Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 156, Issue -, Pages -Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2021.107616
Keywords
Linear structure; Gaussian loading; First excursion probability; Decoupling; Operator norm
Categories
Funding
- Research Foundation Flanders (FWO) [12P3519N]
- Alexander von Humboldt foundation
- ANID (National Agency for Research and Development, Chile) under its program FONDECYT [1180271]
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This paper introduces a fully decoupled approach for a specific class of problems, which simplifies the RBO problem with discrete design variables to solving a deterministic optimization problem followed by a single reliability analysis, resulting in improved numerical efficiency without compromising the accuracy of the optimal design.
Reliability-based optimization (RBO) offers the possibility of finding an optimal design for a system according to a prescribed criterion while explicitly taking into account the effects of uncertainty. However, due to the necessity of solving simultaneously a reliability problem nested in an optimization procedure, the corresponding computational cost is usually high, impeding the applicability of the methods. This computational cost is even further enlarged when one or several design variables must belong to a discrete set, due to the requirement of resorting to integer programming optimization algorithms. To alleviate this issue, this contribution proposes a fully decoupled approach for a specific class of problems, namely minimization of the failure probability of a linear system subjected to an uncertain dynamic load of the Gaussian type, under the additional constraint that the design variables are integer-valued. Specifically, by using the operator norm framework, as developed by the authors in previous work, this paper shows that by reducing the RBO problem with discrete design variables to the solution of a single deterministic optimization problem followed by a single reliability analysis, a large gain in numerical efficiency can be obtained without compromising the accuracy of the resulting optimal design. The application and capabilities of the proposed approach are illustrated by means of three examples. (c) 2021 Elsevier Ltd. All rights reserved.
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