Multidisciplinary Inverse Reliability Analysis Based on Collaborative Optimization with Combination of Linear Approximations

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 analy...

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Bibliographic Details
Published in:Mathematical problems in engineering 2015-01, Vol.2015 (2015), p.1-11
Main Authors: Zhang, Li-Xiang, Zhou, Jing-Tao, Wang, Ye-Dong, Jing, Shi-Kai, Meng, Xin-Jia, Liu, Ji-Hong
Format: Article
Language:English
Subjects:
Approximation
Computational efficiency
Constraints
Design optimization
Electronic packaging
Failure analysis
Inverse
Mathematical models
Methods
Multidisciplinary
Multidisciplinary design optimization
Optimization
Reliability analysis
Reliability engineering
Searching
Subsystems
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2015/964238