欢迎来到天天文库
浏览记录
ID:36655674
大小:1.12 MB
页数:9页
时间:2019-05-13
《基于多属性决策的气动隐身多目标优化》由会员上传分享,免费在线阅读,更多相关内容在学术论文-天天文库。
1、学兔兔www.xuetutu.com第48卷第l3期机械工程学报V_o1.48NO.132012年7月JOURNAL0FMECHANICALENGINEERINGJu1.2012DoI:1O.3901/JME.2012.13.132基于多属性决策的气动隐身多目标优化木廖炎平刘莉龙腾(北京理工大学宇航学院北京100081)摘要:针对多目标优化结果排序与选择的多属性决策(Multi。attributedecisionmaking,MADM)I"n~题,将多目标优化与MADM相结合,提出基于MADM的多目标优化方法,并将该方法应用于跨声
2、速前掠翼(Forward-sweptwing,FSW)气动隐身多目标优化中,优化结果提高了跨声速FSW的气动和隐身性能。采用类别形状函数变换法(Class.shapefunctiontransformation,CST)方法对翼型几何外形进行描述,实现FSW气动和隐身多学科优化设计模型的参数化描述。建立基于N—s方程的计算流体力学方法的FSW气动分析模型和基于矩量法的计算电磁学方法的FSW隐身分析模型。将Pareto多目标遗传算法得到的Pareto非劣解集构成MADM矩阵,采用基于模糊熵权的改进的逼近理想解的排序法(Modifie
3、dtechniquefororderpreferencebysimilari够toidealsolution,M.TOPSIS)方案评价方法进行Pareto非劣解排序,最终确定最佳的Pareto非劣解。研究结果验证了所提出方法的有效性,为多目标优化问题提供了一种新的解决途径。关键词:多属性决策多目标优化模糊熵权前掠翼Pareto遗传算法中图分类号:V22lMulti-objectiveAerodynamicandStealthyPerformanceOptimizationBasedonMulti-attributeDecisio
4、nMakingLIAOYanpingLIULiLONGTeng(SchoolofAerospaceEngineering,BeijingInstituteofTechnology,Beijing100081)Abstract:Inviewofmulti-attributedecisionmaking(MADM)problemsforrankingandselectingofmulti--objectiveoptimizationresults,themulti—objectiveoptimizationmethodbasedonM
5、ADMisproposedbycombiningthemulti-objectiveoptimizationwithMADM.Themulti—objectiveaerodynamicandstealthyperformanceoftransonicforward-sweptwing(FSW)issolvedbytheproposedmethod,whichCanimprovetheaerod3namicandstealthyperformanceoftransonicFSWefectively.Theclass-shapefun
6、ctiontransformation(CST)methodisusedtodescribetheparameterizedairfoilgeometry.Theparameterizedmodelsforaerod)namicandstealthyperformanceofFSWareconstructed.TheaerodynamicanalysismodelofFSWisconstructedbycomputationalfluiddynamicsmethodbasedonN—Sequations.Thestealthype
7、rformanceanalysismodelofFSWisconstructedbycomputationalelectromagneticsmethodbasedmethodofmoments.TheMADMdecisionmarxisconstructedbytheParetooptimalsolutionssetsolvedfromParetomulti-objectivegeneticalgorithm,themodifiedtechniquefororderpreferencebysimilaritytoidealsol
8、ution(M-TOPSIS)approachbasedonfuzzyentropyweightisemployedtoranktheParetooptimalsolutionsandultimatelytoidentifythebestParet
此文档下载收益归作者所有