A Comparative Analysis of Machine Learning Techniques for Predicting the Performance of Microchannel Gas Coolers in CO 2 Automotive Air-Conditioning Systems

Shehryar Ishaque, Naveed Ullah and Man-Hoe Kim ()
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Shehryar Ishaque: Institute of Engineering Design Technology (IEDT), Kyungpook National University, Daegu 41566, Republic of Korea
Naveed Ullah: Mechanical Engineering Department, College of Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
Man-Hoe Kim: Institute of Engineering Design Technology (IEDT), Kyungpook National University, Daegu 41566, Republic of Korea

Energies, 2024, vol. 17, issue 20, 1-17

Abstract: The automotive industry is increasingly focused on developing more energy-efficient and eco-friendly air-conditioning systems. In this context, CO 2 microchannel gas coolers (MCGCs) have emerged as promising alternatives due to their low global warming potential (GWP) and environmental benefits. This paper explores the application of machine learning (ML) algorithms to predict the thermohydraulic performance of MCGCs in automotive air-conditioning systems. Using data generated from an experimentally validated numerical model, this study compares various ML techniques, including both linear and nonlinear regression models, to forecast key performance metrics such as refrigerant outlet temperature, pressure drop, and heat transfer rate. Spearman’s correlation was employed to develop performance maps, whereas the R 2 and MSE metrics were used to evaluate the models’ predictive accuracy. The linear models gave around 70% forecasting accuracy for pressure drop across the gas cooler and 97% accuracy for refrigerant outlet temperature, whereas the nonlinear models achieved more accurate predictions, with an accuracy ranging from 71% to 99%. This implies that nonlinear regression generally performs better than linear regression models in assessing the overall thermohydraulic performance of microchannel gas coolers. This research brings forth new ideas on how ML methods can be applied to enhance efficiency and effectiveness in gas coolers, contributing to the development of more eco-friendly automotive air-conditioning systems.

Keywords: thermohydraulic; performance; machine learning; microchannel; gas cooler (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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