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

Abstract

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.