Prediction of Water Vapor Condensation Heat Transfer in Horizontal Channels with Non-Condensable Gases using Machine Learning Methods
This study develops a machine learning framework using dimensionless parameters derived from the Buckingham theorem to accurately and efficiently predict water vapor condensation heat transfer in horizontal channels with non-condensable gases, demonstrating that the XGBoost algorithm outperforms traditional correlations and other models while maintaining high accuracy even with a reduced feature set.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
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