Stochastic modeling of Fourier modes in two-dimensional turbulence via filtered white noise
Provides a simplified stochastic model for turbulence that could aid in heat transport and other applications, but the improvement over existing models is not quantified.
The study identifies a typical time correlation length in Fourier modes of 2D turbulence and proposes a stochastic model using filtered white noise, which reproduces passive tracer transport with effective diffusion matching direct numerical simulations.
Modeling turbulent flows by a random Fourier decomposition is a classical procedure in order to use simplified models of turbulence in heat transport and other applications. We carefully investigate the Fourier time series of two-dimensional turbulent flows forced at intermediate scales and identify significant statistical structures. In particular, we find the existence of a typical time correlation length, and propose a stochastic model for the Fourier components. Finally, we compute the transport of a passive tracer under purely convective dynamics by means of direct numerical simulation of the turbulent flow and compare it with the effective diffusion produced by the stochastic model.