Input-output analysis of the stochastic Navier-Stokes equations: application to turbulent channel flow

TL;DR

This paper enhances stochastic linear models of turbulent channel flow by incorporating nonlinear interactions through stochastic forcing, improving predictions of flow structures across different Reynolds numbers.

Contribution

It introduces a stochastic forcing approach to account for nonlinear wave interactions, improving the model's accuracy in predicting turbulent structures.

Findings

Improved prediction of streaks and vortices in turbulent flows.

Enhanced model accuracy in the logarithmic layer.

Better performance than eddy viscosity-based resolvent analysis.

Abstract

Stochastic linear modelling proposed in Tissot, M\'emin & Cavalieri (J. Fluid Mech., vol. 912, 2021, A51) is based on classical conservation laws subject to a stochastic transport. Once linearised around the mean flow and expressed in the Fourier domain, the model has proven its efficiency to predict the structure of the streaks of streamwise velocity in turbulent channel flows. It has been in particular demonstrated that the stochastic transport by unresolved incoherent turbulence allows to better reproduce the streaks through lift-up mechanism. In the present paper, we focus on the study of streamwise-elongated structures, energetic in the buffer and logarithmic layers. In the buffer layer, elongated streamwise vortices, named rolls, are seen to result from coherent wave-wave non-linear interactions, which have been neglected in the stochastic linear framework. We propose a way to account for the effect of these interactions in the stochastic model by introducing a stochastic forcing, which replace the missing non-linear terms. In addition, we propose an iterative strategy in order to ensure that the stochastic noise is decorrelated from the solution, as prescribed by the modelling hypotheses. We explore the prediction abilities of this more complete model in the buffer and logarithmic layers of channel flows at $Re_\tau=180$, $Re_\tau=550$ and $Re_\tau=1000$. We show an improvement of predictions compared to resolvent analysis with eddy viscosity, especially in the logarithmic layer.