Modeling inertial particle acceleration statistics in isotropic turbulence

Abstract

Our objective is to explain recent Lagrangian acceleration measurements of inertial particles in decaying, nearly isotropic turbulence [Ayyalasomayajula et al., Phys. Rev. Lett. 97, 144507 (2006)]. These experiments showed that as particle inertial effects increased, the variance in the particle acceleration fluctuations was reduced, and the tails of the normalized particle acceleration probability density function (PDF) became systematically attenuated. We model this phenomenon using a base flow that consists of a two-dimensional array of evenly spaced vortices with signs and intensities that vary randomly in time. We simulate a large sample of inertial particles moving through the fluid without disturbing the flow (one-way coupling). Consistent with Bec et al. [J. Fluid Mech. 550, 349 (2006)], we find that our model exhibits preferential concentration or clustering of particles in regions located away from the vortex centers. That is, inertial particles selectively sample the flow field, oversampling regions with high strains and undersampling regions with high vorticities. At low Stokes numbers, this biased “sampling” of the flow is responsible for the reduction in the acceleration variance and partially explains the attenuation of the tails of the acceleration PDF. However, contrary to previous findings, we show that the tails of the PDF are also diminished by “filtering” induced by the attenuated response of the inertial particles to temporal variations in the fluid acceleration: Inertial particles do not respond to fluctuations with frequencies much higher than the inverse of the particle stopping time. We show that larger fluid acceleration events have higher frequencies and hence experience greater filtering by particle inertia. We contrast the vortex model with previous Lagrangian acceleration models by Sawford [Phys. Fluids A 3, 1577 (1991)] and Reynolds [Phys. Fluids 15, L1 (2003)] and show that although these models capture some aspects of the inertial particle behavior, it is necessary to employ a model of the flow with spatial structure to capture the effect of sampling on the inertial particle dynamics.