Method for Sensitivity Analysis of the Uncertain Distribution Parameters Based on Kriging Model and Surrogate Sampling

doi:10.3901/JME.2024.08.370

Abstract

Abstract: For structural system involving inputs with distribution parameter uncertainty, sensitivity indices which can evaluate the effect of uncertain distribution parameters on the statistical eigenvalues of the structural output response are firstly established by taking the output expectation and variance as examples. Then, to overcome the problem that the computational cost of the sensitivity indices is normally too prohibitive to be accepted for the engineering problem, a newly efficient algorithm is proposed for sensitivity analysis in the presence of distribution parameters uncertainty. In the proposed algorithm, the Kriging model between the distribution parameters and the statistical eigenvalues of the structural output response are established, which can solve the problem that the computational cost of the sensitivity analysis increases exponentially with the increase of the dimensions of the input variables and their distribution parameters. Furthermore, by introducing a ‘surrogate sampling probability density function (SS-PDF)’ to sample the input variables efficiently in the process of establishing the Kriging model, the dependency of the computational cost of the output statistical eigenvalues on the dimensionality of the distribution parameters of the input variables is further released. The results of numerical and engineering examples show that the proposed methods require substantially less computation effort while ensuring the calculation accuracy.

Key words: distribution parameter uncertainty, sensitivity analysis, kriging model, surrogate sampling probability density function

CLC Number:

TB114

CHENG Hongxin, LI Luyi, ZHOU Changcong. Method for Sensitivity Analysis of the Uncertain Distribution Parameters Based on Kriging Model and Surrogate Sampling[J]. Journal of Mechanical Engineering, 2024, 60(8): 370-383.

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