Acceleration statistics of heavy particles in turbulence

By J. Bec, Luca Biferale, Guido Boffetta, Antonio Celani, M. Cencini, A. Lanotte, S. Musacchio, Federico Toschi. Journal of Fluid Mechanics, 2005

253 citations recorded by the source corpus.

We present the results of direct numerical simulations of heavy particle transport in homogeneous, isotropic, fully developed turbulence, up to resolution $512^3$ ($R_\lambda\approx 185$). Following the trajectories of up to 120 million particles with Stokes numbers, St, in the range from 0.16 to 3.5 we are able to characterize in full detail the statistics of particle acceleration. We show that: (i) the root-mean-squared acceleration $a_{\rm rms}$ sharply falls off from the fluid tracer value at quite small Stokes numbers; (ii) at a given St the normalized acceleration $a_{\rm rms}/(\epsilon^3/\nu)^{1/4}$ increases with $R_\lambda$ consistently with the trend observed for fluid tracers; (iii) the tails of the probability density function of the normalized acceleration $a/a_{\rm rms}$ decrease with St. Two concurrent mechanisms lead to the above results: preferential concentration of particles, very effective at small St, and filtering induced by the particle response time, that takes over at larger St.

Subfields: Computational Mechanics, Nuclear and High Energy Physics, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/009aeaf64c3eae6bf4a0525311884eb9eb5d8b32

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In Computational Mechanics

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Citations in the corpus

Analyzing preferential concentration and clustering of inertial particles in turbulence (2012, background): “10 taken from Bec et al. (2006) summarizes these results and shows that as the Stokes number gets closer to unity, the relative number of particles in rotational regions tends to decrease, indicating that heavy particles tend to live in strain-dominated regions.”
Acceleration statistics of finite-sized particles in turbulent flow: the role of Faxén forces (2008, result): “Lagrangian numerical studies through direct numerical simulations (DNSs) of very small – computationally assumed to be pointwise – particles have also shown encouraging consistency with experimental measurements for inertial particles (Ayyalasomayajula et al. 2006; Bec et al. 2006; Salazar et al. 20”
Motion of inertial particles with size larger than Kolmogorov scale in turbulent flows (2008, background): “[18] J.”
Dynamics and statistics of heavy particles in turbulent flows (2006, methodology): “This behavior is even more pronounced for the fourth moment of the acceleration [43].”
The effect of Reynolds number on inertial particle dynamics in isotropic turbulence. Part 1. Simulations without gravitational effects (2015, methodology): “…for particle clustering A variety of measures have been proposed to study particle clustering, including Voronoï diagrams (Monchaux et al. 2010), Lyapunov exponents (Bec et al. 2006b), Minkowski functionals (Calzavarini et al. 2008) and radial distribution functions (RDFs) (McQuarrie 1976).”
The effect of Reynolds number on inertial particle dynamics in isotropic turbulence. Part 2. Simulations with gravitational effects (2015, methodology): “2013; Bec et al. 2014; Gustavsson et al. 2014). For sufficiently large separations, the RDFs with and without gravity approach each other. To test the theory in §4.2.1, we compute g(r) from (4.13) using the relative velocity statistics S 2‖ and S p 2⊥ obtained from DNS. In addition, we use the direc”