A novel dynamic simulation methodology for high temperature packed-bed thermal energy storage with experimental validation. (December 2020)

• Record Type: Journal Article
• Authors: Tuttle, Jacob F.; White, Nate; Mohammadi, Kasra; Powell, Kody
• Is Part Of: Sustainable energy technologies and assessments. Volume 42(2021)
• Publication Date: 2020-12
• Subjects: Thermal energy storage -- Packed-bed -- Intra-particle conduction -- Experimental setup -- Simulation -- 1D × 1D Modeling; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2020.100888
• Languages: English
• ISSNs: 2213-1388

Abstract

Abstract: Packed-bed thermal energy storage (TES) is a cost-effective storage option for high temperature applications. This study aims to accurately model the behavior of a packed-bed TES system during transient operation while maintaining low computation time. This is realized through the development of a novel 1D × 1D model accounting for intra-particle conduction – the first dimension representing the overall device in the axial direction, and the second dimension representing spherical storage particles in the radial direction. An experimental system was constructed to validate the simulation model. Temperatures at the system outlet and in the center of the storage bed were predicted within 3.75% and 3.54% of experimental results, respectively. The validated model was then used to predict the behavior of different materials suitable for large-scale applications, such as stone, concrete, and steel. The potential error due to applying the uniform temperature approximation was investigated on different Biot number configurations. A relative error greater than 55% between surface and particle center temperatures was identified for a system with a Biot number of 3. The novel modeling methodology realized computation times similar to models not accounting for this phenomenon, an 87% improvement upon previous work investigating similar methods, with prediction accuracy remaining unaffected.