Enhanced multi-objective genetic algorithm for optimized thermal management in mini heat sinks

This research conducts an in-depth exploration of multi-objective optimization for a mini heat sink with fins using a genetic algorithm (GA). The objective is to reduce both thermal resistance and pump power consumption. The optimization problem consists of the use of increased design freedom: mixed-variable freedom factors, including fin angles, hole dimensions, and their placements. Computational fluid dynamics (CFD) simulations are used to evaluate the performance of the heat sink. A repair function is implemented to refine solutions by restricting continuous variables to specific values, streamlining the optimization process. The results reveal significant trade-offs between thermal resistance and pump power, emphasizing the importance of balancing these factors. The optimization process, completed in 20 h, cuts down the required time by 56 % compared to using a basic mixed variable algorithm. The optimized heat sink designs demonstrate considerable improvements, contributing to advancements in thermal engineering techniques. This study highlights the effectiveness of the proposed genetic algorithm in optimizing thermal management systems and may serve as a reference for future studies.

Duke Scholars

Author Adrian Bejan Thomas Lord Department of Mechanical Engineering and Materia ...