A machine learning approach for predicting heat transfer characteristics in micro-pin fin heat sinks

Micro-pin fin heat sinks are receiving attention for their use in the thermal management of high-heatflux electronics systems since they can help to enhance heat transfer characteristics (owing to their large extended surface area) and flow mixing while requiring relatively low pumping power compared with conventional microchannel heat sinks. Although many studies have determined the thermal performance of micro-pin fin heat sinks over the past several decades, a universal model for predicting the thermal performance of micro-pin fin heat sinks with various geometries and under different operating conditions has not been developed. In this study, we developed universal machine learning models for predicting the thermal performance of micro-pin fin heat sinks of various shapes and under different operating conditions beyond the limits of existing correlations by using power law regression. The database for these models comprised 906 data points amassed from 15 studies. Three machine learning models and a newly proposed regression model were compared with the conventional regression models. The prediction accuracies of each model and complex relations between the geometric shape, operating conditions, and heat transfer performance are discussed by comparing the three machine learning models and the regression model. The machine learning models had mean absolute errors (MAEs) of 7.5-10.9%, representing an approximately fivefold enhancement in the prediction accuracy compared with existing regression correlations. Their MAEs were lower than that of the regression model. Moreover, the machine learning models provided high accuracies for rare geometric shapes and operating conditions, such as a triangular pin shape or the use of R134A as a working fluid. These results showed the superiority of the machine learning models over traditional correlations in terms of the prediction accuracy for the thermal performance of micro-pin fin heat sinks over a wide range of geometric and operating conditions. (c) 2022 Elsevier Ltd. All rights reserved.