Computational Study of Phase Change Heat Transfer and Latent Heat Energy Storage for Thermal Management of Electronic Components Using Neural Networks

The phase change heat transfer of nano-enhanced phase change materials (NePCMs) was addressed in a heatsink filled with copper metal foam fins. The NePCM was made of 1-Tetradecanol graphite nanoplatelets. The heatsink was an annulus contained where its outer surface was subject to a convective cooling of an external flow while its inner surface was exposed to a constant heat flux. The governing equations, including the momentum and heat transfer with phase change, were explained in a partial differential equation form and integrated using the finite element method. An artificial neural network was employed to map the relationship between the anisotropic angle and nanoparticles fractions with the melting volume fraction. The computational model data were used to successfully train the ANN. The trained ANN showed an R-value close to unity, indicating the high prediction accuracy of the neural network. Then, ANN was used to produce maps of melting fractions as a function of design parameters. The impact of the geometrical placement of metal foam fins and concentrations of the nanoparticles on the surface heat transfer was addressed. It was found that spreading the fins (large angles between the fins) could improve the cooling performance of the heatsink without increasing its weight. Moreover, the nanoparticles could reduce the thermal energy storage capacity of the heatsink since they do not contribute to heat transfer. In addition, since the nanoparticles generally increase the surface heat transfer, they could be beneficial only with 1.0% wt in the middle stages of the melting heat transfer.

Automated summary

The heat transfer during phase change in a heatsink filled with nano-enhanced phase change materials (NePCMs) was studied. The NePCM consisted of 1-Tetradecanol graphite nanoplatelets. The heatsink had an annular shape and experienced convective cooling on its outer surface and constant heat flux on its inner surface. The governing equations for momentum and heat transfer were solved using the finite element method. An artificial neural network (ANN) was used to establish the relationship between anisotropic angle, nanoparticle fractions, and melting volume fraction. The trained ANN showed high prediction accuracy and was used to generate melting fraction maps for design parameters. It was found that spreading the metal foam fins improved cooling performance without increasing weight, and nanoparticles had a negative effect on thermal energy storage capacity.