Article: Bragg, AD; Ireland, PJ; Collins, LR; (2015) “On The Relationship Between The Non-local Clustering Mechanism and Preferential Concentration”, Journal of Fluid Mechanics, 780:327-343
Abstract: ‘Preferential concentration’ (Squires & Eaton, Phys. Fluids, vol. A3, 1991, pp. 1169-1178) refers to the clustering of inertial particles in the high strain, low-rotation regions of turbulence. The ‘centrifuge mechanism’ of Maxey (J. Fluid Mech., vol. 174, 1987, pp. 441-465) appears to explain this phenomenon. In a recent paper, Bragg & Collins (New J. Phys., vol. 16, 2014, 055013) showed that the centrifuge mechanism is dominant only in the regime St << 1, where St is the Stokes number based on the Kolmogorov time scale. Outside this regime, the centrifuge mechanism gives way to a non-local, path history symmetry breaking mechanism. However, despite the change in the clustering mechanism, the instantaneous particle positions continue to correlate with high strain, low-rotation regions of the turbulence. In this paper, we analyse the exact equation governing the radial distribution function and show how the non-local clustering mechanism is influenced by, but not dependent upon, the preferential sampling of the fluid velocity gradient tensor along the particle path histories in such a way as to generate a bias for clustering in high strain regions of the turbulence.
We also show how the non-local mechanism still generates clustering, but without preferential concentration, in the limit where the time scales of the fluid velocity gradient tensor measured along the inertial particle trajectories approaches zero (such as white noise flows or for particles in turbulence settling under strong gravity). Finally, we use data from a direct numerical simulation of inertial particles suspended in Navier-Stokes turbulence to validate the arguments presented in this study.
Funding Acknowledgement: National Science Foundation [CBET-0967349]; National Science Foundation
Funding Text: The authors acknowledge financial support from the National Science Foundation through grant CBET-0967349 and through the Graduate Research Fellowship awarded to P.J.I. Computational simulations were performed on Yellowstone Computational and Information Systems Laboratory (2012) (ark:/85065/d7wd3xhc) at the US National Center for Atmospheric Research through its Computational and Information Systems Laboratory (sponsored by the National Science Foundation).