This dataset contains a curated set of 19,164 airfoil shapes from various applications and the data-driven design space of separable shape tensors (PGA space), which can be used as a parameter space for machine-learning applications focused on airfoil shapes. We constructed the airfoil dataset in two main stages. First, we identified 13 baseline airfoils from the NREL 5MW and IEA 15MW reference wind turbines. We reparameterized these shapes using least-squares fits of 8-order CST parametrizations, which involve 18 coefficients. By uniformly perturbing all 18 CST coefficients by +/-20% around each baseline airfoil, we generated 1,000 unique airfoils. Each airfoil was sampled with 1,001 shape landmarks whose x-coordinates followed a cosine distribution along the chord. This process resulted in a total of 13,000 airfoil shapes, each with 1,001 landmarks. In the second phase, we gathered additional airfoils from the extensive BigFoil database, which consolidates data from sources such as the University of Illinois Urbana-Champaign (UIUC) airfoil database, the JavaFoil database, the NACA-TR-824 database, and others. We undertook a thorough pre-processing step to filter out shapes with sparse, noisy, or incomplete data. We also removed airfoils with sharp leading edge and those exceeding our threshold for trailing edge thickness. Additionally, we thinned out the collection of NACA airfoils-- parametric sweeps of NACA airfoils with increasing thickness and camber present in BigFoil database-- by selecting every fourth step in the parameter sweeps. Finally, we regularized the airfoils by reparametrizing them with an 8-order CST parametrization (with 1,001 shape landmarks with x coordinated following cosine distribution along the chord) and removing airfoils with high reconstruction errors. This data pre-processing resulted in a set of 6,164 airfoils. In total, our curated airfoil dataset comprises 19,164 airfoils, each with 1,001 landmarks, and is stored in the curated_airfoils.npz file. Using this curated airfoil dataset, we utilized the separable shape tensors framework to develop a data-driven parameterization of airfoils based on principal geodesic analysis (PGA) of separable shape tensors. This PGA space is provided in PGAspace.npz file.

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.

This dataset is designed to test Machine-Learning techniques on Computational Fluid Dynamics (CFD) data.

It contains two-dimensional RANS simulations of the turbulent flow around NACA 4-digits airfoils, at fixed angle of attack (10 degrees) and at a fixed Reynolds number (3x10^6). The whole NACA family is spawned.

The present dataset contains 425 geometries, 2600 further geometries are published in accompanying repository (10.5281/zenodo.4106752).

For further information refer to: Schillaci, A., Quadrio, M., Pipolo, C., Restelli, M., Boracchi, G. "Inferring Functional Properties from Fluid Dynamics Features" 2020 25th International Conference on Pattern Recognition (ICPR) Milan, Italy, Jan 10-15, 2021

A selection of four different unsteady aerodynamic experiments have been done to prepare a database which will serve for the analysis, investigation and tool validation of airfoil unsteady behavior of wind turbine blades.
The four experiments and selected data are:

• University of Glasgow dynamic stall experiments: NACA0015 and NACA0030 airfoils tested at sinusoidal type motion of the pitch.
• NREL OSU experiments: LS(1)0417MOD, NACA4415 and S809 airfoils tested at sinusoidal type motion of the pitch.
• CENER unsteady airfoil pitching and flapping tests at DTU: NACA643-418 airfoil tested at sinusoidal type motion of the pitch, the flap and combined pitch and flap.
• ForWind airfoil tests under tailored inflow turbulence: DU00W212 airfoil with laminar flow, open grid condition and one sinusoidal dynamic grid condition.

2-D Coanda Airfoil with Tangential Wall Jet. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.

Rectangular Supercritical Wing (Ricketts) - design and measured locations are provided in an Excel file RSW_airfoil_coordinates_ricketts.xls . One sheet is with Non dimensional coordinates (RSW-nd) to be able to compare with other supercritical airfoils. The other sheet (RectSupercriticalWing) has the data which should be used to generate the grids. Benchmark Supercritical Wing are available in PDF file of tables. The data was OCR'd. Matlab code was written to read data line by line to be able to extract the data. The "*" flag associated with points that have deviation greater the specified value were replaced with blank space. Bad lines were omitted - lines that have non-numerical digits. Comparison of Rectangular Supercritical Wing (Ricketts), Benchmark Supercritical Wing, MBB_A3 2D Airfoil tested at DLR - comparison of theoretical and actual.

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy.

INVERTIBLE NEURAL NETWORKS

Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including:

• Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate
• Direct design using computational fluid dynamics (CFD): cost-prohibitive
• Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems

AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK

The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA).

NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES

We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes.

TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM

Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach.

This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this project.

This dataset includes the experimentally obtained airfoil polars of 4 airfoils optimised for inclined struts of a multi-megawatt VAWT. The data and measurement campaign are discussed in the PhD thesis of D. De Tavernier.

Airfoil Performance Degradation due to Roughness and Leading-edge Erosion. The zip file contains analysis, charts, and photos.

Dynamic stall noise is one of the potential sources of amplitude modulations associated with wind turbine noise. This phenomenon is related to the periodic separation and reattachment of the boundary layer on the wind turbine blade suction side during its rotation. Within the framework of the PIBE project (Predicting the Impact of Wind Turbine Noise - https://www.anr-pibe.com/en), experiments were conducted in the anechoic wind tunnel of the École Centrale de Lyon in order to characterize stall noise on a pitching airfoil in both static and dynamic conditions.

In version 1.0.0 of the database, data from the second campaign using an instrumented NACA63(3)418 airfoil in static and dynamic conditions are provided. The static data can be found in the file static_data_NACA63418.h5 that contains:

1. static wall pressure data : lift and pressure coefficients;
2. dynamic wall pressure data : Power Spectral Density (PSD) of fluctuating wall pressure;
3. far-field acoustic data : Power Spectral Density (PSD) of acoustic pressure.

The structure of the file is described in Tree_structure_static_data.pdf. To read the HDF5 file, the Matlab scripts given in read_HDF5_NACA63418_static_Matlab.zip can be used.

The dynamic data can be found in the file dynamic_data_NACA63418.h5 that contains:

1. static wall pressure data : phase-averaged lift coefficients;
2. dynamic wall pressure data : phase-averaged spectrograms of fluctuating wall pressure;
3. far-field acoustic data : phase-averaged spectrograms of acoustic pressure.

The structure of the file is described in Tree_structure_dynamic_data.pdf. To read the HDF5 file, the Matlab scripts given in read_HDF5_NACA63418_dynamic_Matlab.zip can be used. Only the results for a mean angle of attack of 15° and an amplitude of 15° are provided in this file.

2-D Coanda Airfoil with Tangential Wall Jet. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.

This dataset includes experimental data for an oscillating, pitching airfoil. This data set is used to demonstrate the use of VGs to control and suppress dynamic stall. The details of the experiment and data analysis are provided in: D. De Tavernier, C. Ferreira, A. Viré, et al., Controlling dynamic stall using vortex generators on a wind turbine airfoil, Renewable Energy, 2021, 172:1194-1211.

Correlated and ensemble averaged velocity fields of the flow around pitching and plunging flat plate airfoils from 2 facilities. Raw data were recorded in a water tunnel at BUAA and a wind tunnel at TU Darmstadt. Parameters of the flow, correlation algorithm and airfoil motion can be found in Kissing et al. 2020 (under revision). For additional data and information please contact kissing@sla.tu-darmstadt.de. Both datasets are used to compare the baseline case in the first part of the manuscript.

description: ### A2e Atmosphere to Electrons (A2e) is a new, multi-year, multi-stakeholder U.S. Department of Energy (DOE) research and development initiative tasked with improving wind plant performance and mitigating risk and uncertainty to achieve substantial reduction in the cost of wind energy production. The A2e strategic vision will enable a new generation of wind plant technology, in which smart wind plants are designed to achieve optimized performance stemming from more complete knowledge of the inflow wind resource and complex flow through the wind plant. Read more ... ### Project: Leading-edge Erosion Study (LEES) Project #### Airfoil Performance Degradation due to Roughness and Leading-edge Erosion Wind farms often underperform predicted power output by 10 to 30 percent relative to manufacturer predictions. A potential aerodynamic explanation is that blade roughness caused by insect impingement and leading-edge erosion decreases lift and drag as opposed to clean blades. These effects are difficult to test in the field because aerodynamic performance cannot be measured directly and can be affected by many factors that cannot be controlled in field experiments. This project provides aerodynamic performance data using wind tunnel measurements of representative inboard and outboard blade sections contaminated with various types and levels of roughness and leading-edge erosion. Results include aerodynamic load coefficients and measurements of laminar-to-turbulent transition location as functions of Reynolds number and angle of attack for various roughness configurations. Read more ... ### Dataset Overview Airfoil Performance Degradation due to Roughness and Leading-edge Erosion. The zip file contains analysis, charts, and photos. Read more ... --- Data access is enabled only after registering with A2e.; abstract: ### A2e Atmosphere to Electrons (A2e) is a new, multi-year, multi-stakeholder U.S. Department of Energy (DOE) research and development initiative tasked with improving wind plant performance and mitigating risk and uncertainty to achieve substantial reduction in the cost of wind energy production. The A2e strategic vision will enable a new generation of wind plant technology, in which smart wind plants are designed to achieve optimized performance stemming from more complete knowledge of the inflow wind resource and complex flow through the wind plant. Read more ... ### Project: Leading-edge Erosion Study (LEES) Project #### Airfoil Performance Degradation due to Roughness and Leading-edge Erosion Wind farms often underperform predicted power output by 10 to 30 percent relative to manufacturer predictions. A potential aerodynamic explanation is that blade roughness caused by insect impingement and leading-edge erosion decreases lift and drag as opposed to clean blades. These effects are difficult to test in the field because aerodynamic performance cannot be measured directly and can be affected by many factors that cannot be controlled in field experiments. This project provides aerodynamic performance data using wind tunnel measurements of representative inboard and outboard blade sections contaminated with various types and levels of roughness and leading-edge erosion. Results include aerodynamic load coefficients and measurements of laminar-to-turbulent transition location as functions of Reynolds number and angle of attack for various roughness configurations. Read more ... ### Dataset Overview Airfoil Performance Degradation due to Roughness and Leading-edge Erosion. The zip file contains analysis, charts, and photos. Read more ... --- Data access is enabled only after registering with A2e.

A NACA 0018 airfoil in freestream velocity is oscillated in longitudinal, transverse, and angle-of-attack directions with respect to the freestream velocity, known as surge, plunge, and pitch. The lift-based equivalence method introduces phase shifts between these three motions to construct in-phase sinusoidal components for maximum lift, waveform construction. Lift cancellation is also determined with the exact negative pitch and plunge motion amplitudes found from the equivalence method to achieve out-of-phase wave destruction. Lift cancellation occurs when a combination of these motions is sought to obtain a constant lift magnitude throughout the oscillation cycle. To achieve both equivalence and cancellation of lift, a prescribed pure pitch amplitude through the Theodorsen theory equates the corresponding equivalent plunge amplitude and pitch-plunge phase shift. These Theodorsen, linear superposition findings of pitch-plunge are leveraged toward the Greenberg theory to determine a closed-form, surge-pitch-plunge solution through the addition of a surge-plunge phase shift and optimal surge amplitude for lift cancellation. The lift cancellation surge-pitch-plunge amplitudes define the equivalence amplitude investigated here and theoretically limit the experiment to combinations of the first lift harmonic of the Greenberg theory. The analytical results are then compared with experimental lift force measurements and dye visualization. The normalized lift differences due to unsteady wake and boundary-layer behavior are examined to explore the extents of the Greenberg theory for these cases of lift-based equivalence and cancellation.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This dataset contains geometries, simulations results, and experimental results used in the paper "UAV Icing: Aerodynamic Degradation Caused by Intercycle and Runback Ice Shapes on an RG-15 Airfoil". The aim of the paper is to quantify the aerodynamic influence of intercycle and runback ice using wind tunnel tests and numerical simulations. This dataset contains the clean wing geometry and the geometries of both intercycle ice shapes, the lift and drag coefficients as simulated numerically, and the force measurements from the wind tunnel tests.

This dataset accompanies the paper "Aeroacoustic Benchmarking of Trailing-edge Noise from a NACA 633–018 Airfoil with Trailing-edge Serrations" published in the AIAA Journal. (https://doi.org/10.2514/1.J061630).

This dataset includes experimental data for an oscillating, pitching airfoil. This data set is used to demonstrate the use of VGs to control and suppress dynamic stall. The details of the experiment and data analysis are provided in: D. De Tavernier, C. Ferreira, A. Viré, et al., Controlling dynamic stall using vortex generators on a wind turbine airfoil, Renewable Energy, 2021, 172:1194-1211.

Current methods for obtaining pressure profiles on airfoils require complex pressure-scanner setups, which are expensive, very time-consuming, have multiple points of potential failure, and physically change the structure/loading on a test airfoil, especially in the case of smaller airfoils. This severely reduces the frequency at which such testing can be done, and the number of airfoils on which such testing can be practically performed.

To address these issues, IEM proposes the Low Profile Aerodynamic Testing Tape (LPATT) derived from and building upon millions of dollars in miniature wireless sensor research and patented and patent-pending technology and innovations. LPATT will be an ultra-thin, easily applied pressure and temperature wireless sensor node (other sensing parameters possible) that can be applied in any location with minimal time and effort and NO modifications to the airframe of any kind, with minimal impact on airstream passage. LPATT will match or exceed current-technology measurement accuracy, be self-powered, will store all information onboard and be capable of either real-time transmission of data or of transmitting the data later on demand, and may be designed with 50 or more pressure sensors with high sampling rates. Data interface between LPATT will be accomplished using a Data Collection Interface System (DCIS) to collect, process, and transmit data to outside systems.

The use of LPATT will drastically reduce the cost and time involved in wind-tunnel and flight test pressure sensing trials and may offer direct benefits in other areas such as providing the data for operation of performance adaptive aeroelastic wing shape controls in future aircraft designs. IEM will work with Dr. Michael Amitay of Rensselaer Polytechnic Institute, who will provide access to and expertise with a functional laboratory wind tunnel for tests and demonstrations of LPATT prototypes.