In this thesis the geometrical configuration of a processor heat sink is optimized. Two objective functions, operational cost and maximum temperature, have been chosen and the design space has been limited to three degrees of freedom represented by number of fins, fin height and fin thickness.
The optimization is simulation-based and carried out using multi-objective particle swarm optimization (MO-PSO). Two variants of MO-PSO has been selected, adapted and implemented. The first is a variant developed by Coello and Lechuga (CL-method) which is based on density of the pareto front, whereas the second is a variant developed by Fieldsend and Singh (FS-method) based on distance to the pareto front. Simulations have been performed using a state-of-the-art immersed boundary flow solver called IBOFlow developed by Fraunhofer-Chalmers research centre.
Comparisons presented in this thesis show that FS-method requires less evaluations to find the Pareto front, whereas the CL-method explores the Pareto front more evenly. The number of particles does not seem to give an apparent effect on exploration it is rather the distribution of initial points that determines the exploration. Many evaluated points have been observed to be clustered in objective space, especially in FS-method. This observation triggered the idea of a filter.
In the current work it has been found that the number of simulations can be significantly reduced by using filters. However, the threshold used in the filter must be chosen conservatively to prevent coarsening of the Pareto front. It has also been found that the Pareto front can be refined by projection of infeasible points onto the boundary of the feasibility region. The improvement from projection comes at the expense of additional evaluations.
At the end of the thesis, optimal designs of the heat sink are proposed based on lexicographic method, Analytical Hierarchy Process, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), respectively.