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This work studies the effectiveness of several machine learning techniques for predicting extreme events occurring in the flow around an airfoil at low Reynolds. For certain Reynolds numbers the aerodynamic forces exhibit intermittent…

Fluid Dynamics · Physics 2021-08-13 Samuel H. Rudy , Themistoklis P. Sapsis

We report the experimental evidence of the existence of a random attractor in a fully developed turbulent swirling flow. By defining a global observable which tracks the asymmetry in the flux of angular momentum imparted to the flow, we can…

We propose a framework for studying predictability of extreme events in complex systems. Major conceptual elements -- direct cascading or fragmentation, spatial dynamics, and external driving -- are combined in a classical age-dependent…

Adaptation and Self-Organizing Systems · Physics 2007-08-14 Andrei Gabrielov , Vladimir Keilis-Borok , Ilya Zaliapin

To characterize fluctuations in a turbulent flow, one usually studies different moments of velocity increments and dissipation rate, $\overline{(v(x+r)-v(x))^{n}}\propto r^{\zeta_{n}}$ and $\overline{{\cal E}^{n}}\propto Re^{d_{n}}$,…

Fluid Dynamics · Physics 2019-05-29 Victor Yakhot , Diego A. Donzis

The transitional and well-developed regimes of turbulent shear flows exhibit a variety of remarkable scaling laws that are only now beginning to be systematically studied and understood. In the first part of this article, we summarize…

Fluid Dynamics · Physics 2017-01-04 Nigel Goldenfeld , Hong-Yan Shih

Accurate prediction of the transition from laminar flow to turbulence remains an unresolved challenge despite its importance for understanding a variety of environmental, biological, and industrial phenomena. Well over a century of…

Fluid Dynamics · Physics 2023-01-10 John O. Dabiri , Nina Mohebbi , Matthew K. Fu

Extreme events gain the attention of researchers due to their utmost importance in various contexts ranging from finance to climatology. This brings such recurrent events to the limelight of attention in interdisciplinary research. A…

Data Analysis, Statistics and Probability · Physics 2022-05-18 Sayantan Nag Chowdhury , Arnob Ray , Syamal K. Dana , Dibakar Ghosh

An experiment was performed using Dual-plane-SPIV in the LMFL boundary layer facility to determine all of the derivative moments needed to estimate the average dissipation rate of the turbulent kinetic energy, $\varepsilon$, and its…

We study the problem of predicting rare critical transition events for a class of slow-fast nonlinear dynamical systems. The state of the system of interest is described by a slow process, whereas a faster process drives its evolution and…

Computational Physics · Physics 2020-12-14 Soon Hoe Lim , Ludovico Theo Giorgini , Woosok Moon , J. S. Wettlaufer

A convolutional encoder-decoder-based transformer model is proposed for autoregressively training on spatio-temporal data of turbulent flows. The prediction of future fluid flow fields is based on the previously predicted fluid flow field…

Fluid Dynamics · Physics 2023-03-31 Aakash Patil , Jonathan Viquerat , Elie Hachem

Turbulence may appear as a complex process with a multitude of scales and flow patterns, but still obeys simple physical principles such as the conservation of momentum, of energy, and the maximum entropy principle. The latter states that…

Fluid Dynamics · Physics 2019-04-23 T. -W. Lee

A proposal for a calculational program in fluid turbulence is presented. It is proposed that the fluid probability density functional has an attractor for its time-evolution, just as the dynamical system itself has. The evolution of the…

Fluid Dynamics · Physics 2007-05-23 Edsel A. Ammons

We consider transition to strong turbulence in an infinite fluid stirred by a gaussian random force. The transition is {\bf defined} as a first appearance of anomalous scaling of normalized moments of velocity derivatives (dissipation…

Fluid Dynamics · Physics 2017-08-02 Victor Yakhot , Diego Donzis

We propose Echo State Networks (ESNs) to predict the statistics of extreme events in a turbulent flow. We train the ESNs on small datasets that lack information about the extreme events. We asses whether the networks are able to extrapolate…

Fluid Dynamics · Physics 2022-04-13 Alberto Racca , Luca Magri

In stochastic resonance, a periodically forced Brownian particle in a double-well potential jumps between minima at rare increments, the prediction of which poses a major theoretical challenge. Here, we use a path-integral method to find a…

Data Analysis, Statistics and Probability · Physics 2020-04-02 L. T. Giorgini , S. H. Lim , W. Moon , J. S. Wettlaufer

In this perspective, we consider the development of statistical hydrodynamics, focusing on the way in which the intrinsic stochasticity of turbulent phenomena was identified and is being explored. A major purpose of our discussion is to…

Fluid Dynamics · Physics 2026-01-01 Gregory L. Eyink , Nigel Goldenfeld

The models that are based of fractional derivatives should be highlighted among promising new models to describe turbulent fluid flows. In the present work, a steady-state flow in a duct is considered under the condition that the turbulent…

Numerical Analysis · Computer Science 2016-07-20 Alexander G. Churbanov , Petr N. Vabishchevich

A simple analytical model for a turbulent flow is proposed, which considers the flow as a collection of localized spatial structures that are composed of elementary "cells" in which the state of the particles (atoms or molecules) is…

Fluid Dynamics · Physics 2013-04-09 Sergei F. Chekmarev

The complex flow features resulting from the laminar-turbulent transition (LTT) in a sudden expansion pipe flow, with expansion ratio of 1:2 subjected to an inlet vortex perturbation is investigated by means of direct numerical simulations…

The onset of shear flow turbulence is characterized by turbulent patches bounded by regions of laminar flow. At low Reynolds numbers localized turbulence relaminarises, raising the question of whether it is transient in nature or it becomes…

Fluid Dynamics · Physics 2014-03-31 M. Avila , A. P. Willis , B. Hof