2004-01-1995

Towards Large Eddy Simulation in Internal-CombustionEngines: simulation of a compressed tumble flow.

Vincent Moureau*, Iain Barton***IFP, Rueil-Malmaison (France)

Christian Angelberger*, Thierry Poinsot****CERFACS, Toulouse (France)

Copyright © 2004 SAE International

ABSTRACT

The development of the Large Eddy Simulation (LES) 3DCFD code AVBP to yield a CFD tool able to predict cyclicvariability in Internal Combustion (IC) engines isreported. In a first step the implementation of an ArbitraryLagrangian Eulerian (ALE) method into AVBP isdescribed, allowing to move solid boundaries. Then theprinciples and implementation of the ConditionedTemporal Interpolation (CTI) mesh managementtechnique is described, and some specific adaptationsfor LES simulations are discussed. Finally a firstvalidation of the so obtained LES IC engine code ispresented by comparing predictions with findings on thesquare piston experiment.

INTRODUCTION

Among the technologies that will potentially be at thebasis of future highly efficient, near-zero emission, costeffective powertrains, combustion processes likeControlled Auto-Ignition (CAI) or Homogeneous ChargeCompression Ignition (HCCI) play a key role. They havethe potential to reduce pollutant production at the source,allowing to comply with future more and more stringentemission regulations, without the need for complex andexpensive after-treatment systems. One of the factorslimiting at present the full exploitation of the potential ofsome of these new combustion techniques is theoccurrence of important cycle to cycle variations in partsof the engine operating range. Cyclic variability inengines are not yet fully understood, as they are theresult of an important number of phenomena hardlyaccessible to experimental investigations. Giving theautomotive engineers the means to predict such cyclicvariability, and more generally unsteady enginephenomena like warm-up or engine transients, can beviewed as a major challenge for a more effective designof the combustion engines of the future. Due to thedifficulty of experimental investigations in this field,Computational Fluid Dynamics (CFD) simulations appearas the best candidate for providing such a design tool.

Today's standard in engine simulation are ReynoldsAveraged Navier-Stokes (RANS) methods. They allow toaccurately predict the mean characteristics of stabilisedengine operation, and are therefore widely used tochoose the most promising engine configuration, beforegoing to actually test it. They thus contribute to reducethe cost of development, allowing to integrate animportant number of physical knowledge into enginedesign. Yet these techniques are inherently not adaptedto predict unsteady phenomena. A better candidate for atool allowing to predict them are Large Eddy Simulation(LES) techniques. These techniques have proven theirgreat potential when applied to the prediction of acousticinstability in gas turbine combustion chambers [9], andthey start to be developed for piston engine applicationsas discussed by Celik et al [4].

LES and RANS techniques differ in the way they addressthe present impossibility to resolve all the scales presentin engine flows, and especially those related toturbulence, combustion and liquid jets. RANSsimulations are based on a statistical averaging to solveonly the mean flow. This implies that modelling concernsthe whole spectrum of scales, which in turn makes thepredictivity of RANS simulations dependant on the qualityof the models used. The statistical averaging alsoextremely complicates addressing unsteady phenomena.In LES, a spatial or temporal filtering is used to representthe large turbulent scales of the flow, which are directlyresolved, while the small scales are modelled. In LES,modelling thus concerns a much smaller part of thespectrum, which leads to an improvement of predictivityas compared to RANS. LES inherently allows to addresslarge scale unsteady phenomena, and thus has a goodpotential to predict engine unsteadiness.

In LES and RANS, the effect of the modelled part of theturbulent spectrum on the resolved part is assumed to bediffusive, and it is often taken into account by introducinga turbulent viscosity. The level of turbulent viscositydirectly depends on the amount of modelled energyleading to high levels for RANS and far less importantlevels for LES. This explains the different requirements

of LES and RANS in terms of numerical schemes. InRANS, the main requirement is to be robust and stableon distorted meshes. Numerical dissipation is only asecond-orderaspect.MostofRANScodes likeKIVA [2]use upwind schemes known to be very stable, butdissipative. Inversely, in LES, vortices above and at theresolution limitmustbeaccurately resolved,withas fewnumericaldissipationaspossible.Thisimpliestheuseofprecise and energy-conserving numerical schemes, asFinite-VolumeCentred-Differencing(FVCD)schemes.

LESandRANSnot only differ by the requests imposedon numerics, they also imply a different simulationprocedure and exploitation of results. One RANS cyclegivesdirectlyaccesstomeanvalues.RealisingthesameobjectivewithLESwouldleadtoaCPUtimethat iswaybeyond reach of present supercomputers, as apotentially high number of consecutive engine cycleswould have to be computed to reach a statisticalconvergence of the results. On the short to mediumterm, theobjectiveof LESwill not be to compute meanenginecharacteristics,adomainRANScansatisfactorilycoverat reasonableCPUcosts,but rather tobeused incomplement toRANS,addressingunsteadyphenomenainaccessible to it. Experience gained with LES so farindicates that even a small number of engine cyclescouldbe sufficient to gain insight into theoccurrence ofcyclicvariability,whichwillhelptosuppressorlimitthemby design. This will necessitate however thedevelopmentofenginesimulationcodesabletoaddressall related phenomena like turbulence, acoustic waves,combustionandknock,aswellasliquidsprays.

The objectives of the present paper is to present firstdevelopmentsperformedtoupdatetheLESsolverAVBPto be able to perform LES engine simulations, with thelongtermobjectiveofrealisingaCFDtoolabletopredictcyclic variability in ICengines.Workhas focused so faronconservingthenumericalpropertiesofthecodewhenthe mesh is deforming and on the mesh managementusing a Conditioned Temporal Interpolation (CTI)techniquecoupledtoahigh-order interpolator.Thecodevalidation is performed by comparing simulation resultsto a complete database of Particle Image Velocimetry(PIV) fields obtained with a square-piston experimentwhich generates and compresses a strong tumblemotion. The influence on turbulence of assuming a 2Dflow instead of simulating the 3D reality is studied, aswellastheeffectsofthenumericalschemes'accuracy.

THEAVBPCODE

The numerical developments presented in this articlehave all been implemented in the AVBP code which isjointly developed by CERFACS and IFP. AVBP is aparallel LES and DNS solver of the reactive multi-componentcompressibleNavier-Stokesequationson2Dand 3D unstructured hybrid grids. The closure of theseequations is done introducing a perfect-gas stateequation for the mixture and a temperature dependententhalpy tabulation for each component. A calorificcapacity ratio r for the mixture is defined locally

dependingonthethermodynamicconditions.Thisaspectis very important in piston engine simulations becausethe theoretical thermodynamicefficiency is a function ofthemeanofthegasconstant r .

AVBPNUMERICS

ThedesignofAVBPhasbeen focusedonguaranteeinga linear parallel efficiency : multiplying the number ofprocessors by two divides the CPU time by the sameratio. These performances have been obtained using ameshpartitioningalgorithmwhichbalancestheCPUloadon each processor and suitable message-passinglibraries. To minimise interface exchanges betweenprocessors,AVBPnumericalschemesarebasedon thecell-vertex method [13] which naturally ensures a highcompactness.Themainconvectiveschemesareafinite-volume Lax-Wendroff type scheme (LW) and a finite-element two-step Taylor-Galerkin scheme (TTGC) [5].Thesetwoschemesarerespectively2ndand3rdorder intime and space. The diffusive scheme is a typical 2nd

ordercompactscheme.ElementtypeshandledbyAVBPare triangles and quadrangles in 2D and tetrahedrons,prisms, pyramids and hexahedrons in 3D. The timeintegrationisfullyexplicittomaximiseaccuracy.

TURBULENCEMODELS

The turbulent LES models of AVBP are a classicalSmagorinsky, a filtered Smagorinsky and a WallAdaptingLinearEddy (WALE)model. TheSmagorinskymodel [16] has been heavily tested and used in LESsimulations. It is known to correctly predict the globalturbulentquantitiesbutalsotoover-dissipatewhenusedfor transient flows [14]. The filtered Smagorinsky modelissimilartotheSmagorinskymodelbut thevelocity fieldis explicitly filtered with a higher-filter width before theevaluation of the turbulent viscosity. It has beendeveloped to better represent the local phenomena ofthe flow [3].Finally, theWALEmodel [3],alsobasedonthe Smagorinsky model, is used for wall-bounded flowsattemptingtorecoverthescalinglawsofthewall.

IMPLEMENTATIONOFANALEMETHODFORMOVINGWALLS

Because AVBP is a code primary written for turbo-machinery, a first step was to implement a numericalmethod to handle moving boundaries. This kind ofmethodsadaptedtodeformingdomainsareofinterestinmany practical applications : fluid-structure interactions,free-surfaceflow,reciprocatingengines,…Ineachcase,the properties of the boundary movement - velocity,deformation rate, frequency - determine the moreadapted. For IC engine configurations, movingboundaries are the piston and the valves that have atranslation movement along a fixed axis. Body-force orArbitrary-Lagrangian Eulerian (ALE) methods arecommonlyusedforthistypeofperiodicdisplacements.

Thebody-forcemethod[19]consistsinimposingagivenspeedonanarbitrarysurfacewhichdoesnotnecessarily

coincide with mesh vertices. Complex deformingconfigurationscanherebyeasilybesimulatedusingfixedCartesian grids. The important drawback is thatadvancedboundary-modellingasimposingcharacteristicboundary conditions or wall laws [17] is difficult andcomplicated.

TheALE[1]method isanappealingalternative inwhicheachmeshvertexihasagivenmovingspeed ( )i tX andthe computational domain boundaries )(tΩ∂ coincidewithmeshvertices.Thistechniqueisparticularlyadaptedto deforming unstructured meshes. The vertex speed ( )i tX isaninputofthesimulationandmustthereforebe

calculatedindependentlywithaCTImethodforexampleasdescribedinthenextSection.

TheALEformalismhasbeenusedinAVBPtoredeveloppre-existing numerical finite-volume and finite-elementconvectiveschemes.Thenewschemesareverysimilarto the original ones and the mesh movement is simplytakenintoaccountintroducingadditionalcorrectingtermsduetotranslationanddilatation.Thesetermswhichhavebeen rigorously determined [12], ensure that numericalproperties - convergence order and precision - are thesameon fixedandmovinggrids.Specialcarehasbeenpaid to the geometric conservation properties of theschemes in order to avoid the generation of spuriousperturbationswhendeformingthegrid[8].Severalsimpletestcaseshavevalidatedthenumericalmethod[12]andillustratedtheenumeratedproperties.

THECTIMESHMANAGEMENT

Given the complexity of IC engine geometry,characterised in particular by piston and valves movingatdifferentspeedsalongdifferentaxes, it isessential toemploy a robust mesh management technique. Itsprincipal goal is to provide a means of moving all themesh nodes in time in a way that ensures an accurateevolution of the moving boundaries while avoiding theoccurrenceofnegativecellvolumes.

Previous studies using an implicit solver, KIFP, haveinvestigated different approaches to moving the meshwithandwithoutflowcalculations[20,21].Fromthisworkthe main conclusion is that standard methods of meshmovement techniques, such as the spring analogy [15],are not easily applicable to IC engine combustionchambers,wherethemovementsofthevalveandpistonboundaries significantly deform the mesh. As aconsequence of this, the Conditioned TemporalInterpolation (CTI) mesh management technique wasdeveloped [18]. In this section we recall the basicprinciples of this technique and describe itsimplementation into AVBP, as well as the necessarymodificationsforLES.

BASICPRINCIPLES

The starting point of the CTI mesh managementtechnique is to decompose the IC engine cycle in a

numberofmeshphases.Eachphaseischaracterisedbyaspecificandconstantmeshconnectivity.Astartmeshis created (using standard CAD packages) at the startangleofeachmeshphase.Theboundariesof this startmesharethenstretchedwithintheCADsoftwaretoyieldthe node positions at the crank angle corresponding tothe end of the mesh phase. The final mesh obtained iscalled the target mesh for that respective mesh phase,and so the only difference with the start mesh and thetargetmeshare thenodepositions.Thestartandtargetmeshesdonotcomprisenegativevolumes, thedifficultythat faces the mesh management is to move theboundariesappropriatelyduring theCFDsimulationandtoensurethatmeshqualityremainsacceptable.

For each mesh phase - a vector field consisting of theco-ordinate differences between the positions of thenodes in the start and target grid is stored.Anabsolutepercentagedifference field isdefinedas thepercentageofthedisplacementalongthepathleadingfromthestarttothetargetmesh.Ifall thenodeshaveazeroabsolutepercentagevalue,thiswouldcorrespondtothestartgrid.Ifallnodeshavea100%absolutepercentagevalue,thiswouldcorrespondtothetargetgrid.

The difficulty of calculating how to move all the nodesalongtheirpredefinedpathsisdirectlyrelatedtothefactthatthepistonandvalvesaremovingatdifferentspeeds,whichimpliesthattheabsolutepercentagefieldchangesat difference rates on different physical boundaries. Toovercome this, a smoothed percentage field has to becalculatedwithinthecomputationaldomain.Notethattheadopted method for smoothing cannot ensure positivecell volumes, and some level of trial-and-error isnecessary to achieve an acceptable result. This is whymesh-only simulations are very useful for initialcomputations.

IMPLEMENTATIONINAVBP

AVBPisaparallelsolverbasedonmeshdecomposition[10], themeshdecomposition isan important factor thatrequired some adaptation using the CTI technique.However, the space discretisation in AVBP is nodecentered[13],whichisadvantageousforgridmovement,since the mesh management requires moving the gridnodes directly. The velocity of mesh nodes is thereforeeasily computed and used in numerical convectiveschemes[12].

The actual percentage calculations used in this paperfollow the work by Torres & Zolver in [18]. A relativepercentage field is calculated at each time step,representing the displacement that grid points need tomove from their current position to their target position.Torres&Zolver found that suchamethodology using aweighted curvature term based on a zeroth-orderLaplacian solver was the most generic in application. Itwas found to work well for multi-valve and single-valveconfigurations.

In order to test the implementation of the CTImethodology into AVBP, a purely mesh-only simulationof thecombustionchamberofasinglevalvePSAXU10engine has been performed. The mesh used in thesesimulations was simply a coarse RANS mesh. Thisallowedus toperforma fast testingof thequality of theresulting mesh management, no attempt being done toperformLESsimulationsatthisstage.Figure1showsacut through this mesh at 5°CA (degrees crank angle),26°CAand48°CA,obtainedwithamesh-onlysimulationusingtheCTImethodimplementedinAVBP.

Figure1:Thegridsystemsat5°CA,26°CAand48°CA,respectively.

Figure2:ThegridsystemsatCADof4,5beforethevalveisopenedandat5justafteritisopened,respectively.

ThesimulationshowninFigure1andFigure2 ispartofa full engine cycle starting at 4°CA, where the valve istreated as being closed. At 5°CA the valve opens, theevolutionofthegridfromitsclosedstatetoitsopenstateis shown in Figure 2 focussing on the grid in the valveregion. This evolution demonstrates clearly a change inmesh phase. The entire simulation runs from 4°CA to360°CA.Itconsistsof5meshphases.Thefirstand lasttwo phases have a closed valve system. The secondmeshphaserepresentstheopeningofthevalveandtheexpansion of the piston, while the third phase isessentially the closing of the valve. The last two meshphasesessentiallyrepresentthecompression.

In order to cope with the partitioning of the grid, thesolutionofthepercentagefieldrequiredsomecare.Theoptimisation of the percentage field need to be doneinvolvingall themeshpartitions.While this is somewhatexpensive, the increase in CPU cost was only of theorderof10%comparedwithsimulationsthatdidnotcalltheCTIroutine.Aconvergencecriterionof0.1%wassetfor the maximum change of percentage fieldcalculations. For the configuration shown in Figure 1,parallelisationtestsonaSGIOrigin2000machinewithatotal of eight processors show a near linear increase inCPUefficiency,asshowninFigure3.Thedropfromtheidealspeed-uplineformorethan5processorsiscausedby an increase in the number of nodes on partitionboundariesthatrequireupdatingduringthesimulation.

Figure3:TheparallelperformanceofthecoderunningtheCTItechnique,showingSpeed-Upvs.numberofprocessors.

The disadvantage of the CTI approach is that a newtarget mesh is required whenever a moving boundarychanges direction (as a new displacement field isrequired).Also, thepaths that thegrid nodes can movealong are prescribed and do not change according tolocal deformation in the grid structure. This problem isobviously overcome, by changing the mesh topologybetween mesh phases, which in turn means that a"remapping" of the flow field variables has to beperformedfromthetargetgridoftheprecedingphasetothe start grid of the current mesh phase. "Remapping"meansthatresultsareinterpolatedbetweentwomeshes

withdifferentgrid indexingandconnectivity. In thebasicCTIversionwrittenforRANSapplications,theremappingprocess is achieved by solving a least square fitinterpolationofasecond-orderpolynomialonatightlocalstencil. Typically, the remapping process is requiredwhen the grid becomes too coarse or too distorted:Figure4showsacuttroughtheXU10gridat55°CA,justbeforeand justafter the remappingprocess.As canbeseenfromFigure4,thegridsysteminthemainchamberis coarsewhich is causedby the previous expansion ofthegrid,remappingtothefinergridallowsthesimulationtocontinuewithoutanyfurtherdeteriorationintheresultscaused by the coarseness of the grid. In fact thisapproach can have some benefits in particular for LESsimulations and explicit code solvers. Namely, theremapping allows the user to implement the mostappropriate grid system during a particular evolution ofthe simulation, which evidently is critical when thecomputationaldomainitselfchangesform.

Figure4:Thegridsystemsat55°CA,beforeremapping(left),afterremapping(right).

INTERPOLATIONBETWEENMESHPHASES:ADAPTATIONSFORLES

As explained in the previous Section and illustrated onFigure 4, the "remapping" process between two phasesis of primary importance for explicit codes to keep ahomogenous cell width. The interpolation must be asprecise as possible not to modify the variable fields.RANS fields represent statistically mean variables, theydo not contain any high-frequency structures. Theinterpolationschemecanthereforebelimitedtoasimplegradient or a second-order polynomial reconstruction.HoweverLESfields,whichareafilteredrealisationoftheturbulent flow, contain small vortices with a frequencyclosetothecut-off.Inthiscase,higher-orderandlarger-stencilinterpolationschemesmustbeused.

Figure 5 illustrates the accuracy differences of threeinterpolators for which the stencil and the order aredifferent. The considered simulation is a LES of aHomogenousIsotropicTurbulence (HIT). Interpolation isusedtoswitch froma333 regularmesh toa493 regularmesh. Turbulent spectrums are given before and afterthe "remapping". These results underline that a largestencil width and a high discretisation order of theinterpolation are necessary for ensuring a level ofspectral quality compatible with LES simulations.Otherwise, the results obtained with the 1st order tight

stencil indicate that energy is lost even at well resolvedlevels during interpolation. These conclusions wereconfirmed using different mesh refinements andcoarsenings. Note that a problem that still needs to beresolved is the exchange of kinetic energy betweenresolved and sub-grid scale, which will be the object offutureresearch.

Figure5:SpectrumcomparisonofinterpolatedLESfieldsfrom333

(initial)to493meshesforaHomogenousIsotropicTurbulenceflow.

FIRST APPLICATION: THE SQUARE PISTON

EXPERIMENTALSET-UP

Piston

Intake channel

Windows

Laser sheet

Location of guillotine device

Figure6:sketchofthesquarepistonengine

The square piston experiment has been designed atIMFT to study the compression and the disruption of atumble vortex [3,11]. The main goal was to betterunderstandtheflowstructuresandphenomenainSpark-Ignition (SI) engines. It was specifically designed with aview to validate LES simulations. The experimental set-up is shown in Figure 6: it is composed of a square

pistonwhichhasasinusoidalmotion,aguillotinetoclosethe flat intake channel, a plenum chamber at ambientpressureandmultipleopticalaccessesforParticleImageVelocimetry (PIV) measurements. The intake channelcomesoutinthelowerpartofthechambertogenerateastrong tumble motion during the intake stroke. Theexperiment is run with a four-stroke cycle : intake,compression,expansionandexhaust.Theavailabledatahavebeenobtainedwithavolumetriccompression ratioof 4 and a crankshaft speed of 206 rpm. The pistondimensionsare100x100mm²,thestrokeis75mm.andtheintakechannelis300mmlong.

MorethanonehundredPIVfieldshavebeentakeninthevertical symmetry plane at different crank angles. Fromthese one can compute mean and fluctuation fields forthetwospeedcomponentsoftheplane.

NUMERICALSET-UP

Both2Dand3DLESsimulationshavebeencarriedoutduring several cycles to compute mean and fluctuatingvelocity profiles. A 3D mesh of 270000 vertices withprisms, tetrahedra and hexahedra elements is given onFigure 7 as an example. For 2D and 3D meshes,guillotineopeningandclosingaresimulatedandFigure8showsthecelldeformationsduringthesephases.

Figure7:globalviewofa3Dhybridmesh

The simulations are performed using the Smagorinskyturbulent model and a simple "law of the wall" modelbased on the linear/log laws. The mesh managementwas realised with the CTI method and the large stencil,2nd order interpolation. The engine cycle wasdecomposed into25meshphases. Numerical schemesdescribedinSection"TheAVBPCode"arecomparedtounderlinetheeffectsoftheconvergenceorder.

Figure8:detailofthe2Dhybridmeshwhenclosingtheguillotine.

RESULTS

Instantaneousfields

Instantaneous fieldsare interesting to haveaqualitativeevaluation of the different simulations and to see if theresults are in agreement with experimental findings.Although 2D LES simulations are not physically sounddue to the three-dimensional nature of turbulence, theywereusedforbeingabletocomparenumericalschemesat low CPU costs. We also present their results toillustrate that performing LES in 2D meshes, which canseem tempting in terms of CPU time, will lead toerroneous results, a fact that is less evident for RANSsimulations.

Figure9:2D(left),3D(centre)andexperimental(right)instantaneousvelocityfieldswithsamenormalisationatTop-DeadCentre(TDC).ComputationalfieldshavebeenobtainedwiththeLax-Wendroffscheme.

Figure10:instantaneousvelocityfieldsofthecompression,45°CrankAngles(CA)BeforeTDC(BTDC),obtainedwithLax-Wendroff(left)andTTGC(right)schemeson2Dmeshes.Thevelocitynormalisationisthesame.

Figure9presents instantaneous fieldsat theendof thecompressionstrokefor2Dand3Dsimulationscomparedto experimental fields. The first noticeable remark isaboutthestructureoftheflow.The2Dfieldiscomposedof threebigcontra-rotatingvorticeswhichdonotappearin 3D or in the experiments. The second remarkconcernstherotationspeedofthevorticeswhichisveryimportant in 2D compared to the others. The disruptionof the tumble is a typical 3D effect that could becompared to a forced Kolmogorov cascade. In 2D, thetumblestretchingisnotpossibleandtheKelvin theoremdemonstrates in this case that a compressionalong thex-axis induces a expansion along the y-axis. Thisdeformation is unstable and leads to a tumble break-down into smaller vortices, conserving the kineticmomentum. Therefore 2D simulations have to bediscriminated for LES of turbulence compression likethosefoundinSIengines.

Anotherpointofimportanceistheeffectofthenumericalscheme on the flow structures. The Lax-Wendroffscheme is known to be dissipative and more accurateschemes specifically tailored for LES exist such asTTGCpresented in previous Sections. A comparison of2DinstantaneousfieldsduringthecompressionstrokeisproposedonFigure10.WhereastheLWvelocityfield isvery smooth, the TTGC field includes a lot of smallvorticesand thepresenceof threebigvortices isnotasclear.ForagivenresolutionTTGCallowstheconvectionof finer structures and a potential better prediction ofsub-gridturbulence.

Meanprofiles

The instantaneous field comparisons have yieldedqualitative remarks that can be quantitatively confirmedbyplottingmeanprofiles.These1Dvelocity profilesareextracted from mean fields as illustrated on Figure 11and they are presented on Figure 12 to Figure 14 fordifferentcrankanglesduringintakeandcompression.

Figure11:1Dcutinthemedianplaneformeanprofilecomparisons.

Figure12:meanvelocityprofilesat270°CABTDC(intake).

2D mean fields have been obtained averaging the 15th

lastcyclesofa20-cyclecomputation.DuetoCPUcosts,

only six 3D cycles have been simulated and averaged.More cycles will be available in the future to computebetter converged means. Concerning experimentalfields, about 120 PIV realisations were available foraveraginginthedatabase.

Figure13:meanvelocityprofilesat90°CABTDC(compression).

Figure14:meanvelocityprofilesatTDC(endofcompression).

Figure12 shows that the intake stroke is well predictedby 2D computations. Since only few cycles have beenaveragedforthe3Dcase, it ismoredifficult toconcludeon the predictivity. Figure 13 and Figure 14 are takenduringtheendofthecompressionstroke.Instantaneousobservations have shown that this stroke can not besimulatedaccuratelywith2Dmethodsandmeanprofilesconfirmtheseremarks.2Dprofilesgivetoohighlevelsofu whereas 3D profiles are in a far better agreement. At90°CA BTDC, a big non-physical vortex occupies thetotality of the 2D combustion chamber and it is brokendownintothreevorticesatTDC.

Thesecomparisonsconfirm that2Dsimulationshave tobe discriminated for IC engine flows as expected. Theyare unable to predict correctly the disruption of thetumblemotion.However3Dsimulationsseempromisingandadditionaltestswithdifferentturbulencemodelsandfiner grids need to be performed to confirm these firstresults.

CONCLUSIONS&PERSPECTIVES

In this paper the developments performed to make theunstructuredparallelLESCFDcodeAVBPapplicableforIC engine applications were described, along with firstresultsobtainedonaengine-likecoldflow.

An ALE methodology was implemented and tested incombinations with 2nd order finite volume and 3rd orderfinite element convective schemes. The formulation ofthemethodwasshowntomovethegridwithoutaffectingnumerical characteristics as precision and order ofconvergence,ascomparedtofixedgridsimulations.

Inordertoprepare ICenginesimulations, theCTImeshmanagement technique was then implemented into theAVBP code. A first mesh-only application to the PSAXU10 engine served in order to validate theimplementation in the context of unstructured grids anddomain decomposition parallelism. The additional costsforCTIwereshownnot toexceed10%ascompared tosimulations without it. Parallel efficiency was shown notto be affected, the linear speed-up characteristic ofAVBPbeingpreservedwithCTI. Itwasalso shown thatinterpolation between meshes with different topologies,asneededduringthesimulationofanenginecycleusingCTI,hadtobecarefullyadaptedforLES,inordertokeepas much as possible the spectral characteristics of theresolvedflow.

Finally first validations of the capabilities of the soachieved IC engine LES code AVBP were presented.ComparisonsofLESsimulationsobtainedon2Dand3Dmeshes and using two convective schemes werecompared with experimental findings for the squarepistonset-up.Thesimulationsclearlyshowed thatalbeitthemeanflow isessentially2Dduring the intakephase,onlyafull3Dsimulationisabletocapturethebreakdownof the tumble during piston compression. Thesimulations also showed that using the 3rd order TTGCconvectiveschemeallowed,foragivenmeshresolution,

toresolvemoreflowdetailsnearthecut-offscalethana2ndordercentredLWscheme,adefinitiveadvantageforLESsimulations.Themeanvelocityprofilesobtainedbyphaseaveragingsixconsecutive3Denginecycleswithastandard Smagorinsky LES turbulence model combinedto wall laws and using a LW scheme showedencouragingreproductionofmeanexperimentalprofiles.

Futurework ispresentlyconcernedwithapplyingaLESturbulencemodelbasedonatransportequationforsub-grid scale kinetic energy to the square piston, with theaim of improving turbulence predictions duringcompression. Efforts are also made to analyse moredeeply the simulation outcomes, in particular bycomparing predictions of velocity fluctuations over alarge number of engine cycles with experimentalfindings.Theaim is thentorealiseLESsimulationsofarealconfiguration.

Workisalsocurrentlyunderwaytodevelopanautomaticmesh management technique, adapted inflow/outflowboundary conditions for LES engine simulation, as wellas a LES combustion model for premixed spark-ignitedcombustion. The final objective will be to demonstratethe feasibility of industrial LES engine simulations, andtheirabilitytopredictcyclicvariationsinSIICengines.

ACKNOWLEDGMENTS

TheauthorswouldliketoexpresstheirgratitudetoProf.Jacques Borée for kindly providing them theexperimental database on the square piston. They arealso indebted to Marc Zolver & Arnaud Torres for theirgreathelpinadaptingtheCTItechniquetoAVBP.

Thisworkwasmadepossibleby the financialsupportofthe European Commission (LESSCO2 project, contractnumberENK6-CT-2002-00616)andofIFP'sTechniquesforEnergyApplicationsDivision.

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CONTACT

Dr.ChristianAngelbergerIFPTAER1021&4avenueduBois-Préau92852Rueil-Malmaison(France).Tel:+33147525745Fax:+33147527068christian.angelberger@ifp.fr