Abstract: CFD-DEM modelling of particle-laden turbulent flow is challenging in terms of the required and obtained CFD resolution, heavy DEM computations, and the limitations of the method. Here, we assess the efficiency of a particle-tracking solver in OpenFOAM with RANS-DEM and LES-DEM approaches under the unresolved CFD-DEM framework. Furthermore, we investigate aspects of the unresolved CFD-DEM method with regard to the coupling regime, particle boundary condition and turbulence modelling. Applying one-way and two-way coupling to our RANS-DEM simulations demonstrates that it is sufficient to include one-way coupling when the particle concentration is small (O ~ 10−5). Moreover, our study suggests an approach to estimate the particle boundary condition for cases when data is unavailable. In contrast to what has been previously reported for the adopted case, our RANS-DEM results demonstrate that simple dispersion models considerably underpredict particle dispersion and previously observed reasonable particle dispersion were due to an error in the numerical setup rather than the used dispersion model claiming to include turbulence effects on particle trajectories. LES-DEM may restrict extreme mesh refinement, and, under such scenarios, dynamic LES turbulence models seem to overcome the poor performance of static LES turbulence models. Sub-grade scale effects cannot be neglected when using coarse mesh resolution in LES-DEM and must be recovered with efficient modelling approaches to predict accurate particle dispersion.
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Abstract: CFD-DEM modelling of particle-laden turbulent flow is challenging in terms of the required and obtained CFD resolution, heavy DEM computations, and the limitations of the method. Here, we assess the efficiency of a particle-tracking solver in OpenFOAM with RANS-DEM and LES-DEM approaches under the unresolved CFD-DEM framework. Furthermore, we investigate aspects of the unresolved CFD-DEM method with regard to the coupling regime, particle boundary condition and turbulence modelling. Ap...
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