Progressively data-augmented explicit algebraic Reynolds stress model (PDA-EARSM) enhanced for the prediction of secondary flows based on RANS $k-\omega$ SST turbulence model. Developed by Fluid Mechanics & Turbulence research group at Aarhus University.

PDA2DEARSM - Implementation of the PDA2DEARSM RANS model as proposed by Rincón and Amarloo (2023) for OpenFOAM.

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Implementation of the progressively data-augmented explicit algebraic Reynolds stress model (PDAEARSM) enhanced for the prediction of secondary flows based on standard RANS $k-\omega$ SST. The model has been developed for 2D flows but its applicability has been tested on 3D flows, yielding similar results. Three coefficients can be modified by the user to change the model's performance. Standard optimised values are given by default in the model.

The code is known to work with OpenFOAM v2312 and previous ESI versions.

Mario Javier Rincón [email protected] Ali Amarloo [email protected]

1. Download the source code using git:

     git clone https://github.com/AUfluids/PDA2DEARSM.git
   

2. Enter the directory where the source code has been extracted, and compile it by typing:

     wmake
   

3. Add the following line to the controlDict of your case:

     libs ( "libPDA2DEARSMIncompressibleTurbulenceModels" ) ;
   

4. Specify

     RASModel kOmegaSSTPDA2DEARSM;
   

in turbulentProperties.

5. Add the subdictionary

     PDA2DEARSM 2;  // 0: off | 1: ModelI | 2: ModelII
   

to turbulentProperties.

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;
     }

For more details, refer to the publication at: Progressive augmentation of Reynolds stress tensor models for secondary flow prediction by computational fluid dynamics driven surrogate optimisation or arXiv

Qualitative results for a duct flow of aspect ratio 1 at bulk Reynolds number 3500 for Model II: Quantitative results:

Please, cite this library using the following DOI: https://doi.org/10.1016/j.ijheatfluidflow.2023.109242

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},
     keywords = {Turbulence modelling, RANS, Progressive augmentation, Surrogate modelling, Kriging, Secondary flows}
     }

For release-specific DOIs, click on the badge and find the DOI corresponding to the desired version in the version list.

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