Narrow Results

A direct numerical simulation of Navier–Stokes equation coupled to the Lagrangian tracking of individual particles was used to predict the dispersion of deposited micro-particulate matter in turbulent channel flows on the walls. The different interaction conditions between particles and walls were considered for particles with Stokes numbers ranging from 0.1 to 10⁴. The particle deposition rates were predicted accurately because of the accurate calculation of turbulence and particle dispersion. It was found the interaction between the turbulent particles and the walls determined the re-entrainment mechanism of inertial particles away from the wall. The dispersion of deposition of particles were independent of the wall conditions in the partial diffusional and whole diffusion-impaction regime, consistent with a log–log law with particle Stokes number, which was found to be . The deposition rate decreased with decreasing adhesion of the wall in the inertia-moderated regime. The present results may be helpful for establishing and evaluating accurate prediction models of micro-particle deposition rates in various engineering applications.

High resolution lattice-Boltzmann simulations of turbulent channel flow on the Quadrics parallel machine are presented. The parallel performance is discussed together with some preliminary results concerning the vorticity structures which appear near the wall layer and their influence on the scaling laws.

A set of governing equations for flows in the vegetated domain has been derived with the assumption that the vegetation is of straight and rigid cylinder. The effect of vegetation on flow motion is represented by the additional inertial and drag forces exerted by the vegetation on the ambient flow. The derived governing equations are incorporated into an existing 3D turbulent free surface flow model. The new model is validated by available experimental data for open channel flows passing through the vegetation with different vegetation size and density. The numerical results agree very well to the experiment data.

Numerical and experimental turbulence simulations are nowadays reaching the size of the so-called big data, thus requiring refined investigative tools for appropriate statistical analyses and data mining. We present a new approach based on the complex network theory, offering a powerful framework to explore complex systems with a huge number of interacting elements. Although interest in complex networks has been increasing in the past years, few recent studies have been applied to turbulence. We propose an investigation starting from a two-point correlation for the kinetic energy of a forced isotropic field numerically solved. Among all the metrics analyzed, the degree centrality is the most significant, suggesting the formation of spatial patterns which coherently move with similar vorticity over the large eddy turnover time scale. Pattern size can be quantified through a newly-introduced parameter (i.e. average physical distance) and varies from small to intermediate scales. The network analysis allows a systematic identification of different spatial regions, providing new insights into the spatial characterization of turbulent flows. Based on present findings, the application to highly inhomogeneous flows seems promising and deserves additional future investigation.

This article is devoted to the study of some bidimensional fluid model with a source term implying oscillations. In this context, we can construct a family of exact solutions u_ε indexed by ε ∈ ]0, 1], defined on a strip [0, T] × ℝ² with fixed, and involving three types of scales: ε^-2t for a boundary layer at time t = 0, ε^-2x₁ for oscillations in the direction x₁, and ε^-1x₂ for a concentration near the position x₂ = 0. We show that the propagation and the interaction of these singularities produce a turbulent effect that manifests itself through the addition of some (unusual) diffusion when describing the underlying modulation equations.

The dynamics of the Reynolds stress tensor is determined by an evolution equation coupling geometrical effects and turbulent source terms. The effects of the mean flow geometry are shown up when the source terms are neglected. Then, the Reynolds stress tensor is expressed as the sum of three tensor products of vector fields, which are governed by a distorted gyroscopic equation. Along the mean flow trajectories and in the directions of the vector fields, the fluctuations of velocity are determined by differential equations whose coefficients depend only on the mean flow deformation.