Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74987
Title: Distributed collaborative probabilistic design of multi-failure structure with fluid-structure interaction using fuzzy neural network of regression
Authors: Song, LK
Wen, J
Fei, CW 
Bai, GC
Keywords: Distributed collaborative response surface method
Fuzzy neural network of regression
Multi-failure modes
Probabilistic design
Turbine blisk
Issue Date: 2018
Publisher: Academic Press
Source: Mechanical systems and signal processing, 2018, v. 104, p. 72-86 How to cite?
Journal: Mechanical systems and signal processing 
Abstract: To improve the computing efficiency and precision of probabilistic design for multi-failure structure, a distributed collaborative probabilistic design method-based fuzzy neural network of regression (FR) (called as DCFRM) is proposed with the integration of distributed collaborative response surface method and fuzzy neural network regression model. The mathematical model of DCFRM is established and the probabilistic design idea with DCFRM is introduced. The probabilistic analysis of turbine blisk involving multi-failure modes (deformation failure, stress failure and strain failure) was investigated by considering fluid–structure interaction with the proposed method. The distribution characteristics, reliability degree, and sensitivity degree of each failure mode and overall failure mode on turbine blisk are obtained, which provides a useful reference for improving the performance and reliability of aeroengine. Through the comparison of methods shows that the DCFRM reshapes the probability of probabilistic analysis for multi-failure structure and improves the computing efficiency while keeping acceptable computational precision. Moreover, the proposed method offers a useful insight for reliability-based design optimization of multi-failure structure and thereby also enriches the theory and method of mechanical reliability design.
URI: http://hdl.handle.net/10397/74987
ISSN: 0888-3270
EISSN: 1096-1216
DOI: 10.1016/j.ymssp.2017.09.039
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