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Active Kriging-Based Adaptive Importance Sampling for Reliability and Sensitivity Analyses of Stator Blade Regulator

Hong Zhang1, Lukai Song1,2,*, Guangchen Bai1
1 School of Energy and Power Engineering, Beihang University, Beijing, 100191, China
2 Research Institute of Aero-Engine, Beihang University, Beijing, 100191, China
* Corresponding Author: Lukai Song. Email:
(This article belongs to this Special Issue: Computer-Aided Structural Integrity and Safety Assessment)

Computer Modeling in Engineering & Sciences 2023, 134(3), 1871-1897. https://doi.org/10.32604/cmes.2022.021880

Received 10 February 2022; Accepted 09 May 2022; Issue published 20 September 2022

Abstract

The reliability and sensitivity analyses of stator blade regulator usually involve complex characteristics like high-nonlinearity, multi-failure regions, and small failure probability, which brings in unacceptable computing efficiency and accuracy of the current analysis methods. In this case, by fitting the implicit limit state function (LSF) with active Kriging (AK) model and reducing candidate sample pool with adaptive importance sampling (AIS), a novel AK-AIS method is proposed. Herein, the AK model and Markov chain Monte Carlo (MCMC) are first established to identify the most probable failure region(s) (MPFRs), and the adaptive kernel density estimation (AKDE) importance sampling function is constructed to select the candidate samples. With the best samples sequentially attained in the reduced candidate samples and employed to update the Kriging-fitted LSF, the failure probability and sensitivity indices are acquired at a lower cost. The proposed method is verified by two multi-failure numerical examples, and then applied to the reliability and sensitivity analyses of a typical stator blade regulator. With methods comparison, the proposed AK-AIS is proven to hold the computing advantages on accuracy and efficiency in complex reliability and sensitivity analysis problems.


Keywords

Markov chain Monte Carlo; active Kriging; adaptive kernel density estimation; importance sampling

Cite This Article

Zhang, H., Song, L., Bai, G. (2023). Active Kriging-Based Adaptive Importance Sampling for Reliability and Sensitivity Analyses of Stator Blade Regulator. CMES-Computer Modeling in Engineering & Sciences, 134(3), 1871–1897.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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