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Research Papers

A Sequential Sampling Strategy to Improve Reliability-Based Design Optimization With Implicit Constraint Functions

[+] Author and Article Information
Xiaotian Zhuang

School of Computing, Informatics, and Decision Systems Engineering,Arizona State University,Tempe, AZ 85287xiaotian.zhuang@asu.edu

Rong Pan1

School of Computing, Informatics, and Decision Systems Engineering,Arizona State University,Tempe, AZ 85287rong.pan@asu.edu

1

Corresponding author.

J. Mech. Des 134(2), 021002 (Feb 03, 2012) (10 pages) doi:10.1115/1.4005597 History: Received February 22, 2011; Revised October 26, 2011; Published February 03, 2012

Reliability-based design optimization (RBDO) has a probabilistic constraint that is used for evaluating the reliability or safety of the system. In modern engineering design, this task is often performed by a computer simulation tool such as finite element method (FEM). This type of computer simulation or computer experiment can be treated a black box, as its analytical function is implicit. This paper presents an efficient sampling strategy on learning the probabilistic constraint function under the design optimization framework. The method is a sequential experimentation around the approximate most probable point (MPP) at each step of optimization process. Our method is compared with the methods of MPP-based sampling, lifted surrogate function, and nonsequential random sampling. We demonstrate it through examples.

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Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 1

Max ERI sample point in design space. The initial samples are marked by “ + ,” additional samples are marked by “o,” and Gmin' is the latest additional sample selected by the ERI criterion.

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Figure 2

Algorithmic flowchart

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Figure 3

Max ERI sample point in response space

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Figure 5

3D shape of G function

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Figure 6

Feasible region with true G function

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Figure 7

RBDO feasible region of Ĝ by sequential ERI sampling

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Figure 8

RBDO feasible region Ĝ function by MPP based sampling

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Figure 9

RBDO feasible region by lifting response function

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Figure 10

RBDO feasible region of Ĝ by nonsequential random sampling

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Figure 11

A thin-walled box beam demo

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Figure 12

Contour plots of Von-Mises of the finite element model of thin-walled box beam

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