首页|Kyungpook National University Researcher Provides New Study Findings on Machine Learning (A Comparative Analysis of Machine Learning Techniques for Predicting t he Performance of Microchannel Gas Coolers in CO2 Automotive Air-Conditioning Sy stems)
Kyungpook National University Researcher Provides New Study Findings on Machine Learning (A Comparative Analysis of Machine Learning Techniques for Predicting t he Performance of Microchannel Gas Coolers in CO2 Automotive Air-Conditioning Sy stems)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on artificial in telligence. According to news reporting from Daegu, South Korea, by NewsRx journ alists, research stated, "The automotive industry is increasingly focused on dev eloping more energy-efficient and eco-friendly air-conditioning systems." Financial supporters for this research include Korea Institute of Energy Technol ogy Evaluation And Planning. The news editors obtained a quote from the research from Kyungpook National Univ ersity: "In this context, CO2 microchannel gas coolers (MCGCs) have emerged as p romising alternatives due to their low global warming potential (GWP) and enviro nmental benefits. This paper explores the application of machine learning (ML) a lgorithms to predict the thermohydraulic performance of MCGCs in automotive air- conditioning systems. Using data generated from an experimentally validated nume rical model, this study compares various ML techniques, including both linear an d nonlinear regression models, to forecast key performance metrics such as refri gerant outlet temperature, pressure drop, and heat transfer rate. Spearman's cor relation was employed to develop performance maps, whereas the R2 and MSE metric s were used to evaluate the models' predictive accuracy. The linear models gave around 70% forecasting accuracy for pressure drop across the gas c ooler and 97% accuracy for refrigerant outlet temperature, whereas the nonlinear models achieved more accurate predictions, with an accuracy rangi ng from 71% to 99%."
Kyungpook National UniversityDaeguSo uth KoreaAsiaAutomobilesCyborgsEmerging TechnologiesMachine LearningTransportation
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