Machine learning-based thermal performance study of microchannel heat sink under non-uniform heat load conditions

Abstract

The parallel microchannel heat sink stands as a pivotal solution in managing high heat flux electronics due to its efficient heat transfer characteristics and ease of manufacturing. While numerous studies have explored the thermal performance and flow characteristics of microchannel heat sinks, most have focused on uniform heat loads or relied heavily on numerical methods. This study presents an experimental system tailored to generate data for analyzing the thermal performance of microchannel heat sinks under various conditions. Leveraging this dataset, four distinct machine learning models Artificial Neural Network (ANN), XGBoost, LightGBM, and K-nearest neighbor (KNN) were trained using 22 input features, totalling 560 data points categorised into geometry parameters, heating patterns, and boundary conditions details. The models were tasked with predicting six response variables: the average base temperature of the heat sink, temperature change (ΔT), hotspot temperature, heat transfer coefficient (h), Nusselt number (Nu), and thermal resistance (Rth). Among the four machine learning models, XGBoost exhibited a good predictive accuracy of an average R2 value of 0.98 and MAE values of 2.1 across all responses. Furthermore, the study delved into the impact of varying input features on prediction accuracy, revealing a consistent enhancement in accuracy with the inclusion of more features across all models.