Abstract
For engineering products with uncertain input variables and distribution parameters, a sampling-based sensitivity analysis methodology was investigated to efficiently determine the influences of these uncertainties. In the calculation of the sensitivity indices, the nonlinear degrees of the performance function in the subintervals were greatly reduced by using the integral whole domain segmentation method, while the mean and variance of the performance function were calculated using the unscented transformation method. Compared with the traditional Monte Carlo simulation method, the loop number and sampling number in every loop were decreased by using the multiplication approximation and Gaussian integration methods. The proposed algorithm also reduced the calculation complexity by reusing the sample points in the calculation of two sensitivity indices to measure the influence of input variables and their distribution parameters. The accuracy and efficiency of the proposed algorithm were verified with three numerical examples and one engineering example.
Highlights
A variety of uncertainties are inherent in engineering products due to various factors, which inevitably affects product performances, especially for nonlinear and complex engineering products
We considered one type of sparse variable whose distribution type is determinate, while its distribution parameters are uncertain [41,42]
A sampling-based sensitivity analysis method is proposed, which considers the uncertainties of input variables and their distribution parameters
Summary
A variety of uncertainties are inherent in engineering products due to various factors, which inevitably affects product performances, especially for nonlinear and complex engineering products. De Carlo [36] developed a global SA computation method with correlation variables by incorporating optimal space-filling quasi-random sequences into an existing, importance sampling-based kernel regression sensitivity method. In these SA methods, the uncertainty information of the input variables is assumed to be determinate. Wang [43] proposed an improved analytical variance-based sensitivity analysis method to calculate the sensitivity indices of uncertain input variables and their distribution parameters. In actual engineering problems, there is often serious coupling, and there may be multiple different distribution types To solve these issues, a sampling-based sensitivity analysis method, considering the uncertainties of input variables and their distribution parameters simultaneously, was proposed.
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