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Information Reuse for Importance Sampling in Reliability-Based Design Optimization

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  • Chaudhuri, Anirban
  • Kramer, Boris
  • Willcox, Karen E.

Abstract

This paper introduces a new approach for importance-sampling-based reliability-based design optimization (RBDO) that reuses information from past optimization iterations to reduce computational effort. RBDO is a two-loop process—an uncertainty quantification loop embedded within an optimization loop—that can be computationally prohibitive due to the numerous evaluations of expensive high-fidelity models to estimate the probability of failure in each optimization iteration. In this work, we use the existing information from past optimization iterations to create efficient biasing densities for importance sampling estimates of probability of failure. The method involves two levels of information reuse: (1) reusing the current batch of samples to construct an a posteriori biasing density with optimal parameters, and (2) reusing the a posteriori biasing densities of the designs visited in past optimization iterations to construct the biasing density for the current design. We demonstrate for the RBDO of a benchmark speed reducer problem and a combustion engine problem that the proposed method leads to computational savings in the range of 51% to 76%, compared to building biasing densities with no reuse in each iteration.

Suggested Citation

  • Chaudhuri, Anirban & Kramer, Boris & Willcox, Karen E., 2020. "Information Reuse for Importance Sampling in Reliability-Based Design Optimization," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
  • Handle: RePEc:eee:reensy:v:201:y:2020:i:c:s0951832019301620
    DOI: 10.1016/j.ress.2020.106853
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    References listed on IDEAS

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    1. Jean-Marie Cornuet & Jean-Michel Marin & Antonietta Mira & Christian P. Robert, 2012. "Adaptive Multiple Importance Sampling," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 39(4), pages 798-812, December.
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    6. repec:dau:papers:123456789/10690 is not listed on IDEAS
    7. Jinghong Liang & Zissimos P. Mourelatos & Jian Tu, 2008. "A single-loop method for reliability-based design optimisation," International Journal of Product Development, Inderscience Enterprises Ltd, vol. 5(1/2), pages 76-92.
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    Cited by:

    1. Jiang, Chen & Yan, Yifang & Wang, Dapeng & Qiu, Haobo & Gao, Liang, 2021. "Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    2. Yang, Meide & Zhang, Dequan & Jiang, Chao & Han, Xu & Li, Qing, 2021. "A hybrid adaptive Kriging-based single loop approach for complex reliability-based design optimization problems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Rocchetta, Roberto & Crespo, Luis G., 2021. "A scenario optimization approach to reliability-based and risk-based design: Soft-constrained modulation of failure probability bounds," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
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    7. Hao, Peng & Yang, Hao & Wang, Yutian & Liu, Xuanxiu & Wang, Bo & Li, Gang, 2021. "Efficient reliability-based design optimization of composite structures via isogeometric analysis," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    8. Jiang, Zhiyuan & Huang, Xianzhen & Wang, Bingxiang & Liao, Xin & Liu, Huizhen & Ding, Pengfei, 2024. "Time-dependent reliability-based design optimization of main shaft bearings in wind turbines involving mixed-integer variables," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    9. Ling, Chunyan & Lu, Zhenzhou & Zhang, Xiaobo, 2020. "An efficient method based on AK-MCS for estimating failure probability function," Reliability Engineering and System Safety, Elsevier, vol. 201(C).

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