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

被引：0
|
作者：
Zhang W. [1 ]
Lü Z. [1 ]
机构：
[1] Institute of Aeronaut, Northwestern Polytechnical University, Xi'an
来源：
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2022年 / 58卷 / 06期
关键词：
Adaptive learning; EFF learning function; Structural reliability; Surrogate model; U learning function;
D O I：
10.3901/JME.2022.06.263
中图分类号：
学科分类号：
摘要：
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. © 2022 Journal of Mechanical Engineering.
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页码：263 / 273
页数：10
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