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research-article

High-Dimensional Reliability-based Design Optimization Involving Highly Nonlinear Constraints and Computationally Expensive Simulations

[+] Author and Article Information
Meng Li

Student member of ASME, PhD Student, Department of Mechanical Engineering, Iowa State University, Ames, IA 50011 USA
meng@iastate.edu

Mohammadkazem Sadoughi

Student member of ASME, PhD Student, Department of Mechanical Engineering, Iowa State University, Ames, IA 50011 USA
sadoughi@iastate.edu

Chao Hu

Member of ASME, Assistant Professor, Department of Mechanical Engineering and Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011 USA
chaohu@iastate.edu

Zhen Hu

Member of ASME, Assistant Professor, Department of Industrial and Manufacturing Systems Engineering, University of Michigan-Dearborn, Dearborn, MI 48128 USA
zhennhu@unich.edu

Amin Toghi Eshghi

Student Member of ASME, PhD Student, Department of Mechanical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250 USA
amint1@umbc.edu

Soobum Lee

Member of ASME, Assistant Professor, Department of Mechanical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250 USA
sblee@umbc.edu

1Corresponding author.

ASME doi:10.1115/1.4041917 History: Received June 16, 2018; Revised October 23, 2018

Abstract

Reliability-based design optimization (RBDO) aims at optimizing the design of an engineered system to minimize the design cost while satisfying reliability requirements. However, it is challenging to perform RBDO under high-dimensional uncertainty due to the often prohibitive computational burden. In this paper, we address this challenge by leveraging a recently developed method for reliability analysis under high-dimensional uncertainty. The method is termed high-dimensional reliability analysis (HDRA). The HDRA method optimally combines the strengths of univariate dimension reduction (UDR) and kriging-based reliability analysis to achieve satisfactory accuracy with an affordable computational cost for high-dimensional reliability analysis problems. In this paper, we improve the computational efficiency of high-dimensional RBDO by pursuing two new strategies: (i) a two-stage surrogate modeling strategy is adopted to first locate a highly probable region of the optimum design and then locally refine the accuracy of the surrogates in this region; and (ii) newly selected samples are updated for all the constraints during the sequential sampling process in HDRA. Results of two mathematical examples and one real-world engineering example suggest that the proposed HDRA-based RBDO (RBDO-HDRA) method is capable of solving high-dimensional RBDO problems with higher accuracy and comparable efficiency than the UDR-based RBDO (RBDO-UDR) and ordinary kriging-based RBDO (RBDO-kriging) methods.

Copyright (c) 2018 by ASME
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