Algebraic Reynolds Stress Model (ARSM) for Compressible Shear Flows

2011 ◽  
Author(s):  
Carlos Gomez ◽  
Sharath Girimaji
Keyword(s):  
Reynolds Stress ◽  
Shear Flows ◽  
Reynolds Stress Model ◽  
Stress Model ◽  
Algebraic Reynolds Stress Model

Performance Evaluation of a Non-Linear Explicit Algebraic Reynolds Stress Model for Surface Mounted Obstacles

2020 ◽  
Author(s):  
Benjamin H. Taylor ◽  
Tausif Jamal ◽  
D. Keith Walters
Keyword(s):  
Reynolds Stress ◽  
Eddy Viscosity ◽  
Numerical Data ◽  
Reynolds Stress Model ◽  
Stress Model ◽  
Rans Model ◽  
Non Linear ◽  
Additional Strain ◽  
Dynamic Hybrid ◽  
Algebraic Reynolds Stress Model

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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