Machine Learning-Based Filtered Drag Model for Cohesive Gas-Particle Flows

Josef Tausendschön*, Sankaran Sundaresan, Mohammadsadegh Salehi, Stefan Radl

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The accuracy of filtered two-fluid model simulations critically depends on constitutive models for corrections that account for the effects of inhomogeneous structures at the sub-grid level. The complexity of accounting these structures increases with cohesion. In the present study, a dataset from filtered Euler-Lagrange simulations with systematic variations of the cohesion level and the filter length was created to investigate the development of a machine learning-based drag correction model for liquid bridge-induced cohesive gas-particle flows. A-priori tests revealed that these models afford robust and accurate predictions of the drag correction and the actual drag force. Further it was demonstrated that an anisotropic drag correction model is more accurate than an isotropic model.

Original languageEnglish
Pages (from-to)1373-1386
Number of pages14
JournalChemical Engineering and Technology
Volume46
Issue number7
DOIs
Publication statusPublished - Jul 2023

Keywords

  • Cohesive gas-particle flow
  • Drag correction model
  • Filtered simulations
  • Machine learning

ASJC Scopus subject areas

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

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