Summary of Reducing Spatial Discretization Error on Coarse Cfd Simulations Using An Openfoam-embedded Deep Learning Framework, by Jesus Gonzalez-sieiro et al.
Reducing Spatial Discretization Error on Coarse CFD Simulations Using an OpenFOAM-Embedded Deep Learning Framework
by Jesus Gonzalez-Sieiro, David Pardo, Vincenzo Nava, Victor M. Calo, Markus Towara
First submitted to arxiv on: 13 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Fluid Dynamics (physics.flu-dyn)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method enhances the quality of low-resolution computational fluid dynamics (CFD) simulations by reducing spatial discretization errors using deep learning. A feed-forward neural network interpolates velocities from cell centers to face values, approximating down-sampled fine-grid data. The framework incorporates OpenFOAM and allows automatic differentiation of CFD physics using a discrete adjoint code version. A fast communication method between TensorFlow and OpenFOAM accelerates training. Results show reduced errors (120% to 25%) for simulations within the training distribution and about 50% error reduction for those outside, with affordable training time and data samples due to local feature exploitation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses artificial intelligence to improve computer simulations of fluid flow. It takes a fine-resolution simulation and simplifies it into a low-resolution one, then uses a special kind of neural network to make the simplified simulation more accurate. The method works by looking at how velocities change between different points in space, and using that information to create a better approximation of what’s happening. This can be useful for simulating complex fluid flow problems, like those found in aircraft or ship design. |
Keywords
» Artificial intelligence » Deep learning » Neural network
