aerogio/DL-ZE-turbulence-model
Coupling TensorlFlow deep learning models to OpenFOAM for RANS simulations
This project helps computational fluid dynamics (CFD) engineers and researchers in civil and architectural engineering apply deep learning to their simulations. It integrates a trained deep learning model into OpenFOAM to predict eddy viscosity, using velocity magnitude and wall distance as inputs. The output is a more efficient and potentially more accurate turbulence model for RANS simulations within a built environment context.
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Use this if you are performing RANS CFD simulations in OpenFOAM, particularly for built environment applications, and want to incorporate deep learning for turbulence modeling without altering your existing OpenFOAM solver.
Not ideal if you are not using OpenFOAM or are working on turbulence models that require full Reynolds Stress closure or other complex physics beyond zero-equation models.
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May 17, 2023
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