Artificial intelligence - computational fluid dynamics study close contact melting of phase change materials in a finned shell-tube energy storage

The critical challenge of improving the latent-heat thermal energy storage (LHTES) efficiency was addressed in this study, as it is essential for advancing sustainable energy solutions. A new approach is proposed that uses heated liquid phase change materials (PCM) in direct contact with solid PCM to enhance the speed of thermal energy storage processes. Computational fluid dynamics and artificial neural networks were employed to examine the effects of various parameters on melting efficiency and temperature distribution. These parameters included PCM inlet pressures (5–15 Pa), inlet temperatures (311–359 K), hot water flow (HWF) inlet temperatures (311–359 K), HWF velocities (0.0067–0.0268 m/s), and the configuration of inlet/outlet ports. It was found that increasing HWF inlet temperature significantly accelerated the melting process. A case with the highest HWF inlet temperature (359 K), exhibited a remarkable 64.82 % improvement in melting rate compared to the baseline. Furthermore, optimization of PCM temperature and port configuration yielded approximately 30 % enhancement in system performance. It was concluded that higher inlet temperatures not only promoted more uniform melting but also significantly reduced melting time. The proposed method could substantially expedite melting and enhance heat transfer in LHTES systems.