Dynamically relevant recurrent flows obtained via a nonlinear recurrence function from two-dimensional turbulence
Edward M. Redfern, Andrei L. Lazer, Dan Lucas

TL;DR
This paper introduces a method to extract and analyze unstable recurrent flows in two-dimensional turbulence using nonlinear triad variables, improving the identification of extreme events and aiding turbulence statistics reconstruction.
Contribution
The study presents a novel approach using nonlinear triad variables for more effective detection of unstable recurrent flows in turbulence simulations, reducing symmetry issues and enhancing analysis.
Findings
Unstable periodic orbits span larger dissipation rates and extreme events.
Triad variables naturally weight active spatial modes better than Euclidean norms.
A simple heuristic weighting based on instability predicts turbulence statistics effectively.
Abstract
This paper demonstrates the efficient extraction of unstable recurrent flows from two-dimensional turbulence by using nonlinear triads to diagnose recurrence in direct numerical simulations. Nearly recurrent episodes are identified from simulations and then converged using a standard Newton- GMRES-hookstep method, however with much greater diversity than previous studies which performed this 'recurrent flow analysis'. Unstable periodic and relative periodic orbits are able to be identified which span larger values of dissipation rate, i.e. corresponding to extreme bursting events. The triad variables are found to provide a more natural way to weight the greater variety of spatial modes active in such orbits than a standard Euclidian norm of complex Fourier amplitudes. Moreover the triad variables build in a reduction of the continuous symmetry of the system which avoids the need to…
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Taxonomy
TopicsFluid Dynamics and Turbulent Flows
