Multi-objective design optimization of a plate-fin heat sink using a teaching-learning-based optimization algorithm

This paper presents the multiobjective design optimization of a plate-fin heat sink equipped with flow-through and impingement-flow air cooling systems using teaching-learning-based optimization algorithm. Two objective functions known as entropy generation rate and material cost with five constraints have been taken to measure the performance of the heat sink. Number of fins, height of fins, spacing between two fins and oncoming air velocity are considered as the design variables. The dynamic heat dissipation performance of plate-fin heat sink is investigated using finite element software ANSYS 12.1. The results show the better or competitive performance of the TLBO algorithm over the other optimization algorithms considered by the previous researchers for the same problem.