Neural-network-based filtered drag model for gas-particle flows

Yundi Jiang, Jari Kolehmainen, Yile Gu, Yannis G. Kevrekidis, Ali Ozel, Sankaran Sundaresan

Research output: Contribution to journalArticlepeer-review

78 Scopus citations


Filtered two-fluid model (fTFM) for gas-particle flows require closures for the sub-filter scale corrections to interphase drag force and stresses, the former being more significant. In this study, we have formulated a neural-network-based model to predict the sub-grid drift velocity, which is then used to estimate the drag correction. As a part of the neural network model development effort, we derived a transport equation for drift velocity and then performed a budget analysis to conclude that an algebraic model for drift velocity in terms of the filtered variables that are resolved in a fTFM simulation is adequate, and the model should include the filtered gas-phase pressure gradient as a marker in addition to the filtered particle volume fraction and the filtered gas-solid slip velocity. Both a priori and a posteriori analyses reveal that the present model for drift velocity when used in a fTFM simulation is able to capture the fine-grid simulation results quite well.

Original languageEnglish (US)
Pages (from-to)403-413
Number of pages11
JournalPowder Technology
StatePublished - Mar 15 2019

All Science Journal Classification (ASJC) codes

  • General Chemical Engineering


  • Drag force
  • Drift velocity
  • Filtering approach
  • Fluidized bed
  • Sub-grid modeling
  • Two-fluid model


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