Summary of Geometry-aware Pinns For Turbulent Flow Prediction, by Shinjan Ghosh et al.
Geometry-aware PINNs for Turbulent Flow Prediction
by Shinjan Ghosh, Julian Busch, Georgia Olympia Brikis, Biswadip Dey
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA); Fluid Dynamics (physics.flu-dyn)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 parametric PINN surrogate model is a novel geometry-aware approach that can predict flow fields for NACA 4-digit airfoils in turbulent conditions, including unseen shapes and inlet flow conditions. This model combines a local+global embedding approach with a RANS formulation of the Navier-Stokes equations and a 2-equation k-epsilon turbulence model to predict turbulent flows at near real-time. The model is trained using limited CFD data from 8 different NACA airfoils and validated on unknown airfoils at unseen Reynolds numbers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new computer program can help design airplane wings that work better in bad weather. It uses special math equations to figure out how air moves around different shapes of wing, even ones it’s never seen before. This is important because airplane wings need to be good in all kinds of weather, not just calm and clear skies. |
Keywords
» Artificial intelligence » Embedding
