一种新的自适应Kriging法停止准则及其在涡轮盘疲劳寿命可靠性中的应用

doi:10.3901/JME.2022.06.263

机械工程学报 ›› 2022, Vol. 58 ›› Issue (6): 263-273.doi: 10.3901/JME.2022.06.263

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一种新的自适应Kriging法停止准则及其在涡轮盘疲劳寿命可靠性中的应用

张文鑫,吕震宙

西北工业大学航空学院西安 710072

收稿日期:2021-05-05 修回日期:2021-10-20 出版日期:2022-03-20 发布日期:2022-05-19
通讯作者: 吕震宙,女,1966年出生,教授,博士研究生导师。主要研究方向为飞行器总体设计、飞行器结构设计、飞行器可靠性工程、安全工程。E-mail:zhenzhoulu@nwpu.edu.cn
作者简介:张文鑫,男,1992年出生,博士研究生。主要研究方向为飞;行器可靠性工程。E-mail:qnq_zh@hotmail.com
基金资助:
国家自然科学基金资助项目(51775439)。

New Stopping Criterion of Adaptive Kriging Method and Its Application in Fatigue Life Reliability for Turbine Disk

ZHANG Wenxin, Lü Zhenzhou

Institute of Aeronaut, Northwestern Polytechnical University, Xi'an 710072

Received:2021-05-05 Revised:2021-10-20 Online:2022-03-20 Published:2022-05-19

摘要/Abstract

摘要： 自适应Kriging结合Monte Carlo模拟(AK-MCS)是估计结构失效概率的高效方法。AK-MCS在自学习过程中需要停止准则掌控其自学习的程度，然而目前的停止准则没有准确地将自学习过程和失效概率估计值的精度联系起来。为此，提出一个基于失效概率置信区间的停止准则，该停止准则首先计算失效概率估计值的方差，然后依据切比雪夫不等式将失效概率估计值方差转化为失效概率估计值的置信区间，并以置信区间长度小于给定阈值作为停止准则。所列的简单算例以及涡轮盘低周疲劳寿命可靠性工程算例均将该方法与同类研究进行了对比，验证了该方法的精度。

关键词: 自适应学习, 代理模型法, U学习函数, EFF学习函数, 结构可靠性

Abstract: Active learning reliability method combining Kriging and Monte Carlo simulation(AK-MCS) is an efficient method in estimating structural failure probability. In the adaptive learning process of AK-MCS, the stopping criterion takes control of the level of the adaptive learning. However, existing stopping criterions do not contact the adaptive learning process with the accuracy of failure probability. Therefore, a new stopping criterion based on the confidence interval of failure probability is proposed. Firstly, the stopping criterion calculates the variance of estimated failure probability. Besides, the variance of estimated failure probability is converted into the confidence interval of estimated failure probability by Chebyshev's theorem. In the end, the stopping criterion is defined as the length of a confidence interval. Numerical examples and an engineering example of low-cycle fatigue life reliability for a turbine disk verify the accuracy of the proposed method comparing with similar researches.

Key words: adaptive learning, surrogate model, U learning function, EFF learning function, structural reliability

中图分类号:

TB114

张文鑫, 吕震宙. 一种新的自适应Kriging法停止准则及其在涡轮盘疲劳寿命可靠性中的应用[J]. 机械工程学报, 2022, 58(6): 263-273.

ZHANG Wenxin, Lü Zhenzhou. New Stopping Criterion of Adaptive Kriging Method and Its Application in Fatigue Life Reliability for Turbine Disk[J]. Journal of Mechanical Engineering, 2022, 58(6): 263-273.

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一种新的自适应Kriging法停止准则及其在涡轮盘疲劳寿命可靠性中的应用

New Stopping Criterion of Adaptive Kriging Method and Its Application in Fatigue Life Reliability for Turbine Disk

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