ABSTRACT Phase change material (PCM)‐based thermal energy storage systems offer a promising solution to bridge the mismatch between intermittent solar energy availability and continuous energy demand. However, their practical use is often constrained by the inherently low thermal conductivity of PCMs, which leads to sluggish melting and incomplete heat transfer. This study aims to enhance the thermal performance of a horizontal shell‐and‐tube latent heat storage unit employing paraffin‐based PCM RT42 through a hybrid computational optimization framework combining “computational fluid dynamics” (CFD) and a “genetic algorithm” (GA). A two‐dimensional axisymmetric CFD model developed in ANSYS Fluent simulates the melting behavior of RT42 under natural convection and conduction. The GA, implemented in Python, optimizes fin geometry specifically fin length and inclination angle—to minimize total melting time and maximize energy efficiency. The optimized configuration achieved a 30% reduction in melting (charging) time and a 15%–20% improvement in thermal efficiency compared with the baseline system. Sensitivity analysis revealed that excessive PCM thickness and fin spacing hinder heat transfer, while fluid velocities above 0.03 m/s offer minimal benefit. The results demonstrate that the CFD–GA integrated approach provides a robust and scalable method for optimizing PCM‐based storage units in solar thermal systems. This research establishes a reproducible framework for designing high‐performance TES units, promoting efficient and sustainable solar energy utilization.
Kumar et al. (Mon,) studied this question.