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

04 Jun 2022, 01:00 (modified: 10 Oct 2022, 11:52)NeurIPS 2022 Datasets and Benchmarks Readers: Everyone
Keywords: Computational Fluid Dynamics, Navier–Stokes Equations, Partial Differential Equations, Physical Metrics, Geometric Deep Learning, Graph Neural Networks, Point Clouds, Surrogate Models, Reduced Order Models, Meshes, Physically Constrained Deep Learning, Numerical Simulation, Fluid Mechanics
TL;DR: We propose a high fidelity aerodynamic dataset of Reynolds-Averaged Navier–Stokes (RANS) simulations over airfoils
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 \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.
Supplementary Material: pdf
Dataset Url: Root folder: Machine learning ready dataset : Full OpenFOAM dataset: Scores and visualizations for the proposed models: GitHub repository to reproduce the experiments: GitHub repository to run new simulations:
License: Open Data Commons Open Database License v1.0. The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use a database while maintaining this same freedom for others.
Author Statement: Yes
Contribution Process Agreement: Yes
In Person Attendance: Yes
23 Replies