Efficient strategy for reliability-based optimization design of multidisciplinary coupled system with interval parameters

被引:13
|
作者
Wang, Ruixing [2 ,3 ]
Luo, Yan [1 ]
机构
[1] Beijing Univ Chem Technol, Sch Mech & Elect Engn, Beijing 100029, Peoples R China
[2] Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing 100190, Peoples R China
[3] Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China
基金
中国国家自然科学基金;
关键词
Multidisciplinary design optimization; Non-probabilistic reliability; Uncertainty propagation analysis; Gradient information; Interval reliability displacement; NUMBER PROGRAMMING METHOD; TOPOLOGY OPTIMIZATION; CONVEX MODEL; UNCERTAINTY; ROBUST;
D O I
10.1016/j.apm.2019.05.030
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Non-probabilistic reliability based multidisciplinary design optimization has been widely acknowledged as an advanced methodology for complex system design when the data is insufficient. In this work, the uncertainty propagation analysis method in multidisciplinary system based on subinterval theory is firstly studied to obtain the uncertain responses. Then, based on the non-probabilistic set theory, the interval reliability based multidisciplinary design optimization model is established. Considering that the gradient information of interval reliability cannot be acquired in the whole design domain, which causes convergence difficulties and prohibitive computation, an interval reliability displacement based multidisciplinary design optimization method is proposed to address the issue. In the proposed method, the interval reliability displacement is introduced to measure the degree of interval reliability. By doing so, not only the connotation of the interval reliability is guaranteed, but more importantly, the partial gradient region for interval reliability is equivalently converted into full gradient region for reliability displacement. Consequently, the gradient information can be acquired under any circumstances and thus the convergence process is highly accelerated by utilizing the gradient optimization algorithms. (C) 2019 Elsevier Inc. All rights reserved.
引用
收藏
页码:349 / 370
页数:22
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