Full text

Turn on search term navigation

Copyright © 2015 Xin-Jia Meng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Multidisciplinary reliability is an important part of the reliability-based multidisciplinary design optimization (RBMDO). However, it usually has a considerable amount of calculation. The purpose of this paper is to improve the computational efficiency of multidisciplinary inverse reliability analysis. A multidisciplinary inverse reliability analysis method based on collaborative optimization with combination of linear approximations (CLA-CO) is proposed in this paper. In the proposed method, the multidisciplinary reliability assessment problem is first transformed into a problem of most probable failure point (MPP) search of inverse reliability, and then the process of searching for MPP of multidisciplinary inverse reliability is performed based on the framework of CLA-CO. This method improves the MPP searching process through two elements. One is treating the discipline analyses as the equality constraints in the subsystem optimization, and the other is using linear approximations corresponding to subsystem responses as the replacement of the consistency equality constraint in system optimization. With these two elements, the proposed method realizes the parallel analysis of each discipline, and it also has a higher computational efficiency. Additionally, there are no difficulties in applying the proposed method to problems with nonnormal distribution variables. One mathematical test problem and an electronic packaging problem are used to demonstrate the effectiveness of the proposed method.

Details

Title
Multidisciplinary Inverse Reliability Analysis Based on Collaborative Optimization with Combination of Linear Approximations
Author
Xin-Jia, Meng; Shi-Kai, Jing; Ye-Dong, Wang; Jing-Tao, Zhou; Li-Xiang, Zhang; Ji-Hong, Liu
Publication year
2015
Publication date
2015
Publisher
John Wiley & Sons, Inc.
ISSN
1024123X
e-ISSN
15635147
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
1731737671
Copyright
Copyright © 2015 Xin-Jia Meng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.