The influence of an anisotropic Langevin dispersion model on the prediction of micro- and nanoparticle deposition in wall-bounded turbulent flows

This paper deals with the issues of stochastic dispersion models and associated best practice responses for the investigation of micro- and nanoparticle deposition in turbulent flows. For such applications, Reynolds averaged turbulence models are widely used in combination with particle Lagrangian tracking, due to their relative simplicity and computational efficiency. Such approaches imply to generate the instantaneous velocity of the fluid at particle location to reproduce the effect of turbulence on particle transport. The default dispersion model used in most CFD codes is an eddy lifetime model, which frequently overestimates the deposition rates. In this work, a simple method is proposed to implement a three-dimensional stochastic dispersion model based on the Langevin equation in the Fluent^® commercial code. Comparisons are provided between this model, complemented by the simulation of Brownian effects, and available numerical data obtained using either an eddy lifetime model or a simple Langevin model. Computations are carried out in horizontal and vertical channel flows and in circular pipe flows as well. The use of the proposed anisotropic Langevin model is shown to improve the accuracy of deposition prediction in the whole range of particle inertia.