Antonio Celani

Dynamics and statistics of heavy particles in turbulent flows

We present the results of direct numerical simulations (DNS) of turbulent flows seeded with millions of passive inertial particles. The maximum Reynolds number is Re λ∼ 200. We consider particles much heavier than the carrier flow in the limit when the Stokes drag force dominates their dynamical evolution. We discuss both the transient and the stationary regimes. In the transient regime, we study the growth of inhomogeneities in the particle spatial distribution driven by the preferential concentration out of intense vortex filaments. In the stationary regime, we study the acceleration fluctuations as a function of the Stokes number in the range St ∈ [0.16:3.3]. We also compare our results with those of pure fluid tracers (St = 0) and we find a critical behavior of inertia for small Stokes values. Starting from the pure monodisperse statistics we also characterize polydisperse suspensions with a given mean Stokes, .

Acceleration statistics of heavy particles in turbulence

We present the results of direct numerical simulations of heavy particle transport in homogeneous, isotropic, fully developed turbulence, up to resolution

Particle trapping in three-dimensional fully developed turbulence

512^3

Bacterial strategies for chemotaxis response

(

Acceleration and vortex filaments in turbulence

R_\lambda\approx 185

Clustering and collisions of heavy particles in random smooth flows

). 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

Lagrangian statistics of particle pairs in homogeneous isotropic turbulence

a_{\rm rms}

Fronts in passive scalar turbulence

sharply falls off from the fluid tracer value at quite small Stokes numbers; (ii) at a given St the normalized acceleration

Front propagation in laminar flows

a_{\rm rms}/(\epsilon^3/\nu)^{1/4}

Noninvasive inference of the molecular chemotactic response using bacterial trajectories

increases with