CFD Application in Automotive Industry

T. Kobayashi1, M. Tsubokura2

1 Japan Automobile Research Institute1-1-30, Shibadaimon, Minato-KuTokyo, 105-0012, Japankobaya@jari.or.jp

2 Hokkaido UniversityGraduate School of EngineeringDivision of Mechanical and Space EngineeringN13, W8, Kita-kuSapporo, 060-8628, Japanmtsubo@eng.hokudai.ac.jp

Summary With the growing interest in environmental issues, the automotiveindustry is required to implement a range of measures such as increasing fuelefficiency or decreasing pollutants from exhaust gases. It is no doubt thatCFD is an encouraging technology to develop an innovative idea by providingvaluable data which conventional experimental methods can not measure.Compared with other industries, the automotive industry has been taking theinitiative in introducing Computer Aided Engineering (CAE) at various stagesof manufacturing. Thus, we can look into the state of the art of engineeringCFD by reviewing its applications in automotive engineering.

1 Introduction

The main concern of automotive CFD is the treatment of turbulence. Infact, we have to treat generally higher Reynolds-number turbulence up toRe ∼ O(106), but it will be impossible to apply Direct Numerical Simula-tion (DNS) in the foreseeable future. Especially in the development stage ofa new model, we have to obtain a reasonable numerical prediction withina couple of days at reasonable costs. Thus, conventional Reynolds-AveragedNavier-Stokes (RANS) simulation has been the most popular method, andcommercial softwares are generally utilized to avoid excessive developmentcost for the CFD code. In addition to the accuracy of the solver, easiness ofmesh generation, robustness and parallel efficiency of the solver are consid-ered when we select commercial software among various candidates. Analysisscales are up to some million numerical meshes using computer cluster sys-

E.H. Hirschel et al. (Eds.): 100 Vol. of ‘Notes on Num. Fluid Mech.’, NNFM 100, pp. 285–295.springerlink.com c© Springer-Verlag Berlin Heidelberg 2009

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tems, but occasionally, some ten million scale simulations are conducted on asupercomputer at the research stage.

With rapid development of hardware systems, unsteady or transient turbu-lence simulation now comes within range. The validity of the unsteady simula-tion is, in addition to its high accuracy compared with RANS, its applicabilityof predicting unsteady aerodynamic phenomena including aeroacoustics. Twopromising methods for the time-dependent simulation are the Large EddySimulation (LES) and the Lattice Boltzmann Method (LBM). The LES isbased on the spatially filtered Navier-Stokes equations, while the LBM isbased on the mesoscopic Boltzmann equation. The validity of the LBM is itshandiness of mesh generation and robustness of the numerical scheme, whileits problem is that the effect of the numerical model on the macroscopic flowprocess is unclear. In both cases, the main issue to be solved is the treatmentof the solid surface where the very thin turbulent boundary layer appears.

2 Vehicle Aerodynamics

For years, wind tunnels have been the main tool to assess vehicle aerodynamicperformance. However, owing to the high cost for measurements, as well asthe shorter time period of the vehicle design process, CFD is expected to bean alternative to the experimental measurements. In fact, CFD has been mostextensively used in this category in the automotive industry.

The panel method was the only CFD available around thirty years ago,when a powerful computer was not available. In that method, fluid is sup-posed to be irrotational and velocity-potential is solved via a boundary in-tegral equation transformed from the Laplace equation. The method is usedtogether with the boundary layer method for the viscous treatment. The typ-ical example [1] applied to a vehicle body is shown on the left of Fig. 1. It isimpressive that even such an ad hoc method somehow properly capture thepressure distribution on the upper surface, while serious discrepancy is foundon the vehicle floor and the rear end region where the flow separates from thebody.

In the 80’s, remarkable progress of supercomputers and engineering work-stations made it possible to conduct 3-D Navier-Stokes simulation. Two majormethods for the turbulence treatment at the time were: RANS in which turbu-lence is expressed as the Reynolds stress in the momentum equations and onlythe mean quantities are solved, and quasi DNS in which only larger eddies aresolved in a time dependent simulation and smaller turbulent eddies are dissi-pated numerically by the upwind method. Generally the unstructured finitevolume (FV) method is adopted for the former method, while the boundary-fitted coordinate (BFC) is employed for the latter case. The total numericalmesh number available was less than one hundred thousands at the time anda simplified car shape without an engine room was treated.

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Fig. 1. Surface pressure distributions on the vehicle center line. Left: Panel method(reprinted with permission from SAE Paper 920338 c©1992 SAE International).Right: LES and RANS.

GFlops-class supercomputers were available in the 90’s, and full-scale ve-hicle aerodynamic simulation with some million numerical meshes was goingto be possible at the time. A landmark of a time-depending 3-D CFD for afull-scale vehicle was conducted in 1990 [2] using a quasi-DNS method withthe upwind K-K scheme. CFD was realized to be a powerful tool for the vehi-cle aerodynamic assessment. However at the same time, some problems wererecognized, such as the strong dependency on the adopted turbulence models,numerical treatments including the boundary conditions, numerical meshesand schemes [3].

In the 2000’s, RANS methods were going to be commonly utilized in theautomotive industry as a supplementary tools of wind tunnel measurements.The problems of RANS are: one is its strong dependence on turbulence modelas mentioned above, and the other is its difficulty of capturing the unsteadyflow characteristics. In addition, recently greater attention is paid to unsteadyaerodynamic forces generated from sudden steering action, overtaking, or crosswind; all of which are difficult to estimate not only by a RANS method butalso by a wind tunnel test, and an alternative method to the conventionalmanners is strongly desired. LES will be an encouraging solution to the prob-lem, because it can reproduce unsteady turbulence characteristics with highaccuracy, but in turn it requires excessively large computational resources.Consequently only few attempts have been made so far to apply LES to theassessment of vehicle aerodynamics. In 2002, the world-fastest massively par-allel supercomputer Earth Simulator (ES) was developed in Japan, whichconsists of 5120 vector processors with the peak performance of 40TFlops.In 2006, high-performance computing LES of the flow around vehicles wasconducted on ES to investigate the validity of the large-scale LES on vehicleaerodynamics [4]. They used some ten million numerical meshes and com-pared the results with the conventional RANS (standard k- model) method.

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As shown on the right of Fig. 1, surface pressure distributions of LES andexperimental data show excellent correlation on both top and bottom surfaceof the vehicle, while relatively large discrepancy appears between RANS andexperiments on the bottom surface.

In 2007, high-performance computing (HPC)-LES of flows around a full-scale formula car and a motorcycle were conducted on ES using some ten toone hundred million meshes [5] (see Fig. 2).

Fig. 2. HPC-LES of unsteady flow around a full-scale formula car and a motorcycle:Snap-shots of the surface pressure.

LES successfully predicted the lift coefficient, which is only about 1% largerthan the wind tunnel data. Compared with the results of the panel methodand LES in Fig. 1, which describes the progress of CFD for the thirty years,improvement of the underbody profile should be noted. Our next target con-cerning CFD and vehicle aerodynamics will be the estimation of unsteadyaerodynamic forces acting on vehicles in the conditions of such as a suddencross-wind and a steering action, or overtaking.

3 Thermal Management and Cabin Environment

Coupling of flow and heat transfer and predicting such multiphysics phe-nomena is an important issue in CFD. In the automotive applications, suchproblems appear in vehicle thermal management in an engine room, or in theindoor thermal environment. In the case of natural convection in which buoy-ancy dominates the flow, some sophisticated turbulence models are requiredfor better prediction. In addition to heat convection, radiation and even con-duction must be taken into consideration for the total thermal management.Reproduction of a solid body surface in detail is indispensable for the betterprediction of heat transfer, and surface geometry of targets is usually com-plicated both in an engine room or a vehicle cabin. Accordingly, applications

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of CFD to this category go back not so long a way compared with the vehi-cle aerodynamics. The history began in the late 1980’s when a supercomputerwas available. Especially recently, with the advent of new energy systems suchas the hybrid electric system or the fuel cell, importance of CFD in the fieldis going to be more important than ever.

In addition to the thermal treatment, we need additional advanced numer-ical techniques for rotary machines such as a radiator fan or a blower fan forair conditioning. Physical modeling of a radiator as a porous media is also animportant issue to properly predict the flow rate coming into the engine roomthrough the front grille. The latest topic in the thermal management in anengine room is the consideration of the outer external flow around a vehiclefor the inner simulation. The state of the art of simulations made with LBMis shown in Fig. 3 [6].

Fig. 3. Thermal management of an engine room with LBM: Temperature distribu-tions with-out (left) and with (right) hot surface conditions (reprinted with permis-sion from SAE Paper 2007-01-0100 c©2007 SAE International).

To exert a greater effect of air conditioning performance from the limitedcapacity, the assessment of the cabin thermal environment is conducted by

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CFD. In such a simulation, radiation and solar insolation strongly affect thetotal environment. Fig. 4 shows the flow and heat transfer predicted by aRANS method, which indicates the effect of radiation on the total temperatureprofiles on the surface of the human model [7].

Fig. 4. Comfort assessment for the indoor environment considering heat radiation.

In addition to such difficulty in treating the physical condition, biologicalproblems arise in the cabin CFD. For thermal comfort evaluation, the goalis to predict accurately the surface temperature of the human body, whichis strongly affected by the biological heat balance inside the body. Thus asophisticated heat transfer model for humans must be constructed. We needfurther progress concerning this matter.

In addition to the integrative objects mentioned above, CFD for elementaltargets are also popular in this field. Fig. 5 shows a result for an air condition-ing duct with very complicated geometry [8], which contains six ventilationnozzles, four in the dashboard and two in the B-pillars, four nozzles to thefloor, one main defroster and two side defrosters. Total of about three millionmeshes are employed for RANS simulation using a commercial code. Fig. 5shows the fraction of total flow rate through each branch of the air distribu-tion system, which indicate that CFD shows relatively good agreement withexperimental measurements.

Prediction of frost or moisture condensation patterns in a cabin or relatedequipments is also of great concern. We have to treat phase change in somesituation. Fig. 6 shows an example of moisture and natural convection simu-lation inside a headlamp with complicated geometry [9]. The simulation con-

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Fig. 5. Duct simulation for the air ventilation and conditioning system (reprintedwith permission from SAE Paper 1999-01-1200 c©1999 SAE International).

siders radiation as well as moisture transfer resourcefully by applying a newlydeveloped technique for the treatment of solid fluid inter-face. As shown inFig. 6, CFD well predicts the moisture condensation pattern observed in theexperiment.

Fig. 6. Moisture and natural convection simulation inside an headlamp: Front viewof the numerical mesh and the moisture condensation after 20 min (reprinted withpermission from SAE Paper 2005-01-1449 c©2005 SAE International).

4 Internal Combustion Engine

Present IC (Internal Combustion) engines have been drastically changed morethan ever. Owing to a combination of a turbo-charger, EGR (Exhaust GasRecirculation), common-rail injection system, DPF (Diesel Particulate Filter)and electric engine control system, the performance of diesel engines has beenmuch improved. At the same time, the engine system has become very com-plicated and needs an optimized control. Now, a diesel has to reduce exhaustgas emissions as low as a gasoline engine and also the cost. On the other hand,

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a gasoline engine has to improve thermal efficiency as much as a diesel evenfollowed by some cost up. Application of CFD in this category is relativelynew, because we have to treat cavitation, two phase flow with phase changeand chemical reaction, as well as the complicated geometry. The targets ofCFD for IC engines are categorized to the following three topics: simple gasflow, fuel supply, and reacting flow.

In the gas flow simulation, air charging system, EGR system, VVA systemand in-cylinder flow control are targeted. Especially in the VVA (VariableValve Actuation) system, the optimization of open/close valve timings andvalve lift amount are carried out by CFD.

The fuel supply and mixture formation processes is relatively challenging,because we have to consider cavitation with erosion or the gas-liquid two-phase flow phenomenon. In most cases, the treatments of interfaces betweenliquid and gas phases, surface tension, turbulence at its interface and phasechange phenomenon must be well modeled numerically [10, 11]. Especially, ascavitation is a very rapid phenomenon, calculation stability is quite bad. Itis a big issue what kind of calculation scheme and technique should be used.The in-nozzle flow CFD is useful not only for the prediction of cavitation anderosion but also for boundary conditions at the nozzle exit for the calculationof spray inside the cylinder [12]. The mixture formation inside the cylinder isalso important. The Lagrangian method of DDM (Discrete Droplet Model)that treats droplets by grouping them into several thousands parcels is widelyused.

The evaporation process is modeled in a phenomenological way based onmany experimental results. The mixture formation inside a cylinder dependson both convective flow and turbulent diffusion. As the in-cylinder gas motionis very complex and strongly unsteady, a standard turbulence model such as ak-model is not necessarily appropriate. Moreover, cycle-to-cycle flow variationexists in an engine flow field. To resolve this, an application of LES is stronglyexpected [13, 14].

Reacting flows are the most advanced field in the last years in the modelingof flow and chemical reactions. The demand of predicting chemical reactionsbecame quite strong since a new combustion technology of HCCI (Homo-geneous Charge Compression Ignition) has been introduced. In HCCI, theignition timing must be controlled precisely, which is achieved by controllingmany items spatially and temporally, such as EGR, fuel concentration, gastemperature and gas pressure. This is actually very difficult using conven-tional map-control systems, instead, model-based control that needs CFD isused [15]. Predicting the ignition onset timing is also an important issue, andchemical elementary reactions must be solved. However, the reaction mech-anism is too complex to solve for an actual engine in 3D at the moment. Inthe near future, the prediction ability of ignition onset timing as well as otherphysical properties will be improved enough for practical use.

The flame propagation process of a spark ignition engine varies from cycleto cycle. In order to design a production engine, the cycle-to-cycle variation

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should be reduced. Figure 7 shows calculated results [14] of flame surfacedensity using LES for four consecutive engine cycles. This information will bequite useful for the engine design.

Fig. 7. LES of flow and combustion in an IC engine: the flame surface densityat three crank angles for the 4 cycles (reprinted with permission from SAE Paper2007-01-0151 c©2007 SAE International).

5 Aeroacoustics

Notorious aeroacoustics relating to vehicles is: wind noise generated by theA-pillar and door mirror, wind buffeting noise generated by a sunroof, or fannoise generated by a radiator fan or an air conditioning fan. If the target aero-dynamic sound is supposed to be generated at low Mach numbers conditionaround the source object, far-field sound can be estimated from the Lighthill-Curle’s equation, which can be solved separately from the flow simulation. Thetypical aeroacoustics such as the wind noise or the fan noise can be treatedin this manner. Under this assumption, the time history of the pressure dis-tribution on the solid surface is solved by CFD to predict the wind noise. Infact, if we can estimate the source of the noise through CFD, it is going tobe a valuable tool for aeroacoustic problems. However owing to the relativelyhigh computational cost for the unsteady 3-D simulation, its history has justbegun.

Even though the mathematical method for the noise prediction itself hasalready been matured, we have to treat the numerical method for flow sim-

294 T. Kobayashi, M. Tsubokura

ulation more carefully to properly capture the broad spectra of turbulencefluctuation. Unsteady simulation methods such as LES [16] or the LatticeBoltzmann Method (LBM) will be promising for this purpose. Fig. 8 showsa computational analysis of underbody wind noise sources using LBM [17].The predicted fluctuating pressure level for the 400 Hz 1/3 octave band showsgood qualitative agreement with experimental measurements.

Fig. 8. Underbody wind noise predicted by LBM: 400 Hz 1/3 octave band (reprintedwith permission from SAE Paper 2007-01-2400 c©2007 SAE International).

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