Volume fraction gradient effects in compressible particle-laden flow

ORAL

Abstract

The effect of volume fraction gradients in compressible particle-laden flow is investigated using data from particle-resolved simulations of flows through random beds of fixed particles. Accurate prediction of particle forces is essential for simulations of engineering systems such as pneumatic conveying, fluidized beds and more. The results of the present study shows that the forces on the particles can be predicted reasonably well by a drag law developed for homogeneous flow conditions; however, a systematic underprediction with steepening volume fraction gradient is observed. This indicates that particle gradient effects should be accounted for in the drag law to obtain more accurate results. Sharper volume fraction gradients also result in increasing pseudo turbulent stress contributions in the momentum balances. Nevertheless, the overall direct contribution of the pseudo turbulence remains small for the cases considered here.

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Publication: Vartdal, M & Jain, SS Improving volume-averaged particle-laden flow models using resolved simulations
of particle diffusers., Proceedings of the Summer Program 2022, CTR, 173–184 (2022)

Presenters

  • Magnus Vartdal

    • FFI

Authors

  • Magnus Vartdal

    • FFI

  • Andreas N Osnes

    • Norwegian Defence Research Establishment
    • FFI

  • Suhas Jain

    • Woodruff School of Mechanical Engineering, Georgia Institute of Technology, USA. Center for Turbulence Research, Stanford Universty, USA
    • Georgia Institute of Technology, Flow Physics and Computational Sciences Lab
    • Woodruff School of Mechanical Engineering, Georgia Tech
    • Flow Physics and Computational Science Lab, Georgia Institute of Technology, Atlanta, Georgia, 30332, USA
    • Woodruff School of Mechanical Engineering, Georgia Institute of Technology; Center for Turbulence Research, Stanford University
    • George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA