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The KolmogorovObukhov Statistical Theory of Turbulence
Abstract
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 NavierStokes equation. The additive noise in the stochastic NavierStokes 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 KolmogorovObukhov {'}62 scaling hypothesis with the SheLeveque intermittency corrections. Then we compute the invariant measure of turbulence writing the stochastic NavierStokes equation as an infinitedimensional Ito process and solving the linear KolmogorovHopf 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 BarndorffNilsen, and compare well with PDFs from simulations and experiments.
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