Physical Insights on Structure and Reynolds Stress in Turbulent Channel Flow

2006 ◽  
Author(s):  
Ihab Girgis ◽  
Garry Brown
Keyword(s):  
Reynolds Stress ◽  
Channel Flow ◽  
Turbulent Channel Flow

The effect of the Taylor-Görtler vortex on Reynolds stress transport in the rotating turbulent channel flow

2010 ◽  
Vol 53 (4) ◽  
pp. 725-734 ◽  
Author(s):  
ZiXuan Yang ◽  
GuiXiang Cui ◽  
ChunXiao Xu ◽  
Liang Shao ◽  
ZhaoShun Zhang
Keyword(s):  
Reynolds Stress ◽  
Channel Flow ◽  
Turbulent Channel Flow

Comparative study of Reynolds stress budgets of thermally and calorically perfect gases for high-temperature supersonic turbulent channel flow

2018 ◽  
Vol 233 (11) ◽  
pp. 4222-4234 ◽  
Author(s):  
Xiaoping Chen ◽  
Hua-Shu Dou ◽  
Qi Liu ◽  
Zuchao Zhu ◽  
Wei Zhang
Keyword(s):  
High Temperature ◽  
Reynolds Stress ◽  
Channel Flow ◽  
Velocity Gradient ◽  
Vibrational Energy ◽  
Turbulent Channel Flow ◽  
Mean Flow ◽  
Perfect Gas ◽  
Critical Position ◽  
Stress Budgets

To study the Reynolds stress budgets, direct numerical simulations of high-temperature supersonic turbulent channel flow for thermally perfect gas and calorically perfect gas are conducted at Mach number 3.0 and Reynolds number 4800 combined with a dimensional wall temperature of 596.30 K. The reliability of the direct numerical simulation data is verified by comparison with previous results ( J Fluid Mech 1995, vol. 305, pp.159–183). The effects of variable specific heat are important because the vibrational energy excited degree exceeds 0.1. The viscous diffusion, pressure–velocity gradient correlation, and dissipation terms in the Reynolds stress budgets for TPG, except the streamwise component, are larger than those for calorically perfect gas close to the wall. Compressibility-related term decreases when thermally perfect gas is considered. The major difference for both gas models is mainly due to variations in mean flow properties. Inter-component transfer related to pressure–velocity gradient correlation term can be distinguished into inner and outer regions, whose critical position is approximately 16 for both gas models.

Modeling the pressure strain correlation in turbulent flows using deep neural networks

2021 ◽  
pp. 095440622110429
Author(s):  
Jyoti P Panda ◽  
Hari V Warrior
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Turbulent Flows ◽  
Reynolds Stress ◽  
Channel Flow ◽  
Turbulence Modeling ◽  
Critical Role ◽  
Turbulent Channel Flow ◽  
Correlation Models ◽  
Turbulence Data

The pressure strain correlation plays a critical role in the Reynolds stress transport modeling. Accurate modeling of the pressure strain correlation leads to the proper prediction of turbulence stresses and subsequently the other terms of engineering interest. However, classical pressure strain correlation models are often unreliable for complex turbulent flows. Machine learning–based models have shown promise in turbulence modeling, but their application has been largely restricted to eddy viscosity–based models. In this article, we outline a rationale for the preferential application of machine learning and turbulence data to develop models at the level of Reynolds stress modeling. As an illustration, we develop data-driven models for the pressure strain correlation for turbulent channel flow using neural networks. The input features of the neural networks are chosen using physics-based rationale. The networks are trained with the high-resolution DNS data of turbulent channel flow at different friction Reynolds numbers (Reλ). The testing of the models is performed for unknown flow statistics at other Reλ and also for turbulent plane Couette flows. Based on the results presented in this article, the proposed machine learning framework exhibits considerable promise and may be utilized for the development of accurate Reynolds stress models for flow prediction.

Budgets of Reynolds stress, kinetic energy and streamwise enstrophy in viscoelastic turbulent channel flow

2001 ◽  
Vol 13 (4) ◽  
pp. 1016-1027 ◽  
Author(s):  
Costas D. Dimitropoulos ◽  
R. Sureshkumar ◽  
Antony N. Beris ◽  
Robert A. Handler
Keyword(s):  
Kinetic Energy ◽  
Reynolds Stress ◽  
Channel Flow ◽  
Turbulent Channel Flow

scholarly journals Simulation of Rotating Channel Flow With Heat Transfer: Evaluation of Closure Models

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Alan S. Hsieh ◽  
Sedat Biringen ◽  
Alec Kucala
Keyword(s):  
Heat Transfer ◽  
Heat Flux ◽  
Reynolds Stress ◽  
Channel Flow ◽  
Turbulence Models ◽  
Turbulent Channel Flow ◽  
Turbulent Heat ◽  
Stress Distributions ◽  
Mean Velocity ◽  
Closure Models

A direct numerical simulation (DNS) of spanwise-rotating turbulent channel flow was conducted for four rotation numbers: Rob=0, 0.2, 0.5, and 0.9 at a Reynolds number of 8000 based on laminar centerline mean velocity and Prandtl number 0.71. The results obtained from these DNS simulations were utilized to evaluate several turbulence closure models for momentum and heat transfer transport in rotating turbulent channel flow. Four nonlinear eddy viscosity turbulence models were tested and among these, explicit algebraic Reynolds stress models (EARSM) obtained the Reynolds stress distributions in best agreement with DNS data for rotational flows. The modeled pressure–strain functions of EARSM were shown to have strong influence on the Reynolds stress distributions near the wall. Turbulent heat flux distributions obtained from two explicit algebraic heat flux models (EAHFM) consistently displayed increasing disagreement with DNS data with increasing rotation rate.

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