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A turbulence characteristic length scale for compressible flows
Authors:Jing-Lei Xu  You-Fu Song  Sheng-Cheng Ji  Jun-Qiang Bai
Institution:1. National Key Laboratory of Aircraft Engine, School of Energy and Power engineering, Beihang University, Beijing, China;2. Aeronautical Science and Technology Research Institute of COMAC, Beijing, China;3. School of Aeronautics, Northwestern Polytechnical University, Shanxi, China
Abstract:The current RANS models are generally established and calibrated under incompressible condition and these kinds of models could succeed in predicting many features of incompressible flows. However, these models extended to the high-speed, compressible flows are always less accurate. In the paper, a compressible von Kármán length scale is proposed for compressible flows considering the variable densities. It contains no empirical coefficients and is based on phenomenological theory. In the turbulent kinetic equation, the extra unclosed terms induced by non-constant densities are treated as dissipation terms and the equation is closed algebraically via the introduction of the von Kármán length scale. The original and the proposed von Kármán length scale lead to two different kinds of SAS (scale adaption simulation) models, KDO (turbulence kinetic energy dependent only) and CKDO (compressible KDO), respectively. Compressible mixing layer with significant compressibility is studied within standard k–?, k–ω, KDO turbulence models and their compressible versions. The compressibility effects such as the reduced mixing layer thickness, growth rate and turbulence intensity can be reproduced by CKDO. The new length scale can improve the performances of the model in predicting the mixing layer thickness, stream-wise velocity and Reynolds shear stresses when the convective Mach number is 0.8. Besides, the new length scale also leads to accurate computed growth rate when the convective Mach number ranges from 0.1 to 1.0.
Keywords:Turbulence model  mixing layer  compressibility correction  supersonic flow
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