Stochastic Lagrangian Dynamics of Vorticity. I. General Theory

TL;DR

This paper develops a stochastic Lagrangian framework for vorticity in wall-bounded flows, simplifying previous theories and providing a numerical scheme validated on turbulent channel flow data, revealing exponential growth of invariants' variance.

Contribution

It simplifies Constantin-Iyer's stochastic vorticity theory for wall-bounded flows and introduces a Monte Carlo numerical scheme validated on turbulent data.

Findings

Validation of stochastic Cauchy invariants conservation in turbulent flow events

Exponential growth of invariant variances backward in time

Connection between vorticity generation and statistical relations in turbulence

Abstract

Prior mathematical work of Constantin and Iyer (2008, 2011) has shown that incompressible Navier-Stokes solutions possess infinitely-many stochastic Lagrangian conservation laws for vorticity, backward in time, which generalize the invariants of Cauchy (1815) for smooth Euler solutions. We simplify this theory for the case of wall-bounded flows by appealing to the Kuz'min (1983)-Oseledets (1989) representation of Navier-Stokes dynamics, in terms of the vortex-momentum density associated to a continuous distribution of infinitesimal vortex rings. The Constantin-Iyer theory provides an exact representation for vorticity at any interior point as an average over stochastic vorticity contributions transported from the wall. We discuss relations of this Lagrangian formulation with the Eulerian theory of Lighthill (1963)-Morton (1984) for vorticity generation at solid walls, and also with a statistical result of Taylor (1932)-Huggins (1994), which connects dissipative drag with organized cross-stream motion of vorticity and which is closely analogous to the "Josephson-Anderson relation" for quantum superfluids. We elaborate a Monte Carlo numerical Lagrangian scheme to calculate the stochastic Cauchy invariants and their statistics, given the Eulerian space-time velocity field. The method is validated using an online database of a turbulent channel-flow simulation (Graham et al. 2016), where conservation of the mean Cauchy invariant is verified for two selected buffer-layer events corresponding to an "ejection" and a "sweep". The variances of the stochastic Cauchy invariants grow exponentially backward in time, however, confirming earlier observations of Lagrangian chaos in channel-flow turbulence.