mthsmcd/MachineLearningTurbulenceModels
OpenFOAM implementation of turbulence models driven by machine learning predictions.
This project offers tools for computational fluid dynamics (CFD) engineers and researchers to improve the accuracy of RANS simulations. By incorporating predictions from machine learning models, it refines how turbulence is modeled. Users provide RANS simulation data and high-fidelity (DNS/LES) fields, and the system outputs corrected turbulence parameters that lead to more accurate fluid flow predictions.
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Use this if you need to enhance the precision of your Reynolds-averaged Navier–Stokes (RANS) simulations for complex fluid flows by integrating data-driven turbulence model corrections.
Not ideal if you are not using OpenFOAM for your CFD simulations or do not have access to high-fidelity (DNS/LES) reference data for training your machine learning models.
Stars
48
Forks
9
Language
C++
License
GPL-3.0
Category
Last pushed
Jun 11, 2024
Commits (30d)
0
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