Comparison of GRNN and RF algorithms for predicting heat transfer coefficient in heat exchange channels with bulges

• Published in: Applied thermal engineering Vol. 217; p. 119263
• Main Authors: Zheng, Xiao, Yang, Ruomiao, Wang, Qifan, Yan, Yuchao, Zhang, Yu, Fu, Jiahong, Liu, Zhentao
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 25.11.2022
• Subjects: General Regression Neural Network; Heat exchange channel; Heat transfer coefficient; Machine learning; Random Forests; Random Forests; General Regression Neural Network; Heat transfer coefficient; Heat exchange channel; Machine learning
• ISSN: 1359-4311
• DOI: 10.1016/j.applthermaleng.2022.119263

•Machine learning is applied in the heat exchange channels.•Different ML algorithms performance is compared for predicting heat transfer coefficient.•Neuron-based algorithms are more applicable to heat transfer field.•The combination of machine learning and sensitivity analysis shows a bright future.•Machine learning is combined with genetic algorithms to improve structural optimization. In the research of heat transfer, heat exchanger plays an important role in highly integrated and high-precision thermal management to ensure the thermal balance. In previous studies, traditional experiments and CFD simulations consume lots of time and computational resources, while the heat transfer correlations have large errors. Hence, this research aims to establish a reliable method to predict the Heat Transfer Coefficient (HTC) of heat exchange channels more quickly and accurately. In this paper, General Regression Neural Network (GRNN) and Random Forests (RF) models, which are trained by hundreds of CFD simulation results, are adopted to predict the heat-exchange performances of channels with different height bulges. The prediction results show that the HTC of channels with different bulge arrangements are accurately predicted, supported by R2 > 0.97 in both training and validation sets. Also, it shows that GRNN is more applicable to heat exchange channels than RF. Besides, it can be inferred from the prediction results that the front-end bulge has a significant impact on the overall HTC, as does the uniformity of the bulge height. In conclusion, machine learning algorithms have great potential in predicting the HTC of channels, and the GRNN algorithm may perforce better when calculate other complex heat transfer problems.