Eddy diffusivities of inertial particles under gravity

The large-scale/long-time transport of inertial particles of arbitrary mass density under gravity is investigated by means of a formal multiple-scale perturbative expansion in the scale-separation parametre between the carrier flow and the particle concentration field. The resulting large-scale equation for the particle concentration is determined, and is found to be diffusive with a positive-definite eddy diffusivity. The calculation of the latter tensor is reduced to the resolution of an auxiliary differential problem, consisting of a coupled set of two differential equations in a (6+1)-dimensional coordinate system (3 space coordinates plus 3 velocity coordinates plus time). Although expensive, numerical methods can be exploited to obtain the eddy diffusivity, for any desirable non-perturbative limit (e.g. arbitrary Stokes and Froude numbers). The aforementioned large-scale equation is then specialized to deal with two different relevant perturbative limits: i) vanishing of both Stokes time and sedimenting particle velocity; ii) vanishing Stokes time and finite sedimenting particle velocity. Both asymptotics lead to a greatly simplified auxiliary differential problem, now involving only space coordinates and thus easy to be tackled by standard numerical techniques. Explicit, exact expressions for the eddy diffusivities have been calculated, for both asymptotics, for the class of parallel flows, both static and time-dependent. This allows us to investigate analytically the role of gravity and inertia on the diffusion process by varying relevant features of the carrier flow, as e.g. the form of its temporal correlation function. Our results exclude a universal role played by gravity and inertia on the diffusive behaviour: regimes of both enhanced and reduced diffusion may exist, depending on the detailed structure of the carrier flow.Comment: 8 figures (12 plots), submitted to JF

Anomalous scaling of a passive scalar advected by the Navier--Stokes velocity field: Two-loop approximation

The field theoretic renormalization group and operator product expansion are applied to the model of a passive scalar quantity advected by a non-Gaussian velocity field with finite correlation time. The velocity is governed by the Navier--Stokes equation, subject to an external random stirring force with the correlation function $\propto \delta(t-t') k^{4-d-2\epsilon}$. It is shown that the scalar field is intermittent already for small $\epsilon$, its structure functions display anomalous scaling behavior, and the corresponding exponents can be systematically calculated as series in $\epsilon$. The practical calculation is accomplished to order $\epsilon^{2}$ (two-loop approximation), including anisotropic sectors. Like for the well-known Kraichnan's rapid-change model, the anomalous scaling results from the existence in the model of composite fields (operators) with negative scaling dimensions, identified with the anomalous exponents. Thus the mechanism of the origin of anomalous scaling appears similar for the Gaussian model with zero correlation time and non-Gaussian model with finite correlation time. It should be emphasized that, in contrast to Gaussian velocity ensembles with finite correlation time, the model and the perturbation theory discussed here are manifestly Galilean covariant. The relevance of these results for the real passive advection, comparison with the Gaussian models and experiments are briefly discussed.Comment: 25 pages, 1 figur

Random field sampling for a simplified model of melt-blowing considering turbulent velocity fluctuations

In melt-blowing very thin liquid fiber jets are spun due to high-velocity air streams. In literature there is a clear, unsolved discrepancy between the measured and computed jet attenuation. In this paper we will verify numerically that the turbulent velocity fluctuations causing a random aerodynamic drag on the fiber jets -- that has been neglected so far -- are the crucial effect to close this gap. For this purpose, we model the velocity fluctuations as vector Gaussian random fields on top of a k-epsilon turbulence description and develop an efficient sampling procedure. Taking advantage of the special covariance structure the effort of the sampling is linear in the discretization and makes the realization possible

Subtle statistical behavior in simple models for random advection-diffusion

Simple models for advection-diffusion with a statistical velocity field are studied here. These models involve advection by a time-independent random shear flow together with a constant mean flow. Several new and surprisingly subtle phenomena are developed here for the statistical behavior in these models. These new phenomena include: 1) mathematical criteria and examples with ill-posed evolution equations for the second order correlations and the mean statistics; 2) explicit sensitive dependence of the large scale, long time renormalization theory on parameters of the problem, such as the mean flow, the infrared cut-off, and the molecular diffusivity, for both the second order correlations and the mean statistics. This surprising sensitive dependence is explained in a self-consistent fashion both through mathematical theory and explicit examples

Spectral methods for mesoscopic models of pattern formation

In this paper we present spectral algorithms for the solution of mesoscopic equations describing a broad class of pattern formation mechanisms, focusing on a prototypical system of surface processes. These models are in principle stochastic integrodifferential equations and are derived directly from microscopic lattice models, containing detailed information on particle–particle interactions and particle dynamics. The enhanced computational efficiency and accuracy of spectral methods versus finite difference methods are also described

Spectral methods for mesoscopic models of pattern formation

In this paper we present spectral algorithms for the solution of mesoscopic equations describing a broad class of pattern formation mechanisms, focusing on a prototypical system of surface processes. These models are in principle stochastic integrodifferential equations and are derived directly from microscopic lattice models, containing detailed information on particle–particle interactions and particle dynamics. The enhanced computational efficiency and accuracy of spectral methods versus finite difference methods are also described