دانلود مقاله ISI انگلیسی شماره 152877
ترجمه فارسی عنوان مقاله

توزیع طراحی احتمالی مشارکتی ساختار چند شکستی با تعامل ساختاری مایع با استفاده از شبکه عصبی فازی رگرسیون

عنوان انگلیسی
Distributed collaborative probabilistic design of multi-failure structure with fluid-structure interaction using fuzzy neural network of regression
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
152877 2018 15 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Mechanical Systems and Signal Processing, Volume 104, 1 May 2018, Pages 72-86

ترجمه کلمات کلیدی
طراحی احتمالی، چند حالت شکستن، روش سطح پاسخ پاسخ توزیع شده، شبکه عصبی فازی رگرسیون، توربین بلیس،
کلمات کلیدی انگلیسی
Probabilistic design; Multi-failure modes; Distributed collaborative response surface method; Fuzzy neural network of regression; Turbine blisk;
پیش نمایش مقاله
پیش نمایش مقاله  توزیع طراحی احتمالی مشارکتی ساختار چند شکستی با تعامل ساختاری مایع با استفاده از شبکه عصبی فازی رگرسیون

چکیده انگلیسی

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.