China Mechanical Engineering ›› 2014, Vol. 25 ›› Issue (12): 1694-1699.

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Frame's Nonlinear Reliability Optimization Based on Interval Uncertainty

Li Weiping;Wang Zhenxing;Zhang Baozhen;Dou Xiandong;Liu Chao   

  1. State Key Laboratory of Advanced Design and Manufacture for Vehicle Body,Hunan University,Changsha,410082
  • Online:2014-06-26 Published:2014-06-27
  • Supported by:
    National High-tech R&D Program of China (863 Program) (No. 2012AA111802)

基于区间不确定性的车架非线性可靠性优化

李伟平;王振兴;张宝珍;窦现东;柳超   

  1. 湖南大学汽车车身先进设计制造国家重点实验室,长沙,410082
  • 基金资助:
    国家高技术研究发展计划(863计划)资助项目(2012AA111802);教育部长江学者与创新团队发展计划资助项目(531105050037)

Abstract:

Nonlinear finite element analysis method was used herein, and using ABAQUS software to analyze the stiffness and strength of the frame. Based on the results of the finite element model, the thicknesses of some beams which were more effects on the structural strength and the mass were chosen as the interval of design variables. The frame material density and Poisson's ratio were taken as uncertain variables. Building an approximate model between the design variables and stress by using TPS-HDMR, the Kriging model was utilized to build an approximate model among the design variables and mass. NSGA-Ⅱ and IP-GA was used to carry two objective optimization, at the same time a reliability index was introduced as the constraint, and then the Pareto optimal solution set was obtained.

Key words: uncertainty, high dimensional model, Kriging model, genetic algorithm, reliability, frame

摘要:

采用非线性有限元分析方法,用ABAQUS软件对车架的刚度和强度进行了分析。基于分析结果选取对结构强度和质量影响比较大的梁的厚度作为区间的设计变量,把车架材料的密度和泊松比作为不确定量,利用高维模型(TPS-HDMR)构建了设计变量与应力之间的近似模型,运用Kriging模型构建了设计变量与质量的近似模型。采用遗传算法中的NSGA-Ⅱ方法和隔代遗传算法,对车架应力和质量两目标进行了优化,并加入可靠度作为约束,得到了Pareto最优解集。

关键词: 不确定性;高维模型, Kriging模型, 遗传算法;可靠性, 车架

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