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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2023, Vol. 44 ›› Issue (12): 227670-227670.doi: 10.7527/S1000-6893.2022.27670

• Solid Mechanics and Vehicle Conceptual Design • Previous Articles    

An efficient surrogate method for analyzing parameter global reliability sensitivity

Wanying YUN1,2,3(), Zhenzhou LYU2   

  1. 1.Innovation Center NPU Chongqing,Northwestern Polytechnical University,Chongqing 400000,China
    2.School of Aeronautics,Northwestern Polytechnical University,Xi’an 710072,China
    3.Research & Development Institute of Northwestern Polytechnical University in Shenzhen,Shenzhen 518063,China
  • Received:2022-06-22 Revised:2022-07-14 Accepted:2022-07-28 Online:2022-08-18 Published:2022-08-17
  • Contact: Wanying YUN E-mail:wanying_yun@nwpu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(12002237);Natural Science Foundation of Chongqing(CSTB2022NSCQ-MSX0861);Guangdong Basic and Applied Basic Research Foundation(2022A1515011515);Fundamental Research Funds for the Central University(D5000211035)

Abstract:

To give an efficient analysis of parameter reliability global sensitivity, this paper proposes a method by integrating the single-loop importance sampling technique and the adaptive Kriging model. First, a single-loop importance sampling algorithm for estimating the parameter reliability global sensitivity is constructed based on the Bayes theorem, Metropolis-Hastings algorithm and Edgeworth expansion, which unifies the analyses of reliability and parameter reliability global sensitivity. Based on the proposed single-loop importance sampling algorithm, the estimation of the parameter reliability global sensitivity is converted into the identification of states (failure or safety) of all unconditional importance sampling samples, so that each parameter reliability global sensitivity can be evaluated by repeatedly using the unconditional importance sampling samples. Secondly, the Kriging model surrogating the performance function is adaptively constructed to approximate the optimal importance sampling Probability Density Function (PDF) and then generate the corresponding importance samples. Finally, the Kriging model used to construct the approximately optimal importance sampling PDF is continuously updated among the candidate sampling pool of the generated importance samples until the states of all importance samples are accurately identified by the Kriging model. Based on the accurately identified states of all importance samples, parameter global reliability sensitivity of each uncertain distribution parameter is assessed.Results of a numerical example and a missile wing structure verify the efficiency and accuracy of the proposed method.

Key words: parameter reliability global sensitivity analysis, Bayes theorem, surrogate model, Metropolis-Hastings algorithm, importance sampling, Edgeworth expansion

CLC Number: