Application of Machine Learning to Predict Thermal Performances of Heat Sinks

- In the present study, the capabilities of two machine learning (ML) regression methods, support vector regression (SVR) and kernel ridge regression (KRR), to predict heat transfer coefficients (HTCs) in air-cooled heat sinks (HSs) are evaluated. Within the laminar regime, HSs with different geometrical parameters and at five different Reynolds numbers are considered for the simulations. Since the focus of the present study is the proof-of-concept, the ML-based models are developed using limited numbers of input data. The input data are prepared by solving three-dimensional equations of continuity, momentum, and energy inside the channels of HSs. Results indicate that both SVR and KRR predict HTCs with excellent accuracy and within ±1.9% of simulated values. The present study suggests that both SVR and KRR are promising design tools to predict hydrothermal performances of thermal systems using sufficiently large and accurate input data. Such precise ML-based models will be excellent alternatives to expensive experimental and computational efforts that are required to develop physics-based correlations for predicting hydrothermal performances of engineering systems. (Re). Simulations are performed for each a five Re, . The training dataset is selected randomly from 83% of the input data; the remaining data are the testing dataset.