J4 ›› 2014, Vol. 41 ›› Issue (6): 111-117.doi: 10.3969/j.issn.1001-2400.2014.06.019

• 研究论文 • 上一篇    下一篇

随机变量下的热传导结构拓扑优化设计

尤芳1,2;陈建军1;曹鸿钧1;谢永强1   

  1. (1. 西安电子科技大学 机电工程学院,陕西 西安  710071;
    2. 西北农林科技大学 机电工程学院,陕西 杨凌  712100)
  • 收稿日期:2014-01-07 出版日期:2014-12-20 发布日期:2015-01-19
  • 作者简介:尤芳(1973-),女,讲师,西安电子科技大学博士研究生,E-mail: youfang@nwsuaf.edu.cn.
  • 基金资助:

    国家自然科学基金资助项目(51175398);中央高校基本科研业务费专项资金资助项目(K5051304016)

Topology optimization design of heat conduction structures with random variables

YOU Fang1,2;CHEN Jianjun1;CAO Hongjun1;XIE Yongqiang1   

  1. (1. School of Mechano-electronic Engineering, Xidian Univ., Xi'an  710071, China;
    2. College of Mechanical & Electronic Engineering, Northwest A & F University, Yangling  712100, China)
  • Received:2014-01-07 Online:2014-12-20 Published:2015-01-19

摘要:

研究具有随机变量的稳态热传导结构在散热弱度约束下的拓扑优化设计问题.建立了以单元相对导热系数为设计变量,导热材料体积极小化为目标函数,满足散热弱度可靠性指标为约束条件的稳态热传导结构的拓扑优化设计数学模型.基于随机因子法,利用代数综合法推导出散热弱度的数字特征的计算表达式.采用渐进结构优化方法(Evolutionary Structural Optimization,ESO)求解,并利用过滤技术消除优化过程中的数值不稳定性现象.通过两个算例验证文中模型及求解策略、方法的合理性和有效性.

关键词: 热传导, 随机变量, 散热弱度, 可靠性, 随机因子法, 拓扑优化

Abstract:

Topology optimization design of a heat conduction structure with random variables under dissipation of heat transport potential capacity constraint is discussed. The mathematical model of topology optimization, based on the probabilistic reliability index with dissipation of heat transport potential capacity constraint, is constructed. The total volume of heat conductive material is to be minimized and the relative thermal conductivity of elements is regarded as the design variable here. The computational expressions for numerical characteristics of dissipation of heat transport potential capacity based on the random factor method are presented. Evolutionary structural optimization method is used in optimization. A filtering technique is employed to eliminate numerical instabilities in process of topology optimization. Two numerical examples are presented to demonstrate the feasibility and effectiveness of the optimal model and the solution.

Key words: heat conduction, random variables, dissipation of heat transport potential capacity, reliability, random factor method, topology optimization