Next: Random phases vs Gaussian
Noisy spectra, long correlations, and intermittency in wave turbulence
Phys. Rev. E 69, 066608 (2004)
04.30.Nk , 24.60.-k, 52.35.Ra, 92.10.Cg, 87.15.Ya
Abstract:
We study the k-space fluctuations of the waveaction about its mean
spectrum in the turbulence of dispersive waves. We use a minimal model
based on the Random Phase Approximation (RPA) and derive evolution
equations for the arbitrary-order one-point moments of the wave
intensity in the wavenumber space. The first equation in this series
is the familiar Kinetic Equation for the mean waveaction spectrum,
whereas the second and higher equations describe the fluctuations
about this mean spectrum. The fluctuations exhibit a nontrivial
dynamics if some long coordinate-space correlations are present in the
system, as it is the case in typical numerical and laboratory
experiments. Without such long-range correlations, the fluctuations
are trivially fixed at their Gaussian values and cannot evolve even if
the wavefield itself is non-Gaussian in the coordinate space. Unlike
the previous approaches based on smooth initial k-space cumulants, our
approach works even for extreme cases where the k-space fluctuations
are absent or very large and intermittent. We show, however, that
whenever turbulence approaches a stationary state, all the moments
approach the Gaussian values.
The concept of Wave Turbulence (WT), which describes an ensemble of
weakly interacting dispersive waves, significantly enhanced our
understanding of the spectral energy transfer in complex systems like
the ocean, the atmosphere, or in
plasmas [1,2,3,4,5]. This theory also became a
subject of renewed interest recently, (see,
e.g. [6,7,8,9]). Traditionally, WT
theory deals with derivation and solutions of the Kinetic Equation
(KE) for the mean waveaction spectrum (see e.g. [1]). However,
all experimentally or numerically obtained spectra are ``noisy'',
i.e. exhibit k-space fluctuations which contain a complimentary to the
mean spectra information. Such fluctuations will be studied (for the
first time) in this manuscript.
Next: Random phases vs Gaussian
Dr Yuri V Lvov
2007-04-11