Abstract
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.
Original language | English |
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Article number | 116235 |
Journal | Chemical Engineering Science |
Volume | 230 |
Early online date | 24 Oct 2020 |
DOIs | |
Publication status | E-pub ahead of print - 24 Oct 2020 |
Keywords
- CFD
- Data-driven modeling
- Multiphase flow
ASJC Scopus subject areas
- Chemistry(all)
- Chemical Engineering(all)
- Industrial and Manufacturing Engineering