journal of wind engineering and industrial aerodynamics volume 81 issue 1-3 1999 [doi...

*Corresponding author. Department of Mechanical Engineering, Imperial College of Science, Techno-logy and Medicine, London SW7 2BX, UK. Tel.: #44-171-594-7031; fax: #44-171-581-7921.

E-mail address: [email protected] (A.D. Gosman)

Journal of Wind Engineeringand Industrial Aerodynamics 81 (1999) 21}39

Developments in CFDfor industrial and environmental applications

in wind engineering

A.D. Gosman!,",*!Computational Dynamics Limited, Olympic House, 317 Latimer Road, London W10 6RA, UK

"Department of Mechanical Engineering, Imperial College of Science, Technology and Medicine,London SW7 2BX, UK

Abstract

An overview is provided of the capabilities and limitations of CFD as a tool for windengineering, with particular reference to commercial CFD codes. The status of the modelling ofturbulence, heat and mass transfer is brie#y reviewed and developments in computer solutionmethodology are outlined, with emphasis on geometry-handling and mesh-generation capabili-ties and parallel computing. Examples are shown of recent applications in the built environ-ment and other industries which illustrate the current state of art. The overall conclusion is that,although there are well-known weaknesses in the physics modelling, the level of predictionaccuracy is already su$cient for some purposes. ( 1999 Elsevier Science Ltd. All rightsreserved.

Keywords: Wind engineering; CFD; Environment

1. Introduction

1.1. Nature of yuids-related problems in the built environment

Flows within and around buildings and other structures and related thermal anddispersion phenomena play an essential role in determining the environment of the

0167-6105/99/$ - see front matter ( 1999 Elsevier Science Ltd. All rights reserved.PII: S 0 1 6 7 - 6 1 0 5 ( 9 9 ) 0 0 0 0 7 - 0

populace which use them and sometimes the integrity of the structures themselves.For these reasons, there is strong incentive to "nd ways of modelling these phe-nomena. The most recent of these is computational #uid dynamics (CFD).

Steady-state and transient wind loadings are often necessary inputs to structuresdesign, especially those which are large and/or may be exposed to severe weatherconditions. The latter possibility particularly applies to bridges and o!shore structureslike oil platforms. For bridges it is often also necessary to consider the #uid/ structuresinteraction aspect, in which the wind-induced deformations may feedback into the #owitself, causing at worst ampli"cation of the forces, leading to structural failure.

E!ects on populace can consist, at the relatively benign level, of comfort andlong-term health; and in severe cases, immediate risk of injury. Comfort is in#uencedby a combination of local air velocity, temperature and humidity (along with radiativeheat transfer) in a complex way [1,2]. The comfort measures are somewhat subjectiveand therefore, the accuracy of the thermo#uids information required for their evalu-ation is arguably not high. At the other extreme, environmental safety considerationsoften require the possibility of "re or other types of release of harmful gases to beconsidered, where the best possible prediction of rates of spread and dispersion andassociated local instantaneous temperature and concentration levels is needed [3,4].Long-term exposure to low-level pollutants generated by automobile and electricitygenerating plant emissions provide yet another area where wind-induced dispersionanalysis is required [5,6].

At the fundamental level, all of these #ow-related phenomena obey the same basiclaws of physics as in any other circumstances, be it around and within aircraft, ships orautomobiles, in the ducts and chambers of gas turbines or power station furnaces, etc.However, each speci"c area has its own particular characteristics and priorities. Thus,large-scale wind engineering, involving consideration of the atmospheric boundarylayer, as in#uenced by thermal strati"cation, surface roughness, Coriolis and othere!ects, is more statistical than deterministic in character, particularly because it isseldom if ever possible to "nd the necessary meteorological data to assemble well-de"ned boundary conditions [6]. For this reason, such studies are often performed onwind-tunnel models, which are used for CFD &calibration' and/or for obtaining datafor design purposes [7]. However, although wind tunnels can provide well-de"nedand controllable conditions, they cannot span the range of conditions in the atmo-spheric boundary layer: so there is an inevitable uncertainty in the use of these studiesfor the real application.

There is also a strong interest in large-scale unsteady e!ects for reasons explainedabove. These present additional challenges to both CFD and wind-tunnel studies.Unsteady e!ects are also present in dispersion processes and can be important; forexample, in the case of particularly dangerous gaseous substances the peak levels ofexposure are often as important, if not more so, than the time-mean values.

Internal #ows in rooms and larger spaces are low-speed and often strongly buoy-ancy-in#uenced [8] features which, for reasons explained later, o!er particular chal-lenges for CFD simulation (and, for that matter, for experimental model studies).

A further characteristic of problems of the wind engineering and the built environ-ment is that they typically involve a wide range of scales. Thus, for example, external

22 A.D. Gosman / J. Wind Eng. Ind. Aerodyn. 81 (1999) 21}39

wind-loading and dispersion studies may require simultaneous analysis of a regionof the atmospheric boundary layer, the general character of the #ow around a groupof buildings embedded therein and the localised details around a particular buildingof interest in the group. Internal #ow studies can involve scales from room size downto the details and dimensions of ventilation supply grills and the natural convectionboundary layers on the surfaces of radiators, windows and walls. Attempts to limitattention to particular regions in such applications inevitably requires estimation ofboundary conditions, with attendant uncertainties.

Finally, it should be noted that built environment analysis sometimes requiresa total system approach, involving more than one type of tool [8]. This will parti-cularly apply if it is to be used in the future as part of a control strategy. Buildingheating, ventilation and air conditioning (HVAC) design is an obvious example ofsuch a need, as yet un"lled, because there are complex #ow and thermal interactionsbetween the di!erent rooms and interconnecting hallways and ducts, the HVAC plantand the external environment, which cannot be practically addressed by one all-embracing CFD simulation. Nevertheless, as described below, there are now manyuseful ways in which CFD can be applied in the built environment.

1.2. Scope and contents of paper

This paper provides an overview of the current capabilities of CFD for applicationsin the built environment and illustrates some of them by way of examples. Theemphasis is on general-purpose industrial CFD code methodology rather thanspecialised techniques and software developed within research institutes. This is not todecry the research codes } they are often pointers to future directions for industrialcode development. In what follows, Section 2 outlines the mathematical modellingapproaches employed to describe the physics of the #ows and Section 3 outlines thenumerical solution techniques. For the sake of brevity, the presentation is devoid ofequations but references are provided to the relevant literature. The applicationexamples are presented in Section 4 along with an indication of future developments,and an overall summary is given in Section 5.

2. Physics modelling

2.1. Turbulence

As is well}known, the cornerstone of industrial CFD is the Navier}Stokes equationset, expressed for turbulent #ows in terms of suitably-averaged velocities and pres-sures to make them amenable to numerical solution without excessive computingoverheads. The conventional and still most widely}employed approach is time-averaging, also described as Reynolds averaging, in which the dependent variablestake their time-averaged values and the equations are then referred to as theReynolds-Averaged Navier}Stokes (RANS) set. (This approach is nowadays oftengeneralised to ensemble averaging, in which case it is also applicable to non-stationary

A.D. Gosman / J. Wind Eng. Ind. Aerodyn. 81 (1999) 21}39 23

#ows, albeit with some ambiguity about how to distinguish between turbulent andnon-turbulent, non-stationary components.)

Averaging results in an additional set of six unknown terms appearing in mo-mentum conservation equations, loosely interpreted as additional pseudo-stressesarising from the turbulent motions of all scales. In the case of the RANS equations,these are termed the &Reynolds stresses'; and the additional equations that are derivedto determine them are known as turbulence models. The derivation necessarilyrequires a (substantial) degree of approximation due to the complexities of theturbulence. This situation has resulted in a plethora of models, each of which generallyworks well for the particular types of #ow for which it was developed, but notnecessarily for others. There are numerous detailed reviews by specialists of theavailable models (e.g. [9,10]), some of which focus speci"cally on their performance onapplications in wind engineering and the built environment. Thus, only a few salientpoints will be made here.f The RANS equations can only provide limited information about the unsteady

aspects of turbulent #ows: so they are not best suited for this purpose, apart fromsome speci"c instances.

f The available RANS models range from a large number of variants of the well-known k}e approach, which is probably the simplest type that is practically useful,to full di!erential Reynolds stress transport models. In general terms, the morecomplex models tend to better represent the e!ects of turbulence anisotropy, whichcan be important in some applications, including those involving turbulent disper-sion and buoyancy e!ects [10]. However, they usually o!er insu$cient bene"ts inreturn for the substantial extra e!ort required to solve them.

f Buoyancy and low Reynolds number e!ects in both the near-wall and bulk #owregions have proved to be particularly di$cult to capture correctly in turbulencemodels, although there have been some signi"cant advances recently [11]. How-ever, in the case of near-wall #ows these often require direct calculation of boundarylayers rather than use of wall functions, which can be excessively expensive.

f Because there is no clearly superior model which works well over a wide range ofapplications, commercial CFD codes tend to o!er a number of options from theclasses mentioned above. Unfortunately, and as a re#ection of the state of the art,the only reliable guide to selection from these for a particular application isprevious experience on a similar problem.

f Certain types of application appear to defy accurate solution by the availableRANS models. A notable example is the #ow around buildings, where the pressuredistributions, wake structures and turbulence characteristics on or near somesurfaces are not well captured by any RANS model [12,13].Over the past decade large eddy simulation (LES), an alternative turbulence

modelling approach, has shown increasing promise to overcome the limitations andde"ciencies of Reynolds averaging. In LES, spatial averaging is performed on the scaleof the computational grid spacing, with the result that modelling is only required ofthe pseudo-stress terms that represent turbulent motions below this scale. Thesmall-scale motions are believed to be more regular in behaviour. Representations ofthem have come to be known as subgrid scale (SGS) models. Again there are recent


reviews [14,15] which summarise the state of the art in this "eld, from which thefollowing observations are drawn.f LES is, for obvious reasons, much better suited to prediction of unsteady e!ects

than the RANS, since it computes directly all but the small-scale, high-frequencycomponents.

f Subgrid turbulence modelling is not an exact science and already a signi"cantnumber of alternative SGS models have appeared in the literature [14,16]. Fortu-nately, however, the variability in performance between &reasonable' SGS modelstends to be much less than between RANS alternatives (albeit sometimes stillappreciable), presumably because the former need only represent a small part of theturbulence spectrum.

f Although LES is at a much earlier stage of development than RANS modelling,there are already applications where it is proving to be superior. One such exampleis the #ow around a building [12,17,18].

f LES is inherently more expensive than RANS, because it always calculates theunsteady motions and requires adequate temporal resolution and su$ciently longintegration times to reach a statistically representative #ow state. By contrast,RANS methodology allows a direct, economical iterative approach to the steadystate (if one exists).

f Advances in CFD methodology and computer hardware have, nevertheless, made itfeasible to apply LES to industrial problems and it is now emerging as an option incommercial CFD codes.

f There are still substantial unresolved problems in LES, including the challenge toeconomically model the near-wall region and di$culties in specifying #ow details atother boundaries, notably inlets [14].

2.2. Heat/mass transfer

As already noted, many #ow-related problems in the built environment also involvesimultaneous heat and/ or mass transfer: examples are heating and air conditioning;"re and smoke spread; and gaseous pollution. In the case of heat transfer, all threemodes } convection, conduction and radiation } often need to be taken into account.In addition, consideration of solar radiation [8] is often required.

The basic equations governing these processes are the di!erential set for conserva-tion of energy and mass of individual chemical components. In the case of heatconduction and radiation, these can be formulated without approximation (al-though for radiation simpli"ed forms are sometimes used for economy) and areincorporated in most general-purpose CFD codes. Some also cater for solar radiationtransport.

Convective heat and mass transfer requires consideration and modelling of turbu-lence e!ects, which have been addressed mainly in the RANS context, although thereis an increasing e!ort in LES. The issues and approaches are similar to those for the#ow "eld modelling with some additions:f Accurate prediction of the relevant #ow "eld features is a prerequisite to good

heat/mass transfer modelling.


f Surface heat transfer is particularly sensitive to details of the wall boundary layers,including their turbulence structure } ironically often more than the #ow itself.Thus, predictions of heat transfer coe$cients may be less accurate than frictionfactors. This is sometimes compensated for pragmatically, by employing empiricalheat transfer coe$cient correlations in the CFD model [26].

f Buoyancy-in#uenced #ows are particularly challenging to model due to the stronginteractions between the #ow and density "elds, which can either augment ordiminish the turbulence, according to whether the #ow is unstably or stablystrati"ed, respectively. Stable strati"cation can lead to locally low Reynolds num-bers and additional associated modelling di$culties [8,11].

f Pollutant dispersion modelling from localised sources involves similar issues as heattransfer, including sensitivity to turbulence anisotropy, even in simple boundarylayer #ows [7].

f Many of these issues and developments are at the leading edge of turbulenceresearch and therefore may not all be re#ected in current commercial CFD codes.On the other hand, it should also be recognised that even the existing models inthese codes often give useful results and acceptable accuracy, particularly in relationto comfort requirements.

2.3. Combined CFD and systems modelling

As noted earlier, it is impractical and unnecessary to attempt to model the#uid/thermal behaviour of an entire building and its HVAC system using CFD. Thepractice to date has been to focus on particular parts (e.g. individual rooms) andmodel them in isolation, using estimated or measured boundary conditions. On theother hand, it is not uncommon, at least for large buildings, to use so-called &lumpedparameter' or &systems'models which consider the whole building and HVAC compo-nents but use simpli"ed and approximate representations of each part. There is nowa move towards integrating systems and CFD approaches, as has already happened inother industries, so that the latter can be employed to provide local, detailed analysiswhile the former provides the boundary conditions and interactions with other parts.

3. CFD methodology and codes

3.1. Overview

In this section, the main elements of the methodology used in current commercialCFD codes to solve the governing equations will be brie#y outlined: they comprise thespace discretisation (i.e. computational mesh), equation discretisation and solutionalgorithm. Commercial CFD is, however, more than just numerics; codes must caterfor mesh generation and other problem setup operations, along with solution controland results display. It must also be geared to producing results rapidly and placingminimum demands on the user. The last-named requirements encourage exploitationof new computer hardware and software developments.


3.2. Geometry-handling via unstructured meshes

One of the most dramatic and signi"cant areas of advance in CFD over the pastdecade has been in mesh #exibility, allowing virtually unlimited geometry-handlingcapability and local resolution control [19]. Thus, from a starting point of purelyCartesian meshes, with associated shape and re"nement restrictions, most codes nowo!er some or all of the following:f Unstructured body-"tting meshes, i.e. those whose elements/cells can be put to-

gether in a Lego-like fashion rather than be constrained to a regular block structure.f Option of working with hexahedra or tetrahedra or other polyhedral cell shapes;

and, in some instances, with mixtures thereof (termed hybrid meshes), whichfacilitates mesh generation and resolution control.

f Ability to locally subdivide existing cells to produce &embedded re"nement' asa means of better capturing geometrical or #ow details.

f Freedom to join together separate mesh blocks without requiring continuity attheir interface (&arbitrary or unconstrained interfacing') } another measure whichmakes mesh generation easier and also facilitates design changes.

f Dynamic features, including distortion, sliding and addition or removal of cellsduring a calculation, to cater for moving surfaces. These capabilities are useful inspeci"c applications areas, like #uid}structure interactions, where the structuraldeformations are signi"cant and have to be accommodated by the #uids mesh; andwind inducement by moving vehicles, where relative motion between di!erent partsof the mesh must be allowed.Many of these features will be further described and illustrated later, in Section 4.

3.3. Discretisation and solvers

The major commercial CFD codes all use "nite-volume methodology, in which thegoverning equations are discretised in their volume-integral form, enabling the under-lying conservation laws to be enforced on each individual cell as well as overall. Ingeneral, a selection of spatial and temporal discretisation schemes is o!ered, rangingin the former case from "rst to third order and in the latter up to second order. Thechoice is a compromise between accuracy and numerical stability, although progressis being made in providing both at the second-order level, which is generally regardedas the minimum requirement for LES.

Solution algorithms are mainly implicit, both to allow rapid iterative solution forsteady-state calculations and also freedom from the local time-step restrictions ofexplicit methods in unsteady calculations, a bene"t even in LES. Many codes useSIMPLE-like segregated solution strategies, although there some also o!er coupledmethods for steady-state applications. Simultaneous equation solution is usually byconjugate gradient or multigrid procedures.

3.4. Automatic and solution-adaptive meshing

The advances in mesh #exibility have proceeded hand in hand with the develop-ment of techniques for rapid automated mesh generation, with one tending to drive


Fig. 1. Speedup performance data for several industrial cases calculated with the STAR-HPC code ona distributed-memory parallel computer. Speedup is de"ned as ratio of computing times on one andN processors.

the other. This capability was "rst o!ered for all-tetrahedral meshes, but is nowavailable for other types, including &trimmed hexahedral'meshes, comprised predomi-nantly of hexahedra, with &polyhedral' boundary cells formed when their corners oredges are allowed to be cut o! by the boundaries of the model to ensure body-"tting.It is also possible, for both types, to introduce layers of prisms adjacent to walls,allowing better resolution of boundary layers, provided of course that the CFD codeis able to work with these types of hybrid grid. Examples of meshes generated in theseways will be shown in Section 4.

A recent development for industrial CFD has been the possibility to adaptivelyre"ne the mesh during the course of a #uids calculation to produce better resolution,using the one or more of the #exible mesh features described earlier. Currently,the re"nement in commercial codes is usually based on some user-selected feature(s)like local velocity or temperature gradient, which is somewhat subjective. However,more rigorous error measuring techniques are beginning to emerge.

3.5. Parallel computing

Computer hardware developments have been another important contributor to theadvance of industrial CFD. At one end of the scale, the speed and memory capacity ofPCs are now such that they can be used for the smaller commercial applications,especially if rapid turnaround and high accuracy are not essential.

However, in many of those industries in which CFD has already proven its worth,there are pressures for faster turnaround, greater accuracy and ever-larger solution


domains, the last-named because by moving the boundaries to regions where condi-tions are reasonably well known, the need for guesswork or measurements is reducedor even eliminated. An illustrative example would be study of the #ow arounda building in the proximity of others, whose in#uence should ideally be represented byincluding them in the calculation. Realistic mesh sizes for such applications aremoving from the many hundreds of thousands into the millions; but despite this theyare feasible thanks to the adaptation of commercial CFD codes to the new breed ofparallel computer, based on relatively low-cost, high- performance &commodity'RISCprocessors (as found in high-end workstations) and standard programming languagesand operating systems. Fig. 1 shows that it is possible for a range of real industrialapplications to obtain speedups on these machines which are closely proportional tothe number of processors. This means, for example, that on a 10-processor machinea calculation requiring one day on a workstation could be done in around 2.5 h.

4. Example applications

4.1. Wind ewects on, near and within structures

Fundamental studies like those reported in Ref. [13] show quite clearly thelimitations of RANS models in predicting the wind-generated #ows around building-like structures. Nevertheless, the achievable level of accuracy is viewed as acceptablefor some purposes and has led to CFD being used in a variety of applications, asillustrated by the examples presented in this section.

Fig. 2 shows a proposed shopping complex, for which CFD simulations have beenperformed to investigate the wind e!ects of various di!erent building designs andarrangements [20]. The type of computational mesh used is also displayed inFig. 2 } it is body-"tted and unstructured and in this instance, was produced in a semi-automatic way using a range of meshing &tools'. Also shown in this plot is thenear-surface wind strength distribution for this particular design, generated by a windentering from the upper left quadrant. Fig. 3 shows aspects of the overall andnear-surface wind "elds produced by one of the other designs, for the same incomingdirection. The presence of strong vortical structures generated by the upwind buildingis clearly evident.

Public transport is another area of application of CFD, for wind engineering andenvironmental comfort and safety reasons. Fig. 4 shows a computer model of an entiremetro station [21], including the ticket hall, which has two substantial coveredground-level entrances, the tunnels and escalators leading to the train platforms anda train. A part of the ticket hall and associated computational mesh are shown inFig. 5. It can be seen that, here again, the #exibility of unstructured meshing isrequired by the irregular topology. In addition, use is also made of arbitrary interfac-ing to allow di!erent styles of entrance structure to be tried. One of the aspects ofinterest is the e!ect of wind strength and direction on the air motions generated withinthe station, features of which are displayed in Fig. 5. The simulation also includednearby buildings which are not shown.


Fig. 2. Proposed shopping complex, showing computational mesh and predicted near-surface windstrength. Courtesy of BSRIA.

CFD studies have also been performed of the induced #ows within metro traintunnels and platforms caused by the train motion [21]. The latter can be fullysimulated by codes which have dynamic mesh change capabilities, including slidinginterfaces. Such studies have also extended to "re simulations, allowing the rate ofspread of combustion gases from various hypothetical locations and strengths of "reto be calculated.

Fig. 6 shows a simulation of the spread of an e%uent plume from a low-levelvent located within a building complex. Calculations of this kind are used to helpto position the vent so as to minimise the exposure of nearby populace to theplume.

Another area of CFD application is to o!shore structures, like the semi-submersibleaccommodation platform depicted in Fig. 7 and the subject of a combined windtunnel and CFD study [22]. Features of interest here include wind loads, plotted inthe same "gure for a particular angle of heel, and #ow patterns (Fig. 8). The latter haveimplications for safety (including takeo! and landing of helicopters) and comfort, andalso explosion and "re consequences. This study concluded that for CFD to becompetitive with the wind tunnel for the wind loading aspects of this particularapplication, more work was needed in the areas of rapid mesh generation, accuracyimprovement and computing speed. Fortunately, as outlined earlier, these have beenthe focus of development in CFD and substantial improvements have been made inthe last few years.


Fig. 3. Alternative shopping complex arrangement without tower building, showing predicted features ofoverall #ow and near-surface wind strength. Courtesy of BSRIA.

Fig. 4. Design of metro station for which full CFD simulations were performed, including e!ects ofsurrounding buildings (not shown). Courtesy of London Underground.


Fig. 5. Part of metro station of Fig. 4 showing unstructured mesh (including arbitrary interfacing) andaspects of #ow "eld. Courtesy of London Underground.

Fig. 6. Simulation of plume dispersal from low-level vent located in building complex.

4.2. Interior HVAC applications

The possibility of using CFD for internal #ow simulations was demonstrated bymodel studies some years ago (see, for example Ref. [23]), although these did not byand large provide a good testing ground for buoyancy e!ects which introduce theadditional modelling di$culties mentioned earlier. Nevertheless, quite extensive usehas been made of commercial CFD codes in this area, for all manner of applicationsranging from rooms [24,26] to large atriums [8], conference halls, auditoriums andstadiums and airport buildings.

By way of an example, extracts from simulations of the ventilation of an auditoriumare shown in Fig. 9, showing the general layout, and Fig. 10, depicting air motion and


Fig. 7. Calculated wind-generated pressure loads on semi-submersible accommodation platform at 153inclination. Courtesy of Danish Maritime Institute.

Fig. 8. Flow "eld around accommodation platform of Fig. 7. Courtesy of Danish Maritime Institute.

&age' (i.e. residence time) in a vertical bisecting plane. This study enabled the air supplyand extraction arrangements to be designed to provide an acceptable environment forall occupants.


Fig. 9. CFD model of auditorium, including ventilation apertures. Courtesy of Sulzer Innotec.

Fig. 10. Predicted velocity and air age distributions in section through auditorium of Fig. 9. Courtesy ofSulzer Innotec.

4.3. Pollutant dispersion

Wind-induced pollutant dispersion is another area of application of CFD. Earlystudies, like those described in Ref. [7], focused on relatively &simple' situations, likethe spread of a chimney plume over #at terrain or escaped gas dispersal behinda single building, using special research codes.

Commercial codes are now being used to study somewhat more complex situations,like the pollutant spread in &street canyons', i.e. city streets bounded by relatively tallbuildings. Fig. 11 shows preliminary results for one such study currently in progress


Fig. 11. Example comparisons of predicted and measured concentration pro"les in model street canyoncase.

[25] where, in this case, both CFD calculations and wind tunnel experiments arebeing performed on a model arrangement of four buildings, allowing the accuracy ofprediction to be assessed. In these cases, it was moderate } as shown in Fig. 11,concentration levels were replicated to within the repeatability of the measurements($10%), but the e!ects of small changes in building alignment were not completelycaptured. Of course, the calculations also provide a substantial amount of additionalinformation about #ow features and dispersion mechanisms which would be time-consuming to obtain by wind-tunnel measurement and not feasible in the full-scalesituation.

4.4. Future prospects

Although the foregoing examples indicate that commercial CFD software is alreadybeing used for diverse applications in the built environment, the full capabilities andpotential are still far from being exploited. This may be due in part to economics } thecost bene"ts of performing CFD simulations may not be su$ciently apparent to theindustrial concerns and their clients. Here, it is up to the CFD companies anddesigners and architects to provide clear evidence of the economic bene"ts.

Another factor may be ease of use } CFD has traditionally been viewed as&high-tech' and requiring highly specialised personnel. This, however, is no longertrue because CFD methodology and codes have been developed to the stage wherethey are much easier to use (but still require a reasonable level of knowledge of #owphysics to properly interpret and exploit the results). Again, it is necessary forprotagonists of the technology to demonstrate that it is now much more &friendly' thanhitherto.


Fig. 12. Automobile passenger compartment with automatically-generated trimmed hexahedral mesh.

An indication of the full potential can be gained from the automotive industry,which has its own wind engineering and built environment problems. Those which arecurrently being addressed using CFD include:f Flow over a vehicle including the underside and wheel wells, allowing aerodynamic

forces to be calculated, ventilation apertures to be sited, brake cooling systems to bedesigned, etc. [27,28]. Also, when combined with calculation of

f Flow and heat transfer in the engine compartment enables predictions of thermalloads on underhood components [28,29]. This has also been extended to

f Coupled CFD and systems code analysis of the entire cooling system comprisingradiator, condenser, heater, etc.

f Passenger compartment HVAC analysis [2], including HVAC unit and ducting tooutlets.

f Radiator, condenser and HVAC component (e.g. fans, heat exchangers) design.Some representative examples of these analyses, which also illustrate the use of the

advanced automeshing tools which are now available, will now be presented. Fig. 12shows the passenger compartment of a car, with driver, which has been rapidlymeshed using a method which works by &trimming' an initially all-hexahedral mesh so


Fig. 13. Predicted velocity magnitude distribution in section through passenger compartment of Fig. 12.

that it body-"ts the geometry, producing some multi-faceted polyhedral cells at theoutermost surface of the mesh in the process. Aspects of the predicted #ow "eld forthis case are plotted in Fig. 13.

Finally, Fig. 14 displays the predicted pressure distribution on a large freight truckcalculated using an automatically generated tetrahedral mesh, portions of which arealso shown. This further illustrates that geometrical complexity is no longer anobstacle to rapid analysis, thanks to mesh generation and CFD solver developments.

5. Conclusions

CFD has already made inroads as a new tool for use in wind engineering. Manyapplications have already been made to thermo#uids problems in the built environ-ment but there is potential for much more, as has already been demonstrated in otherindustrial sectors.

The fact that commercial CFD code developers have made dramatic improvementsto versatility, ease of use and speed (thanks also to hardware developments) shouldprovide help to expand the takeup of the technology by industries concerned withwind engineering. Work still needs to be done to improve the accuracy of the physicsmodelling in speci"c areas, but existing modelling can produce useful results in manyapplications.


Fig. 14. Calculated pressure distribution on large truck, performed using automatically-generated tet-rahedral mesh.

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journal of wind engineering and industrial aerodynamics volume 81 issue 1-3 1999 [doi...

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