Initial Evaluation of an Algebraic Reynolds Stress Model for Compressible Turbulent Shear Flows

Author(s):  
Dennis Yoder



1977 ◽  
Vol 99 (3) ◽  
pp. 593-595 ◽  
Author(s):  
Ronald M. C. So

In [1] Gessner and Emery proposed a closure scheme for the Reynolds-stress equations such that the resultant equations are algebraic and can be solved for the various stress components for corner flows. This note summarizes the earlier work on such closure models for different kinds of turbulent shear flows which was omitted by Gessner and Emery and comments on the general applicability of the model proposed by Gessner and Emery.







Author(s):  
Benjamin H. Taylor ◽  
Tausif Jamal ◽  
D. Keith Walters

Abstract The presence of complex vortical structures, unsteady wakes, separated shear layers, and streamline curvature pose considerable challenges for traditional linear Eddy-Viscosity (LEV) models. Since Non-Linear Eddy Viscosity Models (NEV) models contain additional strain-rate and vorticity relationships, they can provide a better description for flows with Reynolds stress anisotropy and can be considered to be suitable alternatives to traditional EVMs in some cases. In this study, performance of a Non-Linear Explicit Algebraic Reynolds Stress Model (NEARSM) to accurately resolve flow over a surface mounted cube and a 3D axisymmetric hill is evaluated against existing experimental and numerical studies. Numerical simulations were performed using the SST k-ω RANS model, SST k-ω-NEARSM, SST-Multiscale LES model, and two variants of the Dynamic Hybrid RANS-LES (DHRL) model that include the SST k-ω and the SST k-ω-NEARSM as the RANS models. Results indicate that the SST k-ω RANS model fails to accurately predict the flowfield in the separated wake region and although the SST-NEARSM and SST-Multiscale LES models provide an improved description of the flow, they suffer from incorrect RANS-LES transition caused by Modeled Stress Depletion (MSD) and sensitivity to changes in grid resolution. The SST-DHRL and the SST-NEARSM-DHRL variants provide the best agreement to experimental and numerical data.







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