1,721,009 research outputs found
Irreversibility and response functions in the turbulent energy cascade
Full text linkThe statistical properties of turbulent flows are fundamentally different from those of systems at equilibrium due to the presence of an energy flux from the scales of injection to those where energy is dissipated by the viscous forces: a scenario dubbed “direct energy cascade”. Here, we aim at characterizing the non-equilibrium properties of turbulent cascades in a shell model of turbulence by studying an asymmetric time-correlation function and the relaxation behavior of an energy perturbation, measured at scales smaller or larger than the perturbed one. We shall contrast the behavior of these two observables in both non-equilibrium
(forced and dissipated) and equilibrium (inviscid and unforced) cases. Finally, we shall show
that equilibrium and non-equilibrium physics coexist in the same system, namely at scales larger
and smaller, respectively, of the forcing scale
Non-equilibrium statistical mechanics of the turbulent energy cascade: irreversibility and response functions
Full text linkThe statistical properties of turbulent flows are fundamentally different
from those of systems at equilibrium due to the presence of an energy flux from
the scales of injection to those where energy is dissipated by the viscous
forces: a scenario dubbed "direct energy cascade". From a statistical mechanics
point of view, the cascade picture prevents the existence of detailed balance,
which holds at equilibrium, e.g. in the inviscid and unforced case. Here, we
aim at characterizing the non-equilibrium properties of turbulent cascades in a
shell model of turbulence by studying an asymmetric time-correlation function
and the relaxation behavior of an energy perturbation, measured at scales
smaller or larger than the perturbed one. We shall contrast the behavior of
these two observables in both non-equilibrium (forced and dissipated) and
equilibrium (inviscid and unforced) cases. Finally, we shall show that
equilibrium and non-equilibrium physics coexist in the same system, namely at
scales larger and smaller, respectively, of the forcing scale.Comment: REVTeX 4.2, 15 pages, 10 figure
Chaotic advection and relative dispersion in an experimental convective flow
No full textLagrangian motion in a quasi-two-dimensional, time-dependent, convective flow is studied at different Rayleigh numbers. The particle tracking velocimetry technique is used to reconstruct Lagrangian trajectories of passive tracers. Dispersion properties are investigated by means of the recently introduced finite size Lyapunov exponent analysis. Lagrangian motion is found to be chaotic with a Lyapunov exponent which depends on the Rayleigh number as Ra1/2. The power law scaling is explained in terms of a dimensional analysis on the equation of motion. A comparative study shows that the fixed scale method makes more physical sense than the traditional way of looking at the relative dispersion at fixed times. © 2000 American Institute of Physics.The transport and mixing properties such as Lagrangian motion, chaotic advection and relative dispersion of passive impurities in a quasi two-dimensional, time-dependent, convective flow were studied at different Rayleigh numbers. The Lagrangian trajectories of passive tracers were reconstructed using the particle tracking velocimetry and finite size Lyapunov exponent analysis investigated the dispersion properties
Experimental evidence of chaotic advection in a convective flow
No full textLagrangian chaos is experimentally investigated in a convective flow by means of Particle Tracking Velocimetry. The Finite Size Lyapunov Exponentanalysis 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 exponent depends on the Rayleigh number as Ra 1/2. A simple dimensional argument for explaining the observed power law scaling is proposed
The role of data in model building and prediction: a survey through examples
Full text linkThe goal of Science is to understand phenomena and systems in order to predict their development and gain control over them. In the scientific process of knowledge elaboration, a crucial role is played by models which, in the language of quantitative sciences, mean abstract mathematical or algorithmical representations. This short review discusses a few key examples from Physics, taken from dynamical systems theory, biophysics, and statistical mechanics, representing three paradigmatic procedures to build models and predictions from available data. In the case of dynamical systems we show how predictions can be obtained in a virtually model-free framework using the methods of analogues, and we briefly discuss other approaches based on machine learning methods. In cases where the complexity of systems is challenging, like in biophysics, we stress the necessity to include part of the empirical knowledge in the models to gain the minimal amount of realism. Finally, we consider many body systems where many (temporal or spatial) scales are at play-and show how to derive from data a dimensional reduction in terms of a Langevin dynamics for their slow components
Reinforcement learning for pursuit and evasion of microswimmers at low Reynolds number
No full textWe consider a model of two competing microswimming agents engaged in a pursue-evasion task within a low-Reynolds-number environment. Agents can only perform simple maneuvers and sense hydrodynamic disturbances, which provide ambiguous (partial) information about the opponent's position and motion. We frame the problem as a zero-sum game: The pursuer has to capture the evader in the shortest time, while the evader aims at deferring capture as long as possible. We show that the agents, trained via adversarial reinforcement learning, are able to overcome partial observability by discovering increasingly complex sequences of moves and countermoves that outperform known heuristic strategies and exploit the hydrodynamic environment
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
