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Research Papers: Design Automation

Mixed Efficient Global Optimization for Time-Dependent Reliability Analysis

[+] Author and Article Information
Zhen Hu

Department of Mechanical and
Aerospace Engineering,
Missouri University of Science and Technology,
290D Toomey Hall,
400 West 13th Street,
Rolla, MO 65409-0500
e-mail: zh4hd@mst.edu

Xiaoping Du

Professor
Department of Mechanical and
Aerospace Engineering,
Missouri University of Science and Technology,
272 Toomey Hall,
400 West 13th Street,
Rolla, MO 65409-0500
e-mail: dux@mst.edu

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 21, 2014; final manuscript received December 14, 2014; published online February 16, 2015. Assoc. Editor: Gary Wang.

J. Mech. Des 137(5), 051401 (May 01, 2015) (9 pages) Paper No: MD-14-1628; doi: 10.1115/1.4029520 History: Received September 21, 2014; Revised December 14, 2014; Online February 16, 2015

Time-dependent reliability analysis requires the use of the extreme value of a response. The extreme value function is usually highly nonlinear, and traditional reliability methods, such as the first order reliability method (FORM), may produce large errors. The solution to this problem is using a surrogate model of the extreme response. The objective of this work is to improve the efficiency of building such a surrogate model. A mixed efficient global optimization (m-EGO) method is proposed. Different from the current EGO method, which draws samples of random variables and time independently, the m-EGO method draws samples for the two types of samples simultaneously. The m-EGO method employs the adaptive Kriging–Monte Carlo simulation (AK–MCS) so that high accuracy is also achieved. Then, Monte Carlo simulation (MCS) is applied to calculate the time-dependent reliability based on the surrogate model. Good accuracy and efficiency of the m-EGO method are demonstrated by three examples.

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Figures

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Fig. 1

Ymax from independent EGO and mixed EGO and the true values

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Fig. 2

A vibration problem

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Fig. 3

One response Y at the mean value point of random variables

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Fig. 4

Corroded beam subjected to stochastic loading

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