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ISBN:978-0-323-88457-0

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Publisher: MatthewDeans

AcquisitionsEditor: CarrieBolger

EditorialProjectManager: MarianaKuhl

ProductionProjectManager: PoulouseJoseph

CoverDesigner: GregHarris

TypesetbyTNQTechnologies

Contributorsix

Forewordxi

Preface xv

1.Introduction1

BalajiMohan,PinakiPal,JihadBadra,YuanjiangPeiandSibenduSom

1. Industrialrevolution1

2. Artiﬁcialintelligence,machinelearning,anddeeplearning2

3. Machinelearningalgorithms3

4. Artiﬁcialintelligence basedfuel-engineco-optimization4

5. Summary16 References16

SECTION1ArtiﬁcialIntelligencetooptimizefuelformulation

2.Optimizationoffuelformulationusingadaptivelearningand artiﬁcialintelligence27

JulianeMueller,NamhoKim,SimonLapointe,MatthewJ.McNenly, MagnusSjöbergandRussellWhitesides

1. Introductionandmotivation27

2. Mixed-modecombustionandfuelperformancemetrics28

3. Aneuralnetworkmodeltopredictfuelresearchoctanenumbers31

4. Optimizationproblemformulationanddescriptionofsolutionapproaches32

5. Numericalexperimentsandresults37

6. Discussion40

7. Summaryandconcludingremarks42 Acknowledgments43 References43

3.Artiﬁcialintelligence enabledfueldesign47

KiranK.Yalamanchi,AndreNicolleandS.ManiSarathy

1. Transportationfuels47

2. Applicationofartiﬁcialintelligencetofuelformulation52

3. Conclusionsandperspectives58 Acknowledgments60 References60 v

SECTION2ArtiﬁcialIntelligenceandcomputational ﬂuid dynamicstooptimizeinternalcombustion engines

4.Engineoptimizationusingcomputational ﬂuiddynamics andgeneticalgorithms71

AlbertoBroatch,RicardoNovella,JoséM.Pastor,JosepGomez-Soriano andPeterKellySenecal

1. Introduction71

2. Modelingframeworkandaccelerationstrategies74

3. Optimizationmethods79

4. Summaryandconcludingremarks97 References98

5.Computational ﬂuiddynamics guidedenginecombustion systemdesignoptimizationusingdesignofexperiments103

YuanjiangPei,AnqiZhang,PinakiPal,LeZhao,YuZhangand SibenduSom

1. Introduction103

2. Methodologies106

3. Arecentapplication113

4. Recommendationsforbestpractice118

5. Conclusionsandperspectives120 Acknowledgments121 References121

6.Amachinelearning-geneticalgorithmapproachfor rapidoptimizationofinternalcombustionengines125

JihadBadra,OpeoluwaOwoyele,PinakiPalandSibenduSom

1. Introduction125

2. Engineoptimizationproblemsetup127

3. Traininganddataexamination129

4. Machinelearning-geneticalgorithmapproach132

5. Automatedmachinelearning-geneticalgorithm141

6. Summary156 Acknowledgments156 References156

7.Machinelearning drivensequentialoptimizationusing dynamicexplorationandexploitation159

OpeoluwaOwoyeleandPinakiPal

1. Introduction159

2. ActiveMLoptimization(ActivO)160

3. Casestudy1:two-dimensionalcosinemixturefunction165

4. Casestudy2:computational ﬂuiddynamics(CFD)-basedengine optimization171

5. Conclusions179 Acknowledgments180 References180

SECTION3ArtiﬁcialIntelligencetopredictabnormal enginephenomena

8.Artiﬁcial-intelligence-basedpredictionandcontrolof combustioninstabilitiesinspark-ignitionengines185

BryanMaldonado,AnnaStefanopoulouandBrianKaul

1. Introduction185

2. Casestudy:artiﬁcial-intelligence-enhancedmodelingofdilute spark-ignitioncycle-to-cyclevariability189

3. Casestudy:neuralnetworksforcombustionstabilitycontrol193

4. Casestudy:learningreferencegovernorformodel-freedilute limitidentiﬁcationandavoidance199

5. Summary204 References205

9.Usingdeeplearningtodiagnosepreignitioninturbocharged spark-ignitedengines213

EshanSingh,NursuluKuzhagaliyevaandS.ManiSarathy

1. Introduction213

2. Preignitiondetectionusingmachinelearningalgorithm215

3. Activationfunctions221

4. Experimentsanddataextraction222

5. Machinelearningmethodology224

6. Model1:Inputfromprincipalcomponentanalysis230

7. Model2:Timeseriesinput231

Contributors

JihadBadra

TransportTechnologiesDivision,ResearchandDevelopmentCenter,SaudiAramco, Dhahran,EasternProvince,SaudiArabia

AlbertoBroatch

CMT ThermalMotors,PolytechnicUniversityofValencia,CaminodeVera,Valencia, Spain

JosepGomez-Soriano

CMT ThermalMotors,PolytechnicUniversityofValencia,CaminodeVera,Valencia, Spain

BrianKaul

OakRidgeNationalLaboratory,Knoxville,TN,UnitedStates

NamhoKim

SandiaNationalLaboratories,Livermore,CA,UnitedStates

NursuluKuzhagaliyeva

CleanCombustionResearchCenter(CCRC),KingAbdullahUniversityofScienceand Technology,Thuwal,WesternProvince,SaudiArabia

SimonLapointe

LawrenceLivermoreNationalLaboratory,Livermore,CA,UnitedStates

BryanMaldonado

OakRidgeNationalLaboratory,Knoxville,TN,UnitedStates

MatthewJ.McNenly

LawrenceLivermoreNationalLaboratory,Livermore,CA,UnitedStates

BalajiMohan

TransportTechnologiesDivision,ResearchandDevelopmentCenter,SaudiAramco, Dhahran,EasternProvince,SaudiArabia

JulianeMueller

LawrenceBerkeleyNationalLaboratory,Berkeley,CA,UnitedStates

AndreNicolle

AramcoFuelResearchCenter,AramcoOverseas,Rueil-Malmaison,Paris,France

RicardoNovella

CMT ThermalMotors,PolytechnicUniversityofValencia,CaminodeVera,Valencia, Spain

OpeoluwaOwoyele

EnergySystemsDivision,ArgonneNationalLaboratory,Lemont,IL,UnitedStates

PinakiPal

EnergySystemsDivision,ArgonneNationalLaboratory,Lemont,IL,UnitedStates

JoséM.Pastor

CMT ThermalMotors,PolytechnicUniversityofValencia,CaminodeVera,Valencia, Spain

YuanjiangPei

AramcoAmericas:AramcoResearchCenter Detroit,Novi,MI,UnitedStates

S.ManiSarathy

CleanCombustionResearchCenter(CCRC),KingAbdullahUniversityofScienceand Technology,Thuwal,WesternProvince,SaudiArabia

PeterKellySenecal

ConvergentScience,Inc.,Madison,WI,UnitedStates

EshanSingh

CleanCombustionResearchCenter(CCRC),KingAbdullahUniversityofScienceand Technology,Thuwal,WesternProvince,SaudiArabia

MagnusSjöberg

SandiaNationalLaboratories,Livermore,CA,UnitedStates

SibenduSom

EnergySystemsDivision,ArgonneNationalLaboratory,Lemont,IL,UnitedStates

AnnaStefanopoulou

DepartmentofMechanicalEngineering,UniversityofMichigan,AnnArbor,MI, UnitedStates

RussellWhitesides

LawrenceLivermoreNationalLaboratory,Livermore,CA,UnitedStates

KiranK.Yalamanchi

CleanCombustionResearchCenter(CCRC),KingAbdullahUniversityofScienceand Technology,Thuwal,WesternProvince,SaudiArabia

AnqiZhang

AramcoAmericas:AramcoResearchCenter Detroit,Novi,MI,UnitedStates

YuZhang

AramcoAmericas:AramcoResearchCenter Detroit,Novi,MI,UnitedStates

LeZhao

EnergySystemsDivision,ArgonneNationalLaboratory,Lemont,IL,UnitedStates

Foreword

Theinternalcombustionengine(ICE)powersourworld.Fromcarsand long-haultruckstoagriculturalandconstructionequipments,liquid-and gaseous-fueledenginestouchpracticallyeveryaspectofourlives.ICEshave beenaroundfortwohundredyears,andthevehiclestheypowerpermeated themassesmorethanacenturyago.Thesemachineshaveimprovedour livesbyliftingpeopleoutofpovertyandgivingusthefreedomtoliveand workwhereweplease.

Today’sICElooksvastlydifferentfroman1800s-eraengine.Signiﬁcant advancesinairdelivery,fueldelivery,andemissionscontrolsystemshave madethemodernengineamuchcleanerandmoreefﬁcientmachine.Even withtheseadvances,however,thecombustionengineisnotsustainablein itscurrentform.Thereisnowaglobalracefordecarbonization,whichis leadingtothedevelopmentofnewinnovationsatanunprecedentedpace.

TheICEnolongerhastogoalone.Technologiessuchasbattery electricandhydrogenfuelcellvehiclesholdgreatpromiseforhelpingus achieveacleaner,greener,andmorediversemobilityfuture.Practically, though,thesetechnologiescanbeonly part ofthesolution.Westillneed engines alotofthem buttheengineoftomorrowmustbedifferent fromtoday’smachines;theengineoftomorrowmustbedecarbonized. Achievingafullydecarbonizedenginerequiresimprovementsinengine technologies,hybridization,andlow-orno-carbonfuels.

Howdowegetthere?Thepathforwardwillbeacombinationof improvedsimulationthroughtechniquessuchascomputational ﬂuiddynamicsandadvancesinenginehardwareandcontrols.Virtualization throughsimulationhasbeen,andcontinuestobe,powerfulforoptimizing newenginetechnologies.Asanexample,theMazdaSKYACTIV-Xwas developedvirtuallybeforeanyphysicalprototypeswerebuilt,andthis successisjustthestartofwhatistocome.Butsimulationmethodsaloneare notenoughtogetuswhereweneedtogo,atleastnotintheshortamount oftimewehavetogetthere.

Theenginerepresentsalargeandcomplicateddesignspace,encompassingfuels,combustionchamberdesign,fuelinjection,ignitionstrategies, andmore.Thisisamassive,multi-objectiveoptimizationproblemwithnot justone,butacollectionofoptima.Makingthemostofthisparameter spacewillrequirecontinuedadvancesinenginecontroltechnologies,

sensors,andonboardcomputers.Theseadvancesarealreadyleadingto unprecedentedopportunity,butalsotoanever-expandingandunmanageabledesignspaceformodernengines.Currenttrendsareshowingan exponentialincreaseintheparameterspace,andthistrendisexpectedto continuefortheforeseeablefuture.Howcanwepossiblyevaluateand implementeverydesigncombinationto ﬁndthebestsolution?Thecurrent inabilitytorealizethepotentialofthisparameterspaceefﬁcientlyand effectivelyisleadingtosuboptimalenginesinthemarket.

Fortunately, “bigscience” toolshaveevolvedalongsidetheengineitself. Supercomputing,advancednumericalmethods,andevenartiﬁcialintelligence(AI)arenowatourdisposal.Aswithsomanydisciplinestoday, machinelearning(ML)hasopenedanewrealmofpossibilitiesfor advancingengines.Bymarryingbigscience/datasciencewithvirtualrepresentationsoftheengine,wecan(andwill!)achievetheultimateICE design.AI/MLwillbridgeatomisticmodelingbreakthroughswithengineeringsimulations,andAI/MLcontrolswillalsoenableautonomous, intelligentsystemscontrols.Thesesystemswillhavetheabilitytolearn, adapt,andmanipulateenginecontrolstooperateattheedgeofstabilityto maximizeefﬁciencyandminimizeemissionsunderever-changingvehicle demands.

ThisbookdoesanexcellentjobcoveringtherelativelynewtopicofAI appliedtoenginesimulationandexperimentation.Insteadoftryingto covertheentiredesignspaceatonce,theauthorsofeachchapterfocusona singleaspect,suchasfuelformulation,enginecalibrationparameters, combustionchamberdesign,detectingabnormalenginephenomena(e.g., lowpressureignition),andmore.Furthermore,theauthorsdonotfocus solelyonthecurrentlearningalgorithms theyalsoprovideahistorical perspectiveandcoveroptimizationmethodsbasedontheprinciplesof evolution,forexample,geneticalgorithms.

Thisbookprovidesathoroughandtimelyoverviewofwhere AI-assistedengineoptimizationstandstoday,makingitavaluablereference fornewresearchersandseasonedenginedesignersalike.Theeditorshave succeededinchoosingnotonlytherighttopicstocoverbutalsotheright expertstowriteaboutthem.

WeareenteringabravenewworldfortheICE.Ourenginesmustbe cleanerandgreenerthanevertostayrelevantintoday’sdecarbonizing society.Thisbooklaysoutthemostpromisingtoolswehaveavailableto

ensuretheICEreachesitsfullpotential.Throughtherightcombinationof humanintelligenceandAI,advancedengineswillhelpusdecarbonize transportation.

Dr.KellySenecal Co-founder,ConvergentScience Madison,WI,UnitedStates

Dr.RobertWagner Director,BuildingsandTransportationScienceDivision, OakRidgeNationalLaboratory Knoxville,TN,UnitedStates

Preface

Internalcombustionenginesarenotgoingtogoawayanytimesoon. Internalcombustionengineiscurrentlytheprincipalprimemoverinthe transportationsector,anditisexpectedtomaintainitsdominancein theforeseeablefuture.Wetherefore,stronglybelievethatimprovingthe efficiencyandreducingtheemissionsfrominternalcombustionenginesare themosteffectivemeasurestohaveanimmediateandprominentimpacton theenvironment.Ouraiminthistextistoprovideanexpositionofvarious engineandfueloptimizationtechniquesandtoshowcasesomeoftheir applications.Specifically,thedesignoptimizationusingadvancedartificial intelligenceanddata-drivenmethodsisthefocusofthistext.Wehopethat thereaderswillenjoyitand,moreimportantly,learnfromit.

Thetextisprimarilyadaptedfrompreviousarchivalpublicationsmade bythevariousactiveresearchgroupsinthisspecificarea.Theauthorslistis acombinationofleading,reputable,andactiveexpertsinthe fieldofinternalcombustionenginesandartificialintelligence.Wearegratefultothe authorsfortheirinvaluablecontributionsandaretrulyhonoredtohave workedwithsuchanelitegroupofworld-renownedresearchers.

JihadBadra,TeamLeader

EngineCombustionTeam,TransportTechnologiesDivision, ResearchandDevelopmentCenter,SaudiAramco,Dhahran, EasternProvince,SaudiArabia

PinakiPal,ResearchScientist EnergySystemsDivision,ArgonneNationalLaboratory, Lemont,IL,UnitedStates

YuanjiangPei,TeamLeader

ComputationalModelingTeam,AramcoAmericas:Aramco ResearchCenter Detroit,Novi,MI,UnitesStates

SibenduSom,Manager

Multi-PhysicsComputationSection,EnergySystemsDivision, ArgonneNationalLaboratory,Lemont,IL,UnitedStates

CHAPTER1 Introduction

BalajiMohan1,PinakiPal2,JihadBadra1,YuanjiangPei3 and SibenduSom2

1TransportTechnologiesDivision,ResearchandDevelopmentCenter,SaudiAramco,Dhahran,Eastern Province,SaudiArabia; 2EnergySystemsDivision,ArgonneNationalLaboratory,Lemont,IL,United States; 3AramcoAmericas:AramcoResearchCenter Detroit,Novi,MI,UnitedStates

1.Industrialrevolution

The firstindustrialrevolutionusedsteampowerforproductionand mechanization.Thesecondusedelectricpower,whichalsoledtomass production.Theadvancementsinelectronicsandinformationtechnology ledtothethirdindustrialrevolution.Nowbuildinguponthethird,the digitalrevolutionistakingplacefromthelatelastcentury.Thetechnology breakthroughsin fieldssuchasartificialintelligence(AI),bigdataanalytics, internetofthings,robotics,autonomousvehicles,unmannedaerialvehicles, three-dimensional(3D)printing,modeling,intelligentsensing,cloud computing,mobility,andaugmented/virtualrealityaremultiplyingthe enormouspossibilitiestoadvanceindustriestomakethemsmarter[1].

Fig.1.1 showsthestagesofrevolutiontheindustriallandscapehasgone throughovertheperiod.AItechnologiesdrivenbybigdatawillfuelthe fourthindustrialrevolution.AlthoughAI’srootscanbetracedbacktothe 1970s,itssubstantialimpactonthelatestindustrialrevolutionhasbeen evidentoverthelastonetotwodecades.

Figure1.1 Stagesoftheindustrialrevolution.

ArtiﬁcialIntelligenceandDataDrivenOptimizationof InternalCombustionEngines

ISBN978-0-323-88457-0 https://doi.org/10.1016/B978-0-323-88457-0.00002-3

2 ArtiﬁcialIntelligenceandDataDrivenOptimizationofInternalCombustionEngines

2.Artiﬁcialintelligence,machinelearning,anddeep learning

AIhasalreadyoccupiedanessentialplaceinourlives,fromself-drivingcars tovirtualassistants.Theexponentialincreaseincomputingpoweroverthe lastdecadesandthevastamountofdatacollectedthroughthestartofthe thirdrevolutionhavepavedthepathforAIdevelopmentandprogressin recentyears.ThetermsAI,machinelearning(ML),anddeeplearning(DL) aremostlyusedinterchangeably.However,thesetermsaredifferentby definition. Fig.1.2 showsthehierarchyofAI,ML,andDL.AIisthe scienceofmakingthingssmart.Inbroadterms,itisadisciplinetomake computersperformcomplexhumantasks,solvecomplexproblems,and makeintelligentdecisions[2].MLisasubsetofAI,anditisanapproachto achieveAIthroughsystemsthatcanlearnfromexperienceto findpatterns indatasets.In1959,ArthurSamuel,acomputerscientist,coinedtheterm “machinelearning,” whichisdefinedas “computer’sabilitytolearn withoutbeingexplicitlyprogrammed” [3].TheMLalgorithmstakeknown data,understandpatterns,classify,cluster,orpredictnewdata[4].Onthe otherhand,DLisasubsetofML,whichgenerallyusesneuralnetworks withastructuresimilartohumanneuralsystemstoanalyzeandsolvea particularproblem[5].

Hierarchyofartiﬁcialintelligence(AI),machinelearning(ML),anddeep learning(DL).

Figure1.2

3.Machinelearningalgorithms

TherearedifferentMLalgorithmsbasedontheirapplications.Thealgorithmscanbeclassiﬁedintothreecategoriesbasedontheirlearning styles supervised,unsupervised,andreinforcementlearning,asshownin

Fig.1.3 [6 8].Insupervisedlearning,thealgorithmsaretrainedusing labeleddatatogenerateafunctionthatmapsthetargetstotheinputs.The labeleddatapairtheinputswiththeirrespectivetargets.Thetraining processiscontinueduntilthemodelachievesadesiredaccuracyonthe trainingdata.SomecommonlyusedsupervisedMLalgorithmsarelinear regression,logisticregression,decisiontrees,NaïveBayes,neuralnetworks,

nearestneighbors,supportvectormachines,gradientboostedtrees,and randomforest.Inunsupervisedlearning,thealgorithmsuseunlabeleddata toclusterorsegmentthemintodifferentgroupsbyidentifyingspeciﬁc patterns.K-meansclustering,principalcomponentanalysis,association rules,andt-distributedstochasticneighborembeddingarecommonlyused unsupervisedlearningalgorithms.Thereinforcementlearningalgorithm iterativelylearnsfromitsenvironment.Intheprocess,thealgorithmalso referredtoasanagent,learnsfromitsexperienceuntilitexploresthefull rangeofpossiblestates.Itworksonasimplefeedbacksystemtomaximize therewardorminimizetherisk.Thisfeedbackiscalledareinforcement signal.Somecommonlyusedreinforcementlearningalgorithmsare Q-learning,temporaldifferences,MonteCarlotreesearch,andasynchronousactor-criticagents.

TherearemanyapplicationsforMLalgorithmsinday-to-daylives,for example,imagerecognition,virtualassistantslikeSIRI,Alexa,andCortana, speechpredictionsforsearchbyvoicetechnology,trafﬁcpredictionbasedon real-timelocationdata,productrecommendationsbyvariouse-commerce websites,self-drivingcars,emailspam,andmalware ﬁltering,stockmarket diagnosis,automaticlanguagetranslation,andmedicaldiagnosis.The applicationsofMLalgorithmshavefurtherexpendedtooptimizetheperformanceandemissionsfrominternalcombustionengines(ICEs).

4.Artiﬁcialintelligence basedfuel-engine co-optimization

AI-basedco-optimizationofICEandfuelincludesoptimizingengine designandoperatingparameters,fuelformulation,andproperties,and mitigatingtheunwantedandpotentiallydamagingcombustionevents. Optimizationoffuelformulationincludeshigh-throughputscreening, predictionoffuelandmixtureproperties,fuelformulation,anddeveloping reactionmechanismsforfuels.Inaddition,AI-basedapproachescanpredict abnormalcombustioneventssuchaspreignitiontoassistmanufacturersin developingenginessusceptibletothisphenomenonanddiagnosethe preignitionduringon-boarddiagnosisandmitigateit.Thefollowingsectionswillreviewthevariousapplicationsindetail.

4.1Optimizationofinternalcombustionengine

ICEshavebeenaroundsincethe19thcentury,andtheirconceptual identityasafuel-poweredmachinehasnotchangedsince.Despitethe complexityofICEs,therehavebeensigniﬁcanttechnologicalimprovementstotheirperformanceinresponsetofuelefﬁciencyandemission

regulations[9].Manyparametersaffecttheperformanceandemission characteristicsofanICE.Theseincludebutnotlimitedto,fuelcharacteristics[10,11],exhaustgasrecirculation(EGR)[12 14],pistonbowl geometry[15,16],injectionstrategy[17 23],injectiontiming[19,24,25], sprayangle[26 28],andswirlratio[20,29,30].Althoughin-depthstudies ofasingleparametermayprovidevaluableinsightsintoitseffectsonengine performance,theinteractionsbetweenthevariousinputparametersand outputvariablesareinterlinked,nonlinear,andcomplex.

Thetoolsusedtoco-optimizethefuel/enginesystemhaveevolved overtheyears.Until20yearsago,experimentalprototyping(manual)was themainoptimizingmethod.Itwasfollowedbynumericalsimulations, includingcomplex3Dcomputational fluiddynamics(CFD),whichplayed amajorroleinengine/fuelsystemoptimization.Thisdevelopmentwas enabledbythesignificantadvancementsincomputingpower(supercomputers,clusters,andparallelization)andnumericalmodels(turbulence, combustion,spray,heattransfer,meshing,andmovingboundaries).Dueto highdimensionality,complexity,andhighlynonlineardependenciesin enginepropertiesandresponses,bothexperimentalandnumericaloptimizationapproachescanbeinefficientandtakeasignificantamountoftime andefforttoobtainlocalratherthanglobaloptimumdesignsandoperating conditions[31 33].Alternativeapproacheshavebeendevelopedoverthe yearstoovercometheissueswithmanualoptimizations.Thesemethodsin chronologicalorderincludethedesignofexperiments(DoE),genetic algorithms(GAs),ML,andAI,andtheevolutionalongwiththehigh-level enablersandlimitationsareshownin Fig.1.4.Moredetailsabouteachof therecentoptimizationtechniquesareprovidedinthefollowingsections.

4.1.1Designofexperiments

Intheearlydays,engineoptimizationwasperformedmanuallyusingvery fewdesigns,mainlyduetolimitedcomputationalpowerandresources. Later,theDoE-responsesurfacemethodology(RSM)optimizationmethod becameverypopularandwasusedwidelyinengineoptimizationdueto advancementsinthe ﬁeldofinformationtechnology[34 39].Ituses statisticaltechniquestobuildproperresponsesurfacesforoptimization.The DoE-RSMoptimizationisconsideredtheindustry-standardpracticeasthey provideaquicksolutionwithfewerresources.Forinstance,Bessonetal. [40]fromRenaultusedtheCFD-DoEstrategytooptimizethehomogenouschargecompressionignition(HCCI)combustionchamberunderfull loadoperatingpointusingaDoEsizeof30.Hanetal.[41]fromFordused two-pistondesignsandtwoinjectorconﬁgurationstooptimizetheintake

Figure1.4 Evolutionofengineperformanceandemissionoptimizationstrategies.

andsprayinjectionpatternsusingtheCFD-DoEoptimizationapproachof anewlightstratifiedchargedirectinjectionsparkignition(DISI)combustionsystem.Lippertetal.[42]fromGeneralMotorsandSuzukiMotor CorporationsjointlyoptimizedpistonandinjectordesignusingCFD-DoE comprisingthree-pistondesignsandseveninjectorconfigurationsofasmall displacementspark-ignited(SI)directinjection(DI)engineunderstratified operation.Hajirezaetal.[37]fromAVLusedaDoEsizeof16designsto optimizethepistonbowl,sprayconeangle,andintakeportswirllevelofa dieselengineusingCFD.Davisetal.[43]fromGeneralmotorsandRobert Boschjointlydevelopedthe3.6LDOHC4VV6DIengine’scombustion systemusingtheCFD-DoEoptimizationapproachfromsix-pistondesigns andthreeinjectorconfigurations.Reicheetal.[44]fromFordexperimentallyoptimizedthecoldstartfortheEcoBoostengineusingtheDoE techniquewiththreepistonsandfour-portdesigns.Cataniaetal.[45]used experimental-DoEtooptimizenewcombustionsystemdevelopment specificallyorientedtowardpremixedchargecompressionignitionwith three-pistondesigns.Styronetal.[38]usedtheCFD-DoEapproachto optimizetheFord20116.7LPowerStrokedieselenginewithaDoEsizeof 16designscomprisingthreepistons.Rajamanietal.[36]performeda parametricanalysisofpistonbowlgeometryandinjectionnozzleconfigurationusingCFD-DoEwith16designs.Peietal.[39]fromAramco ResearchCentercollaboratedwithArgonneNationalLabandConvergent ScienceInc.tooptimizethepistonbowlgeometry,thenumberofinjector nozzles,totalnozzlearea,nozzleinclusionangle,andthestartofinjection (SOI)ofaheavy-duty(HD)engineusingacomprehensiveCFD-DoE approach.Later,Peietal.[15]usedCFD-DoEcomprising256combustionchamberdesignstooptimizeinjectorspraypatterns,fuelinjection strategies,andin-cylinderswirlmotiontoachievebetterfuelefficiency fromagasolinecompressionignition(GCI)engine.Probstetal.[34]used theCFD-DoE-GAapproachtooptimizefuelconsumptionandNOx emissionsfromadieselengine.

4.1.2Geneticalgorithm

TheadvancementsincomputationalpowerenabledGA[46 48]toevolve asapopularoptimizationapproachamongresearchers.GAtypicallyhasa betterchanceof ﬁndingglobaloptimumsolutionsdespitethepresenceof multiplelocalminima.Itiswidelyusedinacademiaduetotheconsiderable resourcesandtimerequiredtodevelopanefﬁcientapproachtobettersolve aproblem.Forinstance,SenecalandReitz[49]optimizedaDIdiesel

engine’semissionsandfuelconsumptionsimultaneouslyusingtheCFDGAapproach.Risietal.[50]usedCFD-GAtoselectacombustion chamber,givingthebestcompromiseoftheselected ﬁtnessfactorsbasedon theengineemissionslevelsandapenaltyfunctiontoaccountforengine performance.ShiandReitz[51]usedCFD-GAtostudytheeffectsofbowl geometry,spraytargeting,andswirlratioforaHDdieselengineoperating atahighload.Thesimultaneousreductioninemissionsandimprovedfuel consumptionwasachievedinthisoptimizationstudy.Geetal.[52] optimizedthehigh-speedDIdieselengine’sSOI,swirlratio,boostpressure, andinjectionpressureusingtheCFD-GAapproachtoreducefuelconsumptionandpollutantemissionssimultaneously.Hansonetal.[53] optimizedthereactivitycontrolledcompressionignitioncombustionina light-dutymulticylinderengineunderthreeoperatingconditionsintheUS EnvironmentalProtectionAgencylight-dutyFederalTestProcedure(FTP) testtoimprovethermalefﬁciencywhilemaintaininglownitrogenoxide (NO)emissions.Bertrametal.[51]usedexperimentscombinedwithGA andparticleswarmoptimization(PSO)tooptimizeasingleobjective functionrepresentingoxidesofnitrogen(NOx),particulatematter(PM), hydrocarbon(HC),carbonmonoxide(CO),andfuelconsumptionofa dieselengine.Zhangetal.[52]combinedexperimentswithGAandPSO tooptimizeadieselenginefueledwithsoybiodiesel.Thedynamometer time,fueleconomy,andexhaustemissionswereimprovedusingthehybrid GA-PSOalgorithm.Recently,Broatchetal.[54]combinedCFD modelingandGAtechniquetooptimizethecombustionsystemhardware designofahigh-speedDIdieselengine,withrespecttovariousemissions andperformancetargets,includingcombustionnoise.

4.1.3Machinelearning basedalgorithms

WithadvancementsindigitizationandAIalgorithms,MLhasemergedas aneffectivetoolforoptimizingcomplexsystems,suchasenginecombustionconcepts.ManystudieshaveshownthatML-basedoptimizationapproachesperformbetterthantheRSM-basedmethodwhenthedataset’ s complexityincreases[55,56].Further,theMLmodelscanbeusedassurrogatestorepresent/replacetheenginesystem.Artiﬁcialneuralnetworks (ANNs)havebeenutilizedtooptimizeengineperformancesandemissions orrepresenttheenginesystemasasurrogatemodel.Forinstance,Traver etal.[57]employedneuralnetworkstopredictNOx andcarbondioxide (CO2)emissionsusingin-cylinderpressure-basedvariables.Hafneretal. [58]usedfastneuralnetworkmodelsfordieselenginecontroldesign.

Theneuromodelswereintegratedwithanupper-levelemissionoptimizationtooltodeterminetheoptimalenginesettingstominimizethe emissions.Desantesetal.[59]usedHDdieselengineoperatingconditions asdesignparameterstoneuralnetworkstopredictexhaustemissions,such asNOx andPM.Furthermore,theyoptimizedtheenginetomeetEURO IVemissionsstandards.BrahmaandRutland[60]usedneuralnetworksto predictthepressure,temperature,heat flux,torque,andemissionsfroma dieselengine’soperatingparameters.Theyintegratedneuralnetwork modelswithaGAcombinedwithahill-climbingstrategytooptimizethe engine’sentirespeed-torquemap’soperatingparameters.Theneuralnetworksactedasanenginemaptosimulatetheengine’semissionsoverthe FTPHDdieselcyclewithreasonableaccuracyandshortcomputational time.HeandRutland[61]usedANNstooptimizedieselengineemissions usingCFD.Sevencontrolparameters,namely,enginespeed,engineload, theSOI,injectionpressure,fuelmassinthe firstinjection,boostpressure, andEGR,wereusedasinputstotheneuralnetworks.Theneuralnetworks predicted fiveobjectiveparameters:cylinderpressure,cylindertemperature, cylinderwallheattransfer,NOx,andsootemissions.Kesgin[9]useda neuralnetworkandasimpleGAtopredictandoptimizetheeffectsof designandoperationalparametersonnaturalgasengineefficiencyandNOx emissions.Wuetal.[62]usedANNasasurrogatemodelrepresentingthe engine’sresponsetodifferentcontrolvariablecombinationsofvariable valveactuationtechnologywithgreatlyreducedcomputationaltime.The optimalcam-phasingstrategyobtainedatwide-openthrottleforadual independentvariablevalvetiming(VVT)enginewaswell-validatedwith enginedynamometertests.Anandetal.[63]usedANNtopredictSIengineefficiencyandNOx emissionbasedonenginedesignandoperational parameterstrainedonquasi-dimensional,two-zonethermodynamic simulationdata.TheANNmodelwassuggestedasanalternativetothe model-basedsimulationsforreal-timecomputationsintheelectronic controlunit(ECU)duetoitsgoodaccuracyandlowcomputationaltime. Atashkarietal.[64]usedSIengineexperimentaldatacomprisingintake valvetimingandenginespeedasinputsandenginetorqueandfuelconsumptionasoutputstotraingroupmethodofdatahandlingtypeneural network.Then,anondominatedsortinggeneticalgorithm(NSGA-II)was usedtooptimizetheVVTSIengine’sperformance.Ashhab[65]usedfeedforwardANNtopredicttheengineoutputs,pumpingloss,andcylinderair chargeusingintakevalveliftandclosingtimeasinputs.Thisneural networkmodeldevelopedacamlessengineinversecontrolalgorithm,

whichperformedbetterthanthegraphical-basedtechniques.Cruz-Peragon [66]usedacombinationofangularspeedmeasurementsandANNfor combustionfaultdiagnosisinICE.Theexperimentaldatafromthree differentcombustionengineswereutilizedfortrainingtheANN-based combustiondiagnosistooltopredictdefectsinpressurewaveformsand othercombustionindicators,suchasfuelconsumptionandignitiontiming. Kianietal.[67]usedANNtopredicttheenginebrakepower,torque,and exhaustemissions,suchasCO,CO2,NOx,andHC.TheANNwastrained usingexperimentaldatafromSIenginefueledbyethanol-gasolineblendsin differentpercentages(0%,5%,15%,and20%)operatedatdifferentengine speedsandloads.TheauthorssuggestedthattheANNcouldbeusedasa surrogatemodelduetoitsadvantageoffast,accurate,andreliablepredictionswhenothernumericalandmathematicalmodelsfail.Tasdemir etal.[68]modeledtheSIengineperformanceandemissionsusingANN andfuzzyexpertsystem.Themodelwasappliedtopredicttheengine power,torque,specificfuelconsumption(SFC),andHCemissions.The modelperformedwellinpredictingtheengineperformanceandemissions ofnewexperiments.Barmaetal.[69]predictedthedieselengineparameterssuchasmeaneffectivepressure,efficiency,fuelconsumption,air-fuel ratio,andtorquefromenginespeed,load,anddifferentbiodieselanddiesel fuelblendsasinputsusingabackpropagation-basedANN.Themodel showedbetteraccuracyinpredictingengineperformancefueledbybiodieselanddieselfuels.YapandKarri[70]developedatwo-stageANN wherethe firststagepredictionsofpowerandtractiveforceswereusedas thesecondstageANNinputstopredictCO,CO2,HC,andoxygen(O2) fromascooterengine.Themodelwassuggestedtobeusedasavirtual emissionsensorwithoutadditionalequipmentandfurthersubstantiatesthat onlyasingleECUwasrequiredtopredictengineperformanceand emissions.Çayetal.[71]usedANNtopredictthebrake-specificfuel consumption(BSFC),air-fuelratio,COandHCemissionsfromengine speed,torque,fuel flowrate,andfuelblendratiosofamethanol-gasoline blendfueledgasolineengine.TheANNmodel’spredictionsshowedthat methanolasafuelresultedinloweremissionswhilegasolinefuelledto betterengineperformance,andtheexperimentalresultsvalidatedthe predictions.Taghavifaretal.[72]trainedanANNmodeltoactasasurrogatemodeltopredictspraycharacteristics,suchasliquidpenetration lengthandSautermeandiameter.TheANNmodelwastrainedwitha combinationofexperimentalandCFDsimulationdataofspraycharacteristicswhereenginecrankangle,vapormass flowrate,turbulence,and