Modelling and analysis of turbulent datasets using Auto Regressive Moving Average processes-Reference-Cited by-同舟云学术

Modelling and analysis of turbulent datasets using Auto Regressive Moving Average processes

Published:2014-10 Issue:10 Volume:26 Page:105101
ISSN:1070-6631
Container-title:Physics of Fluids
language:en
Short-container-title:Physics of Fluids

Author:

Faranda Davide¹,Pons Flavio Maria Emanuele²,Dubrulle Bérengère¹,Daviaud François¹,Saint-Michel Brice³,Herbert Éric⁴,Cortet Pierre-Philippe⁵

Affiliation:

1. Laboratoire SPHYNX, Service de Physique de l'Etat Condensé, DSM, CEA Saclay, CNRS URA 2464, 91191 Gif-sur-Yvette, France

2. Dipartimento di Scienze Statistiche, Universitá di Bologna, Via delle Belle Arti 41, 40126 Bologna, Italy

3. Institut de Recherche sur les Phénomènes Hors Equilibre, Technopole de Chateau Gombert, 49 rue Frédéric Joliot Curie, B.P. 146, 13 384 Marseille, France

4. Université Paris Diderot - LIED - UMR 8236, Laboratoire Interdisciplinaire des Énergies de Demain, Paris, France

5. Laboratoire FAST, CNRS, Université Paris-Sud, France

Funder

CNRS

Publisher

AIP Publishing

Subject

Condensed Matter Physics,Fluid Flow and Transfer Processes,Mechanics of Materials,Computational Mechanics,Mechanical Engineering

Link

http://aip.scitation.org/doi/pdf/10.1063/1.4896637

Reference43 articles.

1. LOCAL STRUCTURE OF TURBULENCE IN AN INCOMPRESSIBLE VISCOUS FLUID AT VERY HIGH REYNOLDS NUMBERS

2. Supercritical transition to turbulence in an inertially driven von Kármán closed flow

3. Normalized kinetic energy as a hydrodynamical global quantity for inhomogeneous anisotropic turbulence

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