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Creep-Based Reliability Evaluation of Turbine Blade-Tip Clearance with Novel Neural Network Regression
To reveal the effect of high-temperature creep on the blade-tip radial running clearance of aeroengine high-pressure turbines, a distributed collaborative generalized regression extremum neural network is proposed by absorbing the heuristic thoughts of distributed collaborative response surface meth...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6861887/ https://www.ncbi.nlm.nih.gov/pubmed/31671898 http://dx.doi.org/10.3390/ma12213552 |
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author | Zhang, Chun-Yi Wei, Jing-Shan Wang, Ze Yuan, Zhe-Shan Fei, Cheng-Wei Lu, Cheng |
author_facet | Zhang, Chun-Yi Wei, Jing-Shan Wang, Ze Yuan, Zhe-Shan Fei, Cheng-Wei Lu, Cheng |
author_sort | Zhang, Chun-Yi |
collection | PubMed |
description | To reveal the effect of high-temperature creep on the blade-tip radial running clearance of aeroengine high-pressure turbines, a distributed collaborative generalized regression extremum neural network is proposed by absorbing the heuristic thoughts of distributed collaborative response surface method and the generalized extremum neural network, in order to improve the reliability analysis of blade-tip clearance with creep behavior in terms of modeling precision and simulation efficiency. In this method, the generalized extremum neural network was used to handle the transients by simplifying the response process as one extremum and to address the strong nonlinearity by means of its nonlinear mapping ability. The distributed collaborative response surface method was applied to handle multi-object multi-discipline analysis, by decomposing one “big” model with hyperparameters and high nonlinearity into a series of “small” sub-models with few parameters and low nonlinearity. Based on the developed method, the blade-tip clearance reliability analysis of an aeroengine high-pressure turbine was performed subject to the creep behaviors of structural materials, by considering the randomness of influencing parameters such as gas temperature, rotational speed, material parameters, convective heat transfer coefficient, and so forth. It was found that the reliability degree of the clearance is 0.9909 when the allowable value is 2.2 mm, and the creep deformation of the clearance presents a normal distribution with a mean of 1.9829 mm and a standard deviation of 0.07539 mm. Based on a comparison of the methods, it is demonstrated that the proposed method requires a computing time of 1.201 s and has a computational accuracy of 99.929% over 10(4) simulations, which are improvements of 70.5% and 1.23%, respectively, relative to the distributed collaborative response surface method. Meanwhile, the high efficiency and high precision of the presented approach become more obvious with the increasing simulations. The efforts of this study provide a promising approach to improve the dynamic reliability analysis of complex structures. |
format | Online Article Text |
id | pubmed-6861887 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68618872019-12-05 Creep-Based Reliability Evaluation of Turbine Blade-Tip Clearance with Novel Neural Network Regression Zhang, Chun-Yi Wei, Jing-Shan Wang, Ze Yuan, Zhe-Shan Fei, Cheng-Wei Lu, Cheng Materials (Basel) Article To reveal the effect of high-temperature creep on the blade-tip radial running clearance of aeroengine high-pressure turbines, a distributed collaborative generalized regression extremum neural network is proposed by absorbing the heuristic thoughts of distributed collaborative response surface method and the generalized extremum neural network, in order to improve the reliability analysis of blade-tip clearance with creep behavior in terms of modeling precision and simulation efficiency. In this method, the generalized extremum neural network was used to handle the transients by simplifying the response process as one extremum and to address the strong nonlinearity by means of its nonlinear mapping ability. The distributed collaborative response surface method was applied to handle multi-object multi-discipline analysis, by decomposing one “big” model with hyperparameters and high nonlinearity into a series of “small” sub-models with few parameters and low nonlinearity. Based on the developed method, the blade-tip clearance reliability analysis of an aeroengine high-pressure turbine was performed subject to the creep behaviors of structural materials, by considering the randomness of influencing parameters such as gas temperature, rotational speed, material parameters, convective heat transfer coefficient, and so forth. It was found that the reliability degree of the clearance is 0.9909 when the allowable value is 2.2 mm, and the creep deformation of the clearance presents a normal distribution with a mean of 1.9829 mm and a standard deviation of 0.07539 mm. Based on a comparison of the methods, it is demonstrated that the proposed method requires a computing time of 1.201 s and has a computational accuracy of 99.929% over 10(4) simulations, which are improvements of 70.5% and 1.23%, respectively, relative to the distributed collaborative response surface method. Meanwhile, the high efficiency and high precision of the presented approach become more obvious with the increasing simulations. The efforts of this study provide a promising approach to improve the dynamic reliability analysis of complex structures. MDPI 2019-10-29 /pmc/articles/PMC6861887/ /pubmed/31671898 http://dx.doi.org/10.3390/ma12213552 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhang, Chun-Yi Wei, Jing-Shan Wang, Ze Yuan, Zhe-Shan Fei, Cheng-Wei Lu, Cheng Creep-Based Reliability Evaluation of Turbine Blade-Tip Clearance with Novel Neural Network Regression |
title | Creep-Based Reliability Evaluation of Turbine Blade-Tip Clearance with Novel Neural Network Regression |
title_full | Creep-Based Reliability Evaluation of Turbine Blade-Tip Clearance with Novel Neural Network Regression |
title_fullStr | Creep-Based Reliability Evaluation of Turbine Blade-Tip Clearance with Novel Neural Network Regression |
title_full_unstemmed | Creep-Based Reliability Evaluation of Turbine Blade-Tip Clearance with Novel Neural Network Regression |
title_short | Creep-Based Reliability Evaluation of Turbine Blade-Tip Clearance with Novel Neural Network Regression |
title_sort | creep-based reliability evaluation of turbine blade-tip clearance with novel neural network regression |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6861887/ https://www.ncbi.nlm.nih.gov/pubmed/31671898 http://dx.doi.org/10.3390/ma12213552 |
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