AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier–Stokes Solutions

Florent Bonnet · Jocelyn Mazari · Paola Cinnella · Patrick Gallinari

Hall J #1014

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

[ Abstract ]
[ Paper [ Slides [ Poster [ OpenReview
Wed 30 Nov 2 p.m. PST — 4 p.m. PST


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.

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