Efficient Coupling of an Eulerian Flow Solver with a Lagrangian Particle Solver for the Investigation of Particle Clustering in Turbulence

Abstract

Numerical studies show that particles suspended in a turbulent flow tend to cluster due to their inertia (Wang and Maxey, J. Fluid Mech. 256:27–68, 1993; Bec et al., Phys. Rev. Lett. 98:084502, 2007). It was shown by Woittiez et al. (J. Atmos. Sci. 66:1926–1943, 2009) and Onishi et al. (Phys. Fluids 21:125108, 2009) that gravity influences the clustering of small and heavy particles in turbulence. However, these results might be artificially influenced by the periodicity of the used computational domains and also by the turbulence forcing scheme (Rosa et al., J. Phys. Conf. Ser. 318:072016, 2011). In the present study, a new numerical setup to investigate the combined effects of gravity and turbulence on the motion of small and heavy particles is presented, where the turbulence is only forced at the inflow and is advected through the domain by a mean flow velocity. Within a transition region the turbulence develops to a physical state which shares similarities with grid-generated turbulence in wind tunnels. Since the turbulence is decaying in streamwise direction statistical averages can only be performed over small parts of the domain. Hence, a very large number of particles has to be considered to obtain converged statistics compared with the periodic setups of the other numerical studies where averaging can be performed over all particles in the whole domain. This results in the need of a very efficient parallelization strategy. In this study, trajectories of about 43 million small and heavy particles are advanced in time. It is found that specific regions within the turbulent vortices cannot be reached by the particles as a result of the particle vortex interaction. Therewith, the particles tend to cluster outside the vortices. These results are in agreement with the theory of Dávila and Hunt (J. Fluid Mech. 440:117–145, 2001).