High-energy ball milling is a versatile method utilized in mechanochemistry, grinding, and material synthesis. Understanding and predicting the relevant mechanical interactions is crucial for the optimization and up-scaling of these processes. Thus, differentiating between normal and tangential contributions in ball milling collisions is vital due to their distinct mechanical effects on the processed material. Normal interactions cause compression forces, facilitating particle compaction or deformation, while tangential interactions induce shear forces, leading to particle sliding. These distinctions influence energy dissipation mechanisms, affecting particle deformation, fragmentation, and chemical reactions. To study these interactions, a high-energy ball mill is investigated using DEM (Discrete Element Method) simulations to characterize relevant phenomenology. An analysis of how operational parameters affect energy dissipation modes is performed by identifying optimal operational ranges and building master curves that offer predictive capabilities beyond the simulated cases. The results from this work provide the framework for the interpretation of future mechanochemical experiments.

Having proper correlations for hydrodynamic forces is essential for successful CFD-DEM simulations of a fluidized bed. For spherical particles in a fluidized bed, efficient correlations for predicting the drag force, including the crowding effect caused by surrounding particles, are already available and well tested. However for elongated particles, next to the drag force, the lift force and hydrodynamic torque also gain importance. In this work we apply recently developed multi-particle correlations for drag, lift and torque in CFD-DEM simulations of a fluidized bed with spherocylindrical particles of aspect ratio 4 and compare them to simulations with widely used single-particle correlations for elongated particles. Simulation results are compared with previous magnetic particle tracking (MPT) experimental results. We show that multi-particle correlations improve the prediction of particle orientation and vertical velocity. We also show the importance of including hydrodynamic torque.