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Hybrid reliability-based multidisciplinary design optimization with random and interval variables

Author

Listed:
  • Fan Yang
  • Zhufeng Yue
  • Lei Li
  • Dong Guan

Abstract

This article presents a procedure for reliability-based multidisciplinary design optimization with both random and interval variables. The sign of performance functions is predicted by the Kriging model which is constructed by the so-called learning function in the region of interest. The Monte Carlo simulation with the Kriging model is performed to evaluate the failure probability. The sample methods for the random variables, interval variables, and design variables are discussed in detail. The multidisciplinary feasible and collaborative optimization architectures are provided with the proposed method. The method is demonstrated with three examples.

Suggested Citation

  • Fan Yang & Zhufeng Yue & Lei Li & Dong Guan, 2018. "Hybrid reliability-based multidisciplinary design optimization with random and interval variables," Journal of Risk and Reliability, , vol. 232(1), pages 52-64, February.
    • Handle: RePEc:sae:risrel:v:232:y:2018:i:1:p:52-64
    DOI: 10.1177/1748006X17736639
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    References listed on IDEAS

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    1. Durga Rao Karanki & Hari Shankar Kushwaha & Ajit Kumar Verma & Srividya Ajit, 2009. "Uncertainty Analysis Based on Probability Bounds (P‐Box) Approach in Probabilistic Safety Assessment," Risk Analysis, John Wiley & Sons, vol. 29(5), pages 662-675, May.
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