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• W2952474821 abstract "Abstract The performance and reliability of aircraft engine are seriously affected by multiple failures induced by multi-physical loads. Multi-failure probabilistic design is an effective measure to estimate the multi-failure response traits and quantify the multi-failure risk for the improvement of component reliability. In this paper, we propose a neural network regression-distributed collaborative strategy (NNR-DCS) based on a developed two-step error control technique, to improve the efficiency and accuracy of multi-failure probabilistic analysis. We firstly mathematically model NNR-DCS and then introduce the corresponding multi-failure probabilistic framework. With respect to various failure modes such as deformation failure, stress failure and strain failure, the multi-failure probabilistic analysis of a turbine bladed disk is conducted to evaluate the proposed method. From this simulation, we gain the probabilistic distribution features, reliability degree and sensitivity degree of each failure mode and overall failure modes on turbine bladed disk, which provides a useful reference for improving the reliability and performance of aircraft engine. The comparison of methods (Monte Carlo method, RSM, DCRSM, DCFRM, NNR and NNR-DCS) shows that the proposed NNR-DCS holds high efficiency and accuracy for multi-failure probabilistic analysis. The efforts of this study offer an effective way for multi-failure evaluation from a probabilistic perspective and shed light on the multi-objective reliability-based design optimization of complex structures besides turbine bladed disk." @default.
• W2952474821 created "2019-06-27" @default.
• W2952474821 creator A5024196950 @default.
• W2952474821 creator A5061528544 @default.
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• W2952474821 date "2019-09-01" @default.
• W2952474821 modified "2023-10-17" @default.
• W2952474821 title "Multi-failure probabilistic design for turbine bladed disks using neural network regression with distributed collaborative strategy" @default.
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• W2952474821 doi "https://doi.org/10.1016/j.ast.2019.06.026" @default.
• W2952474821 hasPublicationYear "2019" @default.
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