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Attractor local dimensionality, nonlinear energy transfers and finite-time instabilities in unstable dynamical systems with applications to two-dimensional fluid flows

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TLDR
In this paper, the authors examined the geometry of the finite-dimensional attractor associated with fluid flows described by Navier-Stokes equations and related its nonlinear dimensionality to energy exchanges between dynamical components (modes) of the flow.
Abstract
We examine the geometry of the finite-dimensional attractor associated with fluid flows described by Navier–Stokes equations and relate its nonlinear dimensionality to energy exchanges between dynamical components (modes) of the flow. Specifically, we use a stochastic framework based on the dynamically orthogonal equations to perform efficient order-reduction and describe the stochastic attractor in the reduced-order phase space in terms of the associated probability measure. We introduce the notion of local fractal dimensionality to describe the geometry of the attractor and we establish a connection with the number of positive finite-time Lyapunov exponents. Subsequently, we illustrate in specific fluid flows that the low dimensionality of the stochastic attractor is caused by the synergistic activity of linearly unstable and stable modes as well as the action of the quadratic terms. In particular, we illustrate the connection of the low-dimensionality of the attractor with the circulation of energy: (i) from the mean flow to the unstable modes (due to their linearly unstable character), (ii) from the unstable modes to the stable ones (due to a nonlinear energy transfer mechanism) and (iii) from the stable modes back to the mean (due to the linearly stable character of these modes).

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Journal ArticleDOI

Infinite-Dimensional Dynamical Systems in Mechanics and Physics (Roger Temam)

Richard E. Mortensen
- 01 Mar 1991 - 
TL;DR: 5. M. Green, J. Schwarz, and E. Witten, Superstring theory, and An interpretation of classical Yang-Mills theory, Cambridge Univ.
Journal ArticleDOI

Statistically accurate low-order models for uncertainty quantification in turbulent dynamical systems

TL;DR: In this paper, a framework for low-order predictive statistical modeling and uncertainty quantification in turbulent dynamical systems is developed, based on modified quasilinear Gaussian (ROMQG) algorithms.
Journal ArticleDOI

On closures for reduced order models—A spectrum of first-principle to machine-learned avenues

TL;DR: In this article, the effect of the discarded reduced order modes in under-resolved simulations is modeled using data-driven proper orthogonal decomposition (POD) modeling.
Journal ArticleDOI

A variational approach to probing extreme events in turbulent dynamical systems

TL;DR: It is found that the intermittent bursts of the energy dissipation are independent of the external forcing and are instead caused by the spontaneous transfer of energy from large scales to the mean flow via nonlinear triad interactions.
Journal ArticleDOI

Geophysical flows under location uncertainty, Part I Random transport and general models

TL;DR: In this paper, a stochastic flow representation is considered with the Eulerian velocity decomposed between a smooth large scale component and a rough small-scale turbulent component, specified as a random field uncorrelated in time.
References
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Journal ArticleDOI

Deterministic nonperiodic flow

TL;DR: In this paper, it was shown that nonperiodic solutions are ordinarily unstable with respect to small modifications, so that slightly differing initial states can evolve into considerably different states, and systems with bounded solutions are shown to possess bounded numerical solutions.
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Theory of Ordinary Differential Equations

TL;DR: The prerequisite for the study of this book is a knowledge of matrices and the essentials of functions of a complex variable as discussed by the authors, which is a useful text in the application of differential equations as well as for the pure mathematician.
Journal ArticleDOI

Measuring the Strangeness of Strange Attractors

TL;DR: In this paper, the correlation exponent v is introduced as a characteristic measure of strange attractors which allows one to distinguish between deterministic chaos and random noise, and algorithms for extracting v from the time series of a single variable are proposed.
Book

Infinite-Dimensional Dynamical Systems in Mechanics and Physics

Roger Temam
TL;DR: In this article, the authors give bounds on the number of degrees of freedom and the dimension of attractors of some physical systems, including inertial manifolds and slow manifolds.
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Turbulence, Coherent Structures, Dynamical Systems and Symmetry

TL;DR: In this article, the authors present a review of rigor properties of low-dimensional models and their applications in the field of fluid mechanics. But they do not consider the effects of random perturbation on models.
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