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Prediction of compressible turbulent boundary layer via a symmetry-based length model

Published online by Cambridge University Press:  22 October 2018

Zhen-Su She*
Affiliation:
State Key Laboratory for Turbulence and Complex Systems and Department of Mechanics, College of Engineering, Peking University, Beijing, 100871, China
Hong-Yue Zou
Affiliation:
State Key Laboratory for Turbulence and Complex Systems and Department of Mechanics, College of Engineering, Peking University, Beijing, 100871, China
Meng-Juan Xiao
Affiliation:
State Key Laboratory for Turbulence and Complex Systems and Department of Mechanics, College of Engineering, Peking University, Beijing, 100871, China
Xi Chen
Affiliation:
Department of Mechanical Engineering, Texas Tech University, TX 79409-1021, USA
Fazle Hussain
Affiliation:
Department of Mechanical Engineering, Texas Tech University, TX 79409-1021, USA
*
Email address for correspondence: she@pku.edu.cn

Abstract

A recently developed symmetry-based theory is extended to derive an algebraic model for compressible turbulent boundary layers (CTBL) – predicting mean profiles of velocity, temperature and density – valid from incompressible to hypersonic flow regimes, thus achieving a Mach number ($Ma$) invariant description. The theory leads to a multi-layer analytic form of a stress length function which yields a closure of the mean momentum equation. A generalized Reynolds analogy is then employed to predict the turbulent heat transfer. The mean profiles and the friction coefficient are compared with direct numerical simulations of CTBL for a range of $Ma$ from 0 (e.g. incompressible) to 6.0 (e.g. hypersonic), with an accuracy notably superior to popular current models such as Baldwin–Lomax and Spalart–Allmaras models. Further analysis shows that the modification is due to an improved eddy viscosity function compared to competing models. The results confirm the validity of our $Ma$-invariant stress length function and suggest the path for developing turbulent boundary layer models which incorporate the multi-layer structure.

Type
JFM Papers
Copyright
© 2018 Cambridge University Press 

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