This study explores the enhancement of charging performance in a triplex-tube latent heat thermal energy storage system (TTHX) by integrating longitudinal fins and alumina nanoparticles in phase change materials (PCMs). Numerical simulations are conducted to systematically examine the influence of fin length, thickness, number, and orientation, alongside the impact of nano-enhanced PCMs (NEPCMs) to identify optimal configurations for improved charging performance. The results show that incorporating fins accelerates the melting process, with thinner, more numerous fins providing the greatest enhancement. The optimal configuration, consisting of 64 fins with a reduced thickness of 125 µm, achieved an 86% reduction in charging time compared to the baseline case without fins. While adding nanoparticles to the PCM further improved heat transfer, concentrations exceeding 4% led to a decline in the system's overall thermal storage capacity. Among the PCMs studied, RT80-HC outperformed RT82 due to its higher latent heat of fusion and narrower phase-change temperature range. Additionally, horizontal fin configurations demonstrated a slight advantage by increasing the solid–liquid interface area, further enhancing melting efficiency. This study provides a comprehensive analysis of fin optimization and NEPCM integration in TTHXs, offering a better insight into maximizing thermal energy storage performance. The findings contribute to the development of more efficient latent heat thermal energy storage systems, supporting advancements in renewable energy utilization.

Solid-liquid phase transformation of a phase change material in a rectangular enclosure with corrugated fins is studied. Employing a physics-based model, the influence of fin length, thickness, and wave amplitude on the thermal and fluid flow fields is explored. Incorporating fins into thermal energy storage systems enhances the heat transfer surface area and thermal penetration depth, accelerating the melting process. Corrugated fins generate more flow perturbations than straight fins, improving the melting performance. Longer and thicker fins increase the melting rate, average temperature, and thermal energy storage capacity. However, the effect of fin thickness on the thermal characteristics seems insignificant. Larger fin wave amplitudes increase the heat transfer surface area but disrupt natural convection currents, slowing the melting front progress. A surrogate model based on an artificial neural network in conjunction with the particle swarm optimisation is developed to optimise the fin geometry. The optimised geometry demonstrates a 43% enhancement in thermal energy storage per unit mass compared to the case with planar fins. The data-driven model predicts the liquid fraction with less than 1% difference from the physics-based model. The proposed approach provides a comprehensive understanding of the system behaviour and facilitates the design of thermal energy storage systems.