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Within the framework of random multiplicative energy cascade models of fully developed turbulence, expressions for two-point correlators and cumulants are derived, taking into account a proper conversion from an ultrametric to an Euclidean…

We introduce a minimal model of energy transfer through scales to describe, at a qualitative level, the subcritical transition between laminar and turbulent flows, viewed in a statistical physics framework as a discontinuous absorbing phase…

Fluid Dynamics · Physics 2026-01-08 Eric Bertin , Alex Andrix , Gaël Le Godais

We propose a framework for studying predictability of extreme events in complex systems. Major conceptual elements -- hierarchical structure, spatial dynamics, and external driving -- are combined in a classical branching diffusion with…

Geophysics · Physics 2010-03-02 Andrei Gabrielov , Vladimir Keilis-Borok , Sayaka Olsen , Ilya Zaliapin

Direct numerical simulations are used to investigate the individual dynamics of large spherical particles suspended in a developed homogeneous turbulent flow. A definition of the direction of the particle motion relative to the surrounding…

Fluid Dynamics · Physics 2015-06-16 Mamadou Cisse , Holger Homann , Jeremie Bec

Data available in literature from direct numerical simulations of two-dimensional turbulent channels by Lee & Moser (2015), Bernardini et al. (2014), Yamamoto and Tsuji (2018) and Orlandi et al. (2015) in a large range of Reynolds number…

Fluid Dynamics · Physics 2019-01-08 Paolo Orlandi

Machine learning algorithms have opened a breach in the fortress of the prediction of high-dimensional chaotic systems. Their ability to find hidden correlations in data can be exploited to perform model-free forecasting of spatiotemporal…

Optics · Physics 2022-06-08 S. Coulibaly , F. Bessin , M. G. Clerc , A. Mussot

Predicting extreme events in nonlinear dynamical systems is challenging due to a limited understanding of their statistical properties. This study numerically and theoretically investigates the statistical properties of infinite-modal maps…

Chaotic Dynamics · Physics 2026-04-07 Masaki Nakagawa

In many complex systems, large events are believed to follow power-law, scale-free probability distributions, so that the extreme, catastrophic events are unpredictable. Here, we study coupled chaotic oscillators that display extreme…

While deep learning has shown tremendous success in a wide range of domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, training, and inference of such models. In this paper, we aim…

Computational Physics · Physics 2020-06-16 Rui Wang , Karthik Kashinath , Mustafa Mustafa , Adrian Albert , Rose Yu

Elasto-inertial turbulence is a new state of turbulence found in flows with polymer additives . The dynamics of turbulence generated and controlled by such additives is investigated from the perspective of the coupling between polymer…

Fluid Dynamics · Physics 2015-06-12 Yves Dubief , Vincent E. Terrapon , Julio Soria

A calculational approach in fluid turbulence is presented. Use is made of the attracting nature of the fluid-dynamic dynamical system. An approach is offered that effectively propagates the statistics in time. Loss of sensitivity to an…

Fluid Dynamics · Physics 2010-05-18 Edsel A. Ammons

Forecasting extreme precipitation is essential yet challenging due to its rarity and complexity. We develop a large deviation framework to estimate the return times of extreme precipitation events. We first find that the Landau…

Statistical Mechanics · Physics 2026-04-14 Haotian Xie , Haoxian Liu , Jingfang Fan , Ying Tang

Scaling and structural evolutions are contemplated in a new perspective for turbulent channel flows. The total integrated turbulence kinetic energy remains constant when normalized by the friction velocity squared, while the total…

Fluid Dynamics · Physics 2021-05-19 T. -W. Lee

Max-stable processes are the natural extension of the classical extreme-value distributions to the functional setting, and they are increasingly widely used to estimate probabilities of complex extreme events. In this paper we broaden them…

Methodology · Statistics 2016-02-05 Peiman Asadi , Anthony C. Davison , Sebastian Engelke

This paper is concerned with the transition of the laminar flow in a duct of square cross-section. Like in the similar case of the pipe flow, the motion is linearly stable for all Reynolds numbers, rendering this flow a suitable candidate…

Fluid Dynamics · Physics 2010-07-02 Damien Biau , Alessandro Bottaro

We present rigorous estimates for some physical quantities related to turbulent and non-turbulent channel flows driven by a uniform pressure gradient. Such results are based on the concept of stationary statistical solution, which is…

Mathematical Physics · Physics 2007-05-23 Fabio Ramos , Ricardo Rosa , Roger Temam

The transition of the flow in a duct of square cross-section is studied. Like in the similar case of the pipe flow, the motion is linearly stable for all Reynolds numbers; this flow is thus a good candidate to investigate the 'bypass' path…

Fluid Dynamics · Physics 2010-07-02 Damien Biau , Houssam Soueid , Alessandro Bottaro

There are two components in this work that allow solutions of the turbulent channel problem: one is the Galilean-transformed Navier-Stokes equation which gives a theoretical expression for the Reynolds stress; and the second the maximum…

Fluid Dynamics · Physics 2019-07-24 T. -W. Lee

Using various techniques from dynamical systems theory, we rigorously study an experimentally validated model by [Barkley et al., Nature, 526:550-553, 2015], which describes the rise of turbulent pipe flow via a PDE system of reduced…

Fluid Dynamics · Physics 2022-10-19 Maximilian Engel , Christian Kuehn , Björn de Rijk

A central obstacle to understanding the route to turbulence in wall-bounded flows is that the flows are composed of complex, highly fluctuating, and strongly nonlinear states. In the case of pipe flow, models have deepened our understanding…

Fluid Dynamics · Physics 2026-02-24 Santiago J. Benavides , Dwight Barkley
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