Stochastic modeling of particle diffusion in a turbulent boundary layer

Several Continuous Random Walk (CRW) models were constructed to predict turbulent particle diffusion based on Eulerian statistics that can be obtained with Reynolds-Averaged Navier Stokes (RANS) solutions. The test conditions included a wide range of particle inertias (Stokes numbers) with a near-wall injection (y+ = 4) in a turbulent boundary layer that is strongly anisotropic and inhomogeneous. To assess the performance of the models, the CRW results were compared to particle diffusion statistics gathered from a Direct Numerical Simulation (DNS). In particular, comparisons were made with transverse concentration profiles, root-mean-square of particle trajectory coordinates, and mean transverse particle velocity away from the wall. sults showed that accurate simulation required a modified (non-dimensionalized) Markov chain to handle the large gradients in turbulence near the wall as shown by simulations with fluid-tracer particles. For finite-inertia particles, an incremental drift correction for the Markov chain developed herein to account for Stokes number effects was critical to avoiding non-physical particle collection in low-turbulence regions. In both cases, inclusion of anisotropy in the turbulence model was found to be important, but the influence of off-diagonal terms was found to be weak. The results were generally good, especially for long-time and large inertia particles.