A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters
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 performa...
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2021-05-01
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Online Access: | https://www.mdpi.com/2227-7390/9/10/1095 |
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doaj-d0a837b3c0ee4e039bbd93bd2ec619ee2021-05-31T23:51:25ZengMDPI AGMathematics2227-73902021-05-0191095109510.3390/math9101095A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution ParametersXiang Peng0Xiaoqing Xu1Jiquan Li2Shaofei Jiang3College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaFor 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.https://www.mdpi.com/2227-7390/9/10/1095sensitivity analysisdistribution parametersampling calculationunscented transformationGaussian integration |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiang Peng Xiaoqing Xu Jiquan Li Shaofei Jiang |
spellingShingle |
Xiang Peng Xiaoqing Xu Jiquan Li Shaofei Jiang A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters Mathematics sensitivity analysis distribution parameter sampling calculation unscented transformation Gaussian integration |
author_facet |
Xiang Peng Xiaoqing Xu Jiquan Li Shaofei Jiang |
author_sort |
Xiang Peng |
title |
A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters |
title_short |
A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters |
title_full |
A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters |
title_fullStr |
A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters |
title_full_unstemmed |
A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters |
title_sort |
sampling-based sensitivity analysis method considering the uncertainties of input variables and their distribution parameters |
publisher |
MDPI AG |
series |
Mathematics |
issn |
2227-7390 |
publishDate |
2021-05-01 |
description |
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. |
topic |
sensitivity analysis distribution parameter sampling calculation unscented transformation Gaussian integration |
url |
https://www.mdpi.com/2227-7390/9/10/1095 |
work_keys_str_mv |
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1721416377878708224 |