Optimizing packed bed latent heat storage systems via multimodal random packing: A numerical study

Packed bed latent heat storage (PBLHS) systems, leveraging phase change materials (PCM), hold promise for efficient thermal energy storage (TES) as a solution for industrial waste heat recovery (WHR). Traditional unimodal particle arrangements in PBLHS limit their packing density and hence thermal performance. This study introduces a novel approach by coupling random filling arrangements with multimodal PCM mixtures, incorporating up to 20 different particle sizes. A validated 3D numerical model was employed to compare unimodal, bimodal, trimodal, and Gaussian distributions, assessing their effects on energy storage, recovery, and thermal dynamics. Results indicate that multimodal configurations significantly enhance performance. Energy storage capacity improved by up to 15 %, while recovery capacity increased by 14 % compared to unimodal arrangements, driven by higher packing density, optimized void space utilization, and improved heat transfer efficiency. Normalized metrics further revealed up to 17 % improvement in energy storage rate and 11 % improvement in energy recovery rate. Mixtures with higher proportions of smaller and average-sized particles reduced melting times by 8.6 % due to increased surface area and thermal contact points. The bimodal configuration with a particle diameter ratio of 1.5 and a mixture composition ratio of 1:7 achieved the best overall performance. These findings establish a strong foundation for multimodal random packing as a potential cost-effective method to improve PBLHS systems.