Simulation-Driven Modeling and Optimization: ASDOM, Reykjavik, August 2014 PDF
Preview Simulation-Driven Modeling and Optimization: ASDOM, Reykjavik, August 2014
Springer Proceedings in Mathematics & Statistics Slawomir Koziel Leifur Leifsson Xin-She Yang Editors Simulation- Driven Modeling and Optimization ASDOM, Reykjavik, August 2014 Springer Proceedings in Mathematics & Statistics Volume 153 Moreinformationaboutthisseriesathttp://www.springer.com/series/10533 Springer Proceedings in Mathematics & Statistics Thisbookseriesfeaturesvolumescomposedofselectcontributionsfromworkshops and conferences in all areas of current research in mathematics and statistics, includingORandoptimization.Inadditiontoanoverallevaluationoftheinterest, scientific quality, and timeliness of each proposal at the hands of the publisher, individual contributions are all refereed to the high quality standards of leading journals in the field. Thus, this series provides the research community with well-edited, authoritative reports on developments in the most exciting areas of mathematicalandstatisticalresearchtoday. Slawomir Koziel • Leifur Leifsson Xin-She Yang Editors Simulation-Driven Modeling and Optimization ASDOM, Reykjavik, August 2014 123 Editors SlawomirKoziel LeifurLeifsson EngineeringOptimization DepartmentofAerospaceEngineering &ModelingCenter CollegeofEngineering ReykjavikUniversity IowaStateUniversity Reykjavik,Iceland Ames,Iowa,USA Xin-SheYang SchoolofScienceandTechnology MiddlesexUniversity London,UnitedKingdom ISSN2194-1009 ISSN2194-1017 (electronic) SpringerProceedingsinMathematics&Statistics ISBN978-3-319-27515-4 ISBN978-3-319-27517-8 (eBook) DOI10.1007/978-3-319-27517-8 LibraryofCongressControlNumber:2015960792 SpringerChamHeidelbergNewYorkDordrechtLondon ©SpringerInternationalPublishingSwitzerland2016 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof thematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation, broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology nowknownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthisbook arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor theeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinorforany errorsoromissionsthatmayhavebeenmade. Printedonacid-freepaper SpringerInternational PublishingAGSwitzerlandispartofSpringerScience+Business Media(www. springer.com) Preface Accurate modeling and simulation of complex systems often necessitates highly sophisticated but very computationally expensive models, and the costs and time of computational simulations can pose challenging issues in many applications. In addition, the search for optimum designs requires multiple simulation-design cycles,oftenfromhundredstothousandsofevaluationsofdesignobjectives,which makesmodelingandoptimizationtasksextremelychallengingandtime-consuming. Among these challenges, a serious bottleneck for realizing an efficient design optimization process is the high cost of computer simulations with simulation times varying from hours to weeks or even months for large complex systems, which means that even a single set of simulations can be very costly. For a typicaldesign process, differentdesign optionsmay require manyvalidationsand verifications using computer models in order to test “what-if” scenarios so as to providedecision-makerswithrobust,realisticdesignoptions.Thoughthespeedof thecomputerpowerhassteadilyincreasedoverpastdecades,however,suchspeed increasecan slightlyease onlypartof the modelingandsimulationproblems,and these challenging issues still remain largely unresolved.One of the reasons is the ever-increasing demand of the high-accuracy, high-fidelity models for simulating complexsystems. In addition, the search for more sustainable optimum designs makes such simulationtasksmorechallengingtosolve.Evenwithagoodsimulationmodel,the numberofevaluationsandvalidationsofdesignobjectivescanbeveryhigh,varying from several hundreds to thousands or even millions of objective calls. In many cases,suchoptimizationproblemscanbeNP-hard,andthusnoefficientalgorithms exist.Therefore,sometradeoffisneededinpracticetobalancethesolutionaccuracy andpracticalityofsimulationanddesigns. Furthermore,for many applicationsin aerospaceengineering,microwaveengi- neering, gas transport networks, waste management, and system engineering, alternativemethodsorapproximationsmethodsare oftenused.A classofapprox- imation techniques and alternative approaches are the surrogate-based modeling, simulation-driven design optimization, and metaheuristic optimization methods. These surrogate-based and simulation-driven approaches provide the possibility v vi Preface of using faster and cheaper surrogate models to represent expensive models with adequate accuracy and sufficiently reduced simulation times, which consequently makesmanycomplexdesignoptimizationtaskssolvableandachievableinpractice. Amongallthekeyissuesandtechniques,themainaimsarethreefold:toincrease the accuracy of modeling and simulation, to reduce the simulation and design time, and to come up with more robust and sustainable design options. First, to getmoreaccuratesimulationresults,sophisticatedsurrogatemodelsareneededto provide near high-fidelity results; this usually requires many sampling points in the search space in order to produce highly representative surrogates, which will indirectly increase the computationalcosts. Second, in order to reduce simulation and design time, efficient optimization algorithms are needed in addition to the efficient approximate surrogate models. Traditional algorithms such as gradient- basedmethodsandtrust-regionmethodsdonotworkwell.Thus,newmethodssuch asthosebasedonswarmintelligencemethodscanbepromising.Inreality,agood combinationofnewmethodswiththeexistingtraditionalmethodscanoftenobtain goodresults.Finally,researchersanddesignershavetocombineallthetechniques andresourcessoastoproducerobustandsustainabledesignoptions.Somedesign options may satisfy all the design requirements, but they may be very sensitive to manufacturing errors, and they may not be sustainable. Thus, designers often haveto producea diverseset of designoptionsand thusallow decision-makersto choose the most suitable design options under given stringent design constraints. Anysuccessfuldesigncyclerequirestodealwiththeabovethreechallengingissues withsufficientaccuracyinapracticallyacceptabletimelimit. Thiseditedbookprovidesatimelysummaryofsomeofthelatestdevelopments inmodelingandsimulation-drivendesignoptimization.Topicsincludeaerodynamic optimization,gastransportnetworks,antennadesigns,microwavestructures,filter designs, waste management, system identification, crystal nanostructures, sparse grids,andothercomputationallyextensivedesignapplications.Therefore,thisbook can serve as a reference to researchers, lecturers, and engineers in engineering design, modeling, and optimization as well as industry where computationally expensive designs are most relevant. It is our hope that this may help researchers and designs to produce better design tools so as to reduce the costs of the design processaidedbycomputersimulations. Reykjavik,Iceland SlawomirKoziel Ames,IA,USA LeifurLeifsson London,UK Xin-SheYang October2015 Contents Numerical Aspects of Model Order Reduction for Gas TransportationNetworks........................................................ 1 SaraGrundel,NilsHornung,andSarahRoggendorf ParameterStudiesforEnergyNetworkswithExamplesfrom GasTransport .................................................................... 29 TanjaClees Fast Multi-Objective Aerodynamic Optimization Using Space-Mapping-CorrectedMulti-FidelityModelsandKriging Interpolation...................................................................... 55 Leifur Leifsson, Slawomir Koziel, Yonatan Tesfahunegn, andAdrianBekasiewicz AssessmentofInverseandDirectMethodsforAirfoilandWingDesign.. 75 MengmengZhangandArthurWilliamRizzi PerformanceOptimizationofEBG-BasedCommonMode FiltersforSignalIntegrityApplications....................................... 111 CarloOlivieri,FrancescodePaulis,AntonioOrlandi,andSlawomir Koziel Unattended Design of Wideband Planar Filters Using aTwo-StepAggressiveSpaceMapping(ASM)OptimizationAlgorithm.. 135 MarcSans,JordiSelga,AnaRodríguez,ParisVélez,VicenteE. Boria,JordiBonache,andFerranMartín Two-Stage Gaussian Process Modeling of Microwave StructuresforDesignOptimization............................................ 161 J.PieterJacobsandSlawomirKoziel EfficientReconfigurableMicrostripPatchAntennaModeling ExploitingKnowledgeBasedArtificialNeuralNetworks ................... 185 MuratSimsekandAshrfAoad vii viii Contents Expedited Simulation-Driven Multi-Objective Design OptimizationofQuasi-IsotropicDielectricResonatorAntenna............ 207 AdrianBekasiewicz,SlawomirKoziel, WlodzimierzZieniutycz,andLeifurLeifsson OptimalDesignofPhotonicCrystalNanostructures ........................ 233 Abdel-KarimS.O. Hassan, NadiaH. Rafat, andAhmedS.A. Mohamed DesignOptimizationofLNAs andReflectarrayAntennas UsingtheFull-WaveSimulation-BasedArtificialIntelligence ModelswiththeNovelMetaheuristicAlgorithms............................ 261 FilizGünes¸,SalihDemirel,andSelahattinNesil StochasticDecision-Making inWasteManagement Using a Firefly Algorithm-Driven Simulation-Optimization ApproachforGeneratingAlternatives ........................................ 299 RahaImanirad,Xin-SheYang,andJulianScottYeomans Linear and Nonlinear System Identification Using EvolutionaryOptimisation...................................................... 325 K. Worden, I. Antoniadou, O.D. Tiboaca, G. Manson, andR.J.Barthorpe A Surrogate-Model-AssistedEvolutionaryAlgorithmfor ComputationallyExpensive DesignOptimizationProblems withInequalityConstraints..................................................... 347 BoLiu,QingfuZhang,andGeorgesGielen SobolIndicesforDimensionAdaptivityinSparseGrids.................... 371 Richard P. Dwight, Stijn G.L. Desmedt, andPejmanShoeibiOmrani Index............................................................................... 397 Numerical Aspects of Model Order Reduction for Gas Transportation Networks SaraGrundel,NilsHornung,andSarahRoggendorf Abstract Thechapterfocusesonthenumericalsolutionofparametrizedunsteady Eulerian flow of compressible real gas in pipeline distribution networks. Such problemscanleadtolargesystemsofnonlinearequationsthatarecomputationally expensiveto solveby themselves,moreso if parameterstudiesare conductedand thesystemhastobesolvedrepeatedly.Thestiffnessoftheproblemaddsevenmore complexityto thesolutionof these systems. Therefore,we discussthe application of model order reduction methods in order to reduce the computational costs. In particular,weapplytwo-sidedprojectionviaproperorthogonaldecompositionwith the discrete empiricalinterpolationmethodto exemplaryrealistic gasnetworksof differentsize.Boundaryconditionsarerepresentedasinflowandoutflowelements, where either pressure or mass flux is given. On the other hand, neither thermal effects nor more involved network components such as valves or regulators are considered. The numerical condition of the reduced system and the accuracy of its solutions are compared to the full-size formulation for a variety of inflow and outflowtransientsandparameterrealizations. Keywords Gas network simulation • Model order reduction • Proper orthogo- nal decomposition • Discrete empirical interpolation method • Stiff initial-value problems MSC code:65K05(NumericalAnalysis,mathematicalprogrammingmethods) S.Grundel MaxPlanckInstituteMagdeburg,Magdeburg,Germany e-mail:[email protected] N.Hornung((cid:2))•S.Roggendorf FraunhoferSCAI,SanktAugustin,Germany e-mail:[email protected];[email protected] ©SpringerInternationalPublishingSwitzerland2016 1 S.Kozieletal.(eds.),Simulation-DrivenModelingandOptimization, SpringerProceedingsinMathematics&Statistics153, DOI10.1007/978-3-319-27517-8_1
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