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Numerical calculations of a particle-laden turbulent horizontal mixing-layer based on the Eulerian-Lagrangian approach are presented. Emphasis is given to the determination of the stochastic fluctuating fluid velocity seen by the particles in anisotropic turbulence. The stochastic process for the fluctuating velocity is a “Particle Langevin equation Model”, based on the Simplified Langevin Model. The Reynolds averaged Navier-Stokes equations are closed by the standard k-epsilon turbulence model. The calculated concentration profile and the mean, the root-mean-square (rms) and the cross-correlation terms of the particle velocities are compared with particle image velocimetry (PIV) measurements. The numerical results agree reasonably well with the PIV data for all of the mentioned quantities. The importance of the modeled vortex structure “seen” by the particles is discussed.

The ‘Discrete Particle Method’ (DPM) is a versatile numerical tool for improving the understanding of particle flows behavior on a meso-scopic level. A crucial point when using the DPM is the CPU-time consumption for detection of particle collisions. An adaptive algorithm for efficient particle-particle and particle-wall collision detection in a two-dimensional case is presented. The physical domain is hierarchically divided and structured as a quadtree. The algorithm ensures an efficient computation of colliding particle flows by splitting and merging the cells between each time step to keep the number of particles within a proposed range. The numerical performance of the adaptive algorithm is studied by simulating a flow particle in a 90° bend. The computational time of the adaptive algorithm is compared with the simulations performed with a uniform fixed cell structure with optimal size. The adaptive algorithm seems to be mostly advantageous in flows where the particles are not uniformly distributed, in complex geometries, and otherwise where information about the optimal cell size is not known a priori.