Reliability-Based Fatigue Life Prediction for Complex Structure with Time-Varying Surrogate Modeling
To improve the computational efficiency and accuracy of reliability-based fatigue life prediction for complex structure, a time-varying particle swarm optimization- (PSO-) based general regression neural network (GRNN) surrogate model (called as TV/PSO-GRNN) is developed. By integrating the proposed...
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Online Access: | http://dx.doi.org/10.1155/2018/3469465 |
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doaj-985a2f09775740d29c47e060a97f54dd2020-11-24T21:15:36ZengHindawi LimitedAdvances in Materials Science and Engineering1687-84341687-84422018-01-01201810.1155/2018/34694653469465Reliability-Based Fatigue Life Prediction for Complex Structure with Time-Varying Surrogate ModelingLu-Kai Song0Guang-Chen Bai1Cheng-Wei Fei2Jie Wen3School of Energy and Power Engineering, Beihang University, Beijing 100191, ChinaSchool of Energy and Power Engineering, Beihang University, Beijing 100191, ChinaSchool of Energy and Power Engineering, Beihang University, Beijing 100191, ChinaSchool of Energy and Power Engineering, Beihang University, Beijing 100191, ChinaTo improve the computational efficiency and accuracy of reliability-based fatigue life prediction for complex structure, a time-varying particle swarm optimization- (PSO-) based general regression neural network (GRNN) surrogate model (called as TV/PSO-GRNN) is developed. By integrating the proposed space-filling Latin hypercube sampling technique and PSO-GRNN regression function, the mathematical model of TV/PSO-GRNN is studied. The reliability-based fatigue life prediction framework is illustrated in respect of the TV/PSO-GRNN surrogate model. Moreover, the reliability-based fatigue life prediction of an aircraft turbine blisk under multiphysics interaction is performed to validate the TV/PSO-GRNN model. We obtain the distributional characteristics, reliability degree, and sensitivity degree of fatigue failure cycle, which are useful for the turbine blisk design. By comparing the direct simulation (FE/FV model), RSM, GRNN, PSO-GRNN, and TV/PSO-GRNN, we observe that the TV/PSO-GRNN surrogate model is promising to perform the reliability-based fatigue life prediction of the turbine blisk and enhance the computational efficiency while ensuring an acceptable computational accuracy. The efforts of this study offer a useful insight for the reliability-based design optimization of complex structure.http://dx.doi.org/10.1155/2018/3469465 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lu-Kai Song Guang-Chen Bai Cheng-Wei Fei Jie Wen |
spellingShingle |
Lu-Kai Song Guang-Chen Bai Cheng-Wei Fei Jie Wen Reliability-Based Fatigue Life Prediction for Complex Structure with Time-Varying Surrogate Modeling Advances in Materials Science and Engineering |
author_facet |
Lu-Kai Song Guang-Chen Bai Cheng-Wei Fei Jie Wen |
author_sort |
Lu-Kai Song |
title |
Reliability-Based Fatigue Life Prediction for Complex Structure with Time-Varying Surrogate Modeling |
title_short |
Reliability-Based Fatigue Life Prediction for Complex Structure with Time-Varying Surrogate Modeling |
title_full |
Reliability-Based Fatigue Life Prediction for Complex Structure with Time-Varying Surrogate Modeling |
title_fullStr |
Reliability-Based Fatigue Life Prediction for Complex Structure with Time-Varying Surrogate Modeling |
title_full_unstemmed |
Reliability-Based Fatigue Life Prediction for Complex Structure with Time-Varying Surrogate Modeling |
title_sort |
reliability-based fatigue life prediction for complex structure with time-varying surrogate modeling |
publisher |
Hindawi Limited |
series |
Advances in Materials Science and Engineering |
issn |
1687-8434 1687-8442 |
publishDate |
2018-01-01 |
description |
To improve the computational efficiency and accuracy of reliability-based fatigue life prediction for complex structure, a time-varying particle swarm optimization- (PSO-) based general regression neural network (GRNN) surrogate model (called as TV/PSO-GRNN) is developed. By integrating the proposed space-filling Latin hypercube sampling technique and PSO-GRNN regression function, the mathematical model of TV/PSO-GRNN is studied. The reliability-based fatigue life prediction framework is illustrated in respect of the TV/PSO-GRNN surrogate model. Moreover, the reliability-based fatigue life prediction of an aircraft turbine blisk under multiphysics interaction is performed to validate the TV/PSO-GRNN model. We obtain the distributional characteristics, reliability degree, and sensitivity degree of fatigue failure cycle, which are useful for the turbine blisk design. By comparing the direct simulation (FE/FV model), RSM, GRNN, PSO-GRNN, and TV/PSO-GRNN, we observe that the TV/PSO-GRNN surrogate model is promising to perform the reliability-based fatigue life prediction of the turbine blisk and enhance the computational efficiency while ensuring an acceptable computational accuracy. The efforts of this study offer a useful insight for the reliability-based design optimization of complex structure. |
url |
http://dx.doi.org/10.1155/2018/3469465 |
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