Tes-bo/UNet-CFD-FrameWork-cavity
This project trains a U-Net neural network to learn the mapping from boundary conditions (lid velocity and Reynolds number) to flow fields (pressure and velocity). The training data is generated automatically using OpenFOAM CFD simulations.
This project helps engineers and researchers quickly predict fluid flow patterns in a cavity. You provide basic conditions like the lid's speed and fluid properties (Reynolds number), and it rapidly outputs detailed pressure and velocity maps for the entire cavity. It's designed for anyone working with fluid dynamics who needs fast, approximate flow field simulations without waiting for traditional CFD computations.
Use this if you need to rapidly estimate pressure and velocity fields for cavity-driven flows based on boundary conditions, saving significant computational time compared to full CFD simulations.
Not ideal if you require highly precise, validated CFD results for critical engineering design or certification, or if your flow problem is not a cavity-driven flow.
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10
Forks
1
Language
C++
License
GPL-3.0
Category
Last pushed
Jan 13, 2026
Commits (30d)
0
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