A dynamic multiphase turbulence model for coarse-grid simulations of fluidized gas-particle suspensions

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Abstract

The Spatially-Averaged Two-Fluid Model (SA-TFM) is a functional multiphase turbulence model aimed at predicting the influence of unresolved meso-scale structures on the macro-scale flow properties in coarse-grid simulations of gas-particle flows. In this study, we highlight the general applicability of the SA-TFM model due to the dynamic estimation of the model coefficients through test-filters, the anisotro- pic treatment of the Reynolds-stresses and the drag force correction, and additional wall-friction bound- ary conditions for the particle-phase velocity, Reynolds-stress and turbulent kinetic energy. The dynamic approach derives information on the unresolved meso-scale flow properties from the resolved macro- scale variables without the need for any intensive prior considerations of the specific flow structure. Thereby, the drift velocity, a measure for the drag-reduction due to the presence of meso-scale structures is estimated from the turbulent kinetic energies of the phases and the variance of the solids volume frac- tion. We validate the dynamic anisotropic SA-TFM against highly resolved fine-grid Two-Fluid Model simulation data and experimental measurements of Geldart type A and B particles in bubbling to turbu- lent flow regimes. In the course of this extensive study, we find that the predictions for the macro-scale flow properties, such as slip-velocity, bed expansion, volume fraction distribution, and mass-flux, are in good agreement with the experimental and fine-grid simulation data in a vast number of cases, ranging from unbound fluidization to pilot-scale fluidized beds, thus, implying a wide applicability of the dynamic model independent of the underlying grid-size.
Original languageEnglish
Article number117104
Pages (from-to)117104
Number of pages21
JournalChemical Engineering Science
Volume247
DOIs
Publication statusPublished - 16 Jan 2022

Fields of science

  • 204 Chemical Process Engineering
  • 103032 Fluid mechanics

JKU Focus areas

  • Digital Transformation

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