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Research Papers

A Maximum Confidence Enhancement Based Sequential Sampling Scheme for Simulation-Based Design

[+] Author and Article Information
Zequn Wang

e-mail: zxwang5@wichita.edu

Pingfeng Wang

Assistant Professor
e-mail: pingfeng.wang@wichita.edu
Department of Industrial
and Manufacturing Engineering,
Wichita State University
Wichita, KS 67260

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received January 29, 2013; final manuscript received November 4, 2013; published online December 11, 2013. Assoc. Editor: David Gorsich.

J. Mech. Des 136(2), 021006 (Dec 11, 2013) (10 pages) Paper No: MD-13-1034; doi: 10.1115/1.4026033 History: Received January 29, 2013; Revised November 04, 2013

A maximum confidence enhancement (MCE)-based sequential sampling approach is developed for reliability-based design optimization (RBDO) using surrogate models. The developed approach employs the ordinary Kriging method for surrogate model development and defines a cumulative confidence level (CCL) measure to quantify the accuracy of reliability estimation when Monte Carlo simulation is used based on the developed surrogate model. To improve the computational efficiency, an MCE-based sequential sampling scheme is developed to successively select sample points for surrogate model updating based on the defined CCL measure, in which a sample point that produces the largest CCL improvement will be selected. To integrate the MCE-based sequential sampling approach with RBDO, a new sensitivity analysis approach is developed, enabling smooth design sensitivity information to be accurately estimated based upon the constructed surrogate model without incurring any extra computational costs, thus greatly enhancing the efficiency and robustness of the design process. Two case studies are used to demonstrate the efficacy of the developed approach.

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Figures

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Fig. 1

Flowchart of MCE-based sequential sampling scheme for RBDO

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Fig. 2

limit state function contour for case study I

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Fig. 3

Estimated limit states using Kriging models for case study I

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Fig. 4

Sample points used in the design process for case study I

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Fig. 5

Reliability history of three active constraints

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