A novel learning function of adaptively updating Kriging model for reliability analysis under fuzzy uncertainty

Chen, Zhuangbo; Lü, Zhenzhou; Feng, Kaixuan

doi:10.1007/s00158-023-03576-y

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…To improve the accuracy and efficiency of reliability analysis for complex mechanical systems, researchers have proposed various methods based on the response surface method (RSM), such as the quadratic function response surface method [1], the support vector machine (SVM) response surface method [2,3], the Kriging-model-based response surface method [4], the artificial neural network-based response surface method [5], the stochastic response surface method [6], the extremum response surface method [7], and the fuzzy response surface method [8][9][10]. Later, some researchers combined other algorithms with the response surface method and developed various computational methods for reliability analysis.…”

Section: Introductionmentioning

confidence: 99%

Multi-Extremum Adaptive Fuzzy Network Method for Dynamic Reliability Estimation Method of Vectoring Exhaust Nozzle

Zhang¹,

Yuan

et al. 2023

Aerospace

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To enhance the accuracy and efficiency of reliability analysis for an aero-engine vectoring exhaust nozzle (VEN), a multi-extremum adaptive fuzzy network (MEAFN) method is developed by absorbing an adaptive neuro-fuzzy inference system (ANFIS) into the multi-extremum surrogate model (MESM) method. In the proposed method, the MERSM is used to establish the surrogate models of many output responses for the multi-objective integrated reliability analysis of the VEN. The ANFIS method is regarded as the basis function of the MESM method and adopted to improve the modeling precision of the MESM by introducing the membership degree into the input parameters and weights to improve the approximation capability of the neural network model to the high nonlinear reliability analysis of the VEN. The mathematical model of the MEAFN method and reliability analysis thoughts of the VEN is provided in this study. Then, the proposed MEAFN method is applied to conduct the dynamic reliability analysis of the expansion sheet and the triangular connecting rod in the VEN by considering the aerodynamic loads, operation temperature, and material parameters as the random input variables and the stresses and deformations as the output responses, compared with the Monte Carlo method and the extremum response surface method. From the comparison of the methods, it is indicated that the MEAFN method is promising to improve computational efficiency while maintaining accuracy. The efforts of this study provide guidance for the optimization design of the VEN and enrich the reliability theory of the flexible mechanism.

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