A filter-dependent granular temperature model from large-scale CFD-DEM data

Key Points

• The model demonstrates improved accuracy in predicting granular temperature variations across different filtering scenarios, enhancing understanding of granular flow dynamics.
• Key evidence shows that the model adjusts effectively to varying filter settings, aligning with large-scale computational fluid dynamics and discrete element method data.
• Analysis of registry records from large-scale CFD-DEM simulations reveals new insights into granular temperature behavior and its dependence on filter size.
• This significant advancement provides a framework for better modeling in various industrial processes involving granular materials.