Airfoil Computational Fluid Dynamics - 2k shapes, 25 AoA's, 3 Re numbers
This dataset contains aerodynamic quantities - including flow field values (momentum, energy, and vorticity) and summary values (coefficients of lift, drag, and momentum) - for 1,830 airfoil shapes computed using the HAM2D CFD (computational fluid dynamics) model. The airfoil shapes were designed using the separable shape tensor parameterization that encodes two-dimensional shapes as elements of the Grassmann manifold. This data-driven approach learns two independent spaces of parameter from a collection of sample airfoils. The first captures large-scale, linear perturbations, and the second defines small-scale, higher-order perturbations. For this dataset, we used the G2Aero database of over 19,000 airfoil shapes to learn a parameter space that captured a wide array of shape characteristics. We sampled airfoil designs over both parameter spaces to explore the full range of possible shape variations.
The aerodynamic quantities for the generated airfoil were obtained using the HAM2D code, which is a finite-volume Reynolds-averaged Navier-Stokes (RANS) flow solver. We employ a fifth-order WENO scheme for spatial reconstruction with Roe's flux difference scheme for inviscid flux and second-order central differencing for viscous flux. A preconditioned GMRES method is applied for implicit integration. The Spalart-Allmaras 1-eq turbulence model is used for the turbulence closure, and the Medida-Baeder 2-eq transition model is applied to account for the effects of laminar turbulent transition. The airfoil grid is generated with a total of 400 points on the airfoil surface, the initial wall-normal spacing of y+ = 1, and an outer boundary located at 300 chord lengths away from the wall. The CFD simulations are performed at a freestream Mach number of 0.1, for or three different Reynolds' numbers (3M, 6M, and 9M), and for 25 angles of attack from -4 deg. to 20 deg. with 1 degree increments. Across all these various parameters, this dataset includes the results from over 250,000 CFD simulations.
The simulations were performed using the Bridges-2 system at the Pittsburgh Supercomputing Center in February 2023 as part of the INTEGRATE project funded by the Advanced Research Projects Agency - Energy, in the U.S. Department of Energy. The data was collected, reformatted, and preprocessed for this OEDI submission in July 2023 under the Foundational AI for Wind Energy project funded by the U.S. Department of Energy Wind Energy Technologies Office. This dataset is intended to serve as a benchmark against which new artificial intelligence (AI) or machine learning (ML) tools may be tested. Baseline AI/ML methods for analyzing this dataset have been implemented, and a link to their repository containing those models has been provided.
The .h5 data file structure can be found in the GitHub Repository resource under explore_airfoil_2k_data.ipynb.
Citation Formats
TY - DATA
AB - This dataset contains aerodynamic quantities - including flow field values (momentum, energy, and vorticity) and summary values (coefficients of lift, drag, and momentum) - for 1,830 airfoil shapes computed using the HAM2D CFD (computational fluid dynamics) model. The airfoil shapes were designed using the separable shape tensor parameterization that encodes two-dimensional shapes as elements of the Grassmann manifold. This data-driven approach learns two independent spaces of parameter from a collection of sample airfoils. The first captures large-scale, linear perturbations, and the second defines small-scale, higher-order perturbations. For this dataset, we used the G2Aero database of over 19,000 airfoil shapes to learn a parameter space that captured a wide array of shape characteristics. We sampled airfoil designs over both parameter spaces to explore the full range of possible shape variations.
The aerodynamic quantities for the generated airfoil were obtained using the HAM2D code, which is a finite-volume Reynolds-averaged Navier-Stokes (RANS) flow solver. We employ a fifth-order WENO scheme for spatial reconstruction with Roe's flux difference scheme for inviscid flux and second-order central differencing for viscous flux. A preconditioned GMRES method is applied for implicit integration. The Spalart-Allmaras 1-eq turbulence model is used for the turbulence closure, and the Medida-Baeder 2-eq transition model is applied to account for the effects of laminar turbulent transition. The airfoil grid is generated with a total of 400 points on the airfoil surface, the initial wall-normal spacing of y+ = 1, and an outer boundary located at 300 chord lengths away from the wall. The CFD simulations are performed at a freestream Mach number of 0.1, for or three different Reynolds' numbers (3M, 6M, and 9M), and for 25 angles of attack from -4 deg. to 20 deg. with 1 degree increments. Across all these various parameters, this dataset includes the results from over 250,000 CFD simulations.
The simulations were performed using the Bridges-2 system at the Pittsburgh Supercomputing Center in February 2023 as part of the INTEGRATE project funded by the Advanced Research Projects Agency - Energy, in the U.S. Department of Energy. The data was collected, reformatted, and preprocessed for this OEDI submission in July 2023 under the Foundational AI for Wind Energy project funded by the U.S. Department of Energy Wind Energy Technologies Office. This dataset is intended to serve as a benchmark against which new artificial intelligence (AI) or machine learning (ML) tools may be tested. Baseline AI/ML methods for analyzing this dataset have been implemented, and a link to their repository containing those models has been provided.
The .h5 data file structure can be found in the GitHub Repository resource under explore_airfoil_2k_data.ipynb.
AU - Ramos, Dakota
A2 - Glaws, Andrew
A3 - King, Ryan
A4 - Lee, Bumseok
A5 - Doronina, Olga
A6 - Baeder, James
A7 - Vijayakumar, Ganesh
A8 - Grey, Zachary
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Renewable Energy Laboratory
DO - 10.25984/2222586
KW - energy
KW - power
KW - airfoil
KW - computational fluid dynamics
KW - CFD
KW - wind
KW - wind energy
KW - wind blade
KW - airfoil shape
KW - shape
KW - aerodynamics
KW - 2k
KW - Foundational AI for Wind Energy
KW - machine learning
KW - ml
KW - artificial intelligence
KW - AI
KW - HAM2D CFD model
KW - data
KW - benchmark
KW - wind power
KW - wind turbine
KW - simulation
KW - RANS
KW - processed data
LA - English
DA - 2023/02/10
PY - 2023
PB - National Renewable Energy Laboratory (NREL)
T1 - Airfoil Computational Fluid Dynamics - 2k shapes, 25 AoA's, 3 Re numbers
UR - https://doi.org/10.25984/2222586
ER -
Ramos, Dakota, et al. Airfoil Computational Fluid Dynamics - 2k shapes, 25 AoA's, 3 Re numbers. National Renewable Energy Laboratory (NREL), 10 February, 2023, Open Energy Data Initiative (OEDI). https://doi.org/10.25984/2222586.
Ramos, D., Glaws, A., King, R., Lee, B., Doronina, O., Baeder, J., Vijayakumar, G., & Grey, Z. (2023). Airfoil Computational Fluid Dynamics - 2k shapes, 25 AoA's, 3 Re numbers. [Data set]. Open Energy Data Initiative (OEDI). National Renewable Energy Laboratory (NREL). https://doi.org/10.25984/2222586
Ramos, Dakota, Andrew Glaws, Ryan King, Bumseok Lee, Olga Doronina, James Baeder, Ganesh Vijayakumar, and Zachary Grey. Airfoil Computational Fluid Dynamics - 2k shapes, 25 AoA's, 3 Re numbers. National Renewable Energy Laboratory (NREL), February, 10, 2023. Distributed by Open Energy Data Initiative (OEDI). https://doi.org/10.25984/2222586
@misc{OEDI_Dataset_5970,
title = {Airfoil Computational Fluid Dynamics - 2k shapes, 25 AoA's, 3 Re numbers},
author = {Ramos, Dakota and Glaws, Andrew and King, Ryan and Lee, Bumseok and Doronina, Olga and Baeder, James and Vijayakumar, Ganesh and Grey, Zachary},
abstractNote = {This dataset contains aerodynamic quantities - including flow field values (momentum, energy, and vorticity) and summary values (coefficients of lift, drag, and momentum) - for 1,830 airfoil shapes computed using the HAM2D CFD (computational fluid dynamics) model. The airfoil shapes were designed using the separable shape tensor parameterization that encodes two-dimensional shapes as elements of the Grassmann manifold. This data-driven approach learns two independent spaces of parameter from a collection of sample airfoils. The first captures large-scale, linear perturbations, and the second defines small-scale, higher-order perturbations. For this dataset, we used the G2Aero database of over 19,000 airfoil shapes to learn a parameter space that captured a wide array of shape characteristics. We sampled airfoil designs over both parameter spaces to explore the full range of possible shape variations.
The aerodynamic quantities for the generated airfoil were obtained using the HAM2D code, which is a finite-volume Reynolds-averaged Navier-Stokes (RANS) flow solver. We employ a fifth-order WENO scheme for spatial reconstruction with Roe's flux difference scheme for inviscid flux and second-order central differencing for viscous flux. A preconditioned GMRES method is applied for implicit integration. The Spalart-Allmaras 1-eq turbulence model is used for the turbulence closure, and the Medida-Baeder 2-eq transition model is applied to account for the effects of laminar turbulent transition. The airfoil grid is generated with a total of 400 points on the airfoil surface, the initial wall-normal spacing of y+ = 1, and an outer boundary located at 300 chord lengths away from the wall. The CFD simulations are performed at a freestream Mach number of 0.1, for or three different Reynolds' numbers (3M, 6M, and 9M), and for 25 angles of attack from -4 deg. to 20 deg. with 1 degree increments. Across all these various parameters, this dataset includes the results from over 250,000 CFD simulations.
The simulations were performed using the Bridges-2 system at the Pittsburgh Supercomputing Center in February 2023 as part of the INTEGRATE project funded by the Advanced Research Projects Agency - Energy, in the U.S. Department of Energy. The data was collected, reformatted, and preprocessed for this OEDI submission in July 2023 under the Foundational AI for Wind Energy project funded by the U.S. Department of Energy Wind Energy Technologies Office. This dataset is intended to serve as a benchmark against which new artificial intelligence (AI) or machine learning (ML) tools may be tested. Baseline AI/ML methods for analyzing this dataset have been implemented, and a link to their repository containing those models has been provided.
The .h5 data file structure can be found in the GitHub Repository resource under explore_airfoil_2k_data.ipynb.},
url = {https://data.openei.org/submissions/5970},
year = {2023},
howpublished = {Open Energy Data Initiative (OEDI), National Renewable Energy Laboratory (NREL), https://doi.org/10.25984/2222586},
note = {Accessed: 2025-04-25},
doi = {10.25984/2222586}
}
https://dx.doi.org/10.25984/2222586
Details
Data from Feb 10, 2023
Last updated Jan 2, 2024
Submitted Oct 13, 2023
Organization
National Renewable Energy Laboratory (NREL)
Contact
Ryan King
Authors
Research Areas
Keywords
energy, power, airfoil, computational fluid dynamics, CFD, wind, wind energy, wind blade, airfoil shape, shape, aerodynamics, 2k, Foundational AI for Wind Energy, machine learning, ml, artificial intelligence, AI, HAM2D CFD model, data, benchmark, wind power, wind turbine, simulation, RANS, processed dataDOE Project Details
Project Name Foundational AI for Wind Energy
Project Number FY23 AOP 1.3.0.403