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Optimization Methods

1 Optimization Introduction:Inoptimizationofa design , satisfactorysolutionis ,optimizationhasbecomea partofcomputer-aideddesignactivities. Therearetwodistincttypesofoptimizational gorithmswidelyusedtoday.(a) (b) :Anaiveoptimaldesignisachievedbycomparin gafew(limiteduptotenorso) (cost,profit,etc., ) ofeachsolutionis comparedandbestsolutionis isimpossibletoapplysingleformulationproc edureforallengineeringdesignproblems,sin cetheobjectiveinadesignproblemandassocia tedtherefore,designparametersvaryproduct to ,whichthencanbesolvedusinganoptimization algorithm.

identifying the underlying design variables, which are primarily varied during the optimization process. A design problem usually involves many design parameters, of which some are highly sensitive to the proper working of the design. These parameters are called design variables in the parlance of optimization procedures.

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Transcription of Optimization Methods

1 1 Optimization Introduction:Inoptimizationofa design , satisfactorysolutionis ,optimizationhasbecomea partofcomputer-aideddesignactivities. Therearetwodistincttypesofoptimizational gorithmswidelyusedtoday.(a) (b) :Anaiveoptimaldesignisachievedbycomparin gafew(limiteduptotenorso) (cost,profit,etc., ) ofeachsolutionis comparedandbestsolutionis isimpossibletoapplysingleformulationproc edureforallengineeringdesignproblems,sin cetheobjectiveinadesignproblemandassocia tedtherefore,designparametersvaryproduct to ,whichthencanbesolvedusinganoptimization algorithm.

2 Figure1 :Theformulationofanoptimizationproblembe ginswithidentifyingtheunderlyingdesignva riables,whichareprimarilyvariedduringthe optimizationprocess. A designproblemusuallyinvolvesmanydesignpa rameters, Other(notsoimportant)designparametersusu allyremainfixedorvaryin :Theconstraintsrepresentsomefunctionalre lationshipsamongthedesignvariablesandoth erdesignparameterssatisfyingcertainphysi calphenomenonandcertainresourcelimitatio ns. Thenatureandnumberofconstraintstobeinclu dedin theformulationdependontheuser. ,maximumstressis a constraintofa astructurehasregularshapetheyhaveanexact mathematicalrelationofmaximumstresswithd imensions.

3 Butincaseirregularshape, ,smallerthanorequalto,a :Thestress (x)developedanywhereina componentmustbesmallerthanorequaltotheal lowablestrength(Sallowable)ofthematerial . (x) SallowableSomeconstraintsmaybeofgreater- than/ ,thenaturalfrequency(f(x)) ofa systemtobegreaterthan2 Hzorbynotationf(x) :Thedeflection (x)ofa pointinthecomponentmustbeexactlyequalto5 (x)= isverydifficulttohandletheequalityconstr aintsinthealgorithms. Insuchcases, :Previously (x)= 5 Nowit is changedtoinequalityconstraintsasgivenbel ow: (x) 4, (x) :Thenexttaskintheformulationprocedureist ofindtheobjectivefunctionintermsofthedes ignvariablesandotherproblemparameters.

4 Thecommonengineeringobjectivesinvolvemin imizationofoverallcostofmanufacturingorm inimizationofoverallweightofa (expressedinmathematicalform),thereareso meobjectives(suchasaestheticaspectofades ign,ridecharacteristicsofacarsuspensiond esignandreliabilityofa design ) , , minimizingtheoverallweightofthestructure andsimultaneouslybeconcernedinminimizing thedeflectionofa specificpointinthetruss. Intheoptimalproblemformulation,thedesign ermayliketousetheweightofthetruss(asafun ctionofthecrosssectionsofthemembers)asth eobjectivefunctionandhavea constraintwiththedeflectionoftheconcerne dpointtobelessthana Eitherit is tobemaximizedorit hastobeminimized.

5 Usuallytheoptimizationalgorithmswerewrit tenforminimizationproblemsormaximization problems. Althoughinsomealgorithms,someminorstruct uralchangeswouldenabletoperformeithermin imization(or)maximization; minorchangeintheobjectivefunctioninstead ofa changein thealgorithmisforsolvinga minimizationproblem,it canbeeasilychangedtoamaximizationproblem bymultiplyingtheobjectivefunctionby 1 , , = 1, 2, 3, ..N.(1 )Inanygivenproblem, tomakea guessabouttheoptimalsolutionandsetthemin imumandmaximumboundssothattheoptimalsolu tionlieswithinthesetwobounds()( )LUiiix xx ()Lix()Uix13 Ifanydesignvariablecorrespondingtotheopt imalsolutionisfoundtolieonorneartheminim umormaximumbound, ,theoptimizationproblemcanbemathematical lywritteninaspecialformat,knownasnonline arprogramming(NLP) :Denotingthedesignvariablesasa columnvectorx = (x1, )T-,theobjectivefunctionasa scalarquantityf(x),Jinequalityconstraint sasgj(x) 0andKequalityconstraintsashk(x)= 0,wewritetheNLPproblem.

6 Minimizef(x)Subjectto,gj(x) 0j = 1, 2, 3, ..J;hk(x)= 0k = 1, 2, 3, ..K;i = 1, 2, 3, ..N.()( )LUiiix xx 14 Example:1 Optimaldesignofa Theloadingisalsoshownin themembersAC= CE= l =1mOptimize,1. Topologyofthetrussstructure(theconnectiv ityoftheelementsinatruss).2. Onceoptimallayoutis known,crosssectionofeveryelementis , Usingthesymmetryofthetruss,A7= A1;A6= A2;A3= A5 Thus,therearepracticallyfourdesignvariab les(A1toA4).Formulationoftheconstraints: ThetrusscarrythegivenloadP= 2 kN, ,Syt= Syc= 500 MPaandmodulusofelasticityE = 200 GPa. Axialforcesin eachmembersofthetrussare16 MemberAB Pcsc ;MemberBC+ Pcsc ;MemberAC+ Pcot ;MemberBD P(cot + cot );Now,theaxialstresscanbecalculatedbydiv idingtheaxialloadbythecross-sectionalare aofthatmember.

7 Thus,thefirstsetof constraintscanbewrittenas1csc,2ycPSA 2cot,2ytPSA 3csc,2ytPSA 4(cotcot ).2ycPSA + 17In most structures, deflection is a major consideration. In the above truss structure, let us assume that the maximum vertical deflection at C is max = 2 mm. By using Castigliano s theorem, we obtain the deflection constraint:Intheabovestructure,tan = = 2/3. Theothersetofconstraintsarisesfromthesta bilityconsiderationofthecompressionmembe rsAB,BD, membersABandBD:212,2 242(cotcot ). + A A A +++ 18 Inthisproblem, , ,wewritetheobjectivefunctionasThenexttas kistosetlowerandupperboundsforthefourcro sssectionalareas.

8 Wemaychoosetomakeallfourareasliebetween1 0and500mm2. ThusthevariableboundsareasInthefollowing , lA lA lA l++ +19 Subject lA lA lA l++ +4(cotcot ) 0,2ycPSA + ,0sin21 APSyc,0cot22 APSyt,0sin23 APSyt202120, sinEAPl 242(cotcot ) 0, + ,PlEA A A A +++ 21 Example:2 Optimaldesignofa A two-dimensional model of a car suspension systemThecomfortinridinga damperateachwheel(Figure4). Inordertoformulatetheoptimaldesignproble m,thefirsttaskis ,Frontcoilstiffnesskfs,Frontunsprungmass mfu,Rearcoilstiffnesskrs,Rearunsprungmas smru,Fronttyrestiffnesskft,Reardampercoe fficient rReartyrestiffnesskrt,Frontdampercoeffic ientAxle-to-axledistancel,Polarmomentofi nertiaofthecarJ,Aslongtimeis takenfortheconvergenceoftheoptimizationw ithallparametersasdesignvariables,onlyfo urimportantparameters-frontcoilstiffness kfs,rearcoilstiffnesskrs, frontdampercoefficient,andreardampercoef ficient rareconsideredasdesignvariables.

9 Otherdesignparametersarekeptconstant:ms= 1000kgl= mmfu= 70kgl1= mmru= 150kgl2= mkft= 20kg/mmJ= 550kg-m2krt= 20kg/mmf f 23 Usingtheseparameters,differentialequatio nsgoverningtheverticalmotionoftheunsprun gmassatthefrontaxle(q1), thesprungmass(q2), andtheunsprungmassattherearaxle(q4), andtheangularmotionofthesprungmass(q3) arewritten(Fig. 5) The dynamic model of the car suspension system. The above model has four degrees-of-freedom (q1to q4)24(9)(10)(11)(12)Where the forces F1 to F6are calculated as follows:(13)Theparametersd1, d2, d3, andd4aretherelativedeformationsinthefron ttyre,thefrontspring,thereartyre,andther earspringrespectively.

10 Figure5 showsallthefourdegreesoffreedomoftheabov esystem(q1toq4). Therelativedeformationsinspringsandtyres canbewrittenasfollows:2112 2344 4563,,,, ,.ftfsfrsrrtF kdF kdFdF kdFdF kd ======25(14)Thetimevaryingfunctionsf1(t) andf2(t)areroadirregularitiesasfunctions oftime. Anyfunctioncanbeusedforf1(t).Forexample, a bumpcanbemodeledasf1(t)= A sin, whereAistheamplitudeofthebumpandTis caris movingforward,thefrontwheelexperiencesth ebumpfirst,whiletherearwheelexperiencest hesamebumpalittlelater,dependinguponthes peedofthecar. Thus,thefunctionf2(t)canbewrittenasf2(t) = f1( t l/v), wherelis theaxle-to-axledistanceand is thespeedofthecar.


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