Design of rotating heat storage tank filled with unevenly spaced and angled fins: a novel optimization framework of multi-strategy improved sparrow search algorithm

To mitigate the intermittency of solar energy and maximize utilization efficiency, this study introduces a novel hybrid thermal management system that synergizes active rotation with passive non-uniform fins. Another central to this work is the development of a multi-strategy improved sparrow search algorithm (MSISSA), which incorporates sine chaotic mapping, elite reverse learning, fitness guidance, and a greedy selection mechanism. Validation against 12 benchmark functions confirms the superior convergence accuracy and robustness of the developed algorithm. Leveraging this advanced optimizer, we conduct a multi-variable optimization of fin geometry (spacing, height, and angle). The results demonstrate significant performance gains: the optimized configuration reduces the heat storage time by 27.83% and boosts the average heat storage efficiency by 26.83% relative to the baseline design. Additionally, the system elevates temperature uniformity by 8.60%, offering a robust solution for high-efficiency latent heat thermal energy storage.