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      Lagrangian chaos and scalar advection in stochastic fluid mechanics

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          Abstract

          We study the Lagrangian flow associated to velocity fields arising from various models of fluid mechanics subject to white-in-time, \(H^s\)-in-space stochastic forcing in a periodic box. We prove that in many circumstances, these flows are chaotic, that is, the top Lyapunov exponent is strictly positive. Our main results are for the Navier-Stokes equations on \(\mathbb T^2\) and the hyper-viscous regularized Navier-Stokes equations on \(\mathbb T^3\) (at arbitrary Reynolds number and hyper-viscosity parameters), subject to forcing which is non-degenerate at high frequencies. As an application, we study statistically stationary solutions to the passive scalar advection-diffusion equation driven by these velocities and subjected to random sources. The chaotic Lagrangian dynamics are used to prove a version of anomalous dissipation in the limit of vanishing diffusivity, which in turn, implies that the scalar satisfies Yaglom's law of scalar turbulence -- the analogue of the Kolmogorov 4/5 law. Key features of our study are the use of tools from ergodic theory and random dynamical systems, namely the Multiplicative Ergodic Theorem and a version of Furstenberg's Criterion, combined with hypoellipticity via Malliavin calculus and approximate control arguments.

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          Hypoelliptic second order differential equations

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            Ergodic theory of differentiable dynamical systems

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              Ergodicity of the 2D Navier–Stokes equations with degenerate stochastic forcing

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                Author and article information

                Journal
                17 September 2018
                Article
                1809.06484
                65a0040c-3fca-41d7-9185-f8407f2786a2

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                72 pages
                math.AP math.DS math.PR nlin.CD physics.flu-dyn

                Analysis,Differential equations & Dynamical systems,Thermal physics & Statistical mechanics,Probability,Nonlinear & Complex systems

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