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The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Thu, 07 Nov 2024 10:52:52 GMT2024-11-07T10:52:52ZAn empirical mean-field model of symmetry-breaking in a turbulent wake
http://hdl.handle.net/10985/23022
An empirical mean-field model of symmetry-breaking in a turbulent wake
CALLAHAM, Jared L.; RIGAS, Georgios; LOISEAU, Jean-Christophe; BRUNTON, Steven L.
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 highâ€“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.
Wed, 11 May 2022 00:00:00 GMThttp://hdl.handle.net/10985/230222022-05-11T00:00:00ZCALLAHAM, Jared L.RIGAS, GeorgiosLOISEAU, Jean-ChristopheBRUNTON, Steven L.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 highâ€“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.