Machine Learning Algorithm for Predicting Heat Transfer Coefficient

This technical report presents a machine learning-based approach for predicting the hydrothermal performance of water-cooled dimpled ducts, specifically focusing on heat transfer coefficients and pressure drops. The study utilizes an artificial neural network (ANN) model developed from a limited dataset, which was generated through computational fluid dynamics (CFD) simulations. The ANN model effectively identifies patterns among input variables and outputs without relying on existing correlations. The accuracy of the model is validated by comparing its predictions to new performance data, achieving a prediction accuracy within ±17% for heat transfer coefficients and ±19% for pressure drops. The report discusses the challenges associated with traditional empirical correlations in predicting hydrothermal performance, particularly in scenarios with limited data. It emphasizes the potential of machine learning techniques to provide reliable predictions in complex thermal management systems, thereby facilitating the design of efficient cooling solutions under varying operational conditions.