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System reliability-based design optimization using trade-off between uncertainty reduction and design shift

[+] Author and Article Information
Sangjune Bae

Graduate Student, Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, 32611
sjune.bae@ufl.edu

Nam H. Kim

Professor, Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, 32611
nkim@ufl.edu

Seung-gyo Jang

Principal Researcher, Agency for Defense Development, Daejeon, Republic of Korea
jsg4580@add.re.kr

1Corresponding author.

ASME doi:10.1115/1.4041859 History: Received April 12, 2018; Revised October 17, 2018

Abstract

This paper presents a tradeoff between shifting design and controlling sampling uncertainty in system reliability-based design optimization (RBDO) using the Bayesian network. The sampling uncertainty is caused by a finite number of samples used in calculating the reliability of a component, and it propagates to the system reliability. A conservative failure probability is utilized to consider sampling uncertainty. In this paper, the sensitivity of a conservative system failure probability is derived with respect to the design change and the number of samples in a component using Bayesian network along with global sensitivity analysis. In the sensitivity analysis, global sensitivity analysis is used for local sensitivity calculation. The numerical results show that sampling uncertainty can significantly affect the system reliability and needs to be controlled to achieve the desired level of system reliability. Numerical examples show that both shifting design and reducing sampling uncertainty are crucial in the system RBDO.

Copyright (c) 2018 by ASME
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