Stochastic Subgrid Acceleration Model for inertial particles in LES of a high Reynolds number flow

etc15 Tracking Number 436

In the context of Large Eddy Simulation (LES) of turbulent flow laden by solid particles, we propose a modelling of the the interaction of a particle with the unresolved scales of the flow. We consider both particles much smaller and larger than the Kolmogorov length scale. The small scales of high Reynolds number flow are characterized by strong velocity gradients. To account for those gradients, and specifically the turbulent time-scales shorter than the Stokes time, we decompose the particle acceleration in its resolved and residual parts. In the latter, the interactions with the inertial range of the turbulent flow are simulated by a stochastic process evolving along the particle trajectory. For the case of the small particles, we introduced two processes, one for its norm, and another for its direction. Results showed that by introducing the stochastic model for the particle residual acceleration, the particle acceleration statistics from DNS is predicted fairly well. For the particles bigger than the Kolmogorov scale, we propose another stochastic model. We derived a fluctuating drag, simulated by lognormal process. This model gives stretched tails in the particle acceleration distribution invariant with the density and the size of particle as observed experimentally.