北京航空航天大学学报 ›› 2019, Vol. 45 ›› Issue (2): 373-380.doi: 10.13700/j.bh.1001-5965.2018.0301

• 论文 • 上一篇    下一篇

基于改进的动态Kriging模型的结构可靠度算法

魏娟1,2, 张建国1,2, 邱涛1,2   

  1. 1. 北京航空航天大学 可靠性与系统工程学院, 北京 100083;
    2. 北京航空航天大学 可靠性与环境工程技术重点实验室, 北京 100083
  • 收稿日期:2018-05-28 出版日期:2019-02-20 发布日期:2019-03-04
  • 通讯作者: 张建国 E-mail:zjg@buaa.edu.cn
  • 作者简介:魏娟女,硕士研究生。主要研究方向:机械产品可靠性分析与设计;张建国男,博士,教授,博士生导师。主要研究方向:机械可靠性与安全性。
  • 基金资助:
    国家自然科学基金(51675026)

Structural reliability algorithm based on improved dynamic Kriging model

WEI Juan1,2, ZHANG Jianguo1,2, QIU Tao1,2   

  1. 1. School of Reliability and Systems Engineering, Beihang University, Beijing 100083, China;
    2. Science and Technology on Reliability and Environment Engineering Laboratory, Beihang University, Beijing 100083, China
  • Received:2018-05-28 Online:2019-02-20 Published:2019-03-04
  • Supported by:
    National Natural Science Foundation of China (51675026)

摘要: 对于复杂航空航天机械产品,极限状态方程往往表现出隐式、高度非线性的特点,而且通常需要调用有限元分析,从而耗费大量时间。将混合粒子群-模拟退火(PSOSA)算法应用到Kriging模型中相关参数的寻优过程,提高了预测精度。同时结合动态更新机制,逐渐加入样本点,尽可能减少函数的调用次数,从而提高了计算效率,并将该算法应用到结构可靠性分析中。通过案例分析,和传统蒙特卡罗模拟方法、响应面等经典方法进行对比,所提算法与蒙特卡罗模拟方法计算结果更加接近,计算时间大大缩短,效率和精度都明显改进。

关键词: 极限状态函数, 动态更新, Kriging模型, 粒子群-模拟退火(PSOSA)算法, 可靠性

Abstract: For complex aerospace machinery products, the limit state functions are often implicit and highly nonlinear, and the reliability calculation usually requires time-consuming finite element analysis. In this paper, the particle swarm optimization-simulated annealing (PSOSA) algorithm is applied to the optimization of the correlation parameters of the dynamic Kriging model, which improves the prediction accuracy. At the same time, with the dynamic update mechanism, sample points are gradually added to reduce the number of function callsas much as possible, thereby improving the calculation efficiency. The algorithm is applied to the structural reliability analysis. The Monte Carlo method, response surface and other classic algorithms are compared,and the results of the proposed algorithm are closer to those of Monte Carlo method, and the calculation time is greatly shortened, which shows that the efficiency and accuracy of the algorithm are improved significantly.

Key words: limit state function, dynamic update, Kriging model, particle swarm optimization-simulated annealing (PSOSA) algorithm, reliability

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