A time-variant uncertainty propagation analysis method based on a new technique for simulating non-Gaussian stochastic processes

In this paper, the time-variant uncertainty propagation analysis is defined to solve the out-put stochastic process of a time-variant function with uncertainty. And a time-variant uncertainty propagation analysis method is constructed with the combination of an extended orthogonal series expansion method (extended OSE) and sparse grid numerical integration (SGNI). The SGNI serving as a classical uncertainty propagation method is utilized here to solve the moments and autocorrelation function at discrete time points of the time-variant performance function. And the extended OSE is proposed to simulate the out-put stochastic process based on the results from SGNI. By extended OSE, a non-Gaussian stochastic process is represented as the sum of orthogonal time functions with random coefficients, and these coefficients can be directly obtained by discretization of the target process. For these coefficients are correlated and non-Gaussian, the correlated polynomial chaos expansion method (c-PCE) is presented to represent them in terms of correlated standard Gaussian variables, and then the principal component analysis (PCA) is adopted to transform them into independent ones with dimension reduction. Finally we can obtain an explicit expression to represent the non-Gaussian process whatever it is stationary or non-stationary. Three illustrative examples are used to verify the performance of the extended OSE. In addition, two engineering problems are investigated to demonstrate the effectiveness of the time-variant uncertainty propagation method. (c) 2020 Published by Elsevier Ltd.

Automated summary

This paper presents a method for time-variant uncertainty propagation analysis, combining extended orthogonal series expansion method and sparse grid numerical integration, which effectively solves the output stochastic process of a time-variant function. By modeling the orthogonal time functions and correlated coefficients of the stochastic process and reducing dimensionality, an explicit expression for the non-Gaussian process can be obtained.