Thermal Performance Optimization of Nano-enhanced Phase Change Material-Based Heat Pipe Using Combined Artificial Neural Network and Genetic Algorithm Approach

The study focuses on the numerical investigation of Nano-enhanced Phase Change Material (Ne-PCM)-based heat pipes for electronic cooling applications. It uses both Paraffin wax and N-eicosane as PCMs which are combined with Copper Oxide (CuO) Nano-particles at different concentrations of 1%, 3%, 5%, and 7%. The heat input to the heat pipe ranges from 10 - 50 W in an increment of 10 W to simulate realistic operating conditions. The idea is to predict the thermal performance of the heat pipe at various combinations of Nano-particle and PCMs and compare the same to the baseline case of DI Water. The results show a constant drop in the evaporator temperature for the Ne-PCM-assisted heat pipes. Paraffin wax and N-eicosane exhibit maximum reductions of 2.86% and 1.94%, respectively in evaporator wall temperature compared to using conventional DI Water. The maximum evaporator heat transfer coefficients recorded are 257.79 W/m²K, 353 W/m²K, and 265.18 W/m²K for heat pipes using DI Water, Paraffin wax-CuO, and N-eicosane-CuO, respectively. The Nano-particles act as a thermal conductivity enhancer and bring down the heat pipe's temperature with the addition of PCMs. For better prediction of its thermal performance, a predictive model is developed using an Artificial Neural Network (ANN). This model drives the Genetic Algorithm (GA) to identify the optimal configuration resulting in better thermal performance of the heat pipe.