Decoupled reliability-based optimization using Markov chain Monte Carlo in augmented space. (July 2021)

• Record Type: Journal Article
• Authors: Yuan, Xiukai; Liu, Shaolong; Valdebenito, Marcos A.; Faes, Matthias G.R.; Jerez, Danko J.; Jensen, Hector A.; Beer, Michael
• Is Part Of: Advances in engineering software. Volume 157/158(2021)
• Publication Date: 2021-07
• Subjects: Reliability-based design optimization -- Markov chain simulation -- Failure probability function -- Bayes' theorem; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2021.103020
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Failure probability function is estimated by an expression based on samples. The reliability-based optimization problem is fully decoupled. Markov chain Monte Carlo simulation is adopted to generate samples. No reliability analyses are required. Applications to an aircraft inboard flap and a car dynamic are presented. Abstract: An efficient framework is proposed for reliability-based design optimization (RBDO) of structural systems. The RBDO problem is expressed in terms of the minimization of the failure probability with respect to design variables which correspond to distribution parameters of random variables, e.g. mean or standard deviation. Generally, this problem is quite demanding from a computational viewpoint, as repeated reliability analyses are involved. Hence, in this contribution, an efficient framework for solving a class of RBDO problems without even a single reliability analysis is proposed. It makes full use of an established functional relationship between the probability of failure and the distribution design parameters, which is termed as the failure probability function (FPF). By introducing an instrumental variability associated with the distribution design parameters, the target FPF is found to be proportional to a posterior distribution of the design parameters conditional on the occurrence of failure in an augmented space. This posterior distribution is derived and expressed as an integral, which can be estimated through simulation. An advanced Markov chain algorithm is adopted to efficiently generate samples that follow the aforementioned posterior distribution. Also, an algorithm that re-uses information is proposed in combination with sequential approximate optimization to improve the efficiency. Numeric examples illustrate the performance of the proposed framework.