AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions
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 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 AirfRANS under different constraints for generalization
considerations: big and scarce data regime, Reynolds number, and angle of
attack extrapolation.