Journal
CHEMICAL ENGINEERING SCIENCE
Volume 230, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ces.2020.116235
Keywords
Multiphase flow; CFD; Data-driven modeling
Categories
Funding
- ExxonMobil Research Engineering Co
- Syncrude Canada Ltd.
- National Natural Science Foundation of China [51906196]
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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.
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.
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