XGBoost-Based Model for Heat Transfer Coefficient Prediction

This research article presents an innovative XGBoost-based machine learning model designed to accurately predict heat transfer coefficients in liquid cold plates. The study utilizes computational fluid dynamics (CFD) to generate training data, exploring the effects of surface roughness on thermal performance. Results indicate that the model achieves high accuracy, making it a valuable tool for thermal management solutions in engineering applications.