Airfoil Computational Fluid Dynamics - 9k shapes, 2 AoA's

Description

This dataset contains aerodynamic quantities - including flow field values (momentum, energy, and vorticity) and summary values (coefficients of lift, drag, and momentum) - for 8,996 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 data, 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 fixed the linear deformations to be the mean over the database and sampled new shapes over a four-dimensional parameter space of higher-order perturbation. This sampling approaches allows for isolated analysis of non-linear airfoil shape deformations while holding other aspects (e.g., airfoil thickness) approximately constant. 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, Reynolds number of 9M, and at two angles of attack, 4 deg. and 12 deg. 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_9k_data.ipynb.

Resources

Name Format Description Link
21 Airfoil CFD data for 9k shapes in AWS data lake. Data is from a February 2023 simulation; collected, reformatted, and preprocessed for this OEDI submission in July 2023 under the Foundational AI for Wind Energy project. https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=aerodynamic_shapes%2F2D%2F9k_airfoils%2F
21 AWS public dataset program registry page for data released under the Wind AI Bench. The registry page contains information about dataset documentation, access, and contact, for each of the Wind AI Data Lake datasets. https://registry.opendata.aws/nrel-pds-windai/
21 Repository that contains demo notebooks to explore the data. https://github.com/NREL/windAI_bench/tree/main/airfoil_9k

Tags

  • wind-turbine
  • data
  • airfoil
  • processed-data
  • artificial-intelligence
  • benchmark
  • ml
  • wind-power
  • wind
  • wind-blade
  • ham2d-cfd-model
  • power
  • ai
  • foundational-ai-for-wind-energy
  • 9k
  • airfoil-shape
  • shape
  • wind-energy
  • cfd
  • computational-fluid-dynamics
  • aerodynamics
  • simulation
  • machine-learning
  • rans
  • energy

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