Deep Learning and Optimization for Analyzing Serrated Fin’s Heat Transfer Behavior

This research addresses the challenge of improving heat transfer efficiency in serrated fins, which are critical components in thermal management systems used across electronics and energy industries. Efficient heat dissipation under combined radiation and convection conditions is essential for enhancing system reliability and performance. A developed novel deep learning model was optimized by an Evolved Multi-Hybrid Differential Evolution Algorithm (EMHDA) to accurately predict thermal behavior and optimize fin geometry. The results show a linear increase in the heat transfer coefficient from [Formula: see text] as temperature rises from 300 to 700 K. Optimizing fin height, thickness, and spacing reduces entropy generation by up to 83.4% indicated significant thermodynamic improvements. Unlike previous studies, this work integrates complex radiation–convection interactions within a hybrid AI-optimization framework, offering a comprehensive and efficient design tool for advanced heat transfer systems.