Tabulation applied to thermal conduction within complex particle geometries in the Discrete Element Method

Intraparticle models are crucial in the Discrete Element Method when particles are thermally thick. Accurately solving the intraparticle conservation equations using the finite volume method requires high spatial and temporal resolution, which significantly increases computational cost. This study presents a model that relies on tabulation to describe intraparticle heat conduction inside complex-shaped particles. This cost-effective method replaces the computationally expensive finite volume method without compromising the accuracy. The method has been applied to different particle shapes: a cylinder with two different aspect ratios, a cube, a square thin plate, a sphere and an irregular shape. Materials with very different thermal conductivities — glass, limestone, and wood — have also been examined. For wood particles, anisotropic heat conduction is considered as wood possesses directional thermal properties. The particles exchange heat with the surrounding gas by convection, where the gas-phase temperature varies over time between 350 K to 950 K as a superposition of four harmonic functions with different frequencies. The response of the particle surface temperature, core temperature, and internal temperature distributions is compared with results obtained from the finite volume method. The tabulated model accurately reproduced the temperatures of the finite volume method, with maximum root-mean-square deviations of 10 K. A speed-up factor of at least 100 was achieved using the tabulation method compared to the finite volume method, increasing further with higher mesh resolution. • Development of a tabulation-based method for intraparticle heat conduction in thermally thick polyhedral particles. • Tabulation approach tested for different material properties; accuracy improves with higher particle thermal conductivity. • Influence of particle shape is predicted by the model; no clear trend observed within the studied sphericity range. • Achieved up to 5,900x faster calculation time for particle temperature prediction, with a maximum deviation of 1.2 %.