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

doi:10.3901/JME.2022.06.263

Abstract

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

CLC Number:

TB114

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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