A Comparative Study on Optimization in Structural Acoustics [Elektronische Ressource] / Mostafa Ranjbar. Gutachter: Hans-Jürgen Hardtke ; Steffen Marburg ; Ariosto Bretanha Jorge. Betreuer: Hans-Jürgen Hardtke

GutacEineerteidigung:vProf.ergleicUnivhendeh.c.StudieEinreiczurTOptimierungoninProf.derProf.StrukturakustikJorge(ATComparativesenehniscStudyDresdenonMostafaOptimizationterinhabil.StructuralHardtkASteencoustics)AriostoDagIung:SderSwEderRecThenAersit?tTvIM.Sc.(Eng.)ORanjbarNhzur:ErlangungDr.-Ing.desProf.akHans-J?rgenademiscehenDr.-Ing.GradesMarburgDoktor-IngenieurDr.(Dr.-Ing.)BretanhavTorgelegtderderhF09/09/2009akult?tagf?rVMasc25/03/2011hinenacousticAbstractforThisddissertationofpresenobtsandanofexhaustiveeascomparativNewtonesearcstudyalueonhoptimizationevinasstructuralwacoustics.pAicomfeasiblebinationmethoofoinads,commerciallymethoaavanailableTheniteelelemenastoinsoftiswtheareedpactkcase,ageandandativadditionale.g.,user-writtenquadraticprogramsmemoryisoundusedasymptotes,toandmoThedifyrithms,theareshapuseeeofisamethostructure.anThisrepisvdoneanditer-optimizationativmethoelysloandandwithoutaremands.ualtheindstervIfendstion(fasttothenacresultshievcomputationeensignicantthimprosecondvofemenetsdofsequenthemethoobd,jectivyden-Fletceforfunction.problems,ThemooptimizationmpromethocesstrolledconmethotinoptimizationuesgeneticautomaticallysearcunulatedtilativtheandpredenedexactmaximobumFnapproacumwbcomparisonerAofandfunctionofevalgorithmaluationsinisofreac(ahed.
Publié le : samedi 1 janvier 2011
Lecture(s) : 12
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Source : D-NB.INFO/1019002018/34
Nombre de pages : 130
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Einevergleicerteidigung:GutacProf.hendeUnivStudieh.c.zurEinreicOptimierungTinonderProf.StrukturakustikProf.(AJorgeComparativTeesenStudyhnisconDresdenOptimizationMostafainterStructuralhabil.AHardtkcoustics)SteenDAriostoIagSung:SderEwRderTecAhenTersit?tIvOM.Sc.(Eng.)NRanjbarzurhErlangung:desDr.-Ing.akProf.ademiscHans-J?rgenheneGradesDr.-Ing.Doktor-IngenieurMarburg(Dr.-Ing.)Dr.vBretanhaorgelegtTderderFhakult?t09/09/2009f?ragMascVhinen25/03/2011AbstractThisdissertationpresentheanalysisoftsmoanjectivexhaustivsimeoptimizationcomparativstructural-acousticeastudymethoonhoptimizationofinoptimizationstructuralaluated.acoustics.theAerfulcomeriobinationtheofdirections,adcommerciallytsae.g.,vdsailablestatisticalnitetageselemenratetofsoftfast,wtsareexppactotalkwithagemoreandtheyadditionaltheuser-writtenesprogramsmethoisprogrammingusedBrotoconstrainedmomid-rangedifycontheexactshaptabuederivoftheafunction.structure.folloThisds.istagesdoneortediter-ergenceativtheelymethoanddswithoutw.manmid-rangeualininFinallytervuseencantionthetobacandhievtheyeinsignicantime.tbimproforvybridemenderivtstheoffunction,theofobtialjectivd,elimitedfunction.her-Goldfarb-ShannoTheboptimizationmethoprovingcessulti-pcondtinrandomuesd.automaticallymethounalgo-tilhtheannealing,predenede-freemaximtheyumvnjectivumurthermore,bheredofoffunctiondvevdisadvaluationseacisarereacdetails.hed.conThemeasuredesigneed)vlevariablesharearetheoptimizationstructure'sclassiedloandcaldgeometryasymptotesmoulti-pdicationdvducedaluesmethoatitselectedthatsurfaceeectivkmethoey-preduceointime.ts.oThemethoobequippjectiveestructuralofds,thepresenoptimizationoptimizationincludesshortertheofminimizationthisofevtheconsideredroreplacemenotcomplexmeanulti-stagessquarealgorithms.levsecondelativofofstructureobbeornee.g.,soundd(afeasiblegeneralsequenmeasurequadraticofmethotheNewtonvibrationald,sensitivitmemoryyyden-Fletcofmethoaforstructure).oundInproblems,addition,dthemostructuralasymptotes,massmremainsoinconstanmethotandandtrolledthesearcallomethowTheableoptimizationrangesds,ofgeneticdesignrithms,vsearcariableandvulatedaluesareareativrestrictedmethobandyuseprescribexactedalueuppobereandFloawapproacerislimits.wTheforoptimizationcomparisonpromethocedureAisantestedandonantheofnitehelemenalgorithmtrepmoindelTheofofavrectangular(aplateofmadespofandsteel.robustnessTelweacelvoptimizationeddierenevtSomeoptimizationmethomethoaredsasaremediumtestedsloagainstMethoeacofhvingothers.andThesemmethooindsmethoareareconsideredtroeitherasasfastestapprods.ximate,orisexact.eriencedThetheapproofximateeoptimizationanalysismethodsdsdrasticallyusetheeitheroptimizationanIfappropximatedwvoptimizationaluedsofecomeobedjectiveective(fastfunction,reliable)e.g.,acoustichmethoybridthendesigncanoftexpdesirableerimenresultstsaandphdybridcomputationneuralInnetcase,wcanorks,enoretheasapprosuitableximatedtvthealuesandofmthehrstoptimizationandiZusammenfassungDievzuonworliegendedeDissertationendenpr?senvtiertdieeinede,vTergleicstatistischendeundStudieWzurtenOptimierungzwinderdervStrukturakustik.sindEineDieseKVomor-bination(einauserdeneinemalskendungommerziellendieFinite-Elemenneuronalete-SoftertewZielfunktion;are-hPquadratiscakerfahren,etMid-Range-Multi-Punkt-MethoundDiedergenetiscimundRahmenden.derertArbOptimierungsveiterwenteiletKwicOptimierungsgesckeinzelneneltenDiezus?tzlicundhener-ProgrammedasswirdstrukturakustiscvVerwRecen-hdet,oumeisendieerstenGeometrieAbleitungeneinerB.meczul?ssigenhaniscMethohenquenStrukturProgrammierung,zuGSoptimieren.deDerhenautomatiscundheZufalls-Op-VtimierungsprozessB.wirdAlgorithmsoSearclangeulateddurcableitungsfreiehgef?hrt,vbisgenaueneineZielfunktion.vhorgegebwirdeneAnsatzmaximaleDieAnzahlNacanMethoAuswdiskutiert.ertungenvderf?rZielfunktionwindigkerreicRobustheithdentewwird.deEnhentdewurfsvteariablenidensindwirddieVlokezienalenAnaly-Geometrie?nderungendrastiscanderausgewb?hltenf?hrt.Obunder?cybridehen-Netze;punkten.derDasn?herungswZielWderderOptimierungundumfassteitendiederMinimierungz.desMethoEektivwderertesRicdertungen,Scdehall-se-leistung.tiellenDerhenzul?ssigeNewton-MethoBereicBFhVderMethoEnderter?nderlicwurfsvAsymptoten,ariablendewirdkbtrollierteegrenzt.iteration.DasexaktenOptimierungsverfahrener-z.fahrenderwirdheanus,eineraburechhSimtecAnnealing.kigensindPlatteMethoausSieStahlerwerprobt.denZwW?lfdervZumerscergleichiedenederOptimierungsverfahrenerfahreneinwhererdenvmiteinanderendet.vVerglicundhen.hDiesederVdener-erdenfahrenDiesindonenergenzratetMa?wdieederhexakteit)odiederderapproOptimierungsmethoximativ.wDiebapproertet.ximativMethoenderMethoer?nderlicdenAsymptotenvMid-Range-Multi-Punkt-MethoerwwurdenendenezienenOptimierungsvtfahrenwtiziert.edereiterhineinengezeigt,n?herungswdieeisenerwWeinererttenderhenZielfunktion;semethodez.einerB.henHybrid-Designerringerungvf?ronOptimierungExpen?tigteneri-henzeitmeniiAcknoceherwledegmenthetsectivIwhowofouldmemlikisegoals.totoleratingacuxknousedwledgeTtheFP6suppProf.ortinofythismenwfororkbbouldyknothefromInstitutehniscofISolidtheMecAhanicsunitfromPro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e.Implemen.Programs.for.Mo......44.4.5.1.MinimforumFNum.b.er.of.Ob.jectivProgrameQuadraticF.unction.Calculation....Program.metho............46A.55trolledFiniteMethoElemen.t.Mo.del.49Program5.1AnnealingThe.FE.Mo.del.of.RectangularProgramPlateMetho.............................65.Hybrid.Net.orks..........49.5.2.Emplo.ying.of.Bicubic.Splines.to66ReduceSimtheAnnealingNumdb.er.of.Design.V.ariables.....50.5.3.F.requency6.2.10Discretizationabu.h.d...........................69.Con.Random.h.d..................52706OptimizationOptimizationforResultsAlgorithm55d6.1.Original.and.Initial.Designs.for.Rectangular72PlateSummary.Optimization.Considering.1000.Sets.....73.Iteration.of.ds..........55.6.2.Optimization.Results.Considering.One.Set73ofRobustnessInitialtheDesignds....................57.6.2.1.Metho.d.of6.6Farallelizingeasible.Directions..................................7.and.uture.orks.7.1..57.6.2.2.Sequen.tial.Quadratic.Programming........................79.F.w..........58.6.2.3.Metho.d.of.Mo.ving.Asymptotes............A.Remarks.the.tation.Computer.83.Program.Metho.of.ving............60.6.2.4.Limited.Memory83BFProgramGSMethoMethoofdeasiblefor.Bound.Constrained.Problems.....61.6.2.5.Mid-Range.Multi-P.oinA.3tsforMethotialdProgramming.................A.4.for.GS-B.d................62.6.2.6.Newton.Metho.d87.Program.Con.Random.h.d.................A.6.for.ulated.Metho.....................A.7.for.Algorithm64d6.2.7.Hybrid.Design.of.Exp.erimen.ts........viA.8ProgramforHybridMid-range.ofDesign.of.Exp.erimen.ts94.......94.Metho.....Newton.......of.Bibliograph.....Program.oin....92.A.9.Program.forProgramHybriddNeural.Net.w.orks......95.97.ables.101.........A.11.for.Multi-p.ts.d..............93.A.10.ProgramA.12forforTMethoabu.Searc.h.Metho.d....................List.Figures.List.T.99.y.viiviii2A
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