Topics in Model Validation and Uncertainty Quantification, Volume 5: Proceedings of the 31st IMAC, A Conference on Structural Dynamics, 2013 PDF
Preview Topics in Model Validation and Uncertainty Quantification, Volume 5: Proceedings of the 31st IMAC, A Conference on Structural Dynamics, 2013
Conference Proceedings of the Society for Experimental Mechanics Series Todd Simmermacher · Scott Cogan Babak Moaveni · Costas Papadimitriou Editors Topics in Model Validation and Uncertainty Quantification, Volume 5 Proceedings of the 31st IMAC, A Conference on Structural Dynamics, 2013 Conference Proceedings of the Society for Experimental Mechanics Series Series Editor TomProulx Society forExperimentalMechanics,Inc., Bethel, CT, USA Forfurthervolumes: http://www.springer.com/series/8922 Todd Simmermacher • Scott Cogan (cid:129) Babak Moaveni Costas Papadimitriou Editors Topics in Model Validation and Uncertainty Quantification, Volume 5 Proceedings of the 31st IMAC, A Conference on Structural Dynamics, 2013 123 Editors ToddSimmermacher ScottCogan SandiaNationalLaboratories CNRS Albuquerque,NM,USA UniversityofFranche-Comte Besanc¸on,France BabakMoaveni DepartmentofCivilandEnvironmentalEngineering CostasPapadimitriou TuftsUniversity DepartmentofMechanicalandIndustrialEngineering Medford,MA,USA UniversityofThessaly Volos,Greece ISSN2191-5644 ISSN2191-5652(electronic) ISBN978-1-4614-6563-8 ISBN978-1-4614-6564-5(eBook) DOI10.1007/978-1-4614-6564-5 SpringerNewYorkHeidelbergDordrechtLondon LibraryofCongressControlNumber:2013936727 ©TheSocietyforExperimentalMechanics,Inc.2013 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartofthematerialisconcerned,specificallytherights oftranslation, reprinting, reuse ofillustrations, recitation, broadcasting, reproduction onmicrofilms orinany other physical way, and transmission or informationstorageandretrieval,electronicadaptation, computersoftware,orbysimilarordissimilarmethodologynowknownorhereafterdeveloped. Exemptedfromthislegalreservationarebriefexcerptsinconnectionwithreviewsorscholarlyanalysisormaterialsuppliedspecificallyforthepurpose ofbeingenteredandexecutedonacomputersystem,forexclusiveusebythepurchaserofthework.Duplicationofthispublication orpartsthereofis permittedonlyundertheprovisionsoftheCopyrightLawofthePublisher’slocation,initscurrentversion,andpermissionforusemustalwaysbeobtained fromSpringer.PermissionsforusemaybeobtainedthroughRightsLinkattheCopyrightClearanceCenter.Violationsareliabletoprosecutionunderthe respectiveCopyrightLaw. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublicationdoesnotimply,evenintheabsenceofaspecific statement,thatsuchnamesareexemptfromtherelevantprotectivelawsandregulationsandthereforefreeforgeneraluse. Whiletheadviceandinformationinthisbookarebelievedtobetrueandaccurateatthedateofpublication,neithertheauthorsnortheeditorsnorthe publishercanacceptanylegalresponsibility foranyerrorsoromissionsthatmaybemade.Thepublishermakesnowarranty,expressorimplied,with respecttothematerialcontainedherein. Printedonacid-freepaper SpringerispartofSpringerScience+BusinessMedia(www.springer.com) Preface Topicsin ModelValidationandUncertaintyQuantificationrepresentsoneof sevenvolumesof technicalpaperspresented atthe31stIMAC,A ConferenceandExpositiononStructuralDynamics,2013organizedbytheSocietyforExperimental Mechanics, and held in Garden Grove, California February 11–14, 2013. The full proceedings also include volumes on NonlinearDynamics;ExperimentalDynamicsSubstructuring;DynamicsofBridges;DynamicsofCivilStructures;Special TopicsinStructuralDynamics;and,ModalAnalysis. Eachcollectionpresentsearlyfindingsfromexperimentalandcomputationalinvestigationsonanimportantareawithin StructuralDynamics.ModelValidationandUncertaintyQuantificationisoneoftheseareas. ModelVerificationandValidation(V&V),referstotechnologydevelopedtoassesstheaccuracyofnumericalpredictions and quantify the uncertainty and confidence with which predictions are made. V&V includes activities such as code and calculation verification, the design of validation experiments, test-analysis correlation, uncertainty propagation and quantificationforbothmodelsandexperiments,andfiniteelementmodelupdating. Theorganizerswouldliketothanktheauthors,presenters,sessionorganizers,andsessionchairsfortheirparticipationin thistrack. Albuquerque,NM,USA ToddSimmermacher Besanc¸on,France ScottCogan Medford,MA,USA BabakMoaveni Volos,Greece CostasPapadimitriou v Contents 1 OptimalInequalitiestoBoundaPerformanceProbability......................................................... 1 Franc¸oisM.HemezandChristopherJ.Stull 2 RemainingFatigueLifePredictionsConsideringLoadandModelParametersUncertainty .................. 17 MaurizioGobbato,JoelP.Conte,andJohnB.Kosmatka 3 FastComputingTechniquesforBayesianUncertaintyQuantificationinStructuralDynamics ............... 25 CostasPapadimitriouandDimitra-ChristinaPapadioti 4 BayesianUncertaintyQuantificationandPropagationinNonlinearStructuralDynamics .................... 33 DimitriosGiagopoulos,Dimitra-ChristinaPapadioti,CostasPapadimitriou,andSotiriosNatsiavas 5 ProbabilisticDamageIdentificationoftheDowlingHallFootbridgeUsingBayesianFEModelUpdating... 43 ImanBehmaneshandBabakMoaveni 6 ConsideringWavePassageEffectsinBlindIdentificationofLong-SpanBridges ............................... 53 S. Farid Ghahari, M. Ali Ghannad, James Norman, Adam Crewe, Fariba Abazarsa, andErtugrulTaciroglu 7 QuantificationofParametricModelUncertaintiesinFiniteElementModelUpdatingProblem viaFuzzyNumbers...................................................................................................... 67 YildirimSerhatErdogan,MustafaGul,F.NecatiCatbas,andPelinGundesBakir 8 Quantifying Maximum Achievable Accuracy of Identified Modal ParametersfromNoise ContaminatedFreeVibrationData.................................................................................... 75 EricM.Hernandez 9 UsingP-BoxandPiFEtoExpressUncertaintyinModelUpdating................................................ 81 RaminMadarshahian,JuanM.Caicedo,andBorisA.Za´rate 10 RobustModelCalibrationwithLoadUncertainties................................................................. 89 D.Pereiro,S.Cogan,E.Sadoulet-Reboul,andF.Martinez 11 Simulating the Dynamics of the CX-100 Wind Turbine Blade: Model Selection Using aRobustnessCriterion.................................................................................................. 99 KendraL.VanBuren,SezAtamturktur,andFranc¸oisM.Hemez 12 DefiningCoverageofaDomainUsingaModifiedNearest-NeighborMetric..................................... 113 MatthewC.Egeberg,SezAtamturktur,andFranc¸oisM.Hemez 13 OrthogonalityforModalVectorCorrelation:TheEffectsofRemovingDegrees-of-Freedom................. 123 MichaelL.Mains 14 CAEModelCorrelationMetricsforAutomotiveNoiseandVibrationAnalysis................................. 135 QijunZhang,ShawnHui,andKurtSchneider 15 DamageLocalizationUsingaStatisticalTestonResidualsfromtheSDDLVApproach........................ 143 L.Marin,M.Do¨hler,D.Bernal,andL.Mevel vii viii Contents 16 RobustToleranceDesigninStructuralDynamics................................................................... 153 ChaopingZang,JunYang,andM.I.Friswell 17 Uncertainty Propagationin Floating Raft System by FRF-BasedSubstructuring Method forElasticCoupling ..................................................................................................... 165 HuangXiuchang,HuaHongxing,ChenFeng,andXuShiyin 18 CrossingandVeeringPhenomenainCrankMechanismDynamics............................................... 175 ElvioBonisoli,GabrieleMarcuccio,andCarloRosso 19 ValidatingLow-LevelFootfall-InducedVibrationPredictionsinSteelandConcreteStructures.............. 189 MichaelJ.Wesolowsky,JuliaM.Graham,J.ShayneLove,JonK.Galsworthy,andJohnC.Swallow 20 FiniteElementModelUpdatingofanAssembledAero-EngineCasing........................................... 199 ChaopingZang,ShuangchaoMa,andM.I.Friswell 21 ExperimentalModalAnalysisandModellingofanAgriculturalTire............................................ 213 F.Braghin,F.Cheli,S.Melzi,S.Negrini,andE.Sabbioni 22 InternationalSpaceStationModalCorrelationAnalysis........................................................... 221 KristinFitzpatrick,MichaelGrygier,MichaelLaible,andSujathaSugavanam 23 NumericalModelingofVibrationInducedAtomizationofLiquids............................................... 243 JesiEhrhornandWilliamSemke 24 DynamicalModelingandVerificationofUnderwaterAcousticSystem........................................... 255 AhmetLeventAvs¸ar,˙IstekTatar,andCihangirDuran Chapter 1 Optimal Inequalities to Bound a Performance Probability Franc¸oisM.HemezandChristopherJ.Stull Abstract Achallengingproblemencounteredinengineeringapplicationsistheestimationofaprobability-of-failurebased on incomplete knowledge of the sources of uncertainty and/or limited sampling. Theories formulated to derive upper probability bounds offer an attractive alternative because first, they avoid postulating the probability laws that are often unknown and second, they substitute numerical optimization for statistical sampling. A critical assessment of one such technique is presented. It derives upper probability bounds from the McDiarmid concentration-of-measuretheory, which postulatesthatfluctuationsofafunctionaremore-or-lessconcentratedaboutitsmeanvalue.Twoapplicationsofthistheory are presented. The first application analyzes a “toy” polynomial function defined in two dimensions. The upper bounds of probability are calculated and compared to sampling-based estimates of the true-but-unknown probabilities. For this function,theupperboundsobtainedaretoobroadtobeuseful.Theseresultsareconfirmedbyconductingasimilaranalysis onarealengineeringsystem,whereupperboundsofprobabilityassociatedwithresonantfrequenciesofastructuralsystem are estimated. A high-fidelity finite element model, previouslyvalidated using vibration measurements, is used to predict the frequencies. In this application, the uncertainty is introduced by way of material properties and the effective preload of a beam-to-column connection, modeled explicitly. These applications suggest that the theory not only leads to upper boundsthatareinefficientbutthatcanalsobesub-optimaliftheirnumericalestimationisbasedontoofewmodelruns.Itis concludedthatthisparticulartheory,whilemathematicallyattractive,maynotbewellsuitedforengineeringapplications. Keywords Concentration-of-measure (cid:129) Probability-of-failure (cid:129) McDiarmid diameter (cid:129) Parametric uncertainty (cid:129) Model validation(Approvedforunlimited,publicreleaseonSeptember15,2012,LA-UR-12-24769,Unclassified.) 1.1 Introduction Ananalysisofsystemreliabilityestimatesaprobability-of-failure,denotedasP ,where“failure”isdefinedasacondition F wherethe“demand”exceedsthe“capacity”thatanengineeredsystemiscapableofproviding.Demand,forexample,refers toapeakstressresultingfromanappliedload,whilecapacityreferstoamaterialyieldstresscriterion.Anotherapplication would be to require that a performance metric, that defines the capacity C, meets a user requirement, or demand D. The probability-of-failurecanbewritteninagenericsenseas: P =Prob[D≥C], (1.1) F F.M.Hemez((cid:2)) TechnicalStaffMember,LosAlamosNationalLaboratory,X-TheoreticalDesignDivision(XTD-3), POBox1663,MailStopT087,LosAlamos,NM87545,USA e-mail:[email protected] C.J.Stull TechnicalStaffMember,LosAlamosNationalLaboratory,AppliedEngineeringandTechnologyDivision(AET-6), POBox1663,MailStopP915,LosAlamos,NM87545,USA e-mail:[email protected] T.Simmermacheretal.(eds.),TopicsinModelValidationandUncertaintyQuantification,Volume5, 1 ConferenceProceedingsoftheSocietyforExperimentalMechanicsSeries41, DOI10.1007/978-1-4614-6564-5 1,©TheSocietyforExperimentalMechanics,Inc.2013
The list of books you might like
