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
Hlavní autori:	Wang, Jiang, Pan, Yingxiu, Wang, Yechun, Guo, Liejin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.12.2024
Predmet:	Back Propagation Neural Network CNN-LSTM Convection heat transfer coefficient Deep Belief Neural Network MEA-BPNN-Bayesian Multiple Linear Regression Convection heat transfer coefficient Multiple Linear Regression Back Propagation Neural Network CNN-LSTM Deep Belief Neural Network MEA-BPNN-Bayesian
ISSN:	1359-4311
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Shrnutí:	•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.
ISSN:	1359-4311
DOI:	10.1016/j.applthermaleng.2024.124226