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Using the Lagrangian transport of momentum, the Reynolds stress can be expressed in terms of basic turbulence parameters. The Reynolds stress gradient represents the lateral transport of stream-wise momentum, balanced by the u2 transport,…

Fluid Dynamics · Physics 2019-12-11 T. -W. Lee

Using the Lagrangian transport of momentum, the Reynolds shear stress can be expressed in terms of basic turbulence parameters. In this view, the Reynolds stress gradient represents the lateral transport of streamwise momentum, balanced by…

Fluid Dynamics · Physics 2020-07-28 T. -W. Lee

We present a unique method for solving for the Reynolds stress in turbulent canonical flows, which is based on momentum balance for a control volume moving at the local mean velocity. Comparisons with experimental and computational data in…

Fluid Dynamics · Physics 2017-08-04 T. -W. Lee , Jung Eun Park

Using the Lagrangian transport analysis for the turbulence momentum, the Reynolds stress gradient can be expressed as a function of the local momentum flux and force terms. From this perspective of an observer moving at the local mean…

Fluid Dynamics · Physics 2024-07-09 T. W. Lee , J. E. Park

We present a unique method for solving for the Reynolds stress in turbulent canonical flows, based on the momentum balance for a control volume moving at the local mean velocity. A differential transform converts this momentum balance to a…

Fluid Dynamics · Physics 2017-08-17 T. -W. Lee

Models for solving the Reynolds-averaged Navier-Stokes equations are popular tools for predicting complex turbulent flows due to their computational affordability and ability to provide or estimate quantities of engineering interest.…

Fluid Dynamics · Physics 2024-09-10 Ty Homan , Omkar B. Shende , Ali Mani

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

The Reynolds-averaged Navier-Stokes (RANS) equations are widely used in turbulence applications. They require accurately modeling the anisotropic Reynolds stress tensor, for which traditional Reynolds stress closure models only yield…

Fluid Dynamics · Physics 2022-03-23 Haitz Sáez de Ocáriz Borde , David Sondak , Pavlos Protopapas

Reynolds-averaged Navier-Stokes (RANS) equations are widely used in engineering turbulent flow simulations. However, RANS predictions may have large discrepancies due to the uncertainties in modeled Reynolds stresses. Recently, Wang et al.…

Fluid Dynamics · Physics 2018-09-11 Jin-Long Wu , Heng Xiao , Eric Paterson

A new model for the "rapid" part of the velocity/pressure-gradient correlation in the Reynolds averaged Navier-Stokes equations is suggested. It is shown that in an inhomogeneous incompressible turbulent flow, the model that is linear in…

Fluid Dynamics · Physics 2007-05-23 Svetlana V. Poroseva

Reynolds Averaged Navier Stokes (RANS) models represent the workhorse for studying turbulent flows in industrial applications. Such single-point turbulence models have limitations in accounting for the influence of the non-local physics and…

Fluid Dynamics · Physics 2017-04-19 K. Duraisamy , Anand A. , G. Iaccarino

Turbulent problems in industrial applications are predominantly solved using Reynolds Averaged Navier Stokes (RANS) turbulence models. The accuracy of the RANS models is limited due to closure assumptions that induce uncertainty into the…

Fluid Dynamics · Physics 2018-02-20 Atieh Alizadeh Moghaddam , Amir Sadaghiyani

The Reynolds-Averaged Navier-Stokes (RANS) approach remains a backbone for turbulence modeling due to its high cost-effectiveness. Its accuracy is largely based on a reliable Reynolds stress anisotropy tensor closure model. There has been…

This work introduces a novel data-driven framework to formulate explicit algebraic Reynolds-averaged Navier-Stokes (RANS) turbulence closures. Recent years have witnessed a blossom in applying machine learning (ML) methods to revolutionize…

Fluid Dynamics · Physics 2023-01-24 Hongwei Tang , Yan Wang , Tongguang Wang , Linlin Tian

In the present paper a new data-driven model is proposed to close and increase accuracy of RANS equations. The divergence of the Reynolds Stress Tensor (RST) is obtained through a Neural Network (NN) whose architecture and input choice…

Fluid Dynamics · Physics 2022-10-19 Stefano Berrone , Davide Oberto

Reynolds-averaged Navier-Stokes (RANS) equations are presently one of the most popular models for simulating turbulence. Performing RANS simulation requires additional modeling for the anisotropic Reynolds stress tensor, but traditional…

Fluid Dynamics · Physics 2020-12-02 Rui Fang , David Sondak , Pavlos Protopapas , Sauro Succi

With the rising of modern data science, data--driven turbulence modeling with the aid of machine learning algorithms is becoming a new promising field. Many approaches are able to achieve better Reynolds stress prediction, with much lower…

Fluid Dynamics · Physics 2020-06-19 Xianwen Guo , Zhenhua Xia , Heng Xiao , Jinlong Wu , Shiyi Chen

Reynolds-averaged Navier--Stokes (RANS) simulations with turbulence closure models continue to play important roles in industrial flow simulations. However, the commonly used linear eddy viscosity models are intrinsically unable to handle…

Fluid Dynamics · Physics 2019-05-22 Jin-Long Wu , Heng Xiao , Rui Sun , Qiqi Wang

Numerical simulations based on Reynolds-Averaged Navier--Stokes (RANS) equations are widely used in engineering design and analysis involving turbulent flows. However, RANS simulations are known to be unreliable in many flows of engineering…

Fluid Dynamics · Physics 2017-09-19 Jinlong Wu , Rui Sun , Sylvain Laizet , Heng Xiao

Modelling hydrodynamic lubrication is crucial in the design of engineering components as well as for a fundamental understanding of friction mechanisms. The cornerstone of thin-film flow modelling is the Reynolds equation -- a…

Fluid Dynamics · Physics 2022-03-08 Hannes Holey , Andrea Codrignani , Peter Gumbsch , Lars Pastewka
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