Development of Data-Driven Filtered Drag Model for Industrial-Scale Fluidized Beds

Yundi Jiang, Xiao Chen, Jari Kolehmainen, Ioannis G. Kevrekidis, Ali Ozel, Sankaran Sundaresan

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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 languageEnglish
Article number116235
JournalChemical Engineering Science
Volume230
Early online date24 Oct 2020
DOIs
Publication statusPublished - 2 Feb 2021

Keywords

  • CFD
  • Data-driven modeling
  • Multiphase flow

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Industrial and Manufacturing Engineering

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