A hybrid mesoscale closure combining CFD and deep learning for coarse-grid prediction of gas-particle flow dynamics

This study develops filtered two-fluid model (fTFM) closures by coupling computational fluid dynamics (CFD) and deep learning algorithm (DL) for enabling coarse-grid simulations at reactor scales. Mesoscale drag, solids pressure and viscosity are modeled using an isotropic or anisotropic method. Subsequently, a priori analysis and a posteriori analysis of the present models along with other previously proposed clo-sures are conducted. Comparison with the experimental data covering a broad range of operating condi-tions indicates that the mesoscale solids stress can be neglected in bubbling and turbulent fluidization regimes. However, the contribution of solids stress is clearly not insignificant at very low superficial gas velocities. Moreover, the drag model considering the anisotropy shows better prediction performance in the turbulent fluidization regime. In short, the present study develops and validates a DL-fTFM cou-pling algorithm applicable for gas-particle simulations. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study develops filtered two-fluid model (fTFM) closures by coupling computational fluid dynamics (CFD) and deep learning algorithm (DL) for enabling coarse-grid simulations at reactor scales. The mesoscale solids stress can be neglected in bubbling and turbulent fluidization regimes, but is significant at very low superficial gas velocities. The drag model considering the anisotropy shows better prediction performance in the turbulent fluidization regime.