Latent heat storage in a shell-tube is a promising method to store excessive solar heat for later use. The shell-tube unit is filled with a phase change material PCM combined with a high porosity anisotropic copper metal foam (FM) of high thermal conductivity. The PCM-MF composite was modeled as an anisotropic porous medium. Then, a two-heat equation mathematical model, a local thermal non-equilibrium approach LTNE, was adopted to consider the effects of the difference between the thermal conductivities of the PCM and the copper foam. The Darcy–Brinkman–Forchheimer formulation was employed to model the natural convection circulations in the molten PCM region. The thermal conductivity and the permeability of the porous medium were a function of an anisotropic angle. The finite element method was employed to integrate the governing equations. A neural network model was successfully applied to learn the transient physical behavior of the storage unit. The neural network was trained using 4998 sample data. Then, the trained neural network was utilized to map the relationship between control parameters and melting behavior to optimize the storage design. The impact of the anisotropic angle and the inlet pressure of heat transfer fluid (HTF) was addressed on the thermal energy storage of the storage unit. Moreover, an artificial neural network was successfully utilized to learn the transient behavior of the thermal storage unit for various combinations of control parameters and map the storage behavior. The results showed that the anisotropy angle significantly affects the energy storage time. The melting volume fraction MVF was maximum for a zero anisotropic angle where the local thermal conductivity was maximum perpendicular to the heated tube. An optimum storage rate could be obtained for an anisotropic angle smaller than 45°. Compared to a uniform MF, utilizing an optimum anisotropic angle could reduce the melting time by about 7% without impacting the unit’s thermal energy storage capacity or adding weight.

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.