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

Though tractable models for granular temperature founded upon common dimensionless quantities exist, they largely do not take into account a wide enough range of parameters (such as particle volume fraction) to be considered widely applicable. Further, existing models are agnostic to choice of filter or grid size. Because models for granular temperature are critical for capturing the total granular energy of a gas-solid system, addressing this knowledge gap will have important implications for developing more accurate, more widely applicable coarse-grained simulations. In this work, we leverage an extensive set of CFD-DEM data that spans volume fractions and Archimedes numbers relevant to circulating fluidized bed reactors. We apply a range of filter sizes to each of the dataset to analyze the contribution of granular temperature to total granular energy as a function of particle volume fraction, Archimedes number and filter size. Then, we use this data for the basis of improved models for the granular temperature Reynolds number that expand upon existing DNS-based models and take into account filter size.