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
Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 675 ◽  
Author(s):  
T.-W. Lee

There are two components in this work that allow for solutions of the turbulent channel flow problem: One is the Galilean-transformed Navier-Stokes equation which gives a theoretical expression for the Reynolds stress (u′v′); and the second the maximum entropy principle which provides the spatial distribution of turbulent kinetic energy. The first concept transforms the momentum balance for a control volume moving at the local mean velocity, breaking the momentum exchange down to its basic components, u′v′, u′2, pressure and viscous forces. The Reynolds stress gradient budget confirms this alternative interpretation of the turbulence momentum balance, as validated with DNS data. The second concept of maximum entropy principle states that turbulent kinetic energy in fully-developed flows will distribute itself until the maximum entropy is attained while conforming to the physical constraints. By equating the maximum entropy state with maximum allowable (viscous) dissipation at a given Reynolds number, along with other constraints, we arrive at function forms (inner and outer) for the turbulent kinetic energy. This allows us to compute the Reynolds stress, then integrate it to obtain the velocity profiles in channel flows. The results agree well with direct numerical simulation (DNS) data at Reτ = 400 and 1000.


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

2009 ◽  
Vol 23 (03) ◽  
pp. 509-512 ◽  
Author(s):  
SUHUA SHEN ◽  
JIANZHONG LIN

To explore the rheological property in turbulent channel flow of fiber suspensions, the equation of probability distribution function for mean fiber orientation and the Reynolds averaged Navier-Stokes equation with the term of additional stress resulted from fibers were solved with numerical methods to get the distributions of the mean velocity and turbulent kinetic energy. The simulation results show that the effect of fibers on turbulent channel flow is equivalent to an additional viscosity. The turbulent velocity profiles of fiber suspension become gradually sharper by increasing the fiber concentration and/or decreasing the Reynolds number. The turbulent kinetic energy will increase with increasing Reynolds number and fiber concentration.


Author(s):  
Xiaoping Chen ◽  
Hua-Shu Dou ◽  
Qi Liu ◽  
Zuchao Zhu ◽  
Wei Zhang

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.


Author(s):  
Jyoti P Panda ◽  
Hari V Warrior

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.


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