fluid phenomena are needed. CFD sim-ulations with 5 billion cells were carried out for urbanheat island analysis in Tokyo for a 33 km · 33 km area(Ashie and Kono, 2011).

There are a number of reviews on CFD andventilation, such as Awbi (1989), Jones and Whittle

(1992), Chow (1996), Nielsen (2004), Zhai (2006) andChen (2009). Textbooks on CFD include Ferziger andPeric (1999), Wesseling (2000) and Nielsen et al.(2007), on CFD verification and validation: Roache(1998), on turbulence modeling: Wilcox (2006) andSagaut (2001), on building ventilation: Croomeand Roberts (1981), Etheridge and Sandberg (1996)and Awbi (2003).

This review intends to have a different focus fromthe existing reviews. We discuss the growing CFDcapability and challenges for ventilation studies, andsuggest continued needs for analytical and experimen-

tal methods to support the numerical solutions.Examples of the contribution of CFD in revealingnew ventilation phenomena as well as its use beyondbuilding application are also discussed. We do notintend to review the application of CFD in practicaldesign of building ventilation, simply as the scientificliterature does not really reflect the whole picture of the field, and isolated comments will be made in thetext when necessary. We also do not cover theimportant topic of comparison of different turbulencemodels, as it was reviewed, for example, by Chen(2009).

Two major challenges in CFD

CFD solves the governing equations of fluid flow on acomputer and provides both spatial and temporal fieldsolutions of variables such as temperature and velocity,and/or predicts the dispersion of pollutants in a roomor a building, i.e. the computational domain. CFD canalso be used to predict indoor air quality, thermalcomfort, fire and smoke spread, and wind flow aroundbuildings. The effect of gravitational body force can besignificant in airflows in buildings. Owing to relativelysmall temperature differences (with smoke flow as an

exception), the so-called Boussinesq approximation iscommonly used (Tritton, 1988).

In CFD, ventilation parameters such as room/building geometry, supply and exhaust openings, andphysical variables can be flexibly changed. CFD wouldallow scientists to test scenarios that were not possibleusing experimental methods, and have more in-depth

and in-breadth examination of the physical phenome-non, because of relatively low cost and efficiency. CFDsoftware is now widely available, and complex venti-lation problems seem to be easily simulated on a PC ina researchers office.

Adapted from Fujii (2005), Table 1 shows a com-parison of experimental methods and CFD. Bothmethods suffer from possible inherent errors. Themajor accuracy issue in experimental methods is thescaling effect, in particular when small-scale models areused. CFD suffers from both turbulence modelingerrors and numerical errors.

CFD has two major challenges. The same applies toits application in ventilation research. The first is thatturbulence is still only modeling in CFD.

We illustrate this challenge using two examples.First, the flow in a ventilated room is generallyassumed to be a fully developed turbulent flow, andthis flow can be handled by most turbulence models.But in some areas of the room, including the occupiedzone, a low Reynolds number flow can exist at lowroom air supply velocity. Figure 1 shows measure-ments of the maximum velocity in the occupied zone of a room with mixing ventilation from a wall-mounteddiffuser versus the air change rate. The flow is

isothermal (Nielsen, 1992a). It is known from thesimilarity principles that any velocity as, for example,the maximum velocity in the occupied zone is a linearfunction of the air change rate (or the supply velocity)when the flow is a fully developed isothermal turbulentflow. In Figure 1, this is the case for velocities largerthan 0.25 m/s, but the figure indicates that the flow in

Table 1 Comparison of laboratory experiments and computational fluid dynamics (CFD)

for building ventilation (adapted from Fujii, 2005)

Smal l-scal e or ful l-sca le experiments CFD

Water tanks,

small-scale models,

or full-scale rooms

Computers

Measurement methods Numerical algorithms

Manufacturing techniques Programming techniques

(parallel language, etc.)

Model manufacturing CAD interface, grid generation

Data acquisition Postprocessing

Data handling Visualization

Scaling effects

(Reynolds number and

Archimedes number)

Discretization error, turbulence

modeling error (e.g. because of

low Reynolds number in

the predictions)


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the occupied zone is a low Reynolds number flow forvelocities below 0.25 m/s (dotted line). The conven-tional turbulence models may not accurately capturesuch important physical phenomena.

Second, the importance of correct turbulence mod-eling may be seen from predicting a wall jet spread in aroom from a single nozzle in the end wall close to theceiling (see Scha ¨ lin and Nielsen, 2004). The predictedflow fields are shown in Figure 2, with both the k-model with the assumption of isotropic turbulence andthe Reynolds stress model (RSM) considering theanisotropic turbulence.

The wall jet is wrongly predicted by the k- model, asthe wall jet grows at an equal rate parallel to the ceilingand down into the room. It is known that a three-dimensional wall jet grows much faster parallel to theceiling than down into the room. In the RSM, withwall reflection terms, a redistribution of normal stressestakes place near the wall. These dampen the turbulentfluctuations perpendicular to the wall and convert theenergy to fluctuations parallel to the wall. The RSMthus gives a better prediction than the k- model as

compared to the experiments (Scha ¨ lin and Nielsen,2004).

The error as a result of the anisotropic issue maybecome relatively insignificant (and therefore neverrealized) if the room is short and the supply openinghas a large width compared with height (a typicalsupply opening). This error is also small if the flow in

the room is nearly two dimensional, for example, if thesupply opening is 20% of the room width or greater,i.e. when a two-dimensional wall jet in the initial flowresults (data not shown).

Different turbulence models for building airflowswere evaluated, including Reynolds-averaged modeling(Chen, 1996; Murakami, 1998; Abdilghanie et al.,2009) and large eddy simulations (Mochida et al.,2005; Be ´ ghein et al., 2005). Owing to turbulencemodeling difficulties, CFD may not be able to revealthe real physics as in experiments. An increasingnumber of large eddy simulations are carried out for

indoor airflow analysis, which allows the capture of some flow physics (Berrouk et al., 2010; Choi andEdwards, 2008).

The second major challenge in CFD is that thesolutions are still approximate, not exact. Variousnumerical phenomena occur, including numerical dif-fusion and numerical dispersion (Li, 1997), as demon-strated in Figure 3 by the predicted results of a scalarcone 10 units high after one full counterclockwiserotation. The maximum Courant number (velocitytimes the time step, divided by the grid size) used is0.09. The grid is 64 · 64. The numerical diffusion isshown by the reduction in the cone height from

10 units to 8.243 for the QUICK scheme and 5.251for the second-order upwind scheme (SOU). Thenumerical dispersion is shown by the oscillationsbehind the cone for QUICK and in front of the conefor SOU (Li, 1997). Thus, the two commonly usedconvection schemes in ventilation studies are shown togive significant errors for a very simple problem.Questions can be asked whether the same numericaldiffusion and dispersion exist in the CFD simulationsfor complex ventilation problems, and the answer is

Fig. 1 The measured maximum velocity (urm, m/s) in the occu-pied zone of a room versus air change rate (indicating supplyvelocity) (n, h)1) in the case of isothermal mixing ventilation.Proportionality between supply velocity and maximum velocityin the room indicates a fully developed turbulent flow in theoccupied zone for a supply velocity larger than 0.25 m/s and alow Reynolds number flow for lower velocities

(a1) (a2)

(b1) (b2)

Fig. 2 Differences in predicted wall jet spread in a 3D room by the k- model (a1, b1) and the Reynolds stress model (a2, b2), as seen bythe size of the white area where the speed is >3.5 m/s in (a1) and (a2) for the profile near the ceiling, and >3.0 m/s in (b1) and (b2) forthe profile in the mid-vertical plane. The wall jet was issued from the left at the ceiling level (see Scha ¨ lin and Nielsen, 2004)

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most likely positive. In theory, the numerical errorsreduce to an acceptable accuracy when the number of grid points is sufficient or the mesh size is sufficiently

small. The effect of numerical diffusion in recirculatingflow is also demonstrated in a Smith and Huttonscheme by Sørensen and Nielsen (2003b). Numericalaccuracy is expected to improve as computer capacityfurther increases and better numerical schemes aredeveloped.

Owing to turbulence modeling errors and numericalerrors, CFD is not only a replacement for the theoryand experimental methods in ventilation analysis butis also a partner. Obviously, an effective scientificapproach is to combine theory, experiments, and CFDin a way that exploits the inherent strengths of eachmethod. This statement may not be true for engi-

neering design of complex realistic problems, inwhich data from both theory and experiments aregenerally not available, and the required engineeringaccuracy for noncritical applications is often not veryhigh.

Modeling building ventilation – specific challenges

Croome and Roberts (1981) asked two questions: (i)What kind of airflow pattern should be in a spaceoccupied? (ii) How and when can the indoor airflow bepredicted? CFD was mostly used to answer the second

question, but it has also increasingly been used toanswer the first question.The primary process simulated in CFD ventilation

models is the airflow. The primary variables include airspeed, its direction, air temperature, and turbulencequantities. The buoyancy force can be significant inbuilding airflows. An important process that affects theairflow in buildings is the transport of heat andmoisture, including thermal radiation (Li et al.,1993). The related surface condensation is also impor-tant for dampness and mold issues in indoor air. Anunderstanding of the detailed heat transfer through thebuilding envelope using combined CFD and heat

transfer analysis is also useful for building energyefficiency.

For indoor air quality analysis, detailed emission

characteristics at the material surface (Murakamiet al., 2003; Yang et al., 2004; Mo et al., 2005), andbreathing and coughing may be included. For partic-ulate matter, important processes include particledeposition and resuspension. An important processthat affects gaseous pollutants and aerosols is indoorair chemistry.

Two overall ventilation performance indexes areoften used. The air exchange efficiency indicates howefficiently the outdoor air is distributed in the room,while the ventilation effectiveness indicates how effi-ciently the airborne pollutant is removed from theroom. The local mean age of air at a point is defined as

the average time that the air takes to arrive at thatpoint as it first enters the room, and the room mean ageof air is the average of the age of air at all points in theroom. The age of air can be measured using tracer gastechniques. The air exchange efficiency and ventilationeffectiveness have been studied using CFD (Peng et al.,1997).

We next discuss two specific challenges. The first is inthe description of the supply openings. The specificchallenge in representing supply openings is part of more general challenges in properly specifying bound-ary conditions. One major challenge for characterizing

airflow in buildings is the central importance of complex boundary conditions indoors. We now under-stand rather well how to model thermal and flowboundary conditions at walls, supply openings, andnatural ventilation openings (Li and Holmberg, 1994;Fontaine et al., 2005). Efforts made to integrate CFDwith building thermal modeling and multizone airflowmodeling (Axley, 2007) have been reviewed by Chen(2009).

The flow from a diffuser in a ventilation system isdetermined by very small details in the diffuser design.A numerical prediction method should be able tohandle small details in dimensions of one tenth of a

Fig. 3 Perspective plots of the predicted profiles of a scalar conic shape of 10 units high after one full counterclockwise rotation (Li,1997)


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millimeter, as well as dimensions of several meters inthe room. Such a wide range of the geometry neces-sitates incorporating a large number of cells in thenumerical scheme, which increases the prediction costand computing time to a high level.

Various simplifications can be suggested. The mostobvious simplified method is to replace the actual

diffuser with one of less complicated geometry thatsupplies the same momentum of airflow to the room.This may be obtained from a single opening with anarea equivalent to the effective supply area of thediffuser, and such methods were evaluated by Nielsen(2004).

The box method is a method based on specifying thewall jet flow (or free jet flow) or measured data close tothe diffuser (Nielsen, 1973, 1974). The details of theflow in the immediate vicinity of the supply opening areignored, and the supply jet is described by values alongthe surfaces on an imaginary box in front of the

diffuser. Two advantages are obtained by using suchboundary conditions. First, it is not required to use agrid as fine as that needed for full numerical predictionof the wall jet development close to the opening.Second, it is possible to make two-dimensional predic-tions for supply openings that are three-dimensional,provided that the jets develop into a two-dimensionalwall jet or free jet at a given distance from the opening.

The box method is a possibility because the flowclose to the openings can be considered to be aparabolic flow in contrast to the general flow in theroom which often will be an elliptic flow. It is thereforealso a possibility to use the full capacity of the

computer to generate the flow around the diffuserand after that, using the capacity to predict the flow inthe room, with the predicted values from the first runas boundary conditions in the second set of predictions(Kondo and Nagasawa, 2002).

The prescribed velocity method has also been success-fully developed. The inlet profiles are given as bound-ary conditions at the diffuser in the usual way (assimplified boundary conditions), and they can berepresented by a few grid points only. The velocity

profiles are given in the inlet volume (box) in front of the diffuser, and all the other variables are calculated inthis volume as well as in the rest of the room. Thevelocities are thus prescribed for the box in front of thediffuser as the analytical values obtained for a wall jetfrom the diffuser, or they are given as measured values(Gosman et al., 1980; and Nielsen, 1992a,b).

The momentum method is a method where themomentum and mass flow are decoupled in the CFDsimulation of the diffuser, and the initial momentumand mass flow rate from the diffuser are used as theboundary conditions. Chen and Srebric (2001) give adetailed discussion of the momentum method.

Continuous development of computational capacityand speed has undoubtedly made the direct methodswith local grid refinements or multigrid solution, anatural possibility. The diffuser in Figure 4 consists,for example, of 12 small slots which can be adjusted todifferent flow directions. The diffuser is mounted in a

wall below the ceiling. It is a complicated geometrygenerating an asymmetrical three-dimensional flow inthe room, and it can only be introduced in the CFDpredictions by making a detailed description of theboundary conditions corresponding to a partly or fullyresolved diffuser.

Comparisons between measurements and predictionswith the grids in Figure 4b,c show that predictionsbased on 250,000 cells are sufficient to obtain a grid-independent solution (Szczena et al., 2005). Otherdiffuser designs can be much more demanding to thecomputer capacity.

It may be noted that it is always possible to treat a

complicated supply opening such as that shown inFigure 4 as porous medium. We specify the so-calledintrinsic average velocity and the free opening ratio.The mass and momentum conservation are automat-ically satisfied. In addition, all scalars such as turbulentkinetic energy are automatically conserved. This totalconservation method has not been widely tested. Suchtreatment is similar to the porous model used in treecanopy layer analysis and recently in the urbanventilation analysis (Hang and Li, 2010).

(a) (b) (c)

Fig. 4 (a) Air supply diffuser with unsymmetrical adjusted nozzles. (b) Representation of the air supply diffuser using embedded gridrefinement and (c) using unstructured grid

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The second specific challenge is in the treatment of multiple length scales existing in the ventilation prob-lem. Table 2 summarizes a variety of spatial scales of building ventilation problems that have been simu-lated. It covers a range of 0.001–100 m in buildingventilation and up to10,000 m including city ventila-tion. For example, CFD has been used to understand

how the supply air jets are generated and how theydevelop (e.g. Karimipanah and Awbi, 2002), as well ashow human body plumes evolve (e.g. Gao and Niu,2006; Craven and Settles, 2006). It is important torealize that parabolic flows as jets and plumes dependon a detailed description of the source of the flow(momentum source scale in Table 2). The ability tostudy detailed airflows and heat transfer around ahuman body allows the detailed examination of ther-mal comfort of the human body in a room (Gao andNiu, 2005). Most studies have been at the room scale,and most of these considered an empty room. Different

air distribution strategies are evaluated and compared(e.g. Lin et al., 2005). Airflows between rooms can beimportant for overall airflow pattern design, such as inhospitals. CFD has also been useful in analyzing theeffectiveness of open windows, wind catchers, andchimneys in natural ventilation. CFD has been usedeither alone or in combination with the multizoneairflow models for the building scale study (Tan andGlicksman, 2005). Another commonly studied situa-tion is the combined analysis of airflow around andwithin a building, in particular for natural ventilationanalysis. In the latter situation, the wind flows within abuilding cluster can affect the building ventilation. The

neighborhood scale also includes the ventilation of streets. The ventilation problem can be further

extended beyond the building scale up to city scale(Hang et al., 2009a,b; Yang and Li, 2009). Difficultiesarise when multiple scales need to be resolved. To somedegree, the difficulties in treating supply opening is alsoone such multiscale problem.

CFD opens new research areas

CFD has made it possible for a number of newresearch directions to be further explored. Examplesinclude solution multiplicity in building ventilation,inverse CFD modeling, near-body micro-environment,and disease transmission, which the authors are famil-iar with.

Solution multiplicity

Solution multiplicity refers to the existence of morethan one solution of the overall flow patterns in a

room under the same boundary conditions. Differentinitial conditions can lead to different solutions. Insome situations, a solution can be switched to anotherwhen there is a sufficient perturbation. Mu ¨ llejans(1966) first experimentally identified the existence of two solutions in a room with mixed convection. Thesame problem was analyzed using CFD by Nielsenet al. (1979), which is shown in Figure 5. However,solution multiplicity problems seemed to be forgottenin the ventilation community until the phenomenon of multiple solutions was found again in natural ventila-tion through analytical solutions by Nitta (1996), Liand Delsante (1998, 2001) and Hunt and Linden

(2005), and for mixing ventilation by Bjerg et al.(2002). Although the existence of multiple solutionswas not identified by CFD, CFD has provided apowerful tool to understand solution multiplicityphenomena in buildings, in addition to experimentalstudies (Heiselberg et al., 2004; Li et al., 2006; Yanget al., 2006).

Inverse CFD modeling

Identification of the airborne pollutant source loca-tion(s) and strength can be important in identification

of the index patient in an airborne disease outbreakand quick determination of the source origin(s) duringintentional release of chemical or biological pollutantsin a building. Examples include Zhang and Chen(2007) and Liu and Zhai (2008).

Near-body micro-environment

Both thermal comfort and exposure of respiratorydroplets require a full understanding of the near-bodymicro-environment. CFD has made it possible tocalculate the detailed airflow pattern around a humanbody (Gao and Niu, 2005, 2006; Russo and Khalifa,

Table 2 Variable scales of building ventilation problems

Problems Geometric Scale Study examples

Momentum

source scale

0.001–0.1 m

Supply diffusers

Nielsen (1992a,b),

Chen and Srebric (2001),

Nielsen et al. (2007),

Kondo and Nagasawa (2002)

Flow element

scale

0.1–2 m

Coughs and

exhalation puffsBody plumes

Murakami et al. (2000),

Bjørn and Nielsen (2002),

Murakami (2004),Zhu et al. (2006),

Russo et al. (2009)

Room scale 2–20 m

Personalized ventilation

Displacement ventilation,

Mixing ventilation,

Stratum ventilation

Gan (1995), Brohus et al. (2006),

Tian et al. (2008),

Russo et al. (2009)

Building

scale

20–200 m

Multiple rooms

Large enclosures

Lu et al. (1996), Kato et al.

(1995, 1997),

Ji and Cook (2007)

District

scale

200–2000 m

Multiple buildings

Kato and Huang (2009),

Mirzaei and Haghighat (2010)

City scale 2–20 km

City ventilation

Hang and Li (2010)


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2010), including near-body chemistry. The movementof people can also be studied (Choi and Edwards,2008).

Disease transmission

The importance of building ventilation and airflow indisease transmission and control has been known for along time, but it was during and after the 2003 SARSepidemics that CFD became a modeling tool for

disease transmission in buildings. CFD has to somedegree reproduced the possible transmission routes inthe Amoy Gardens outbreak (Yu et al., 2004), thePWH 8A Ward outbreak (Li et al., 2005) and in otheroutbreaks (e.g. Niu and Tung, 2008). Since 2003, therehave been a great number of studies using CFD toimprove hospital ward ventilation (Chao and Wan,2006; Noakes et al., 2006; Qian et al., 2006; Lai andCheng, 2007 and Bolashikov and Melikov, 2009). Anexample of predicted exhaled particle dispersion in amultibed ward is shown in Figure 6.

CFD verification and validation

The challenges on turbulence modeling and numericalerrors also require serious consideration in CFDaccuracy. The correct governing equations and bound-ary conditions, and appropriate numerical algorithmsmust be carefully selected. This can only be done with athorough understanding of the problem before thesimulation takes place. The CFD simulation of aproblem is, at best, as good as the selected governingequations for the problem. A detailed description of CFD quality control in indoor air is given by Sørensenand Nielsen (2003b).

CFD verification deals with the mathematics of themodel, while validation deals with its physics (Oberk-ampf and Trucano, 2002). In other words, verification

is about equations being solved correctly, while vali-dation is about the correct equations being solved. Thefive most common sources of errors in verification(Freitas, 2002) include insufficient spatial discretizationconvergence, insufficient temporal discretization con-vergence, insufficient convergence of an iterativeprocedure, computer round-off, and computer pro-gramming errors.

A number of journals, including the Journal of Fluids Engineering (Roache et al., 1986), the Journalof Heat Transfer (ASME, 1994) and the AIAA Journal(1994), have an editorial policy statement for thecontrol of numerical accuracy, which includes the need

(a)

(b)

Fig. 5 Existence of two solutions (a) and (b) in a room with a heated floor and full width slot, as shown by experimental studies (leftfrom Mu ¨ llejans, 1966) and computational fluid dynamics (CFD) (right from Nielsen et al., 1979), though the experiments and the CFD

predictions do not cover the same geometrical situation

Fig. 6 Dispersion of the suspended 20-lm particles releasedfrom a patient as predicted by computational fluid dynamics in asix-bed ward. The birth time(s) of the particles are shown incolors. Details of the simulations can be found in the study byQian and Li (2010)

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to describe the basic features of the method, that themethods must be at least second-order in space, thatthere is a need to assess artificial viscosity, and thephase error in transient calculations, to establish gridindependence, to address the iterative convergence,and to assess the accuracy of boundary and initialconditions. The Indoor Air journal produced a guest

editorial statement (Sørensen and Nielsen, 2003a) onventilation CFD studies with similar requirements fornumerical accuracy, but with additional requirementsfor validation. A procedure of calculation and report-ing of discretization error estimates in CFD simulationis available for situations when the experimental datamay or may not be available for comparison (Roache,1997).

CFD validation involves comparison of the compu-tational solution with experimental results. A numberof validation data sets are available in the literature(Nielsen, 2010). One needs to quantify or estimate the

error in both the experimental data and CFD resultsbefore comparing the two. One needs to consider thereliability of the experimental data (e.g. because of scaling effects), the modeling and matching of theboundary conditions in the experiment and modeling,as well as the other operating and physical conditions.

From our own experience, the 3D isothermal mixingventilation data set of Restivo (1979) and the 2Dnatural convection case of Cheesewright et al. (1986)are very suitable for new CFD users. The first data setis ideal for testing the use of the basic turbulencemodels without the buoyancy effect and the boundaryconditions for steady-state problems. The second data

set is ideal for testing the inclusion of the buoyancyforce using the Boussinesq approximation, as well asthe wall boundary conditions for predicting heat fluxat the walls. Readers are also recommended to considerthe benchmarking test cases available at http://www.cfd-benchmarks.com.

In practice, validation is a process of refinement of selecting the most suitable turbulence models ormaking the right decisions about the flow assumptions.A CFD user routinely makes some important decisionsabout the governing equations and boundary condi-tions before making a prediction. The fundamental

questions before initiating a prediction are the follow-ing: (i) Is the flow expected to be laminar or turbulent?(ii) Is the flow expected to be two dimensional or threedimensional? (iii) Is the flow expected to have asymmetry plane? (iv) Is the flow expected to haveone, two, or several solutions? (v) Is the flow expectedto be steady or unsteady? Such decisions will influencewhether the solutions obtained are the physical ones.The following example demonstrates the importance of having a high level of fluid mechanical knowledge.

Consider the simple IEA Annex 20 2D test case(Olmedo and Nielsen, 2010). The assumption of turbulent, steady, and two-dimensionality will lead

to the path line solution in Figure 7a with a k-model. It looks correct, but strictly speaking, we donot know whether the flow in reality has moresolutions, or whether it is three dimensional, orperhaps unsteady.

The assumption of turbulent, unsteady, and three-dimensionality will lead to the solution in Figure 7c.The path lines in the middle plane and the velocitydistribution in the lower part of the room show thesteady-state flow similar to the flow found by thesteady-state equations. It is now possible to conclude

that the flow can be predicted with steady-stateequations and even with a set of two-dimensional flowequations.

Beyond building ventilation

The concept of ventilation can be found in a wide rangeof other enclosed or semi-enclosed spaces. In general,ventilation is to provide exchange of fluid into a spaceto replace the existing gas/liquid and distribute the newfluid within the space.

Figure 8 shows an incomplete collection of differentventilation problems. The length and time scales are

(a)

(b)

(c)

Fig. 7 Predicted path lines using different assumptions for the

flow in the simple International Energy Agency (IEA) Annex 202D test case (Olmedo and Nielsen, 2010). (a) Using the k-model and assuming turbulent, steady, and 2D flows; (b) usingthe k- model and assuming turbulent, steady, and 3D flows;and (c) using the k- model and assuming turbulent, unsteady,and 3D flows. The assumption of turbulent, steady, and three-dimensionality will lead to the solution in (b) with a k- model.It is slightly different from (a) in areas below the diffuser. Weknow that the flow in reality could be three dimensional.Examining the solution in the third dimension can confirm thatthe flow is symmetrical. However, we still do not know whetherthe flow has more solutions, although it is unlikely because theflow is symmetrical around the middle plane. Only the trivialsolution with two unsymmetrical solutions to either side is alikely situation (see Scha ¨ lin and Nielsen, 2004)


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approximate. The time scale of ventilation varies froma few seconds (bird lung) to 1000 years (ocean), whilethe length scale ranges from 1 mm to 1000 km. Thefollowing three categories of problems may be found:

• Convection dominated problems – such as ventilationof buildings, caves (Christoforou et al., 1996),streets, swimming pools, greenhouses, fish tanks,human lungs, and even bird lungs.

• Turbulence dominated problems – ventilation of adistrict, valley (Sivertsen et al., 1983), forest canopy(Oliver, 1975; Miller et al., 2007), city, city cluster,atmospheric boundary layer (Rigby et al., 2006),lake, sea, and even ocean (England, 1995).

• Molecular diffusion dominated problems – these aremostly the homes of animals such as caves (Kow-alczk and Froelich, 2010), burrows (Vogel et al.,1973; Wilson and Kilgore, 1978; Maclean, 1981),nests (Ar and Piontkewitz, 1992), fox burrow, owlburrow, mice burrow (Shams et al., 2005), and ter-mite colony (Turner, 1994). The ventilation ability of animals for their homes is limited, and we are

changing their environment by making the groundwarmer and the ground level wind-less. Hence, itmay be fair to predict that we will have a new focusfor understanding ventilation conditions for ani-mals, such as in mice burrows (Hansell, 1993; Liowet al., 2009). Ventilation concerns the health of animals – mammals and non-mammals – plants, andindustry processes, those in the ground, in air, andeven in water.

CFD has already been applied to understand green-house ventilation design and city ventilation (Hanget al., 2009a). It may be anticipated that CFD will findmore applications to other ventilation problems shownin Figure 8 where it may be difficult to apply exper-imental methods directly. In the next 10 years, com-puter speeds are expected to double every 2 years(Orszag and Staroselsky, 2000; Fujii, 2005), and thereis a potential one to two order-of-magnitude increasein computer capability. This means that CFD will bereadily applicable to the larger length scale andmultilength-scale problems such as city ventilation.

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