Reliability‐Based Fatigue Life Prediction for Complex Structure with Time‐Varying Surrogate Modeling

Abstract

To improve the computational efficiency and accuracy of reliability‐based fatigue life prediction for complex structure, a time‐varying particle swarm optimization‐ (PSO‐) based general regression neural network (GRNN) surrogate model (called as TV/PSO‐GRNN) is developed. By integrating the proposed space‐filling Latin hypercube sampling technique and PSO‐GRNN regression function, the mathematical model of TV/PSO‐GRNN is studied. The reliability‐based fatigue life prediction framework is illustrated in respect of the TV/PSO‐GRNN surrogate model. Moreover, the reliability‐based fatigue life prediction of an aircraft turbine blisk under multiphysics interaction is performed to validate the TV/PSO‐GRNN model. We obtain the distributional characteristics, reliability degree, and sensitivity degree of fatigue failure cycle, which are useful for the turbine blisk design. By comparing the direct simulation (FE/FV model), RSM, GRNN, PSO‐GRNN, and TV/PSO‐GRNN, we observe that the TV/PSO‐GRNN surrogate model is promising to perform the reliability‐based fatigue life prediction of the turbine blisk and enhance the computational efficiency while ensuring an acceptable computational accuracy. The efforts of this study offer a useful insight for the reliability‐based design optimization of complex structure.

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