Numerical simulations using two approaches, namely RANS and LES, were conducted to compute inertial particle dispersion from a source point in a turbulent gas flow in a pipe at a high Reynolds number. Numerical predictions were compared to the experimental observations of Arnason (1982) and Arnason and Stock (1984). Stochastic modeling of the turbulent fluctuations seen by inertial particles along their trajectories has been used. In the framework of RANS, the aim is to reconstruct the whole turbulent field whereas in the context of LES, only modeling of SGS fluctuating velocities is sought.
Particle dispersion statistics such as particle concentration, radial velocity and the dispersion coefficient were computed for solid particles that have different inertia and drift. The use of a Langevin-type stochastic approach to model the sub-filter fluctuations has proven crucial for results concerning the small-Stokes-number particles. The stochastic model used has been extensively used in the framework of RANS. Its simplistic extension to predict the sub-filter fluctuations for LES has given very satisfactory results.
Numerical predictions show that, for the same flow, inertial particles with larger diameter (and hence larger Stokes number) can disperse faster than smaller particles (with smaller Stokes numbers). It was proved theoretically that this can be the case if the inertia parameter controls the dispersion. These findings back up the experimental observations of Arnason and Stock.
Comparison of RANS and LES results have shown that the RANS approach is unable to predict particle dispersion statistics as accurately as the LES in particular for inertial particles characterized by a Stokes number smaller than 0.5. For particles with Stokes number higher than 0.5, both LES and RANS predictions compare reasonably well with the experimental results.
Keywords: Pipe flows, Large eddy simulation, particle-laden flow, turbulence, Lagrangian description, small inertial particles, stochastic process, stokes number, dispersion, particle concentration
