基于sko的电除尘器板壳加强筋结构优化-研究

基于sko的电除尘器板壳加强筋结构优化-研究

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时间:2019-01-29

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1、上海交通大学工程硕士学位论文AbstractSTUDYOFELECTRICPRECIPITATORMECHANICALSTRUCTURALOPTIMIZATIONMETHODBASEDONSKOABSTRACTStructuraltopologyoptimizationisthemostchallengingandhottest,aswellasmostdifficultresearchtopicinstructureoptimization.Comparedwiththeconventionalsizeoptimizat

2、ionandshapeoptimization,topologyoptimizationhasmoredesigndegrees,andcangetmorebenefit,sothatitismoreattractiveforengineers.Withthedevelopmentoftopologyoptimization,itisbeingappliedtoengineeringpracticegradually.Forexample,base-structuralmethodusedindiscretetopolog

3、yoptimizationandHomogenizationmethod、Variablethicknessmethod、VariabledensitymethodusedinContinuumtopologyoptimizationandsoon.Thispaperstudiesabionicapproachofstructuraltopologyoptimization—theSKOmethodwhichscarcelyisappliedtothepracticaldesign,andrealizesSKOmethod

4、usingAPDLlanguageandANSYSplatform.SKOmethodisbasedonbiologicaladaptivegrowthlawinnature,thatis,materialshouldbeaddedwheretheloadingisgreat,whilematerialshouldberemovedwheretheloadingissmall.Fortheconceptofstructuraldesignoptimization,itmeansthatstressofeverypointi

5、nthestructureshouldachievethesamestress,inotherwords,theoptimalstructureshouldsatisfytheuniformaxiom.First,therelationshipbetweenYoung’smodulusandthestressintheSKOmethodisstudied,whichisthekeyissuesofSKOmethod.Thenittakesplateshellstructurereinforcementastheresear

6、chobject,establishmathematicalmodelofplateshellstructurereinforcementdistributionbionicdesignmethod,analysisofthebionicdesignmethod,usesthismethodtostudyplateshellstructureofthebasestructureofthe-II-上海交通大学工程硕士学位论文Abstractbuilding,stiffenerbias,crosssectiontypeofde

7、signandreinforcementsectionsizeoptimization.AccordingtoANSYSsecondarydevelopmenttechnology,itrealizesplateshellstructurereinforcementdistributionbionicdesignsoftware.Asapracticalapplication,theoutletstructureoflargeelectrostaticprecipitator(E.P)isoptimizedbythesug

8、gestedmethod.TheresultsshowthatSKOoptimizationmethodcanrapidlyoptimizeshellstructuresandobtaintheapproximateoptimizationmodel.Comparedwithtraditionallys

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