Thermal enhancement of phase change materials using nanoparticles and novel finned structures

Phase change materials (PCMs) have proven vital in thermal energy storage systems due to their remarkable energy density and capacity to sustain a stable temperature. This study examines how adding new dendritic fin structures positioned in the lower region can improve heat transfer and melting kinetics in a molten salt-based nano-enhanced phase change material (NEPCM) with $F{e}_3{O}_4-Cu$ hybrid nanoparticles inside rectangular enclosures. A baseline example without fins, a second case with dendritic fins whose branch lengths decrease toward the bottom (type 1), and a third case with dendritic fins whose branch lengths increase toward the bottom (type 2) are the three different configurations that are examined in this study. The thermal behavior was numerically modelled using the porosity-enthalpy method. We also developed an artificial neural network (ANN) model with a multilayer perceptron architecture that includes two hidden layers to predict melting characteristics and thermal performance parameters, training it on both computational and experimental datasets. When paired with hybrid nanoparticles, total melting was accomplished about 41 % faster. With correlation coefficients above 0.98 and mean relative error below 3.5 % under all test settings, the created ANN model was able to predict melting percent, average temperature, and Nusselt number. The ANN model's sensitivity analysis revealed that the two most important factors influencing thermal performance were the concentration of nanoparticles and the fin branch length ratio. For future studies, it would be beneficial to focus on optimizing the parameters of these dendritic fin structures and to investigate the ideal $F{e}_3{O}_4-Cu$ ratios to achieve maximum thermal performance while ensuring colloidal stability.