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A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters

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  • Xiang Peng

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
    State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China)

  • Xiaoqing Xu

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Jiquan Li

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Shaofei Jiang

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

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.

Suggested Citation

  • Xiang Peng & Xiaoqing Xu & Jiquan Li & Shaofei Jiang, 2021. "A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters," Mathematics, MDPI, vol. 9(10), pages 1-18, May.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:10:p:1095-:d:553353
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    References listed on IDEAS

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    1. Zdeněk Kala, 2022. "Quantification of Model Uncertainty Based on Variance and Entropy of Bernoulli Distribution," Mathematics, MDPI, vol. 10(21), pages 1-19, October.
    2. Congcong Zhou & Zhenzhong Shen & Liqun Xu & Yiqing Sun & Wenbing Zhang & Hongwei Zhang & Jiayi Peng, 2023. "Global Sensitivity Analysis Method for Embankment Dam Slope Stability Considering Seepage–Stress Coupling under Changing Reservoir Water Levels," Mathematics, MDPI, vol. 11(13), pages 1-24, June.

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