基于机器学习的印刷电路板式换热器流动换热预测与仿真

Prediction and simulation of flow and heat transfer for printed circuit plate heat exchanger based on machine learning

基于Zigzag形通道印刷电路板式换热器内跨临界甲烷的流动换热数值模拟结果开展通道内局部对流传热系数与压降机器学习与预测。采用微元分段法提取各通道内局部多物理场参数构建数据库,通过互信息法筛选输入参数,并根据验证集预测效果确定最佳网络结构和超参数。预测结果表明,人工神经网络模型表现最佳,预测对流传热系数的平均绝对百分比误差为2.228%,预测压降则为5.009%。利用机器学习对流动换热参数的预测开发了一种Zigzag形通道印刷电路板式换热器一维仿真方法,实现了通道内流体温度、壁温、对流传热系数和压降的快速准确预测,为换热器设计提供了新的方法。

Based on the numerical simulation results of transcritical methane flow in a Zigzag-channel printed circuit plate heat exchanger,machine learning models were used to predict the local convection heat transfer coefficient and pressure drop in the channel.The local multiple physical parameters along the channel were obtained by the microsegment method to create a database.The input parameters are screened by Mutual Information method,and the optimal network structure and hyper parameters are determined according to the predicting effect of validation set.The predicting results show that the artificial neural network model performs best,with a mean absolute percentage error of 2.228%for predicting heat transfer coefficient and 5.009%for predicting pressure drop.Using machine learning to predict flow heat transfer parameters,a one-dimensional simulation method for Zigzag-shaped channel printed circuit board heat exchangers was developed to achieve rapid and accurate prediction of fluid temperature,wall temperature,convective heat transfer coefficient and pressure drop in the channel,providing a new method for heat exchanger design.