AI Insight
This study investigates the statistical properties of Fourier mode time series in two-dimensional turbulent flows driven by forcing at intermediate scales, identifying a characteristic temporal correlation length within the flow data. The authors develop a stochastic model based on filtered white noise to represent individual Fourier components, capturing these statistical structures. They validate the model by comparing the effective diffusion of a passive tracer computed from direct numerical simulation of the full turbulent flow against predictions from the stochastic model, finding reasonable agreement.
Why it matters
Simplified stochastic models of turbulence have practical applications in simulating heat transport, contaminant dispersion, and other scalar transport problems at reduced computational cost. This work provides a more statistically rigorous foundation for such models by grounding them in identified properties of real turbulent flow data.
arXiv:2605.13671v1 Announce Type: cross
Abstract: 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.
Source: Stochastic modeling of Fourier modes in two-dimensional turbulence via filtered white noise