Transonic separated flow predictions with an eddy-viscosity/Reynolds-stress closure model

AIAA Journal ◽  
1987 ◽  
Vol 25 (2) ◽  
pp. 252-259 ◽  
Author(s):  
D. A. Johnson
Keyword(s):  
Reynolds Stress ◽  
Eddy Viscosity ◽  
Separated Flow ◽  
Closure Model ◽  
Reynolds Stress Closure Model

Second-Moment Closure Model for IC Engine Flow Simulation Using Kiva Code1

1999 ◽  
Vol 122 (2) ◽  
pp. 355-363 ◽  
Author(s):  
S. L. Yang ◽  
B. D. Peschke ◽  
K. Hanjalic
Keyword(s):  
Reynolds Stress ◽  
Eddy Viscosity ◽  
Flow Simulation ◽  
Reynolds Stress Model ◽  
Tensor Component ◽  
Moment Closure ◽  
Stress Model ◽  
Closure Model ◽  
Ic Engine ◽  
Second Moment

The flow and turbulence in an IC engine cylinder were studied using the SSG variant of the Reynolds stress turbulence closure model. In-cylinder turbulence is characterized by strong turbulence anisotropy and flow rotation, which aid in air-fuel mixing. It is argued that solving the differential transport equations for each turbulent stress tensor component, as implied by second-moment closures, can better reproduce stress anisotropy and effects of rotation, than with eddy-viscosity models. Therefore, a Reynolds stress model that can meet the demands of in-cylinder flows was incorporated into an engine flow solver. The solver and SSG turbulence model were first successfully tested with two different validation cases. Finally, simulations were applied to IC-engine like geometries. The results showed that the Reynolds stress model predicted additional flow structures and yielded less diffusive profiles than those predicted by an eddy-viscosity model. [S0742-4795(00)00101-0]

scholarly journals Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

2016 ◽  
Vol 807 ◽  
pp. 155-166 ◽  
Author(s):  
Julia Ling ◽  
Andrew Kurzawski ◽  
Jeremy Templeton
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reynolds Stress ◽  
Network Architecture ◽  
Eddy Viscosity ◽  
Deep Neural Networks ◽  
Test Cases ◽  
Neural Network Architecture ◽  
Stress Anisotropy ◽  
Anisotropy Tensor

There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS) turbulence models that are informed by and can represent a richer set of turbulence physics. This paper presents a method of using deep neural networks to learn a model for the Reynolds stress anisotropy tensor from high-fidelity simulation data. A novel neural network architecture is proposed which uses a multiplicative layer with an invariant tensor basis to embed Galilean invariance into the predicted anisotropy tensor. It is demonstrated that this neural network architecture provides improved prediction accuracy compared with a generic neural network architecture that does not embed this invariance property. The Reynolds stress anisotropy predictions of this invariant neural network are propagated through to the velocity field for two test cases. For both test cases, significant improvement versus baseline RANS linear eddy viscosity and nonlinear eddy viscosity models is demonstrated.

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