TY - JOUR
T1 - Development of Data-Driven Filtered Drag Model for Industrial-Scale Fluidized Beds
AU - Jiang, Yundi
AU - Chen, Xiao
AU - Kolehmainen, Jari
AU - Kevrekidis, Ioannis G.
AU - Ozel, Ali
AU - Sundaresan, Sankaran
N1 - Funding Information:
This work was supported by ExxonMobil Research Engineering Co and Syncrude Canada Ltd . Xiao Chen acknowledges the financial support by the National Natural Science Foundation of China ( 51906196 ).
Publisher Copyright:
© 2020 Elsevier Ltd
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2021/2/2
Y1 - 2021/2/2
N2 - 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.
AB - 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.
KW - CFD
KW - Data-driven modeling
KW - Multiphase flow
UR - http://www.scopus.com/inward/record.url?scp=85095608415&partnerID=8YFLogxK
U2 - 10.1016/j.ces.2020.116235
DO - 10.1016/j.ces.2020.116235
M3 - Article
SN - 0009-2509
VL - 230
JO - Chemical Engineering Science
JF - Chemical Engineering Science
M1 - 116235
ER -