Multidisciplinary Optimization under Uncertainty Using Bayesian Network

Chen, Liang; Mahadevan, Sankaran

doi:10.4271/2016-01-0304

Cited by 7 publications

(1 citation statement)

References 22 publications

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“…Te principle is to deal with the coupling relationship between disciplines through some decoupling methods [20][21][22][23]. At the same time, to optimize the performance of the entire system through a single-layer or multilayer optimization strategy, the multidisciplinary coupling problem is solved hierarchically [24][25][26]. Since the uncertainty in the subsystem is propagated in the multidisciplinary coupling, to quantify the impact of uncertain factors accurately and take advantage of MDO simultaneously, the RBMDO has received extensive attention and has become a research hotspot [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

A Reliability-Based Multidisciplinary Design Optimization Strategy considering Interval Uncertainty Based on the Point-Infilled Kriging Model

Yuan

Zhang

Sun

et al. 2022

Mathematical Problems in Engineering

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Generally, traditional uncertainty design optimization (UDO) methods are based on probability density distribution function or fuzzy membership function. In this situation, a large amount of uncertain information is necessary to construct the UDO model accurately. While, the interval UDO methods require less design information. Only the upper and lower bounds of interval uncertainty are utilized to construct the optimization model. In this study, to enhance the efficiency and accuracy of UBDO considering interval uncertainty, a reliability-based multidisciplinary design optimization (RBMDO) strategy using the point-infilled Kriging model is proposed. In the given method, a double-nested RBMDO model considering interval uncertainty is established. The collaborative optimization is utilized to deal with coupling relationships among complex systems. Then, the point-infilled Kriging response surface strategy is introduced to approximate the RBMDO model. The procedure of the interval multidisciplinary collaborative optimization method based on the Kriging model is discussed. Two examples are given to illustrate the application of the proposed method.

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