Advanced Eddy-Viscosity and Reynolds-Stress Turbulence Model Simulations of Aerospace Applications

2006 ◽  
Author(s):  
Enda Bigarella ◽  
Joao Azevedo
Keyword(s):  
Reynolds Stress ◽  
Turbulence Model ◽  
Eddy Viscosity ◽  
Model Simulations ◽  
Aerospace Applications

Advanced Eddy-Viscosity and Reynolds-Stress Turbulence Model Simluations of Aerospace Applications

AIAA Journal ◽  
2007 ◽  
Vol 45 (10) ◽  
pp. 2369-2390 ◽  
Author(s):  
Enda Dimitri Vieira Bigarella ◽  
João Luiz F. Azevedo
Keyword(s):  
Reynolds Stress ◽  
Turbulence Model ◽  
Eddy Viscosity ◽  
Aerospace Applications

Modeling Separation-Induced Transition Using a Non-Linear Three Equation Turbulence Model and a Reynolds Stress Turbulence Model

2010 ◽  
Author(s):  
Zinon Vlahostergios ◽  
Kyros Yakinthos
Keyword(s):  
Transport Equation ◽  
Reynolds Stress ◽  
Turbulence Model ◽  
Eddy Viscosity ◽  
Turbulence Models ◽  
Viscosity Model ◽  
Transitional Flows ◽  
Eddy Viscosity Model ◽  
Non Linear ◽  
Stress Models

This paper presents an effort to model separation-induced transition on a flat plate with a semi-circular leading edge, by using two advanced turbulence models, the three equation non-linear model k-ε-A2 of Craft et al. [16] and the Reynolds-stress model of Craft [13]. The mechanism of the transition is governed by the different inlet velocity and turbulence intensity conditions, which lead to different recirculation bubbles and different transition onset points for each case. The use of advanced turbulence models in predicting the development of transitional flows has shown, in past studies, good perspectives. The k-ε-A2 model uses an additional transport equation for the A2 Reynolds stress invariant and it is an improvement of Craft et al. [12] non-linear eddy viscosity model. The use of the third transport equation gives improved results in the prediction of the longitudinal Reynolds stress distributions and especially, in flows where transitional phenomena may occur. Although this model is a pure eddy-viscosity model, it borrows many aspects from the more complex Reynolds-stress models. On the other hand, the use of an advanced Reynolds-stress turbulence model, such as the one of Craft [13], can predict many complex flows and there are indications that it can be applied to transitional flows also, since the crucial terms of Reynolds stress generation are computed exactly and normal stress anisotropy is resolved. The model of Craft [13], overcomes the drawbacks of the common used Reynolds-stress models regarding the computation of wall-normal distances and vectors in order to account for wall proximity effects. Instead of these quantities, it employs “normalized turbulence lengthscale gradients” which give the ability to identify the presence of strong inhomogeneity in a flow development, in an easier way. The final results of both turbulence models showed acceptable agreement with the experimental data. In this work it is shown that there is a good potential to model separation-induced transitional flows, with advanced turbulence modeling without any additional use of ad-hoc modifications or additional equations, based on various transition models.

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.

scholarly journals THERMODYNAMIC ANALYSIS OF WIND CATCHER WITH COOLING PADS USING SSG REYNOLDS STRESS TURBULENCE MODEL.

2021 ◽  
Author(s):  
Agarwal A ◽  
◽  
Pitso I ◽  
Letsatsi M.T ◽  
◽  
...  
Keyword(s):  
Reynolds Stress ◽  
Turbulence Model ◽  
Energy Requirement ◽  
Renewable Energy Sources ◽  
Thermal Conditions ◽  
Energy Sources ◽  
Cfd Analysis ◽  
Open Design ◽  
Air Inlet ◽  
Environment Sustainability

With increase in energy requirement, the researchers are looking for energy efficient passive ventilation techniques. The current design concept is based on environment sustainability and use of renewable energy sources is preferred over conventional energy sources. The current research investigates the wind catcher design with cooling pads using techniques of Computational Fluid Dynamics. The CAD model of wind catcher is designed using Creo design software and CFD analysis is conducted using ANSYS CFX software. The CFD analysis is directed at different air inlet velocities and SSG Reynolds stress turbulence model under steady state thermal conditions for both side open design and singe side open designs. The cooling pads have successfully reduced temperature up to 1.65 degrees for single side opening and 2.86 degrees for double side opening design. Maximum air flow rate is achieved with higher air inlet velocities for both design types.

Temperature Corrected Turbulence Model for High Temperature Jet Flow

2004 ◽  
Vol 126 (5) ◽  
pp. 844-850 ◽  
Author(s):  
Khaled S. Abdol-Hamid ◽  
S. Paul Pao ◽  
Steven J. Massey ◽  
Alaa Elmiligui
Keyword(s):  
High Temperature ◽  
Turbulence Model ◽  
Room Temperature ◽  
Eddy Viscosity ◽  
Mixing Layer ◽  
Supersonic Jet ◽  
Turbulence Models ◽  
Jet Flows ◽  
Viscosity Term ◽  
Mixing Layer Flows

It is well known that the two-equation turbulence models under-predict mixing in the shear layer for high temperature jet flows. These turbulence models were developed and calibrated for room temperature, low Mach number, and plane mixing layer flows. In the present study, four existing modifications to the two-equation turbulence model are implemented in PAB3D and their effect is assessed for high temperature jet flows. In addition, a new temperature gradient correction to the eddy viscosity term is tested and calibrated. The new model was found to be in the best agreement with experimental data for subsonic and supersonic jet flows at both low and high temperatures.

Modeling Reichardt’s Formula for Eddy-Viscosity in the Fluid Film of Tilting Pad Thrust Bearings

2017 ◽  
Author(s):  
Xin Deng ◽  
Brian Weaver ◽  
Cori Watson ◽  
Michael Branagan ◽  
Houston Wood ◽  
...  
Keyword(s):  
Shear Stress ◽  
Wall Shear Stress ◽  
Turbulence Model ◽  
High Speed ◽  
Eddy Viscosity ◽  
The Other ◽  
Fluid Film ◽  
Suitable Model ◽  
Velocity Gradients ◽  
Wall Shear

Oil-lubricated bearings are widely used in high speed rotating machines such as those used in the aerospace and automotive industries that often require this type of lubrication. However, environmental issues and risk-adverse operations have made water lubricated bearings increasingly popular. Due to different viscosity properties between oil and water, the low viscosity of water increases Reynolds numbers drastically and therefore makes water-lubricated bearings prone to turbulence effects. The turbulence model is affected by eddy-viscosity, while eddy-viscosity depends on wall shear stress. Therefore, effective wall shear stress modeling is necessary in producing an accurate turbulence model. Improving the accuracy and efficiency of methodologies of modeling eddy-viscosity in the turbulence model is important, especially considering the increasingly popular application of water-lubricated bearings and also the traditional oil-lubricated bearings in high speed machinery. This purpose of this paper is to study the sensitivity of using different methodologies of solving eddy-viscosity for turbulence modeling. Eddy-viscosity together with flow viscosity form the effective viscosity, which is the coefficient of the shear stress in the film. The turbulence model and Reynolds equation are bound together to solve when hydrodynamic analysis is performed, therefore improving the accuracy of the turbulence model is also vital to improving a bearing model’s ability to predict film pressure values, which will determine the velocity and velocity gradients in the film. The velocity gradients in the film are the other term determining the shear stress. In this paper, three approaches applying Reichardt’s formula were used to model eddy-viscosity in the fluid film. These methods are for determining where one wall’s effects begin and the other wall’s effects end. Trying to find a suitable model to capture the wall’s effects of these bearings, with aim to improve the accuracy of the turbulence model, would be of high value to the bearing industry. The results of this study could aid in improving future designs and models of both oil and water lubricated bearings.

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