Thermal Performance Analysis of Combined Notch Fin Geometry Using Artificial Intelligence

ABSTRACT Efficient heat dissipation is essential in automotive and electronics systems to ensure safe operation, improved reliability, and longer component life. This study aims to evaluate the thermal performance of a conventional fin and a geometrically modified combined notch fin under steady‐state natural convection conditions. Experimental testing, ANSYS simulation, and artificial intelligence (AI) based predictive model were employed for analysis. Results showed that the modified fin achieved a heat flux of 39,500 W/m 2 , compared to with 37,750 W/m 2 , for the conventional fin, representing an improvement of 4.64%. Numerical and AI predictions were in close agreement with experimental results, with deviations below 3%. The improved performance of the combined notch fin is attributed to increased effective surface area and enhanced convective heat transfer. The key contribution of this work involves integrating a novel combined notch fin design with experimental, numerical, and AI‐based approaches for rapid reliable thermal performance assessment. The developed model can be effectively applied for rapid thermal design evaluation and optimization of fin‐based heat sinks used in automotive engines, electronic cooling systems, and compact thermal management devices.