Data-driven prediction of convective heat transfer coefficients in internal walls of aero-engine bearing chambers using Mind Evolution Algorithm-Enhanced Bayesian regularization neural networks
•Studied two-phase flow dynamics in aero-engine bearing chambers.•Analyzed factors affecting local convective heat transfer coefficients.•Validated heat transfer predictions using regression and neural network models.•Enhanced accuracy with a Bayesian regularization neural network model. In the bear...
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| Vydané v: | Applied thermal engineering Ročník 257; s. 124226 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.12.2024
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| ISSN: | 1359-4311 |
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| Abstract | •Studied two-phase flow dynamics in aero-engine bearing chambers.•Analyzed factors affecting local convective heat transfer coefficients.•Validated heat transfer predictions using regression and neural network models.•Enhanced accuracy with a Bayesian regularization neural network model.
In the bearing chambers of aero-engines, the complex nature of the oil–gas flow complicates the precise calculation of heat transfer coefficients. Addressing computational discrepancies, firstly, this study establishes a correlation analysis method, which considers the relationship between several independent variables including the geometric shape of the bearing chamber, rotational speed, oil flow rate, thermal sealing air mass, circumferential angle, axial position, and the dependent variable, which is the logarithmic Nusselt number on the internal wall. Secondly, four models are developed using Multiple Linear Regression, Back Propagation Neural Network, Deep Belief Neural Network, and Convolutional Neural Network with Long Short-Term Memory Network, yielding Mean Relative Percentage Errors (MRPE) of 5.14%, 2.80%, 4.66%, and 4.12%, respectively. Due to the limited predictive performance of these models, the neural network topology is further optimized. After repeated trial-and-error calculations, the neural network with three hidden layers was found to be the most effective. Finally, a novel BP Neural Network optimized by the Mind Evolution Algorithm-enhanced with Bayesian regularization (MEA-BPNN-Bayesian) model is proposed forpredicting the local convective heat transfer, achieving an MRPE of 1.71%. The model accurately predicted heat transfer coefficients, opening up new ways to improve heat management in bearing chambers. |
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| AbstractList | •Studied two-phase flow dynamics in aero-engine bearing chambers.•Analyzed factors affecting local convective heat transfer coefficients.•Validated heat transfer predictions using regression and neural network models.•Enhanced accuracy with a Bayesian regularization neural network model.
In the bearing chambers of aero-engines, the complex nature of the oil–gas flow complicates the precise calculation of heat transfer coefficients. Addressing computational discrepancies, firstly, this study establishes a correlation analysis method, which considers the relationship between several independent variables including the geometric shape of the bearing chamber, rotational speed, oil flow rate, thermal sealing air mass, circumferential angle, axial position, and the dependent variable, which is the logarithmic Nusselt number on the internal wall. Secondly, four models are developed using Multiple Linear Regression, Back Propagation Neural Network, Deep Belief Neural Network, and Convolutional Neural Network with Long Short-Term Memory Network, yielding Mean Relative Percentage Errors (MRPE) of 5.14%, 2.80%, 4.66%, and 4.12%, respectively. Due to the limited predictive performance of these models, the neural network topology is further optimized. After repeated trial-and-error calculations, the neural network with three hidden layers was found to be the most effective. Finally, a novel BP Neural Network optimized by the Mind Evolution Algorithm-enhanced with Bayesian regularization (MEA-BPNN-Bayesian) model is proposed forpredicting the local convective heat transfer, achieving an MRPE of 1.71%. The model accurately predicted heat transfer coefficients, opening up new ways to improve heat management in bearing chambers. |
| ArticleNumber | 124226 |
| Author | Guo, Liejin Pan, Yingxiu Wang, Jiang Wang, Yechun |
| Author_xml | – sequence: 1 givenname: Jiang orcidid: 0009-0009-6933-7334 surname: Wang fullname: Wang, Jiang – sequence: 2 givenname: Yingxiu surname: Pan fullname: Pan, Yingxiu – sequence: 3 givenname: Yechun surname: Wang fullname: Wang, Yechun email: wangycc@mail.xjtu.edu.cn – sequence: 4 givenname: Liejin surname: Guo fullname: Guo, Liejin email: lj-guo@mail.xjtu.edu.cn |
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| Cites_doi | 10.1016/j.icheatmasstransfer.2019.104444 10.1115/GT2014-26756 10.1016/j.applthermaleng.2021.117067 10.1115/GTINDIA2012-9620 10.1016/j.energy.2024.130899 10.2514/6.2007-5033 10.1016/j.applthermaleng.2024.123043 10.1016/j.energy.2023.128981 10.1155/S1023621X99000147 10.1016/j.applthermaleng.2018.07.140 10.1016/j.knosys.2020.106622 10.1115/GT2019-91657 10.1115/GT2013-94362 10.1126/science.1127647 10.1115/93-GT-209 10.1016/j.energy.2023.128176 10.1115/1.1924485 10.1016/j.applthermaleng.2024.123255 10.1162/neco.2006.18.7.1527 10.1016/j.applthermaleng.2017.03.126 10.1115/97-GT-261 10.1162/089976602760128018 10.4249/scholarpedia.1482 10.1115/GT2012-68984 10.1115/1.2816687 10.1115/GT2003-38376 10.1115/GT2004-53698 10.1243/09544100JAERO40 10.1016/j.applthermaleng.2023.120698 10.1115/GT2004-53578 10.1115/GT2016-56747 10.1115/GT2011-46259 10.1115/GT2015-43506 10.1115/1.2770480 10.1115/GT2022-82275 10.1126/science.aaw4741 10.1115/1.483209 10.1016/j.energy.2019.05.230 10.1109/CVPR.2015.7299173 10.1115/GT2017-64410 10.1115/1.2906518 10.1109/72.572092 |
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| Keywords | Convection heat transfer coefficient Multiple Linear Regression Back Propagation Neural Network CNN-LSTM Deep Belief Neural Network MEA-BPNN-Bayesian |
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| Title | Data-driven prediction of convective heat transfer coefficients in internal walls of aero-engine bearing chambers using Mind Evolution Algorithm-Enhanced Bayesian regularization neural networks |
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