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.
维普
万方数据