Investigation on the thermal control and performance of PCM-porous media-integrated heat sink systems: Deep neural network modelling employing experimental correlations


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Phase change material (PCM)-based heat sinks can offer reliable and effective thermal management (TM) solutions for increasingly sophisticated applications. A critical aspect of such heat sinks is determining how long it takes them to reach a set-point temperature. However, no generalised method exists in the literature that can predict and interpret the thermal performance of a wide range of PCM-porous media-integrated heat sinks. In this regard, this study examines the heat transfer characteristics of PCM-based heat sinks integrated with various metallic foams through experimental and deep learning (DL) techniques. The experiments are performed for transient TM analysis of various PCM-based heat sinks. Diverse variables, including foam porosity (0.95-0.97), PCM fraction (0.6-0.8), heat flux (0.8-2.4 kW/m2), foam materials (Fe-Ni alloy, Ni and copper) and PCM type (RT-35HC, RT-44HC, RT-54HC and paraffin wax), are investigated in this study. The experimental data are fed to the optimal DL model using the Bayesian surrogate model-tuned hyperparameters. Utilising a correlation analysis, as exemplified by the heat map and correlation plot, in conjunction with explainable artificial intelligence, it has been deduced that the thermal performance of the heat sink is principally influenced by factors such as PCM type, PCM fraction, foam material, foam porosity, and heat flux. Comparing the model's predicted data with the empirical findings, a good agreement was observed. Specifically, the mean absolute error (MAE) for the anticipated temperature and gradient registered at 0.0438 and 0.0054, whilst the mean square error (MSE) manifested values of 0.0579 and 0.0087, respectively. The proposed model can accurately assess the heat sink's thermal performance (correlation coefficient, R2 = 0.99) for various PCM types, fractions, foam materials, applied heat flux and foam porosity.
Bayesian optimisation,Deep learning,Heat sink,Phase change materials,Thermal management
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