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An empirical mean-field model of symmetry-breaking in a turbulent wake

Article dans une revue avec comité de lecture
Author
CALLAHAM, Jared L.
545712 Department of Mechanical Engineering [University of Washington]
RIGAS, Georgios
69530 Imperial College London
ccLOISEAU, Jean-Christophe
134975 Laboratoire de Dynamique des Fluides [DynFluid]
BRUNTON, Steven L.
545712 Department of Mechanical Engineering [University of Washington]

URI
http://hdl.handle.net/10985/23022
DOI
10.1126/sciadv.abm4786
Date
2022-05-11
Journal
Science Advances

Abstract

Improved turbulence modeling remains a major open problem in mathematical physics. Turbulence is notoriously challenging, in part due to its multiscale nature and the fact that large-scale coherent structures cannot be disentangled from small-scale fluctuations. This closure problem is emblematic of a greater challenge in complex systems, where coarse-graining and statistical mechanics descriptions break down. This work demonstrates an alternative data-driven modeling approach to learn nonlinear models of the coherent structures, approximating turbulent fluctuations as state-dependent stochastic forcing. We demonstrate this approach on a high–Reynolds number turbulent wake experiment, showing that our model reproduces empirical power spectra and probability distributions. The model is interpretable, providing insights into the physical mechanisms underlying the symmetry-breaking behavior in the wake. This work suggests a path toward low-dimensional models of globally unstable turbulent flows from experimental measurements, with broad implications for other multiscale systems.

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