Towards Industrial LES/DNS in Aeronau7cs Paving the … · Towards Industrial LES/DNS in Aeronau7cs...
Transcript of Towards Industrial LES/DNS in Aeronau7cs Paving the … · Towards Industrial LES/DNS in Aeronau7cs...
TowardsIndustrialLES/DNSinAeronau7csPavingtheWayforFutureAccurateCFD
CharlesHirschPresident,NUMECAInt.
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• CurrentCFDlimita@ons
• ThevisionsoffutureCFD
• TheTILDAproject
• ExpectedoutcomeforIndustry
Content
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• CFDhasprogressedconsiderablyinthelast50years,duetoalgorithmicdevelopmentsandgrowingcapaci@esincomputerhardwareandHPC
• Thecurrentindustrialprac@cereliesessen@allyonRANSmodeling,despitethestronglimita@onsofpresentdayturbulence(andtransi@on)modelling
• TheuseofCFDhasremainedconfinedtoasmallregionoftheopera@ngdesignspaceduetotheinabilityofcurrentmethodstoreliablypredictturbulentseparatedflows
CurrentCFDcapabili@esandlimita@ons
Flight envelopes and level of confidence in CFD solutions - given by colour gradient (Courtesy: Airbus, A. Abbas (2012))
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CurrentLimita@onsofCFDinAeronau@cs
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• ThereisastrongneedtomovebeyondRANSbasedmodelingandovercomethelimita@onsofturbulencemodels,bymovingtowardsLES/DNSlevelsofsimula@ons
• HybridRANS-LESandwall-modeledLESofferatemporaryintermediateop@on,althoughsignificantmodelingissuesremaintobeaddressedhereaswell
• Toachieveahigherlevelofpredic7vereliability,weneedtoreducethelevelofempiricism
• AgrowingnumberofLES/DNSsimula@onswithfinemeshresolu@onshavebeenproducedinrecentyears,mainlyonbasicsimplifiedconfigura@ons
• WhatistheroadtowardsfullLES/DNScapabili7esatanindustriallevelin
Aeronau7cs?
Howcanweovercometheselimita@ons
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Thevisionsfornextgenera7onCFDTwointeres@ngoverviewsofthefutureofCFDhavebeenprovidedrecentlyby
– NASAvisionCFD-2030(2014)– P.Spalart(2012)andP.SpalartandV.Venkatakrishnan(Aeronau@cal
Journal2016)Plusthe
– TILDAVision(2014)Canbeclassifiedas
– Realis@c:NASAvisionCFD-2030– Pessimis@c:P.SpalartandV.Venkatakrishnan(2016)– Op@mis@c:TILDAvision
CFDFutureVisions
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SpalartFutureCFDVision(2012)
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• Webelievethises@ma@ontobetoopessimis@c!
• Itisbasedonstandardsecondorderschemes
• HOM’sandHPCoffernewopportuni@es,whichcanbelargelyexploited
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FromP.SpalartandV.Venkatakrishnan(2016)– WebelievethereisatendencytowardsoverconfidenceinCFDinsome
circles,eventotheextentofignoringwell-knownsourcesoferror,whichcreatesariskofbacklash,wereCFDtobeblamedforcostlymistakes.
• WenowsummarizeourpredicBonsforturbulencetreatmentattheReynoldsnumbersofinterest– DNSandwall-resolvedLESwillnotbeused.Thechallengesin
physicalmodellingoftransi>onandturbulencewillnotbetrulyovercomeinthiscentury(!!?)
– PureRANScannotbefullyeliminated,butisnottobetrustedaDermassivesepara>on,andul>matelynoteveninboundarylayersinstrongadversepressuregradients.
– TheswitchfromRANStoWMLESwillnothappenglobally,butinstead,hybridsimulaBonswillseetheboundarymoveforwardtograduallyshrinktheRANSregion,reducingittothethinnestareasofboundarylayers,whicharetheleastdifficulttopredictbutcannotbeignored.
SpalartFutureCFDVision(2016)
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Thesees@matesdonottakeintoaccountthepoten@alofferedby:• HighOrderMethods• AdvancesinHPCandtheincreasingcapaci@esofnew
mul@core-mul@threadarchitectures• Thepoten@aladd-onsfrommul@pleGPU’sinfrastructures• AdvancementsinLES/DNSmethodologies,throughimproved
algorithmicdevelopments,suchas– Mul@level,mul@scalemethodologies– Op@malcombina@onsofexplicitandimplicitmethods
Withapoten@algainof3to4ordersofmagnitude!!
TheTILDAVision
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HighOrderMethods(HOM)
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• CurrentCFDcodesarenominallyofsecondorder– ValidstrictlyonCartesiangrids– Onunstructuredgridsthereisagenerallossofaccuracyduetoirregularcellshape
andsizes
• Highordermethods(HOM)onunstructuredgrids– Uptounlimitedorderofaccuracy– Keepstheaccuracyineachcellsincetheorderisdefinedbythepowerofthe
polynomialrepresenta@onineachcell– Provideshighlyaccuratesolu@onsoncoarsegrids– Variousmethodsareavailableandinfurtherdevelopment,towardshigherlevels
ofmaturity:• Discon@nuousGalerkin• SpectralDifferences• FluxReconstruc@on
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• Necessityforcurvedmeshesatboundaries• Moreefficient@meintegra@onmethods• Futurepoten@al
– FromtheIDIHOMprojectitappearsthatHOMisnotyetcompe@@vecomparedtocurrentefficientfinitevolumeCFDcodes,forsteadyRANS
– Butroomiss@llavailableforperformanceimprovements
• However,HOM’sarehighlycompe@@veforunsteadyflows,inpar@cularforCAAandLES/DNS
Challenges of HOM
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TheTILDAProject
Mainobjec7vesoftheTILDAproject(CoordinatedbyNUMECA)Quan7ta7veObjec7ve:• Runa1-10BillionDoFforLES/DNSon50,000+coresin1day;thatisfora
costof1to1.5MCPU-h• Evolu7onofHPCpowerinIndustry
Thisgoalcanbemetbyintroducing:--Efficienthigh-ordermethods(HOM),whichoffertherequestedaccuracyoncoarserunstructuredmeshesandhavethepoten@altomakefullyresolvedsimula@ons(LES,butalsoDNS)feasibleforindustry.--NewmethodologiesforLES/DNS,basedonmul@level,mul@-resolu@on,adap@veandothermethods--Exploitmassiveparallelism,includingmul@-coreandmul@-threadedhardware
Evaluation of HPC, i.e. number of cores vs. years used/in use and extrapolated to the end of TILDA
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PathtoIndustrialLES/DNS
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● Improvements in single thread performance has slowed, but not completely flattened
● Continued increases in the number of cores per
processor and energy efficient accelerators mean Moore’s Law will hold into the near future (~2020-2025)
● Harnessing the power of future systems will
require software that map well onto heterogeneous, high-FLOPs, low memory bandwidth hardware
● High order solvers are demonstrated to
efficiently utilize current leadership systems at scale[2] ○ Piz-Daint system: ~10 PF ○ PyFR: 45% of peak system utilization
● The current path points to exascale systems
deployed by 2025, providing 100X more FLOPs for industrial LES
[1]hmps://www.karlrupp.net/2015/06/40-years-of-microprocessor-trend-data/[2]hmps://www.nas.nasa.gov/assets/pdf/ams/2016/AMS_20160531_Witherden-Vincent.pdf[3]hmps://[email protected]/wordpress/wp-content/uploads/2015/09/[email protected]
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• Budget:around3M€,foraperiodofthreeyears
• NUMECAInt.(Belgium)• DLR(Germany)• ONERA(France)• DassaultAvia@on(France)• SAFRAN(France)• CERFACS(France)• CENAERO(Belgium)• Univ.CatholiquedeLouvain-UCL(Belgium)• Univ.Bergamo(Italy)• ImperialCollege(UK)• TsAGI(Russia)
AssociatedPartners• Airbus• MTU(Germany)• NASAGlenn–HTHuynh(USA)
TILDABudgetandPartners
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Thetechnicalobjec@vesforbringingLESandfutureDNSclosertoindustrialapplica@onsinthemid-term,requireinnova@vedevelopments,ensuringfutureCFDapproachestoofferconfidenceandreliabilitycombinedwithaccuracyanduser-friendliness.HenceintheTILDAprojecttheobjec@vesread:• AdvancemethodstoaccelerateHOMforunsteadysimula>onsofLESandfuture
DNSonunstructuredgrids.• AdvancemethodstoaccelerateLESandfutureDNSmethodologybymul@level,
adap@ve,fractalandsimilarapproachesonunstructuredgrids.• ExtendLES/DNStoindustriallyrelevantapplica@onsusingHOMsonunstructured
gridsforunsteadyflowsaimingatareduc@onof2-3ordersofmagnitudeinCPU>me,togetherwiththeuseoftheinnova@veresearchonLESwithrespecttoadap@vityandmul@levelusewithanaddi>onalpoten>algainof1-2ordersofmagnitude.
• Providegridgenera>onmethodsforHOMonunstructuredgrids,withemphasisonvalidcurvilinearmeshesforcomplexgeometriesincludingboundarylayerandhybridmeshes,whileaccoun>ngforbothmeshandsolu>onquality.
• Moreover,keepafocusonlargescalecomputa@onswithefficientmeshgenera@onalgorithmsandparalleladapta@on,aswellasperformance-orientedload-balancingandpar@@oningstrategies.
TILDAObjec@ves
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Arepresenta@veexampleofTILDAAchievements
• FromP.Vincentetal,ImperialCollege,2016
• 22.5BillionDoF,• Order4,• on5000GPUin35
Hours,onTITAN
NUMECAIngBuroUserMee@ng2017-Nurnberg 2025/04/2017
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• Predic@onofextendedFlightEnvelope,whichhavethepoten@altochangethewayindustryusesCFDinthedesignprocess,– byensuringhighlyreliabledata,insupportofthedesigndecisionprocess,witha
turnaround@meof1day• Enhancedunderstandingoftheunderlyingphysics• Thegenera@onofrepresenta@veLES/DNSdatabasesshouldprovidea
frameworkforimprovementsofTurbulenceandTransi@onmodelsforRANSsimula@ons– Thesesimula@onsprovideaneverseenbeforeamountoffullydetaileddatato
inves@gateallthecontribu@onsto,e.g.k;e;Reynoldsstresses,……,– Andcomparewithexis@ngmodels– Andimprovethemodels
• ThisopensalargeroadtowardsmorereliableRANSmodels,aswellasmorereliablesubgridscalemodelsforWMLES
Expectedoutcomeforindustry
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AnImprovedEARSMModel,basedonextensiveLES/DNSdata
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• Separa7onSensi7veCorrectedExplicitAlgebraicReynoldsStressModel(SSC-EARSM)S.Monté,L.Temmerman,B.Léonard,B.TarBnville,C.Hirsch,ETMM11(2016)
• TheSSC-EARSMisdesignedwiththeaimofbemerpredic@ngseparatedflows.
• ItisconstructedontheSBSL-EARSMmodelofMenteretal.(2012),Jakirlicetal(2015)inwhichthreecorrec@onsareintroducedbasedoniden@fiedweaknessesoftheoriginalmodel.
• Basedonsystema7ccomparisonswithLESandDNSdatabases,comparingdatasuchasshearstressandkine7cturbulentenergydistribu7ons
• Themodifica7onshavebeendesignedtobederpredictthekine7cenergyproduc7on,andtheshearstress,acrossthesepara7onbubble
Turbulentkine@cenergyintheseparatedbubbleofthecurvedbackwardfacingstep.Squares:LESofBentalebetal.(2011).Red:theSBSL-EARSMmodel.Blue:theSSC-EARSMmodel
LES
SBSL-EARSM
SSC-EARSM
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SSC-EARSMTurbulenceModel
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• Thedevelopmentandtheextensivetes@ngoftheSSC-EARSMmodelindicatethatmajorweaknessesoftheSBSL-EARSMmodel,whicharecommontomostRANSmodels,canbeaddressed
• Thedevelopmentofthemodelwasbasedonalargenumberofreferencedata,suchasthecurvedbackwardfacingstepofBentalebetal.(2011),theperiodichill,DNSdatafortheflatplate,………
• TheoutcomeisthattheSSC-EARSMmodelbemerresolvesseparatedflows,includingmorecomplexseparatedflowssuchasthe
– TrapwingofHLPW-1– DrivAercarmodelsofHeretal.(2012)– CRMmodelofDPW-4
TurbulentkineBcenergyk/Uτ^2overaturbulentflatplateatReθ=2,540.Redsolidline:theSBSL-EARSMmodel.Cyansolidline:theSSC-EARSMmodelDots:DNSdatafromP.Schla\er(KTH).
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SSC-EARSMTurbulenceModel-industrialapplica@on
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Exp SBSL-EARSM SSC-EARSM
Fastback
Pressurecoefficientandskinfric@onovertheDrivAercargeometries.
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SSC-EARSMTurbulenceModel--Dragpredic@on
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CRMModelforNASAandJAXADragpredic@on
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AIAADPW4● NASACommonResearchModelinitsWing-Body-Tailconfigura@on● Representa@veofcontemporarytransonictransportaircrar● Wingprofiledesignedforthepurposesofresearchanddevelopment:
○ Strongadversepressuregradientoverthelast10%-15%oflocalchord○ Promotesepara@onofboundarylayertoamplifyeffectofturbulence
model● ExperimentaldatafromNASAAmes11rtransonicwindtunnelandfrom
JAXA,takingintoaccountelas@cdeforma@on
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• UnstructuredgridfromJAXA• Solver:FINE™/OpenfromNUMECAwithSSC-EARSMmodel
JAXAgridsandexperimentaldata
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JAXAgridsandexperimentaldata:CL-Alpha
DatafromJAXAAPC-1Workshop
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JAXAgridsandexperimentaldata:Polar
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DatafromJAXAAPC-1Workshop
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JAXAgridsandexperimentaldata:CM-Alpha
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DatafromJAXAAPC-1Workshop
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• TheTILDAprojectsisac@veun@lNovember2018• A“TILDA”Symposiumisplanned,byNovember2018:
HighFidelityLES/DNSforIndustrialApplica7ons
• Expectedtoini@ateanewdedicatedbi-annualseriesofconferences,coveringa.o.
– ImprovementsinHOMandgridgenera7onforLES/DNS– Industrialrelevantapplica7ons– Understandingofthefundamentalsofturbulenceandtransi7on– Exploita7ontowardsimprovementofWMLES,RANSandtransi7onmodeling
• Willbeorganizedbythenewlyformed“AerospaceEurope”community,composedbythemajorEUscien@ficandindustrialAeronau@calAssocia@ons:
– CEAS,ECCOMAS,EUROMECH,ERCOFTAC,EUROTURBO,EUCASS– AnAssocia@onwithAIAAisunderconsidera@on
Next
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• Variouspresenta@onstofollowwillpresentsomeofthecurrentprogresswithintheTILDAproject
• Manyissuesaboutthesmallscalenear-walltreatmentaretobeinves@gated
• Itisexpectedtoallow,inthenearfuture,extensiveHOMsimula@onsforfullyresolvedLES/DNSwithhighresolu@onatincreasingRe-numbers.
Conclusions
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