We analyse the effect of the Basset history force on the sedimentation or rising of inertial particles in a two-dimensional convection flow. We find that the concept of snapshot attractors is useful to understand the extraordinary slow convergence due to long-term memory: an ensemble of particles converges exponentially fast towards a snapshot attractor, and this attractor undergoes a slow drift for long times. We demonstrate for the case of a periodic attractor that the drift of the snapshot attractor can be well characterized both in the space of the fluid and in the velocity space. For the case of quasiperiodic and chaotic dynamics we propose the use of the average settling velocity of the ensemble as a distinctive measure to characterize the snapshot attractor and the time scale separation corresponding to the convergence towards the snapshot attractor and its own slow dynamics.

We analyze the effect of the Basset history force on the sedimentation of nearly neutrally buoyant particles, exemplified by marine snow, in a three-dimensional turbulent flow. Particles are characterized by Stokes numbers much smaller than unity, and still water settling velocities, measured in units of the Kolmogorov velocity, of order one. The presence of the history force in the Maxey-Riley equation leads to individual trajectories which differ strongly from the dynamics of both inertial particles without this force, and ideal settling tracers. When considering, however, a large ensemble of particles, the statistical properties of all three dynamics become more similar. The main effect of the history force is a rather slow, power-law type convergence to an asymptotic settling velocity of the center of mass, which is found numerically to be the settling velocity in still fluid. The spatial extension of the ensemble grows diffusively after an initial ballistic growth lasting up to ca. one large eddy turnover time. We demonstrate that the settling of the center of mass for such light aggregates is best approximated by the settling dynamics in still fluid found with the history force, on top of which fluctuations appear which follow very closely those of the turbulent velocity field.

The history force is one of the hydrodynamic forces that act on a particle moving through a fluid. It is an integral over the full time history of the particle’s motion and significantly complicates the equations of motion (accordingly it is often neglected). We present here a study of the influence of this force on particles moving in a turbulent flow, for a wide range of particle parameters. It is shown that the magnitude of the history force can be significant and that it can have a considerable effect on the particles’ slip velocity, acceleration, preferential concentration and collision rate. We also investigate the parameter dependence of the strength of these effects.

The equations describing the motion of finite-size particles (inertial particles) contain in their full form the history force. This force is represented by an integral whose accurate numerical evaluation is rather difficult. Here, a systematic way is presented to derive numerical integration schemes of arbitrary order for the advection of inertial particles with the history force. This involves the numerical evaluation of integrals with singular, but integrable, integrands. Explicit specifications of first, second and third order schemes are given and the accuracy and order of the schemes are verified using known analytical solutions.

We present an overview of recent works on the statistical description of turbulent flows in terms of probability density functions (PDFs) in the framework of the Lundgren-Monin-Novikov (LMN) hierarchy. Within this framework, evolution equations for the PDFs are derived from the basic equations of fluid motion. The closure problem arises either in terms of a coupling to multi-point PDFs or in terms of conditional averages entering the evolution equations as unknown functions. We mainly focus on the latter case and use data from direct numerical simulations (DNS) to specify the unclosed terms. Apart from giving an introduction into the basic analytical techniques, applications to two-dimensional vorticity statistics, to the single-point velocity and vorticity statistics of three-dimensional turbulence, to the temperature statistics of Rayleigh-Benard convection and to Burgers turbulence are discussed. (C) 2012 Academie des sciences. Published by Elsevier Masson SAS. All rights reserved.

A systematic investigation of the effect of the history force on particle advection is carried out in a paradigmatic model flow of chaotic advection, the von Kármán flow. All investigated properties turn out to heavily depend on the presence of memory when compared to previous studies neglecting this force. We find a weaker tendency for accumulation and for caustics formation. The Lyapunov exponent of transients becomes larger, the escape rates are strongly altered. Attractors are found to be suppressed by the history force, and periodic ones have a very slow, t(-1/2)-type convergence towards the asymptotic form.

We investigate the single-point velocity probability density function (PDF) in three-dimensional fully developed homogeneous isotropic turbulence within the framework of PDF equations focussing on deviations from Gaussianity. A joint analytical and numerical analysis shows that these deviations may be quantified studying correlations of dynamical quantities like pressure gradient, external forcing and energy dissipation with the velocity. A stationary solution for the PDF equation in terms of these quantities is presented, and the theory is validated with the help of direct numerical simulations indicating sub-Gaussian tails of the PDF.

We investigate the single-point probability density function of the velocity in three-dimensional stationary and decaying homogeneous isotropic turbulence. To this end, we apply the statistical framework of the Lundgren–Monin–Novikov hierarchy combined with conditional averaging, identifying the quantities that determine the shape of the probability density function. In this framework, the conditional averages of the rate of energy dissipation, the velocity diffusion and the pressure gradient with respect to velocity play a key role. Direct numerical simulations of the Navier–Stokes equation are used to complement the theoretical results and assess deviations from Gaussianity.

We study the statistical properties of fully developed hydrodynamical turbulence. To this end, a theoretical framework is established relating basic dynamical features of fluid flows to the shape and evolution of probability density functions of, for example, the vorticity. Starting from the basic equations of motion, the theory involves terms, which presently cannot be calculated from first principles. This missing information is taken from direct numerical simulations. A parallel pseudospectral code is used to to obtain well-resolved numerical simulations of homogeneous isotropic turbulence. The results yield a consistent description of the vorticity statistics and provide insights into the structure and dynamics of fully developed turbulence.

We investigate the dynamics of N moving point vortices, whose vorticity changes periodically between a finite value and zero. The dynamics of such blinking vortices is chaotic, but the degree of chaoticity, expressed in terms of finite size Lyapunov exponents, decreases monotonically with time. In contrast to traditional point vortices, the average diameter of the blinking vortex system increases in time. The average size follows a subdiffusive power law scaling approximately t;{sigma} , with sigma<12 . This expanding vortex system provides a flow which is in between closed and open flows. The advection dynamics generated by the blinking vortices is also studied, and leads to dye distribution patterns which are much more realistic than those of the classical point vortex problems, and can be characterized by a dimension less than 2.