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Poster
AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier–Stokes Solutions
Florent Bonnet · Jocelyn Mazari · Paola Cinnella · Patrick Gallinari

Wed Nov 30 02:00 PM -- 04:00 PM (PST) @ Hall J #1014
in Poster Session 4 »

Surrogate models are necessary to optimize meaningful quantities in physical dynamics as their recursive numerical resolutions are often prohibitively expensive. It is mainly the case for fluid dynamics and the resolution of Navier–Stokes equations. However, despite the fast-growing field of data-driven models for physical systems, reference datasets representing real-world phenomena are lacking. In this work, we develop \textsc{AirfRANS}, a dataset for studying the two-dimensional incompressible steady-state Reynolds-Averaged Navier–Stokes equations over airfoils at a subsonic regime and for different angles of attacks. We also introduce metrics on the stress forces at the surface of geometries and visualization of boundary layers to assess the capabilities of models to accurately predict the meaningful information of the problem. Finally, we propose deep learning baselines on four machine learning tasks to study \textsc{AirfRANS} under different constraints for generalization considerations: big and scarce data regime, Reynolds number, and angle of attack extrapolation.

Keywords: Geometric Deep Learning · graph neural networks · point clouds · partial differential equations · Surrogate Models · Computational Fluid Dynamics · Navier–Stokes Equations · Numerical Simulation · Reduced Order Models · Physical Metrics · Meshes · Physically Constrained Deep Learning · Fluid Mechanics
[ Paper]  [ Slides]  [ Poster]  [ OpenReview] 

Author Information

Florent Bonnet (Extrality - Sorbonne Université)
Jocelyn Mazari (Extrality)
Paola Cinnella (Sorbonne University)

Paola Cinnella is a professor in Computational Fluid Dynamics at Sorbonne University. She graduated summa cum laude in Mechanical Engineering at the Politecnico di Bari in Italy. She got a PhD degree in Fluid Mechanics (summa cum laude) from Ecole Nationale Supérieure d’Arts et Métiers-ENSAM (currently, Arts et Métiers ParisTech) in Paris. Her research interest are numerical methods for fluid flow simulations, including coupling with machine learning algorithms, data-driven models, analysis of turbulent flows, optimization and uncertainty quantification.

Patrick Gallinari (Sorbonne Universite, Criteo AI Lab)

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