@article{200326,
  keywords = {filtered drag model artificial neural network fluidized bed flow},
  author = {Yundi Jiang and Xiao Chen and Jari Kolehmainen and IoannisG. Kevrekidis and Ali Ozel and Sankaran Sundaresan},
  title = {Development of data-driven filtered drag model for industrial-scale fluidized beds},
  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 anal. that the extrapolation of the ANN model to large grid sizes works satisfactorily, and then performed fine-grid simulations for 20 addnl. combinations of gas and particle properties straddling the Geldart A-B transition. We identified the Reynolds number as a suitable addnl. 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. },
  year = {2021},
  journal = {Chem. Eng. Sci.},
  volume = {230},
  pages = {116235},
  publisher = {Elsevier Ltd.},
  isbn = {0009-2509},
  url = {https://doi.org/10.1016/j.ces.2020.116235},
  language = {eng},
}