arXiv subjects
Publications and source records attributed to G. Boffetta.
We study the statistics of pair dispersion in two-dimensional turbulence. Direct numerical simulations show that the pdf of pair separations is in agreement with the Richardson prediction. The pdf of doubling times follows dimensional scaling and shows a long tail which is the signature of close approaches between particles initially seeded with a large separation. This phenomenon is related to the formation of fronts in passive scalar advection.
We review and discuss some different techniques for describing local dispersion properties in fluids. A recent Lagrangian diagnostics, based on the Finite Scale Lyapunov Exponent (FSLE), is presented and compared to the Finite Time Lyapunov Exponent (FTLE), and to the Okubo-Weiss (OW) and Hua-Klein (HK) criteria. We show that the OW and HK are a limiting case of the FTLE, and that the FSLE is the most efficient method for detecting the presence of cross-stream barriers. We illustrate our findings by considering two examples of geophysical interest: a kinematic meandering jet model, and Lagrangian tracers advected by stratospheric circulation.
Different aspects of the predictability problem in dynamical systems are reviewed. The deep relation among Lyapunov exponents, Kolmogorov-Sinai entropy, Shannon entropy and algorithmic complexity is discussed. In particular, we emphasize how a characterization of the unpredictability of a system gives a measure of its complexity. Adopting this point of view, we review some developments in the characterization of the predictability of systems showing different kind of complexity: from low-dimensional systems to high-dimensional ones with spatio-temporal chaos and to fully developed turbulence. A special attention is devoted to finite-time and finite-resolution effects on predictability, which can be accounted with suitable generalization of the standard indicators. The problems involved in systems with intrinsic randomness is discussed, with emphasis on the important problems of distinguishing chaos from noise and of modeling the system. The characterization of irregular behavior in systems with discrete phase space is also considered.
The long time behavior of velocity-magnetic field alignment is numerically investigated in the framework of MHD shell model. In the stationary forced case, the correlation parameter C displays a nontrivial behavior with long periods of high variability which alternates with periods of almost constant C. The temporal statistics of correlation is shown to be non Poissonian, and the pdf of constant sign periods displays clear power law tails. The possible relevance of the model for geomagnetic dynamo problem is discussed.
We investigate Lagrangian relative dispersion in direct numerical simulation of two-dimensional inverse cascade turbulence. The analysis is performed by using both standard fixed time statistics and an exit time approach. Our results are in good agreement with the original Richardson's description in terms of diffusion equation. The deviations are only of the quantitative nature. These deviations, and the observed strong sensitivity to the finite size effects, are associated with the long-range-correlated nature of the particles' relative motion. The correlation, or persistence, parameter is measured by means of a Lagrangian ``turning point'' statistics.
The predictability problem in the inverse energy cascade of two-dimensional turbulence is addressed by means of direct numerical simulations. The growth rate as a function of the error level is determined by means of a finite size extension of the Lyapunov exponent. For error within the inertial range, the linear growth of the error energy, predicted by dimensional argument, is verified with great accuracy. Our numerical findings are in close agreement with the result of TFM closure approximation.
The Reynolds number dependence of the statistics of energy dissipation is investigated in a shell model of fully developed turbulence. The results are in agreement with a model which accounts for fluctuations of the dissipative scale with the intensity of energy dissipation. It is shown that the assumption of a fixed dissipative scale leads to a different scaling with Reynolds which is not compatible with numerical results.
Lagrangian chaos is experimentally investigated in a convective flow by means of Particle Tracking Velocimetry. The Finite Size Lyapunov Exponent analysis is applied to quantify dispersion properties at different scales. In the range of parameters of the experiment, Lagrangian motion is found to be chaotic. Moreover, the Lyapunov depends on the Rayleigh number as Ra^(1/2). A simple dimensional argument for explaining the observed power law scaling is proposed.
High resolution numerical simulations of stationary inverse energy cascade in two-dimensional turbulence are presented. Deviations from Gaussianity of velocity differences statistics are quantitatively investigated. The level of statistical convergence is pushed enough to permit reliable measurement of the asymmetries in the probability distribution functions of longitudinal increments and odd-order moments, which bring the signature of the inverse energy flux. Their scaling laws do not present any measurable intermittency correction. The seventh order skewness is found to increase by almost two orders of magnitude with respect to the third, thus becoming of order unity.
We study relative dispersion of passive scalar in non-ideal cases, i.e. in situations in which asymptotic techniques cannot be applied; typically when the characteristic length scale of the Eulerian velocity field is not much smaller than the domain size. Of course, in such a situation usual asymptotic quantities (the diffusion coefficients) do not give any relevant information about the transport mechanisms. On the other hand, we shall show that the Finite Size Lyapunov Exponent, originally introduced for the predictability problem, appears to be rather powerful in approaching the non-asymptotic transport properties. This technique is applied in a series of numerical experiments in simple flows with chaotic behaviors, in experimental data analysis of drifter and to study relative dispersion in fully developed turbulence.
The problem of error growth due to the incomplete knowledge of the evolution law which rules the dynamics of a given physical system is addressed. Major interest is devoted to the analysis of error amplification in systems with many characteristic times and scales. The importance of a proper parameterization of fast scales in systems with many strongly interacting degrees of freedom is highlighted and its consequences for the modelization of geophysical systems are discussed.
The stability of a sheared magnetic field is analyzed in two-dimensional magnetohydrodynamics with resistive and viscous dissipation. Using a multiple-scale analysis, it is shown that at large enough Reynolds numbers the basic state describing a motionless fluid and a layered magnetic field, becomes unstable with respect to large scale perturbations. The exact expressions for eddy-viscosity and eddy-resistivity are derived in the nearby of the critical point where the instability sets in. In this marginally unstable case the nonlinear phase of perturbation growth obeys to a Cahn-Hilliard-like dynamics characterized by coalescence of magnetic islands leading to a final new equilibrium state. High resolution numerical simulations confirm quantitatively the predictions of multiscale analysis.
The Lagrangian statistics of relative dispersion in fully developed turbulence is numerically investigated. A scaling range spanning many decades is achieved by generating a synthetic velocity field with prescribed Eulerian statistical features. When the velocity field obeys Kolmogorov similarity, the Lagrangian statistics is self similar too, and in agreement with Richardson's predictions. For an intermittent velocity field the scaling laws for the Lagrangian statistics are found to depend on Eulerian intermittency in agreement with a multifractal description. As a consequence of the Kolmogorov law the Richardson law for the variance of pair separation is not affected by intermittency corrections. A new analysis method, based on fixed scale averages instead of usual fixed time statistics, is shown to give much wider scaling range and should be preferred for the analysis of experimental data.
A multifractal-like representation for multi-time multi-scale velocity correlation in turbulence and dynamical turbulent models is proposed. The importance of subleading contributions to time correlations is highlighted. The fulfillment of the dynamical constraints due to the equations of motion is thoroughly discussed. The prediction stemming from this representation are tested within the framework of shell models for turbulence.
The effect of Eulerian intermittency on the Lagrangian statistics of relative dispersion in fully developed turbulence is investigated. A scaling range spanning many decades is achieved by generating a multi-affine synthetic velocity field with prescribed intermittency features. The scaling laws for the Lagrangian statistics are found to depend on Eulerian intermittency in agreement with a multifractal description. As a consequence of the Kolmogorov's law, the Richardson's law for the variance of pair separation is not affected by intermittency corrections.
The predictability problem for systems with different characteristic time scales is investigated. It is shown that even in simple chaotic dynamical systems, the leading Lyapunov exponent is not sufficient to estimate the predictability time. This fact is due the saturation of the error on the fast components of the system which therefore do not contribute to the exponential growth of the error at large errors. It is proposed to adopt a generalization of the Lyapunov exponent which is based on the natural concept of error growing time at finite error size. The method is first illustrated on a simple numerical model obtained by coupling two Lorenz systems with different time scales. As a more realistic example, this analysis is then applied to a toy model of Atmospheric circulation recently introduced by Lorenz.
An efficient method for the construction of a multiaffine process, with prescribed scaling exponents, is presented. At variance with the previous proposals, this method is sequential and therefore it is the natural candidate in numerical computations involving synthetic turbulence. The application to the realization of a realistic turbulent-like signal is discussed in detail. The method represents a first step towards the realization of a realistic spatio-temporal turbulent field.
We investigate the spreading of passive tracers in closed basins. If the characteristic length scale of the Eulerian velocities is not very small compared with the size of the basin the usual diffusion coefficient does not give any relevant information about the mechanism of spreading. We introduce a finite size characteristic time $τ(δ)$ which describes the diffusive process at scale $δ$. When $δ$ is small compared with the typical length of the velocity field one has $τ(δ) \sim λ^{-1}$, where $λ$ is the maximum Lyapunov exponent of the Lagrangian motion. At large $δ$ the behavior of $τ(δ)$ depends on the details of the system, in particular the presence of boundaries, and in this limit we have found a universal behavior for a large class of system under rather general hypothesis. The method of working at fixed scale $δ$ makes more physical sense than the traditional way of looking at the relative diffusion at fixed delay times. This technique is displayed in a series of numerical experiments in simple flows.