Development of data-driven filtered drag model for industrial-scale fluidized beds

Simulations of large-scale gas-particle flows using coarse meshes and the filtered two-fluid model approach depend critically on the constitutive model that accounts for the effects of sub-grid scale inhomogeneous structures. In an earlier study (Jiang et al., 2019), we had demonstrated that an artificial neural network (ANN) model for drag correction developed from a small-scale systems did well in both a priori and a posteriori tests. In the present study, we first demonstrate through a cascading analysis that the extrapolation of the ANN model to large grid sizes works satisfactorily, and then performed fine-grid simulations for 20 additional combinations of gas and particle properties straddling the Geldart A-B transition. We identified the Reynolds number as a suitable additional marker to combine the results from all the different cases, and developed a general ANN model for drag correction that can be used for a range of gas and particle characteristics. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

The study demonstrated the successful extrapolation of an artificial neural network model to large grid sizes and developed a general model for drag correction that can be applied to various gas and particle characteristics. This is important for understanding the behavior of gas-particle flows on a large scale.