Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 38th IMAC, A Conference and Exposition on Structural Dynamics 2020 PDF
Preview Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 38th IMAC, A Conference and Exposition on Structural Dynamics 2020
Conference Proceedings of the Society for Experimental Mechanics Series Zhu Mao Editor Model Validation and Uncertainty Quantification, Volume 3 Proceedings of the 38th IMAC, A Conference and Exposition on Structural Dynamics 2020 Conference Proceedings of the Society for Experimental Mechanics Series SeriesEditor KristinB.Zimmerman,Ph.D. SocietyforExperimentalMechanics,Inc., Bethel,CT,USA TheConferenceProceedingsoftheSocietyforExperimentalMechanicsSeriespresentsearlyfindingsandcasestudiesfrom a wide range of fundamental and applied work across the broad range of fields that comprise Experimental Mechanics. SeriesvolumesfollowtheprincipletracksorfocustopicsfeaturedineachoftheSociety’stwoannualconferences:IMAC, AConferenceandExpositiononStructuralDynamics,andtheSociety’sAnnualConference&Expositionandwilladdress criticalareasofinteresttoresearchersanddesignengineersworkinginallareasofStructuralDynamics,SolidMechanics andMaterialsResearch Moreinformationaboutthisseriesathttp://www.springer.com/series/8922 Zhu Mao Editor Model Validation and Uncertainty Quantification, Volume 3 Proceedings of the 38th IMAC, A Conference and Exposition on Structural Dynamics 2020 Editor ZhuMao Department,DandeneauHall211 UnivofMassachusetts,MechanicalEngg Lowell,MA,USA ISSN2191-5644 ISSN2191-5652 (electronic) ConferenceProceedingsoftheSocietyforExperimentalMechanicsSeries ISBN978-3-030-48778-2 ISBN978-3-030-47638-0 (eBook) https://doi.org/10.1007/978-3-030-47638-0 ©TheSocietyforExperimentalMechanics,Inc.2020 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartofthematerialisconcerned,specificallytherights oftranslation,reprinting,reuseofillustrations,recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionor informationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodologynowknownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublicationdoesnotimply,evenintheabsenceofaspecific statement,thatsuchnamesareexemptfromtherelevantprotectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthors,andtheeditorsaresafetoassumethattheadviceandinformationinthisbookarebelievedtobetrueandaccurateatthedate ofpublication.Neitherthepublishernortheauthorsortheeditorsgiveawarranty,expressedorimplied,withrespecttothematerialcontainedhereinor foranyerrorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictionalclaimsinpublishedmapsandinstitutional affiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface Model Validation and Uncertainty Quantification represents one of eight volumes of technical papers presented at the 38th IMAC, A Conference and Exposition on Structural Dynamics, organized by the Society for Experimental Mechanics, and held in Houston, Texas, February 10–13, 2020. The full proceedings also include volumes on Nonlinear Structures and Systems; Dynamics of Civil Structures; Dynamic Substructures; Special Topics in Structural Dynamics & Experimental Techniques; Rotating Machinery, Optical Methods & Scanning LDV Methods; Sensors and Instrumentation, Aircraft/Aerospace,EnergyHarvesting&DynamicEnvironmentsTesting;andTopicsinModalAnalysis&Testing. Eachcollectionpresentsearlyfindingsfromexperimentalandcomputationalinvestigationsonanimportantareawithin StructuralDynamics.ModelValidationandUncertaintyQuantification(MVUQ)isoneoftheseareas. Modelingandsimulationareroutinelyimplementedtopredictthebehaviorofcomplexdynamicalsystems.Thesetools powerfully unite theoretical foundations, numerical models, and experimental data which include associated uncertainties anderrors.ThefieldofMVUQresearchentailsthedevelopmentofmethodsandmetricstotestmodelpredictionaccuracy and robustness while considering all relevant sources of uncertainties and errors through systematic comparisons against experimentalobservations. Theorganizerswouldliketothanktheauthors,presenters,sessionorganizers,andsessionchairsfortheirparticipationin thistrack. Lowell,MA,USA ZhuMao v Contents 1 VariationalCoupledLoadsAnalysisUsingtheHybridParametricVariationMethod ......................... 1 DanielC.Kammer,PaulBlelloch,andJoelSills 2 BayesianUncertaintyQuantificationintheDevelopmentofaNewVibrationAbsorberTechnology......... 19 NicolasBrötzandPeterF.Pelz 3 ComparisonofComplexityMeasuresforStructuralHealthMonitoring ........................................ 27 HannahDonajkowski,SalmaLeyasi,GregoryMellos,ChuckR.Farrar,AlexScheinker,Jin-SongPei, andNicholasA.J.Lieven 4 SelectionofanAdequateModelofaPiezo-ElasticSupportforStructuralControlinaBeamTruss Structure.................................................................................................................. 41 JonathanLenz,MaximilianSchäffner,RolandPlatz,andTobiasMelz 5 ImpactLoadIdentificationfortheDROPBEARSetupUsingaFiniteInputCovariance(FIC) Estimator................................................................................................................. 51 PeterLander,YangWang,andJacobDodson 6 Real-TimeDigitalTwinUpdatingStrategyBasedonStructuralHealthMonitoringSystems ................. 55 Yi-ChenZhu,DavidWagg,ElizabethCross,andRobertBarthorpe 7 OntheFusionofTestandAnalysis.................................................................................... 65 IbrahimA.Sever 8 DesignofanActuationControllerforPhysicalSubstructuresinStochasticReal-TimeHybrid Simulations............................................................................................................... 69 NikolaosTsokanasandB.Stojadinovic 9 Output-OnlyNonlinearFiniteElementModelUpdatingUsingAutoregressiveProcess........................ 83 JuanCastiglione,RodrigoAstroza,SaeedEftekharAzam,andDanielLinzell 10 AxleBoxAccelerometerSignalIdentificationandModelling...................................................... 87 CyprienA.Hoelzl,LuisDavidAvendanoValencia,VasilisK.Dertimanis,EleniN.Chatzi, andMarcelZurkirchen 11 Kalman-BasedVirtualSensingforImprovementofServiceResponseReplication inEnvironmentalTests.................................................................................................. 93 SilviaVettori,EmilioDiLorenzo,RobertaCumbo,UmbertoMusella,TommasoTamarozzi,BartPeeters, andEleniChatzi 12 VirtualSensingofWheelPositioninGround-SteeringSystemsforAircraftUsingDigitalTwins............. 107 MattiaDalBorgo,StephenJ.Elliott,MaryamGhandchiTehrani,andIanM.Stothers 13 AssessingModelFormUncertaintyinFractureModelsUsingDigitalImageCorrelation ..................... 119 RobinCallens,MatthiasFaes,andDavidMoens vii viii Contents 14 IdentificationofLackofKnowledgeUsingAnalyticalRedundancyAppliedtoStructural DynamicSystems........................................................................................................ 131 JakobHartig,FlorianHoppe,DanielMartin,GeorgStaudter,TugrulÖztürk,ReinerAnderl,PeterGroche, PeterF.Pelz,andMatthiasWeigold 15 AStructuralFatigueMonitoringConceptforWindTurbinesbyMeansofDigitalTwins ..................... 139 JánosZierath,Sven-ErikRosenow,JohannesLuthe,AndreasSchulze,ChristianeSaalbach,ManuelaSander, andChristophWoernle 16 DamageIdentificationofStructuresThroughMachineLearningTechniqueswithUpdatedFinite ElementModelsandExperimentalValidations...................................................................... 143 PanagiotisSeventekidis,DimitriosGiagopoulos,AlexandrosArailopoulos,andOlgaMarkogiannaki 17 ModalAnalysesandMeta-ModelsforFatigueAssessmentofAutomotiveSteelWheels........................ 155 S.Venturini,E.Bonisoli,C.Rosso,D.Rovarino,andM.Velardocchia 18 TowardstheDevelopmentofaDigitalTwinforStructuralDynamicsApplications............................. 165 PaulGardner,MattiaDalBorgo,ValentinaRuffini,YichenZhu,andAidanHughes 19 AnImprovedOptimalSensorPlacementStrategyforKalman-BasedMultiple-InputEstimation............ 181 LorenzoMazzanti,RobertaCumbo,WimDesmet,FrankNaets,andTommasoTamarozzi 20 Towards Population-Based Structural Health Monitoring, Part IV: Heterogeneous Populations, TransferandMapping.................................................................................................. 187 PaulGardner,LawerenceA.Bull,JulianGosliga,NikolaosDervilis,andKeithWorden 21 FeasibilityStudyofUsingLow-CostMeasurementDevicesforSystemIdentificationUsingBayesian Approaches............................................................................................................... 201 AlejandroDuarteandAlbertR.Ortiz 22 KernelisedBayesianTransferLearningforPopulation-BasedStructuralHealthMonitoring................. 209 PaulGardner,LawrenceA.Bull,NikolaosDervilis,andKeithWorden 23 PredictingSystemResponseatUnmeasuredLocationsUsingaLaboratoryPre-Test .......................... 217 RandyMayes,LukeAnkers,andPhilDaborn 24 RobustEstimationofTruncation-InducedNumericalUncertainty ............................................... 223 FrançoisHemez 25 FatigueCrackGrowthDiagnosisandPrognosisforDamage-AdaptiveOperationofMechanical Systems ................................................................................................................... 233 PranavM.Karve,YulinGuo,BerkcanKapusuzoglu,SankaranMahadevan,andMulugetaA.Haile 26 AnEvolutionaryApproachtoLearningNeuralNetworksforStructuralHealthMonitoring ................. 237 TharukaDevendra,NikolaosDervilis,KeithWorden,GeorgeTsialiamanis,ElizabethJ.Cross, andTimothyJ.Rogers 27 BayesianSolutionstoState-SpaceStructuralIdentification........................................................ 247 TimothyJ.Rogers,KeithWorden,andElizabethJ.Cross 28 AnalyzingPropagationofModelFormUncertaintyforDifferentSuspensionStrutModels................... 255 RobertFeldmann,MaximilianSchäffner,ChristopherM.Gehb,RolandPlatz,andTobiasMelz 29 Determining Interdependencies and Causation of Vibration in Aero Engines Using Multiscale Cross-CorrelationAnalysis............................................................................................. 265 ManuKrishnan,IbrahimA.Sever,andPabloA.Tarazaga 30 DynamicDataDrivenModelingofAeroEngineResponse......................................................... 273 ManuKrishnan,SerkanGugercin,IbrahimSever,andPabloTarazaga 31 NonlinearModelUpdatingUsingRecursiveandBatchBayesianMethods ...................................... 279 MingmingSong,RodrigoAstroza,HamedEbrahimian,BabakMoaveni,andCostasPapadimitriou Contents ix 32 TowardsPopulation-BasedStructuralHealthMonitoring,PartI:HomogeneousPopulationsandForms... 287 LawerenceA.Bull,PaulA.Gardner,JulianGosliga,NikolaosDervilis,EvangelosPapatheou, AndrewE.Maguire,CarlesCampos,TimothyJ.Rogers,ElizabethJ.Cross,andKeithWorden 33 ADetailedAssessmentofModelFormUncertaintyinaLoad-CarryingTrussStructure...................... 303 RobertFeldmann,ChristopherM.Gehb,MaximilianSchäffner,AlexanderMatei,JonathanLenz, SebastianKersting,andMoritzWeber 34 RecursiveNonlinearIdentificationofaNegativeStiffnessDeviceforSeismicProtectionofStructures withGeometricandMaterialNonlinearities......................................................................... 315 KalilErazoandSatishNagarajaiah 35 AdequateMathematicalBeam-ColumnModelforActiveBucklingControlinaTetrahedronTruss Structure.................................................................................................................. 323 MaximilianSchaeffner,RolandPlatz,andTobiasMelz 36 SiteCharacterizationThroughHierarchicalBayesianModelUpdatingUsingDispersionandH/VData.... 333 MehdiM.Akhlaghi,MingmingSong,MarshallPontrelli,BabakMoaveni,andLaurieG.Baise 37 BAYESIAN Inference Based Parameter Calibration of a Mechanical Load-Bearing Structure’s MathematicalModel .................................................................................................... 337 ChristopherM.Gehb,RolandPlatzandTobiasMelz 38 Uncertainty Propagation in a Hybrid Data-Driven and Physics-Based Submodeling Method for RefinedResponseEstimation .......................................................................................... 349 BhavanaValetiandShamimN.Pakzad 39 AdaptiveProcessandMeasurementNoiseIdentificationforRecursiveBayesianEstimation.................. 361 KonstantinosE.Tatsis,VasilisK.Dertimanis,andEleniN.Chatzi 40 EffectiveLearningofPost-SeismicBuildingDamagewithSparseObservations ................................ 365 MohamadrezaSheibaniandGeOu 41 EfficientBayesianInferenceofMiterGatesUsingHigh-FidelityModels......................................... 375 ManuelA.Vega,MukeshK.Ramancha,JoelP.Conte,andMichaelD.Todd 42 Two-StageHierarchicalBayesianFrameworkforFiniteElementModelUpdating............................. 383 XinyuJia,OmidSedehi,CostasPapadimitriou,LambrosKatafygiotis,andBabakMoaveni 43 BayesianNonlinearFiniteElementModelUpdatingofaFull-ScaleBridge-ColumnUsingSequential MonteCarlo.............................................................................................................. 389 MukeshK.Ramancha,RodrigoAstroza,JoelP.Conte,JoseI.Restrepo,andMichaelD.Todd 44 OptimalInputLocationsforStiffnessParameterIdentification................................................... 399 DebasishJana,DhirajGhosh,SuparnoMukhopadhyay,andSamitRay-Chaudhuri 45 Modal Identification and Damage Detection of Railway Bridges Using Time-Varying Modes IdentifiedfromTrainInducedVibrations ............................................................................ 405 AshishPal,AsthaGaur,andSuparnoMukhopadhyay 46 Test-AnalysisModalCorrelationofRocketEngineStructuresinLiquidHydrogen–PhaseII................ 413 AndrewM.BrownandJenniferL.DeLessio 47 AnOutput-OnlyBayesianIdentificationApproachforNonlinearStructuralandMechanicalSystems...... 431 SatishNagarajaiahandKalilErazo Chapter 1 Variational Coupled Loads Analysis Using the Hybrid Parametric Variation Method DanielC.Kammer,PaulBlelloch,andJoelSills Abstract Time-domain coupled loads analysis (CLA) is used to determine the response of a launch vehicle and payload systemtotransientforces,suchasliftoff,engineignitionsandshutdowns,jettisonevents,andatmosphericflightloads,such as buffet. CLA, using Hurty/Craig-Bampton (HCB) component models, is the accepted method for the establishment of design-level loads for launch systems. However, uncertainty in the component models flows into uncertainty in predicted system results. Uncertainty in the structural responses during launch is a significant concern because small variations in launchvehicleandpayloadmodeshapesandtheirinteractionscanresultinsignificantvariationsinsystemloads.Uncertainty quantification(UQ)isusedtodeterminestatisticalboundsonpredictionaccuracybasedonmodeluncertainty.Inthispaper uncertainty is treated at the HCB component-model level. In an effort to account for model uncertainties and statistically bound their effect on CLA predictions, this work combines CLA with UQ in a process termed variational coupled loads analysis(VCLA).Themodelingofuncertaintyusingaparametricapproach,inwhichinputparametersarerepresentedby random variables, is common, but its major drawback is the resulting uncertainty is limited to the form of the nominal model. Uncertainty in model form is one of the biggest contributors to uncertainty in complex built-up structures. Model- formuncertaintycanberepresentedusinganonparametricapproachbasedonrandommatrixtheory(RMT).Inthiswork, UQ is performed using the hybrid parametric variation (HPV) method, which combines parametric with nonparametric uncertainty at the HCB component model level. The HPV method requires the selection of dispersion values for the HCB fixed-interface(FI)eigenvalues,andtheHCBmassandstiffnessmatrices.Thedispersionsarebaseduponcomponenttest- analysis modal correlation results. During VCLA, random component models are assembled into an ensemble of random systemsusingaMonteCarlo(MC)approach.CLAisappliedtoeachoftheensemblememberstoproduceanensembleof system-levelresponsesforstatisticalanalysis.Theproposedmethodologyisdemonstratedthroughitsapplicationtoabuffet loadsanalysisofNASA’sSpaceLaunchSystem(SLS)duringthetransonicregime50safterliftoff.Corestage(CS)section shearsandmomentsarerecovered,andstatisticsarecomputed. Keywords Uncertaintyquantification · Hurty/Craig-Bampton · Randommatrix · Modelform · Coupledloadsanalysis Acronyms CLA coupledloadsanalysis CS corestage DCGM diagonalcross-generalizedmassmetric DOF degreesoffreedom FEM finiteelementmodel FI fixed-interface HCB Hurty/Craig-Bampton HPV hybridparametricvariation D.C.Kammer((cid:2))·P.Blelloch ATAEngineering,Inc.,SanDiego,CA,USA e-mail:[email protected];[email protected] J.Sills NASAJohnsonSpaceCenter,Houston,TX,USA e-mail:[email protected] ©TheSocietyforExperimentalMechanics,Inc.2020 1 Z.Mao(ed.),ModelValidationandUncertaintyQuantification,Volume3,ConferenceProceedingsoftheSocietyforExperimental MechanicsSeries,https://doi.org/10.1007/978-3-030-47638-0_1
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