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We propose a Navier-Stokes-driven analysis of the mean and fluctuating wall shear stress (WSS) applied to turbulent channel flow data from direct numerical simulations at friction Reynolds numbers up to $Re_\tau\approx 2000$. Starting from…

Fluid Dynamics · Physics 2026-02-05 Le Yin , Yongyun Hwang , John Christos Vassilicos

We propose the viscous Camassa-Holm equations as a closure approximation for the Reynolds-averaged equations of the incompressible Navier-Stokes fluid. This approximation is tested on turbulent channel flows with steady mean. Analytical…

A model for the pseudo-turbulent Reynolds stress tensor in compressible flows through monodisperse particle clouds is developed based on data from particle resolved numerical simulations. This model extends previous models for the…

Fluid Dynamics · Physics 2025-05-09 Andreas Nygård Osnes , Magnus Vartdal

We solve a Bayesian inverse Reynolds-averaged Navier-Stokes (RANS) problem that assimilates mean flow data by jointly reconstructing the mean flow field and learning its unknown RANS parameters. We devise an algorithm that learns the most…

Fluid Dynamics · Physics 2024-12-17 A. Kontogiannis , P. Nair , M. Loecher , D. B. Ennis , A. Marsden , M. P. Juniper

Reynolds-averaged Navier-Stokes (RANS)-based transition modeling is widely used in aerospace applications but suffers inaccuracies due to the Boussinesq turbulent viscosity hypothesis. The eigenspace perturbation method can estimate the…

Fluid Dynamics · Physics 2022-11-08 Minghan Chu , Weicheng Qian

The Reynolds stress, or equivalently the average of the momentum flux, is key to understanding the statistical properties of turbulent flows. Both typical and rare fluctuations of the time averaged momentum flux are needed to fully…

Fluid Dynamics · Physics 2018-02-12 Freddy Bouchet , J. B. Marston , T. Tangarife

A one-dimensional version of the second-order transition model based on the sheared flow amplification by Reynolds stress and turbulence supression by shearing is presented. The model discussed in this paper includes a form of the Reynolds…

Plasma Physics · Physics 2009-11-13 Ivan Calvo , Benjamin A. Carreras

In the present paper, an aerodynamic investigation of a high-speed train is performed. In the first section of this article, a generic high-speed train against a turbulent flow is simulated, numerically. The Reynolds-Averaged Navier-Stokes…

A six-dimensional Navier-Stokes equation derived by one of the authors(ST) is solved for a turbulent mixing layer to demonstrate that it has a solitary wave solution. Turbulence intensities and Reynolds' stress are calculated using this…

chao-dyn · Physics 2007-05-23 Shunichi Tsuge , Burtsitsig Bai

To study the Reynolds stresses which describe turbulent momentum transport from turbulence affected by large-scale shear and rotation. Three-dimensional numerical simulations are used to study turbulent transport under the influences of…

Solar and Stellar Astrophysics · Physics 2009-11-19 J. E. Snellman , P. J. Käpylä , M. J. Korpi , A. J. Liljeström

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

Scaling of the Reynolds stresses has been sought by many researchers, since it provides a template of universal dynamical patterns across a range of Reynolds numbers. Various statistical and normalization schemes have been attempted, but…

Fluid Dynamics · Physics 2024-07-18 T. -W. Lee , J. E. Park

A novel machine learning algorithm is presented, serving as a data-driven turbulence modeling tool for Reynolds Averaged Navier-Stokes (RANS) simulations. This machine learning algorithm, called the Tensor Basis Random Forest (TBRF), is…

Fluid Dynamics · Physics 2020-04-20 Mikael L. A. Kaandorp , Richard P. Dwight

With this study we investigate the accuracy of deep learning models for the inference of Reynolds-Averaged Navier-Stokes solutions. We focus on a modernized U-net architecture, and evaluate a large number of trained neural networks with…

Machine Learning · Computer Science 2020-10-20 Nils Thuerey , Konstantin Weissenow , Lukas Prantl , Xiangyu Hu

Although Reynolds-Averaged Navier-Stokes (RANS) equations are still the dominant tool for engineering design and analysis applications involving turbulent flows, standard RANS models are known to be unreliable in many flows of engineering…

Computational Physics · Physics 2018-09-11 Jin-Long Wu , Jian-Xun Wang , Heng Xiao , Julia Ling

Modeling statistical properties of motion of a Lagrangian particle advected by a high-Reynolds-number flow is of much practical interest and complement traditional studies of turbulence made in Eulerian framework. The strong and nonlocal…

Statistical Mechanics · Physics 2007-05-23 A. K. Aringazin , M. I. Mazhitov

Iterative coarse-graining procedure based on Wyld's perturbation expansion is applied to the problem of Navier-Stokes turbulence. It is shown that the low-order calculation gives the fixed-point Reynolds number $ Re_{fp}$ (coupling…

Fluid Dynamics · Physics 2013-08-21 Victor Yakhot

Homogeneous anisotropic turbulence simulations are used to determine off-diagonal components of the Reynolds stress tensor and its parameterization in terms of turbulent viscosity and Lambda-effect. The turbulence is forced in an…

Astrophysics · Physics 2011-11-10 P. J. Käpylä , A. Brandenburg

With the ever-increasing use of Reynolds-Averaged Navier--Stokes (RANS) simulations in mission-critical applications, the quantification of model-form uncertainty in RANS models has attracted attention in the turbulence modeling community.…

Fluid Dynamics · Physics 2017-03-28 Heng Xiao , Jian-Xun Wang , Roger G. Ghanem

Accurate and generalizable Reynolds-averaged Navier-Stokes (RANS) models for turbulent flows rely on effective closures, but currently available closures are notoriously unreliable. Kassinos et al. (J. Fluid Mechanics, 428, pp. 213-248,…

Fluid Dynamics · Physics 2026-03-02 Aaron Miller , Sahil Kommalapati , Robert Moser , Petros Koumoutsakos