Case Studies in Thermal Engineering (Sep 2024)
Heat release efficiency Betterment inside a novel-designed latent heat exchanger featuring arc-shaped fins and a rotational mechanism via numerical model and artificial neural network
Abstract
Thermal energy storage (TES) units featuring phase change materials (PCMs) have a crucial impact on efficient energy management. Since energy demands keep changing, and the world increasingly relies on sustainable energy sources, innovation is continuously required. New technologies, materials, and methods for energy storage must be developed to meet these shifting needs effectively. In this study, a triplex-tube heat exchanger (TTHE) was employed as a TES medium, utilizing PCM within the middle tube. Because of its high thermal energy storage capacity, RT50 was selected as the PCM. Four innovative arc-shaped fins were strategically integrated within the PCM space to accelerate heat release. The system was subjected to rotational speeds of 0.1, 0.3, 0.5, 1, and 1.5 rpm. The investigation was carried out in two stages: initially, the impact of varying rotational speeds on the discharging process of the PCM in a finless TTHE was explored; subsequently, the influence of the same speeds on the solidification behavior of the finned TTHE was analyzed. Natural convection and PCM’s solidification process were examined through the enthalpy-porosity method. The findings represented that in the absence of fins, the device’s rotation had a more noticeable impact on the solidification behavior; however, incorporating fins was much more influential. Up to 2850 s, all the finned systems had solidified entirely in the presence or absence of the rotational mechanism, while in the finless system with a rotational speed of 1.5, 30.52 % of the material was still unsolidified until this time. Finally, the influence of three key structural parameters of the fins: α (outer fins arc angle), β (inner fins arc angle), and S (space between fins) on the discharging time of the PCM was analyzed using an artificial neural network (ANN) model. The study offered critical insights for optimizing fin configuration by systematically varying these parameters within defined ranges. An ANN predictive model was also proposed to assist TES system manufacturers and developers in future design and optimization efforts. The results demonstrated that the optimal setting of the TTHE (with the parameters of α = 60°, β = 60°, S = 10 mm) achieved the liquid fraction of 0.1, 80.33 % faster than the finless system.
