Computational Modeling of Latent Heat Thermal Energy Storage in a Shell-Tube Unit: Using Neural Networks and Anisotropic Metal Foam

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