A Turbulent Fluid Mechanics via Nonlinear Mixing of Smooth Flows with Bargmann-Fock Random Fields: Stochastically Averaged Navier-Stokes Equations and Velocity Correlations

Abstract

Let $\mathbb{I\!H}\subset\mathbb{R}^{3}$, with ${Vol}(\mathbb{I\!H})\sim L^{3}$, contain a fluid of viscosity $\nu$ and velocity $\mathrm{U}_{i}(x,t)$ with $(x,t)\in\mathbb{I\!H}\times[0,\infty)$, satisfying the Navier-Stokes equations with some boundary conditions on $\partial\mathbb{I\!H}$ and evolving from initial Cauchy data. Now let $\mathscr{B}(x)$ be a Gaussian random field defined for all $x\in\mathbb{I\!H}$ with expectation $\mathsf{E}\langle\mathscr{B}(x)\rangle=0$, and a Bargmann-Fock binary correlation $\mathsf{E}\big\langle\mathscr{B}(x)\otimes \mathscr{B}({y})\big\rangle=\mathsf{C}\exp(-\|{x}-{y}\|^{2}\lambda^{-2})$ with $\lambda\le {L}$. Define a volume-averaged Reynolds number $\mathbf{Re}(\mathbb{I\!H},t) =(|Vol(\mathbb{I\!H})|^{-1}\int_{\mathbb{I\!H}}\|\mathrm{U}_{i}(x,t)\|d\mu({x}){L}/\nu$. The critical Reynolds number is $\mathbf{Re}_{c}(\mathbb{I\!H})$ so that turbulence evolves within $\mathbb{I\!H}$ for $t$ such that $\mathbf{Re}(\mathbb{I\!H},t)>\mathbf{Re}_{c}(\mathbb{I\!H})$. Let $\psi(|\mathbf{Re}(\mathbb{I\!H},t)-\mathbf{Re}_{c}(\mathbb{I\!H})|)$ be an arbitrary monotone-increasing functional. The turbulent flow evolving within $\mathbb{I\!H}$ is described by the random field $\mathscr{U}_{i}(x,t)$ via a 'mixing' ansatz $\mathscr{U}_{i}(x,t)=\mathrm{U}_{i}(x,t)+\beta\mathrm{U}_{i}(x,t) \big\lbrace\psi(|\mathbf{Re}(\mathbb{I\!H},t)-\mathbf{Re}_{c}(\mathbb{I\!H})|)\big\rbrace \mathbb{S}_{\mathfrak{C}}[\mathbf{Re}(\mathbb{I\!H},t)\big]\mathscr{B}(x)$ where ${\beta}\ge 1$ is a constant and $\mathbb{S}_{\mathfrak{C}}[\mathbf{Re}(\mathbb{I\!H},t)]$ an indicator function. The flow grows increasingly random if $\mathbf{Re}(\mathbb{I\!H},t)$ increases with $t$ so that this is a 'control parameter'. The turbulent flow $\mathscr{U}_{i}(x,t)$ is a solution of stochastically averaged N-S equations. Reynolds-type velocity correlations are estimated.Comment: 64 pages, 5 figure