A comparison of topology optimization and genetic algorithms for the optimization of thermal energy storage composites

Abstract

Purpose The purpose of this paper is to apply two optimization methods to the issue of sensible energy store design. Design/methodology/approach This paper is a comparison of topology optimization and genetic algorithms. Findings Genetic algorithms are prone to converge to local maxima while requiring significantly longer convergence times compared to topology optimization. Topology optimization resulted in structures representing parallel sheets, which are as thin as the grid allows. These configurations can maintain the maximum surface area between the low and high conductivity materials at high refinement, resulting in the best performance. Practical implications Time required for 99 per cent store discharge is decreased by 70 per cent using a 50 × 50 optimization grid at a loading of 10 Vol.%. Originality/value These approaches have not been compared nor applied to this specific problem before. Value is in the key finding that maximization of surface area is only possible with fins/sheets and not tree structures. This dictates the optimal solution for dynamic behaviour.

Comparison between thermal resistances of optimized water-based and gallium-based heat sinks using the genetic algorithm

“…The genetic algorithm is a global search optimization tool which is similar to the Darwinian natural selection process to obtain the optimal solution. It has been used to determine the optimized performance of micro-channel heat sinks (Badenhorst, 2019;Jeevan et al, 2005;Khan et al, 2013;Lee et al, 2007;Luo et al, 2019;Shao et al, 2007Shao et al, , 2009Shao et al, , 2011 and proved to be a fast optimization tool in the exploration of the performance of potential coolants with limited available data (Ghazali-Mohd et al, 2015). Thus, the genetic algorithm was utilized to optimize the design of heat sink in this study.…”