Open Access

ARTICLE

An Uncertainty Analysis and Reliability-Based Multidisciplinary Design Optimization Method Using Fourth-Moment Saddlepoint Approximation

Yongqiang Guo^1,2,*, Zhiyuan Lv³

1 School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, China
2 Beijing Research Institute of Mechanical & Electrical Technology, Ltd., Beijing, 100083, China
3 School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

* Corresponding Author: Yongqiang Guo. Email:

(This article belongs to the Special Issue: Computer-Aided Structural Integrity and Safety Assessment)

Computer Modeling in Engineering & Sciences 2023, 134(3), 1855-1870. https://doi.org/10.32604/cmes.2022.022211

Received 27 February 2022; Accepted 12 April 2022; Issue published 20 September 2022

Abstract

In uncertainty analysis and reliability-based multidisciplinary design and optimization (RBMDO) of engineering structures, the saddlepoint approximation (SA) method can be utilized to enhance the accuracy and efficiency of reliability evaluation. However, the random variables involved in SA should be easy to handle. Additionally, the corresponding saddlepoint equation should not be complicated. Both of them limit the application of SA for engineering problems. The moment method can construct an approximate cumulative distribution function of the performance function based on the first few statistical moments. However, the traditional moment matching method is not very accurate generally. In order to take advantage of the SA method and the moment matching method to enhance the efficiency of design and optimization, a fourth-moment saddlepoint approximation (FMSA) method is introduced into RBMDO. In FMSA, the approximate cumulative generating functions are constructed based on the first four moments of the limit state function. The probability density function and cumulative distribution function are estimated based on this approximate cumulative generating function. Furthermore, the FMSA method is introduced and combined into RBMDO within the framework of sequence optimization and reliability assessment, which is based on the performance measure approach strategy. Two engineering examples are introduced to verify the effectiveness of proposed method.

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Keywords

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