Akademik Veri Yönetim Sistemi
Journal of Energy Storage, cilt.99, 2024 (SCI-Expanded)
This research presents an innovative moving fin design for a latent heat storage unit, with the objective of improving thermal performance. The methodology integrates numerical simulation, a machine learning approach, and a multi-objective genetic optimization algorithm (NSGA-II). The study optimizes fin parameters, such as fin velocity (Vf), fin thickness (tf), fin length (Lf) and initial fin position (yf). The optimization process aims to maximize both power (P) and stored energy per mass (Em), assigning equal importance to both criteria. The outcomes reveal a considerable rise in power with minor compromises in stored energy per mass. The incorporation of moving fins in rectangular enclosure with PCM leads to a significant power boost while preserving stored latent energy with minimal compromise. In comparison to the no-fin condition, fixed 1-fin and fixed 3-fin configurations lead to stored energy losses of 4.16 % and 11.73 %, respectively, accompanied by power increases of 20.48 % and 51.17 %, respectively. Conversely, the optimized condition for maximum power with moving fins incurs a mere 2.73 % stored energy loss but achieves a remarkable 173.92 % increase in power. The optimized design parameters for maximizing power (P) include Vf = 22.5 × 10−3 mm/s, yf = 60 mm, tf = 2 mm, and Lf = 40 mm.