Development of a Filtered CFD-DEM Drag Model with Multiscale Markers Using an Artificial Neural Network and Nonlinear Regression

Abstract

The accuracy of coarse-grained Euler–Lagrangian simulations of fluidized beds heavily depends on the mesoscale drag models to account for the influences of the unresolved subgrid structures. Traditional filtered drag models are regressed with mesoscale markers such as voidage and slip velocities. In this research, a filtered drag was regressed with both mesoscale and macroscale markers using fine-grid Computational Fluid Dynamics–Discrete Element Method (CFD-DEM) simulations. The traditional nonlinear regression method was compared with machine learning regression using an Artificial Neural Network (ANN) implemented in PyTorch and coupled with MFiX. The new drag showed higher accuracy than the Wen–Yu drag and another filtered drag derived from the two-fluid model. The nonlinear regression shows slightly better results than ANN regression in cases with similar R2 values. The utilization of the gas inlet velocity as an additional macroscale marker reduced the errors by up to 55.3% in the tested cases.