Dynamic reliability analysis for structure with temporal and spatial multi-parameter

Abstract

For efficiently estimating the dynamic failure probability of the structure with random variables, stochastic processes and temporal and spatial multi-parameter, an estimation strategy is presented based on the random field transformation. The random field transformation focusing on the dynamic reliability with only one time parameter is further investigated, and it is extended to temporal and spatial multi-parameter issue, which simulates the output as multi-dimensional Gaussian random field. Also, the active learning Kriging method is used to construct the surrogate models for the mean function and auto-covariance function of performance function. After that, the temporal and spatial dynamic failure probability can be obtained by the simulation method. Although it doesn’t need to call the real performance function during the process of simulation method, it is time computationally expensive. To address this issue, the optimization algorithm procedure is established to estimate the dynamic failure probability. Several examples including an aero engine turbine disk and a cylindrical pressure vessel are introduced to illustrate the significance and effectiveness of the proposed methods for analyzing the temporal and spatial multi-parameter dynamic failure probability.