This paper presents an analysis of linear viscous stress Favre averaged turbulence models for computational fluid dynamics (CFD) of fully turbulent round jets with a long straight tube geometry in the near field. Although similar work has been performed in the past with very relevant solutions, considerations were not given for the issues and limitations involved with coupling between an Eulerian and Lagrangian phase, such as in fully two-way coupled CFD-DEM. These issues include limitations on solution domain, mesh cell size, wall modelling, and momentum coupling between the two phases in relation to the particles size. Therefore, within these considerations, solutions are provided to the Navier–Stokes equations with various turbulence models using a three-dimensional wedge long straight tube geometry for fully developed turbulence flow. Simulations are performed with a Reynolds number of 13,000 and 51,000 using two different tube diameters. It is found that a modified k-ε turbulence model achieved the most agreeable results for both the velocity and turbulent flow fields between these two flow regimes, while a modified k-ω SST/BSL also provided suitable results.
In this paper, coupled CFD-DEM simulations of dense particle-laden jet flow are performed using CFDEM® coupling interface that couples LAMMPS-based LIGGGHTS® DEM engine with OpenFOAM CFD framework. Suspensions of mono-sized spherical glass particles with 80 microns diameter and a mass loading of 0.23 and 0.86 are considered. Three different CFD meshes are used with an average mesh resolution dimension of 3.06, 2.67, and 1.86 particle diameters and it is determined that mesh resolution does not change results for void fraction calculation (using the divided model) of the CFD-DEM equations. Samples of particle flux are taken at 0.1, 10, and 20 nozzle diameters along the axial direction of the jet region. The numerical results for particle flux are compared with a well cited experimental data found in literature. The CFD-DEM simulations in turbulent jet flow are found to be highly sensitive to initial particle velocity inputs but the experimental data provide sufficient information to produce comparable results.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.