Comment on "probability distribution of power fluctuations in turbulence (original) (raw)
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Probability distribution of power fluctuations in turbulence
Physical Review E, 2009
We study local power fluctuations in numerical simulations of stationary, homogeneous, isotropic turbulence in two and three dimensions with Gaussian forcing. Due to the near-Gaussianity of the one-point velocity distribution, the probability distribution function (pdf) of the local power is well modelled by the pdf of the product of two joint normally distributed variables. In appropriate units, this distribution is parameterised only by the mean dissipation rate, ǫ. The large deviation function for this distribution is calculated exactly and shown to satisfy a Fluctuation Relation (FR) with a coefficient which depends on ǫ. This FR is entirely statistical in origin. The deviations from the model pdf are most pronounced for positive fluctuations of the power and can be traced to a slightly faster than Gaussian decay of the tails of the one-point velocity pdf. The resulting deviations from the FR are consistent with several recent experimental studies.
Geophysical Research Letters, 1999
Intermittency in fluid turbulence can be emphasized through the analysis of Probability Distribution Functions (PDF) for velocity fluctuations, which display a strong non-gaussian behavior at small scales. Castaing et al. (1990) have introduced the idea that this behavior can be represented, in the framework of a multiplicative cascade model, by a convolution of gaussians whose variances is distributed according to a log-normal distribution. In this letter we have tried to test this conjecture on the MHD solar wind turbulence by performing a fit of the PDF of the bulk speed and magnetic field intensity fluctuations calculated in the solar wind, with the model. This fit allows us to calculate a parameter depending on the scale, which represents the width of the log-normal distribution of the variances of the gaussians. The physical implications of the obtained values of the parameter as well as of its scaling law are finally discussed
Power fluctuations in a closed turbulent shear flow
Physical Review E, 1999
We study experimentally the power consumption P of a confined turbulent flow at constant Reynolds number Re. We analyze in details its temporal dynamics and statistical properties, in a setup that covers two decades in Reynolds numbers. We show that nontrivial power fluctuations occur over a wide range of amplitudes and that they involve coherent fluid motions over the entire system size. As a result, the power fluctuations do not result from averaging of independent subsystems and its probability density function ⌸( P) is strongly non-Gaussian. The shape of ⌸( P) is Reynolds number independant and we show that the relative intensity of fluctuations decreases very slowly as Re increases. These results are discussed in terms of an analaogy with critical phenomena. ͓S1063-651X͑99͒50709-5͔
The Kolmogorov–Obukhov Statistical Theory of Turbulence
Journal of Nonlinear Science, 2013
In 1941 Kolmogorov and Obukhov proposed that there exists a statistical theory of turbulence that should allow the computation of all the statistical quantities that can be computed and measured in turbulent systems. These are quantities such as the moments, the structure functions and the probability density functions (PDFs) of the turbulent velocity field. In this paper we will outline how to construct this statistical theory from the stochastic Navier-Stokes equation. The additive noise in the stochastic Navier-Stokes equation is generic noise given by the central limit theorem and the large deviation principle. The multiplicative noise consists of jumps multiplying the velocity, modeling jumps in the velocity gradient. We first estimate the structure functions of turbulence and establish the Kolmogorov-Obukhov '62 scaling hypothesis with the She-Leveque intermittency corrections. Then we compute the invariant measure of turbulence writing the stochastic Navier-Stokes equation as an infinite-dimensional Ito process and solving the linear Kolmogorov-Hopf functional differential equation for the invariant measure. Finally we project the invariant measure onto the PDF. The PDFs turn out to be the normalized inverse Gaussian (NIG) distributions of Barndorff-Nilsen, and compare well with PDFs from simulations and experiments.
Non-Gaussian probability distribution functions in two-dimensional magnetohydrodynamic turbulence
Europhysics Letters (epl), 2000
Intermittency in MHD turbulence has been analyzed using high resolution 2D numerical simulations. We show that the Probability Distribution Functions (PDFs) of the fluctuations of the Elsasser fields, magnetic field and velocity field depend on the scale at hand, that is they are self-affine. The departure of the PDFs from a Gaussian function can be described through the scaling behavior of a single parameter lambda_r^2 obtained by fitting the PDFs with a given curve stemming from the analysis of a multiplicative model by Castaing et al. (1990). The scaling behavior of the parameter lambda_r^2 can be used to extract informations about the intermittency. A comparison of intermittency properties in different MHD turbulent flows is also performed.
Probability distribution of turbulent irradiance in a saturation regime
Applied Optics, 1979
Application of the central limit theorem to the stochastic equation of propagation suggests that the probability distribution of the complex wave amplitude defined on the geometrical phase front is approximately normal. The resulting irradiance probability density function, valid in the strong scintillation regime, is an exponential multiplied by the modified Bessel function Io both of argument proportional to the irradiance; it is not the Rice-Nakagami density function. Quantitative tests show that this exponential-Bessel function constitutes as good a fit as the log-normal to the irradiance probability data reported in this paper. Since the normal distribution hypothesis is consistent with the stochastic wave equation, the model proposed here should be a simple substitute to the often used but theoretically incorrect log-normal irradiance probability distribution model. When this work was done both authors were with Centre de Recherches pour la Defense Valcartier, P.O. Box 880, Courcelette, Quebec GOA RO; P. L. Wizinowich is now with University of Toronto, Institute for Aerospace Studies, Downsview, Ontario. Received