UniFoil: A Universal Dataset of Airfoils in Transitional and Turbulent Regimes for Subsonic and Transonic Flows

Rohit Sunil Kanchi, Benjamin Melanson, Nithin Somasekharan, Shaowu Pan, Sicheng He

Keywords: Computational Fluid dynamics, Airfoil, Dataset, Turbulence

TL;DR: UniFoil is the largest open source RANS-based airfoil dataset with 500,000 simulations covering transitional and fully turbulent flows across incompressible to compressible regimes.

Abstract:

We present UniFoil, the largest publicly available universal airfoil database based on Reynolds-Averaged Navier–Stokes (RANS) simulations.
It contains over 500,000 samples spanning a wide range of Reynolds and Mach numbers, capturing both transitional and fully turbulent flows across incompressible to compressible regimes.
UniFoil is designed to support machine learning research in fluid dynamics, particularly for modeling complex aerodynamic phenomena.
Most existing datasets are limited to incompressible, fully turbulent flows with smooth field characteristics, thus overlooking the critical physics of laminar–turbulent transition and shock-wave interactions—features that exhibit strong nonlinearity and sharp gradients.
UniFoil fills this gap by offering a broad spectrum of realistic flow conditions.
In the database, turbulent simulations utilize the Spalart–Allmaras (SA) model, while transitional flows are modeled using an $e^N$-based transition prediction method coupled with the SA model.
The database includes a comprehensive geometry set comprising over 4,800 natural laminar flow (NLF) airfoils and 30,000 fully turbulent (FT) airfoils, effectively covering the diversity of airfoil designs relevant to aerospace, wind energy, and marine applications.
This database is also highly valuable for scientific machine learning (SciML), enabling the development of data-driven models that more accurately capture the transport processes associated with laminar–turbulent transition.
UniFoil is freely available under a permissive CC-BY-SA license.

Dataset URL: https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/VQGWC4

Primary Area: AL/ML Datasets & Benchmarks for physics (e.g. climate, health, life sciences, physics, social sciences)

Submission Number: 2115