Abstract
In reliability-based multidisciplinary design optimization, both aleatory and epistemic uncertainties may exist in multidisciplinary systems simultaneously. The uncertainty propagation through coupled subsystems makes multidisciplinary reliability analysis computationally expensive. In order to improve the efficiency of multidisciplinary reliability analysis under aleatory and epistemic uncertainties, a comprehensive reliability index that has clear geometric meaning under multisource uncertainties is proposed. Based on the comprehensive reliability index, a sequential multidisciplinary reliability analysis method is presented. The method provides a decoupling strategy based on performance measure approach (PMA), probability theory and convex model. In this strategy, the probabilistic analysis and convex analysis are decoupled from each other and performed sequentially. The probabilistic reliability analysis is implemented sequentially based on the concurrent subspace optimization (CSSO) and PMA, and the non-probabilistic reliability analysis is replaced by convex model extreme value analysis, which improves the efficiency of multidisciplinary reliability analysis with aleatory and epistemic uncertainties. A mathematical example and an engineering application are demonstrated to verify the effectiveness of the proposed method.
Highlights
With progress in science and technology, the focus on the effect of uncertainty has received increasing attention in engineering design
Aiming at the problem of low computational efficiency caused by the three-layer nesting of the multidisciplinary reliability analysis (MRA), a decoupling strategy for reliability analysis of multidisciplinary system with aleatory and epistemic uncertainties is proposed
Based on the decoupling principle of multidisciplinary reliability analysis and the idea of serialization, this paper proposes a serialization method of multidisciplinary reliability analysis under aleatory uncertainty and epistemic uncertainty
Summary
With progress in science and technology, the focus on the effect of uncertainty has received increasing attention in engineering design. As an indispensable ingredient of reliability-based multidisciplinary design optimization (RBMDO), the multidisciplinary reliability analysis (MRA) plays a decisive role in evaluating the reliability of multidisciplinary systems. Xiang et al [4] proposed a deep reinforcement learning-based sampling method for reliability analysis, which uses a deep neural network as agent to select test points automatically and construct the surrogate model for reliability assessment. Ghoreishi et al [5] proposed a Bayesian surrogate learning for reliability analysis, which increases the minimum number of possible samples from various disciplines to achieve accurate and reliable uncertainty propagation in coupled multidisciplinary systems. These methods all need enough statistical information
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