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

被引：0
|
作者：
Li, Qian [1 ]
Zhang, Rongmin [1 ]
Lin, Zijie [1 ]
Zhan, Qi [1 ]
Cai, Weihua [1 ]
机构：
[1] Energy Resources and Engineering College, Northeast Electric Power University, Jilin, Jilin,132012, China
来源：
Huagong Xuebao/CIESC Journal | 2024年 / 75卷 / 08期
关键词：
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. © 2024 Materials China. All rights reserved;
D O I：
10.11949/0438-1157.20240095
中图分类号：
学科分类号：
摘要：
引用
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页码：2852 / 2864
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