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Airfoil Computational Fluid Dynamics - 2k shapes, 25 AoA's, 3 Re numbers

Publicly accessible License 

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

National Renewable Energy Laboratory (NREL). (2023). Airfoil Computational Fluid Dynamics - 2k shapes, 25 AoA's, 3 Re numbers [data set]. Retrieved from https://dx.doi.org/10.25984/2222586.
Export Citation to RIS
Ramos, Dakota, Glaws, Andrew, King, Ryan, Lee, Bumseok, Doronina, Olga, Baeder, James, Vijayakumar, Ganesh, and Grey, Zachary. Airfoil Computational Fluid Dynamics - 2k shapes, 25 AoA's, 3 Re numbers. United States: N.p., 10 Feb, 2023. Web. doi: 10.25984/2222586.
Ramos, Dakota, Glaws, Andrew, King, Ryan, Lee, Bumseok, Doronina, Olga, Baeder, James, Vijayakumar, Ganesh, & Grey, Zachary. Airfoil Computational Fluid Dynamics - 2k shapes, 25 AoA's, 3 Re numbers. United States. https://dx.doi.org/10.25984/2222586
Ramos, Dakota, Glaws, Andrew, King, Ryan, Lee, Bumseok, Doronina, Olga, Baeder, James, Vijayakumar, Ganesh, and Grey, Zachary. 2023. "Airfoil Computational Fluid Dynamics - 2k shapes, 25 AoA's, 3 Re numbers". United States. https://dx.doi.org/10.25984/2222586. https://data.openei.org/submissions/5970.
@div{oedi_5970, title = {Airfoil Computational Fluid Dynamics - 2k shapes, 25 AoA's, 3 Re numbers}, author = {Ramos, Dakota, Glaws, Andrew, King, Ryan, Lee, Bumseok, Doronina, Olga, Baeder, James, 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.}, doi = {10.25984/2222586}, url = {https://data.openei.org/submissions/5970}, journal = {}, number = , volume = , place = {United States}, year = {2023}, month = {02}}
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

Dakota Ramos

National Renewable Energy Laboratory NREL

Andrew Glaws

National Renewable Energy Laboratory NREL

Ryan King

National Renewable Energy Laboratory NREL

Bumseok Lee

National Renewable Energy Laboratory NREL

Olga Doronina

National Renewable Energy Laboratory NREL

James Baeder

The University of Maryland

Ganesh Vijayakumar

National Renewable Energy Laboratory NREL

Zachary Grey

National Institute of Standards and Technology NIST

DOE Project Details

Project Name Foundational AI for Wind Energy

Project Number FY23 AOP 1.3.0.403

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