Computational Modelling and Analysis of Heat Transfer in Microchannel Heat Exchangers Using Machine Learning

Abstract

 Microchannel heat exchangers (MHEs) are becoming increasingly popular due to their compact size, high heat transfer efficiency, and potential for integration in various applications. However, accurately predicting and analyzing heat transfer in MHEs remains a challenging task due to the complex fluid dynamics and thermal behavior within the microchannels. In this study, we propose a computational modeling and analysis approach for heat transfer in MHEs using machine learning techniques. A numerical model is developed based on the conservation equations for mass, momentum, and energy. The model takes into account the effects of fluid flow, convection, and conduction within the microchannels.  The solving these equations directly can be computationally expensive and time-consuming, especially for large-scale systems or complex geometries. We leverage the power of machine learning algorithms to build an efficient surrogate model. We employ a supervised learning approach and train the model using a dataset generated from the numerical simulations. Several machine learning algorithms, including random forest, support vector regression, and neural networks, are evaluated and compared for their predictive performance. The models are trained using a subset of the dataset and validated against the remaining data to ensure their generalizability. The surrogate model is trained, it can be used for rapid and efficient prediction of heat transfer performance in MHEs. By inputting the relevant parameters, such as fluid properties and channel dimensions, the model provides accurate predictions of heat transfer coefficients and pressure drops. This enables engineers and researchers to optimize the design and operation of MHEs without the need for extensive numerical simulations. The proposed approach not only reduces computational costs but also provides valuable insights into the underlying physics of heat transfer in MHEs. By analyzing the feature importance derived from the machine learning models .This study demonstrates the effectiveness of machine learning in computational modeling and analysis of heat transfer in microchannel heat exchangers. The developed surrogate model offers a promising tool for efficient design optimization and performance prediction of MHEs, paving the way for advancements in the field of microscale heat transfer.