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arXiv · 2302.03450

Effect of Drag Force Modeling on the Flow of Electrostatically Charged Particles

Gizem Ozler·Mustafa Demircioglu·Holger Grosshans
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Abstract

In CFD simulations of two-phase flows, accurate drag force modeling is essential for predicting particle dynamics. However, a generally valid formulation is lacking, as all available drag force correlations have been established for specific flow situations. In particular, these correlations have not been evaluated for particle-laden flows subjected to electrostatic forces. The paper reports the effect of drag force modeling on the flow of electrically charged particles. To this end, we implemented different drag force correlations to the open-source CFD tool pafiX. Then, we performed highly-resolved Direct Numerical Simulations (DNS) using the Eulerian-Lagrangian approach of a particle-laden channel flow with the friction Reynolds number of 180. The simulations generally revealed a strong influence of the precise drag correlation on particles in the near-wall region and a minor effect on the particles far from the walls. Due to their turbophoretic drift, particles accumulate close to the channel walls. For uncharged particles, the simulations show large deviations of the particle concentration profile in the near-wall region depending on the drag force correlation. Therefore, the disturbance of the flow surrounding a particle by a nearby wall or other particles is important for its drag. Driven by electrostatic forces, charged particles accumulate even closer to the wall. Contrary to the uncharged cases, when the particles carry a high charge (in our case one femto-coulomb), we found minor effects of drag force modeling on particle concentration profiles. In conclusion, for the investigated conditions, we propose to account for the effect of nearby particles and walls on the drag of low- or uncharged particles.

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Gizem Ozler, Mustafa Demircioglu, Holger Grosshans. 2023-02-07. Effect of Drag Force Modeling on the Flow of Electrostatically Charged Particles. https://doi.org/10.1016/j.apt.2023.103951

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