AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions
This provides a standardized dataset for researchers in computational fluid dynamics and machine learning to benchmark surrogate models, addressing a gap in real-world fluid dynamics data.
The authors tackled the lack of reference datasets for data-driven models in fluid dynamics by developing AirfRANS, a high-fidelity dataset for approximating Reynolds-Averaged Navier-Stokes solutions over airfoils, and introduced metrics and deep learning baselines to evaluate model performance under various generalization constraints.
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.