Effects of dimensional wall temperature on Reynolds stress budgets in a supersonic turbulent channel flow with thermally perfect gas

2018 ◽  
Vol 32 (8-9) ◽  
pp. 315-325 ◽  
Author(s):  
Xiaoping Chen ◽  
Fei Fei
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.


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

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.


2001 ◽  
Vol 13 (4) ◽  
pp. 1016-1027 ◽  
Author(s):  
Costas D. Dimitropoulos ◽  
R. Sureshkumar ◽  
Antony N. Beris ◽  
Robert A. Handler

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