Abstract: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 inpu… Show more
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