Parametric analysis of a PCM-based heat sink for electronic device thermal management

Abstract

Integrating conventional Heat Sinks with Phase Change Materials (HS-PCM) is a novel strategy for the thermal management of electronic devices. Although various studies attempted to investigate the impact of PCM on the operating temperature of electronic devices, the influence of PCM properties on the performance of the HS-PCM system remained unknown. Therefore, in the present study, an accurate three-dimensional HS-PCM system is designed and simulated to evaluate the effect of PCM properties, including thermal conductivity, enthalpy, specific heat capacity, and liquidus temperature, on the operating temperature of this system. In this order, the HS-PCM system is simulated at multiple operating conditions during both the working and cooling phases. Notably, an experimental setup is fabricated to compare the numerical outputs against the experimental data. Additionally, the Support Vector Regression with Hybrid (HSVR) kernels is used to predict the performance of the HS-PCM system. The hyperparameters of this machine learning model are optimized by implementing Grey Wolf Optimizer (GWO). It is found that raising the liquidus temperature of the PCM can have a minor impact on the working duration of the chipset while it dramatically impacts the cooling process of the system. Also, increasing the enthalpy and heat capacity of the PCM has a favorable effect on the working time of the chipset but extends the cooling process. Thermal conductivity is the only parameter which its enhancement has a positive impact on both the working and cooling duration of the chipset. Raising the thermal conductivity of the PCM from 0.05 W/(m·K) to 0.8 W/(m·K) improves the working duration of the chipset from 22 min to 24 min. Based on the outcomes, the most effective factor on the working time of the chipset is the enthalpy, followed by heat capacity, thermal conductivity, and the liquidus point of the PCM. According to the findings, the GWO-HSVR model can accurately forecast the outcomes of the HS-PCM system at the both working and cooling phases. In the testing process of the model, the values of R2 for the working and cooling phases are calculated to be 0.99174 and 0.99775, respectively.