Enhancing heat transfer efficiency in heat exchangers: A fusion of computational fluid dynamics and genetic algorithm for circular baffle optimization

Optimizing the performance of heat exchangers is critical for enhancing energy efficiency in industrial applications. Traditional methods often fail to balance heat transfer enhancement and pressure loss. This study addresses this gap by integrating Computational Fluid Dynamics (CFD) and Genetic Algorithms (GA) to optimize circular baffle heat exchangers, targeting both Nusselt number (Nu) and friction factor (f). CFD simulations were conducted over Reynolds numbers from 30,000 to 70,000, with water as the working fluid. Key geometric parameters, baffle diameter ratio (di/D), spacing ratio (S/D), and hole count (N) were investigated. The results show that reducing the baffle spacing from S/D = 5.56 to S/D = 2.381 led to a 42 % increase in Nu, while f increased by 89 %. Using GA, optimal configurations were identified, achieving a maximum Nu of 1588.1 and a corresponding f of 12.522 at Re = 70,000. This novel approach bridges the gap between maximizing heat transfer and minimizing pressure drop, providing a new pathway for efficient heat exchanger design.