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.

Citation

Badenhorst, H. (2019), "A comparison of topology optimization and genetic algorithms for the optimization of thermal energy storage composites", International Journal of Numerical Methods for Heat & Fluid Flow, Vol. 29 No. 9, pp. 3454-3471. https://doi.org/10.1108/HFF-01-2019-0034