ABAQUS关于固有频率的提取方法.docx
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ABAQUS关于固有频率的提取方法.docx
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ABAQUS关于固有频率的提取方法
Abaqus固有频率提取
6.3.5 Naturalfrequencyextraction
Products:
Abaqus/Standard Abaqus/CAE Abaqus/AMS
References
∙“Procedures:
overview,” Section6.1.1
∙“Generalandlinearperturbationprocedures,” Section6.1.2
∙“Dynamicanalysisprocedures:
overview,” Section6.3.1
∙*FREQUENCY
∙“Configuringafrequencyprocedure”in“Configuringlinearperturbationanalysisprocedures,” Section14.11.2oftheAbaqus/CAEUser'sManual
Overview
Thefrequencyextractionprocedure:
∙performseigenvalueextractiontocalculatethenaturalfrequenciesandthecorrespondingmodeshapesofasystem;
∙willincludeinitialstressandloadstiffnesseffectsduetopreloadsandinitialconditionsifgeometricnonlinearityisaccountedforinthebasestate,sothatsmallvibrationsofapreloadedstructurecanbemodeled;
∙willcomputeresidualmodesifrequested;
∙isalinearperturbationprocedure;
∙canbeperformedusingthetraditionalAbaqussoftwarearchitectureor,ifappropriate,thehigh-performanceSIMarchitecture(see “UsingtheSIMarchitectureformodalsuperpositiondynamicanalyses”in“Dynamicanalysisprocedures:
overview,” Section6.3.1);and
∙solvestheeigenfrequencyproblemonlyforsymmetricmassandstiffnessmatrices;thecomplexeigenfrequencysolvermustbeusedifunsymmetriccontributions,suchastheloadstiffness,areneeded.
Eigenvalueextraction
Theeigenvalueproblemforthenaturalfrequenciesofanundampedfiniteelementmodelis
where
isthemassmatrix(whichissymmetricandpositivedefinite);
isthestiffnessmatrix(whichincludesinitialstiffnesseffectsifthebasestateincludedtheeffectsofnonlineargeometry);
istheeigenvector(themodeofvibration);and
M and N
aredegreesoffreedom.
When ispositivedefinite,alleigenvaluesarepositive.Rigidbodymodesandinstabilitiescause tobeindefinite.Rigidbodymodesproducezeroeigenvalues.Instabilitiesproducenegativeeigenvaluesandoccurwhenyouincludeinitialstresseffects.Abaqus/Standardsolvestheeigenfrequencyproblemonlyforsymmetricmatrices.
Selectingtheeigenvalueextractionmethod
Abaqus/Standardprovidesthreeeigenvalueextractionmethods:
∙Lanczos
∙Automaticmulti-levelsubstructuring(AMS),anadd-onanalysiscapabilityforAbaqus/Standard
∙Subspaceiteration
Inaddition,youmustconsiderthesoftwarearchitecturethatwillbeusedforthesubsequentmodalsuperpositionprocedures.Thechoiceofarchitecturehasminimalimpactonthefrequencyextractionprocedure,buttheSIMarchitecturecanoffersignificantperformanceimprovementsoverthetraditionalarchitectureforsubsequentmode-basedsteady-stateortransientdynamicprocedures(see “UsingtheSIMarchitectureformodalsuperpositiondynamicanalyses”in“Dynamicanalysisprocedures:
overview,” Section6.3.1).Thearchitecturethatyouuseforthefrequencyextractionprocedureisusedforallsubsequentmode-basedlineardynamicprocedures;youcannotswitcharchitecturesduringananalysis.Thesoftwarearchitecturesusedbythedifferenteigensolversareoutlinedin Table6.3.5–1.
Table6.3.5–1 Softwarearchitecturesavailablewithdifferenteigensolvers.
SoftwareArchitecture
Eigensolver
Lanczos
AMS
SubspaceIteration
Traditional
SIM
TheLanczossolverwiththetraditionalarchitectureisthedefaulteigenvalueextractionmethodbecauseithasthemostgeneralcapabilities.However,theLanczosmethodisgenerallyslowerthantheAMSmethod.TheincreasedspeedoftheAMSeigensolverisparticularlyevidentwhenyourequirealargenumberofeigenmodesforasystemwithmanydegreesoffreedom.However,theAMSmethodhasthefollowinglimitations:
∙AllrestrictionsimposedonSIM-basedlineardynamicproceduresalsoapplytomode-basedlineardynamicanalysesbasedonmodeshapescomputedbytheAMSeigensolver.See “UsingtheSIMarchitectureformodalsuperpositiondynamicanalyses”in“Dynamicanalysisprocedures:
overview,” Section6.3.1,fordetails.
∙TheAMSeigensolverdoesnotcomputecompositemodaldampingfactors,participationfactors,ormodaleffectivemasses.However,ifparticipationfactorsareneededforprimarybasemotions,theywillbecomputedbutarenotwrittentotheprinteddata(.dat)file.
∙YoucannotusetheAMSeigensolverinananalysisthatcontainspiezoelectricelements.
∙Youcannotrequestoutputtotheresults(.fil)fileinanAMSfrequencyextractionstep.
Ifyourmodelhasmanydegreesoffreedomandtheselimitationsareacceptable,youshouldusetheAMSeigensolver.Otherwise,youshouldusetheLanczoseigensolver.TheLanczoseigensolverandthesubspaceiterationmethodaredescribedin“Eigenvalueextraction,” Section2.5.1oftheAbaqusTheoryManual.
Lanczoseigensolver
FortheLanczosmethodyouneedtoprovidethemaximumfrequencyofinterestorthenumberofeigenvaluesrequired;Abaqus/Standardwilldetermineasuitableblocksize(althoughyoucanoverridethischoice,ifneeded).Ifyouspecifyboththemaximumfrequencyofinterestandthenumberofeigenvaluesrequiredandtheactualnumberofeigenvaluesisunderestimated,Abaqus/Standardwillissueacorrespondingwarningmessage;theremainingeigenmodescanbefoundbyrestartingthefrequencyextraction.
Youcanalsospecifytheminimumfrequenciesofinterest;Abaqus/Standardwillextracteigenvaluesuntileithertherequestednumberofeigenvalueshasbeenextractedinthegivenrangeorallthefrequenciesinthegivenrangehavebeenextracted.
See “UsingtheSIMarchitectureformodalsuperpositiondynamicanalyses”in“Dynamicanalysisprocedures:
overview,” Section6.3.1,forinformationonusingtheSIMarchitecturewiththeLanczoseigensolver.
Input File Usage:
*FREQUENCY,EIGENSOLVER=LANCZOS
Abaqus/CAE Usage:
Stepmodule:
StepCreate:
Frequency:
Basic:
Eigensolver:
Lanczos
ChoosingablocksizefortheLanczosmethod
Ingeneral,theblocksizefortheLanczosmethodshouldbeaslargeasthelargestexpectedmultiplicityofeigenvalues(thatis,thelargestnumberofmodeswiththesamefrequency).Ablocksizelargerthan10isnotrecommended.Ifthenumberofeigenvaluesrequestedis n,thedefaultblocksizeistheminimumof(7, n).Thechoiceof7forblocksizeprovestobeefficientforproblemswithrigidbodymodes.ThenumberofblockLanczosstepswithineachLanczosrunisusuallydeterminedbyAbaqus/Standardbutcanbechangedbyyou.Ingeneral,ifaparticulartypeofeigenproblemconvergesslowly,providingmoreblockLanczosstepswillreducetheanalysiscost.Ontheotherhand,ifyouknowthataparticulartypeofproblemconvergesquickly,providingfewerblockLanczosstepswillreducetheamountofin-corememoryused.Thedefaultvaluesare
Blocksize
MaximumnumberofblockLanczossteps
1
80
2
50
3
45
≥4
35
Automaticmulti-levelsubstructuring(AMS)eigensolver
FortheAMSmethodyouneedonlyspecifythemaximumfrequencyofinterest(theglobalfrequency),andAbaqus/Standardwillextractallthemodesuptothisfrequency.Youcanalsospecifytheminimumfrequenciesofinterestand/orthenumberofrequestedmodes.However,specifyingthesevalueswillnotaffectthenumberofmodesextractedbytheeigensolver;itwillaffectonlythenumberofmodesthatarestoredforoutputorforasubsequentmodalanalysis.
TheexecutionoftheAMSeigensolvercanbecontrolledbyspecifyingthreeparameters:
, ,and .Thesethreeparametersmultipliedbythemaximumfrequencyofinterestdefinethreecut-offfrequencies. (defaultvalueof5)controlsthecutofffrequencyforsubstructureeigenproblemsinthereductionphase,while and (defaultvaluesof1.7and1.1,respectively)controlthecutofffrequenciesusedtodefineastartingsubspaceinthereducedeigensolutionphase.Generally,increasingthevalueof and improvestheaccuracyoftheresultsbutmayaffecttheperformanceoftheanalysis.
Requestingeigenvectorsatallnodes
Bydefault,theAMSeigensolvercomputeseigenvectorsateverynodeofthemodel.
Input File Usage:
*FREQUENCY,EIGENSOLVER=AMS
Abaqus/CAE Usage:
Stepmodule:
StepCreate:
Frequency:
Basic:
Eigensolver:
AMS
Requestingeigenvectorsonlyatspecifiednodes
Alternatively,youcanspecifyanodeset,andeigenvectorswillbecomputedandstoredonlyatthenodesthatbelongtothatnodeset.Thenodesetthatyouspecifymustincludeallnodesatwhichloadsareappliedoroutputisrequestedinanysubsequentmodalanalysis(thisincludesanyrestartedanalysis).Ifelementoutputisrequestedorelement-basedloadingisapplied,thenodesattachedtotheassociatedelementsmustalsobeincludedinthisnodeset.Computingeigenvectorsatonlyselectednodesimprovesperformanceandreducestheamountofstoreddata.Therefore,itisrecommendedthatyouusethisoptionforlargeproblems.
Input File Usage:
*FREQUENCY,EIGENSOLVER=AMS,NSET=name
Abaqus/CAE Usage:
Stepmodule:
StepCreate:
Frequency:
Basic:
Eigensolver:
AMS:
Limitregionofsavedeigenvectors
ControllingtheAMSeigensolver
TheAMSmethodconsistsofthefollowingthreephases:
Reductionphase:
InthisphaseAbaqus/Standardusesamulti-levelsubstructuringtechniquetoreducethefullsysteminawaythatallowsaveryefficienteigensolutionofthereducedsystem.Theapproachcombinesasparsefactorizationbasedonamulti-levelsupernodeeliminationtreeandalocaleigensolutionateachsupernode.Startingfromthelowestlevelsupernodes,weuseaCraig-Bamptonsubstructurereductiontechniquetosuccessivelyreducethesizeofthesystemasweprogressupwardintheeliminationtree.Ateachsupernodealocaleigensolutionisobtainedbased
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