Wenxuan Wang^*, Xiaoyi Wang

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 985-1000, 2020, DOI:10.32604/cmes.2020.09006

Abstract The importance analysis method represents a powerful tool for quantifying the impact of input uncertainty on the output uncertainty. When an input variable is described by a specific interval rather than a certain probability distribution, the interval importance measure of input interval variable can be calculated by the traditional non-probabilistic importance analysis methods. Generally, the non-probabilistic importance analysis methods involve the Monte Carlo simulation (MCS) and the optimization-based methods, which both have high computational cost. In order to overcome this problem, this study proposes an interval important analytical method avoids the time-consuming optimization process. First, the original performance function is… More >

Hui Yin¹, Dejie Yu^1,2, Shengwen Yin¹, Baizhan Xia¹

CMES-Computer Modeling in Engineering & Sciences, Vol.109-110, No.3, pp. 221-246, 2015, DOI:10.3970/cmes.2015.109.221

Abstract This paper presents a new hybrid Chebyshev-perturbation method (HCPM) for the prediction of acoustic field with random and interval parameters. In HCPM, the perturbation method based on the first-order Taylor series that accounts for the random uncertainty is organically integrated with the first-order Chebyshev polynomials that deal with the interval uncertainty; specifically, a random interval function is firstly expanded with the first-order Taylor series by treating the interval variables as constants, and the expressions of the expectation and variance can be obtained by using the random moment method; then the expectation and variance of the function are approximated by using… More >