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

TL;DR

AirfRANS is a comprehensive dataset for two-dimensional steady-state Reynolds-Averaged Navier-Stokes equations over airfoils, enabling improved development and evaluation of data-driven surrogate models in fluid dynamics.

Contribution

This work introduces AirfRANS, a novel dataset for RANS equations over airfoils, along with metrics and baseline deep learning models for various generalization scenarios.

Findings

Deep learning models can predict surface stress forces with high accuracy.

Metrics and visualization tools help assess model performance in boundary layer predictions.

Baseline models demonstrate potential and limitations in different data regimes.

Abstract

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.