Design optimization of fluid machinery: applying computational fluid dynamics and numerical optimization

308 Pages·2019·16.712 MB·English

by  Kim, Kwang-Yong

DesignOptimizationofFluidMachinery

Design Optimization of Fluid Machinery
ApplyingComputationalFluidDynamicsand
NumericalOptimization
Kwang-YongKim
InhaUniversity
Incheon
RepublicofKorea
AbdusSamad
IndianInstituteofTechnologyMadras
Chennai
India
ErnestoBenini
UniversityofPadova
Italy
Thiseditionfirstpublished2019
©2019JohnWiley&SonsSingaporePte.Ltd
Allrightsreserved.Nopartofthispublicationmaybereproduced,storedinaretrievalsystem,ortransmitted,inanyform
orbyanymeans,electronic,mechanical,photocopying,recordingorotherwise,exceptaspermittedbylaw.Adviceonhow
toobtainpermissiontoreusematerialfromthistitleisavailableathttp://www.wiley.com/go/permissions.
TherightofKwang-YongKim,AbdusSamad,andErnestoBeninitobeidentifiedastheauthorsofthisworkhasbeen
assertedinaccordancewithlaw.
RegisteredOffices
JohnWiley&Sons,Inc.,111RiverStreet,Hoboken,NJ07030,USA
JohnWiley&SonsLtd,TheAtrium,SouthernGate,Chichester,WestSussex,PO198SQ,UK
EditorialOffice
TheAtrium,SouthernGate,Chichester,WestSussex,PO198SQ,UK
Fordetailsofourglobaleditorialoffices,customerservices,andmoreinformationaboutWileyproductsvisitusat
www.wiley.com.
Wileyalsopublishesitsbooksinavarietyofelectronicformatsandbyprint-on-demand.Somecontentthatappearsin
standardprintversionsofthisbookmaynotbeavailableinotherformats.
LimitofLiability/DisclaimerofWarranty
Whilethepublisherandauthorshaveusedtheirbesteffortsinpreparingthiswork,theymakenorepresentationsor
warrantieswithrespecttotheaccuracyorcompletenessofthecontentsofthisworkandspecificallydisclaimallwarranties,
includingwithoutlimitationanyimpliedwarrantiesofmerchantabilityorfitnessforaparticularpurpose.Nowarrantymay
becreatedorextendedbysalesrepresentatives,writtensalesmaterialsorpromotionalstatementsforthiswork.Thefact
thatanorganization,website,orproductisreferredtointhisworkasacitationand/orpotentialsourceoffurther
informationdoesnotmeanthatthepublisherandauthorsendorsetheinformationorservicestheorganization,website,or
productmayprovideorrecommendationsitmaymake.Thisworkissoldwiththeunderstandingthatthepublisherisnot
engagedinrenderingprofessionalservices.Theadviceandstrategiescontainedhereinmaynotbesuitableforyour
situation.Youshouldconsultwithaspecialistwhereappropriate.Further,readersshouldbeawarethatwebsiteslistedin
thisworkmayhavechangedordisappearedbetweenwhenthisworkwaswrittenandwhenitisread.Neitherthepublisher
norauthorsshallbeliableforanylossofprofitoranyothercommercialdamages,includingbutnotlimitedtospecial,
incidental,consequential,orotherdamages.
®
MATLAB isatrademarkofTheMathWorks,Inc.andisusedwithpermission.TheMathWorksdoesnotwarrantthe
®
accuracyofthetextorexercisesinthisbook.Thiswork’suseordiscussionofMATLAB softwareorrelatedproductsdoes
notconstituteendorsementorsponsorshipbyTheMathWorksofaparticularpedagogicalapproachorparticularuseofthe
®
MATLAB software.
LibraryofCongressCataloging-in-PublicationData
Names:Kim,Kwang-Yong,1956-author.
Title:Designoptimizationoffluidmachinery:applyingcomputationalfluid
dynamicsandnumericaloptimization/Kwang-YongKim,Professor,Inha
University,Incheon,AbdusSamad,AssociateProfessor,IndianInstituteof
TechnologyMadras,Chennai,India,ErnestoBenini,Professor,University
ofPadova,Italy.
Description:Hoboken,NJ:Wiley,2019.|Includesbibliographicalreferences
andindex.|
Identifiers:LCCN2018044844(print)|LCCN2018045697(ebook)|ISBN
9781119188322(AdobePDF)|ISBN9781119188308(ePub)|ISBN9781119188292
(hardcover)
Subjects:LCSH:Computationalfluiddynamics.
Classification:LCCTA357.5.D37(ebook)|LCCTA357.5.D37K562019(print)|
DDC620.1/064–dc23
LCrecordavailableathttps://lccn.loc.gov/2018044844
CoverDesignbyWiley
CoverImage:CourtesyofKwang-YongKim
Setin10/12ptWarnockProbySPiGlobal,Chennai,India
10 9 8 7 6 5 4 3 2 1
Chihee,Minji,andSoonwook
–Kim
MywifeHusnahara,sonSohailanddaughterArshi
–Samad
Mybelovedfamily
–Benini

vii
Contents
Preface xiii
1 Introduction 1
1.1 Introduction 1
1.2 FluidMachinery:ClassificationandCharacteristics 2
1.3 AnalysisofFluidMachinery 4
1.4 DesignofFluidMachinery 7
1.4.1 DesignRequirements 7
1.4.2 DeterminationofMeanlineParameters 7
1.4.3 MeanlineAnalysis 8
1.4.4 3DBladeDesign 8
1.4.5 Quasi3DThrough-FlowAnalysis 8
1.4.6 Full3DFlowAnalysis 8
1.4.7 DesignOptimization 8
1.5 DesignOptimizationofTurbomachinery 9
References 10
2 FluidMechanicsandComputationalFluidDynamics 11
2.1 BasicFluidMechanics 11
2.1.1 Introduction 11
2.1.2 ClassificationofFluidFlow 11
2.1.2.1 BasedonViscosity 12
2.1.2.2 BasedonCompressibility 12
2.1.2.3 BasedonFlowSpeed(MachNumber) 12
2.1.2.4 BasedonFlowRegime 13
2.1.2.5 BasedonNumberofPhases 14
2.1.3 One-,Two-,andThree-DimensionalFlows 14
2.1.3.1 One-DimensionalFlow 15
2.1.3.2 Two-andThree-DimensionalFlow 15
2.1.4 ExternalFluidFlow 15
2.1.5 TheBoundaryLayer 15
2.1.5.1 TransitionfromLaminartoTurbulentFlow 16
2.2 ComputationalFluidDynamics(CFD) 16
2.2.1 CFDanditsApplicationinTurbomachinery 17
2.2.1.1 AdvantagesofUsingCFD 18
viii Contents
2.2.1.2 LimitationsofCFDinTurbomachinery 18
2.2.2 BasicStepsInvolvedinCFDAnalysis 19
2.2.2.1 ProblemStatement 19
2.2.2.2 MathematicalModel 19
2.2.3 GoverningEquations 19
2.2.3.1 MassConservation 20
2.2.3.2 MomentumConservation 20
2.2.3.3 EnergyConservation 21
2.2.4 TurbulenceModeling 21
2.2.4.1 WhatisTurbulence? 22
2.2.4.2 NeedforTurbulenceModeling 22
2.2.4.3 Reynolds-AveragedNavier–StokesEquations 22
2.2.4.4 TurbulenceClosureModels 23
2.2.4.5 LargeEddySimulation(LES) 27
2.2.4.6 DirectNumericalSimulation(DNS) 27
2.2.5 BoundaryConditions 27
2.2.5.1 Inlet/OutletBoundaryConditions 28
2.2.5.2 WallBoundaryConditions 28
2.2.5.3 Periodic/CyclicBoundaryConditions 28
2.2.5.4 SymmetryBoundaryConditions 29
2.2.6 MovingReferenceFrame(MRF) 29
2.2.7 VerificationandValidation 30
2.2.8 CommercialCFDSoftware 30
2.2.9 OpenSourceCodes 31
2.2.9.1 OpenFOAM 31
References 32
3 OptimizationMethodology 35
3.1 Introduction 35
3.1.1 EngineeringOptimizationDefinition 36
3.1.2 DesignSpace 36
3.1.3 DesignVariablesandObjectives 37
3.1.4 OptimizationProcedure 40
3.1.5 SearchAlgorithm 40
3.2 Multi-ObjectiveOptimization(MOO) 41
3.2.1 WeightedSumApproach 42
3.2.2 Pareto-OptimalFront 42
3.3 Constrained,Unconstrained,andDiscreteOptimization 43
3.3.1 ConstrainedOptimization 43
3.3.2 UnconstrainedOptimization 44
3.3.3 DiscreteOptimization 44
3.4 SurrogateModeling 44
3.4.1 Overview 44
3.4.2 OptimizationProcedure 44
3.4.3 SurrogateModelingApproach 44
3.4.3.1 ResponseSurfaceApproximation(RSA)Model 45
3.4.3.2 ArtificialNeuralNetwork(ANN)Model 46