Progressive data-augmented k-omega SST model as proposed by Amarloo and Rincón (2023) for OpenFOAM. Developed by Fluid Physics & Turbulence research group at Aarhus University.
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Progressive augmentation of the kOmegaSST turbulence model in OpenFOAM. This correction enhances the performance of kOmegaSST turbulence model in capturing the separation flow and secondary flow:
- Separation Flow: specifically optimized for separation after bumps or for cases with high adverse pressure gradient including periodic hills, curved backward-facing step, converging-diverging channel, and parametric bumps.
- Secondary Flow: specifically the Prandtl's second kind of secondary flow (aka corner flow) induced by heterogenities of the Reynolds stresses.
The implementation of the augmented model has been optimized for 2D cases but its applicability has been tested on 3D flows, yielding similar results. Five coefficients can be modified by the user to change the model's performance. Standard optimised values are given by default in the model. More information is available in the publications listed at the end of this file.
The code is known to work with OpenFOAM-v2312 and previous ESI versions.
Ali Amarloo [email protected] Mario Javier Rincón [email protected]
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Download the source code using git:
git clone https://github.com/AUfluids/KOSSTPDA.git -
Depends on your system, you might need to make the Allwmake file executable with the following commands:
cd KOSSTPDA chmod a+x Allwmake -
Compile the model by the following command:
./Allwmake
4.1. Add the following line to the controlDict of your case (for incompressible flow):
libs ( "libPDAIncompressibleTurbulenceModels" );
4.2. In case of compressible flow, add the following line to the controlDict of your case:
libs ( "libPDAcompressibleTurbulenceModels" );
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Specify the following in turbulentProperties.
RASModel kOmegaSSTPDA;
(We suggest first running your case with the standard kOmegaSST and then changing the model to kOmegaSSTPDA, to avoid possible errors)
NOTE: You might have to define the bijDelta term in system/fvSchemes file, here you have an example:
divSchemes
{
div(dev(((2*k)*bijDelta))) Gauss linear;
}
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(Optional) By default, the model activates both secondary and separation effects. If desired, one can change the models as follows:
separationMode 4; //optional - default:4 - off:0 | ModelI:1 | ModelII:2 | ModelIII:3 | ModelIV:4 secondaryMode 2; //optional - default:2 - off:0 | ModelI:1 | ModelII:2
If you use 0, the extra effects are deactivated, and the standard kOmegaSST is used. For info about the differences within these models, users are referred to the publications corresponding to the development of the each model (can be found at the end of the document)
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(Optional) In case of stability and convergence issues, we also suggest the following setting for the new model to be tested. Otherwise, these coefficients are automatically assigned with values corresponding to the models.
//Separation Flow coefficients separationMode 1; separationLambda1 1; separationLambda2 1; C0 -1; C1 0; C2 0; //Secodnary Flow coefficients secondaryMode 1; A0 -1; A1 0; A2 0;
You can also check the test cases of CBFS_Reb13700 and ductFlowAR1Reb3500 in the folder testCases.
Results of using KOSSTPDA for curved backward-facing step with bulk Reynolds number of 13700 I and III:
Results for a duct flow of aspect ratio 1 at bulk Reynolds number 3500 for Model II:
For more details about the separation and secondary flow effects, refer to the publications at:
- Progressive augmentation of turbulence models for flow separation by multi-case computational fluid dynamics driven surrogate optimization
- Progressive augmentation of Reynolds stress tensor models for secondary flow prediction by computational fluid dynamics driven surrogate optimisation
Please, cite this library using the following two publications:
Amarloo and Rincón (2023):
@article{amarloo2023progressive,
title={Progressive augmentation of turbulence models for flow separation by multi-case computational fluid dynamics driven surrogate optimization},
author={Amarloo, Ali and Rinc{\'o}n, Mario Javier and Reclari, Martino and Abkar, Mahdi},
journal={Physics of Fluids},
volume={35},
number={12},
year={2023},
publisher={AIP Publishing}
}
Rincón and Amarloo (2023)
@article{rincon2023progressive,
title = {Progressive augmentation of Reynolds stress tensor models for secondary flow prediction by computational fluid dynamics driven surrogate optimisation},
journal = {International Journal of Heat and Fluid Flow},
volume = {104},
pages = {109242},
year = {2023},
issn = {0142-727X},
doi = {https://doi.org/10.1016/j.ijheatfluidflow.2023.109242},
author = {Mario Javier Rincón and Ali Amarloo and Martino Reclari and Xiang I.A. Yang and Mahdi Abkar},
}
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