Asymptotic predictions on the velocity gradient statistics in low-Reynolds number random flows: Onset of skewness, intermittency and alignments

Abstract: Stirring a fluid through a Gaussian forcing at a vanishingly small Reynolds number produces a Gaussian random field, while flows at higher Reynolds numbers exhibit non-Gaussianity, cascades, anomalous scaling and preferential alignments. Recent works (Yakhot and Donzis, Phys. Rev. Lett., vol. 119, 2017, pp. 044501; Gotoh and Yang, Philos. Trans. Royal Soc. A, vol. 380, 2022, pp. 20210097) investigated the onset of these turbulent hallmarks in low-Reynolds number flows by focusing on the scaling of the velocity increments. They showed that the scalings in random flows at low-Reynolds and in high-Reynolds number turbulence are surprisingly similar. In this work, we address the onset of turbulent signatures in low-Reynolds number flows from the viewpoint of the velocity gradient dynamics, giving insights into its rich statistical geometry. We combine a perturbation theory of the full Navier-Stokes equations with velocity gradient modeling. This procedure results in a stochastic model for the velocity gradient in which the model coefficients follow directly from the Navier-Stokes equations and statistical homogeneity constraints. The Fokker-Planck equation associated with our stochastic model admits an analytic solution which shows the onset of turbulent hallmarks at low Reynolds numbers: skewness, intermittency and preferential alignments arise in the velocity gradient statistics as the Reynolds number increases. The model predictions are in excellent agreement with direct numerical simulations of low-Reynolds number flows.