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 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

This dataset contains data from a three-dimensional large eddy simulation of Mach 0.3 flow over a NACA 0012 airfoil at Reynolds number 23,000, which features a transitional boundary layer, separation over a recirculation bubble, and a turbulent wake. The dataset contains 16,000 time-resolved snapshots of the mid-span and spanwise-averaged velocity fields. All data are stored within hdf5 files, and a Matlab script showing how the data can be read and manipulated is provided. Please see the ‘airfoilLES_README.pdf’ file for more information. We recommend using the ‘airfoilLES_example.zip’ file as an entry point to the dataset.;The dataset is part of “A database for reduced-complexity modeling of fluid flows” (see references below) and is intended to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. The paper introduces the flow setup and computational methods, describes the available data, and provides an example of how these data can be used for reduced-complexity modeling. Users of these data should cite the papers listed below.

Context

NASA dataset obtained from a series of aerodynamic and acoustic tests of two and three-dimensional airfoil blade sections conducted in an anechoic wind tunnel. The data was obtained from UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/airfoil+self-noise

Content

The NASA data set comprises different size NACA 0012 airfoils (n0012-il) (see LINK) at various wind tunnel speeds and angles of attack. The span of the airfoil and the observer position were the same in all of the experiments.

Source

• Donor: Dr. Roberto Lopez robertolopez '@' intelnics.com Intelnics

• Creators: Thomas F. Brooks, D. Stuart Pope and Michael A. Marcolini NASA

Attribute Information:

Input features: 1. f: Frequency in Hertzs [Hz]. 1. alpha: Angle of attack (AoA, α), in degrees [°]. 1. c: Chord length, in meters [m]. 1. U_infinity: Free-stream velocity, in meters per second [m/s]. 1. delta: Suction side displacement thickness (𝛿), in meters [m].

Output: 1. SSPL: Scaled sound pressure level, in decibels [dB].

Relevant Papers

T.F. Brooks, D.S. Pope, and A.M. Marcolini. Airfoil self-noise and prediction. Technical report, NASA RP-1218, July 1989.

K. Lau. A neural networks approach for aerofoil noise prediction. Master’s thesis, Department of Aeronautics. Imperial College of Science, Technology and Medicine (London, United Kingdom), 2006.

R. Lopez. Neural Networks for Variational Problems in Engineering. PhD Thesis, Technical University of Catalonia, 2008.

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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.

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.

Comparison of experimental dynamic stall with a modified Leishman-Beddoes model.

1 ReadMe,

2 Normal force and Moment coefficient for an oscillating flat plate undergoing dynamic stall,

3 Normal force and Moment coefficient for an oscillating NACA 0012 undergoing dynamic stall

4 Normal force and Moment coefficient for an oscillating NACA 0018 undergoing dynamic stall

This data is collected using hot-wire anemometry.A NACA 2412 aerofoil was used in this turbulence study. The standard baseline aerofoil has files beginning with 'aerofoil_nonns', and the aerofoil with negative stiffness inclusions has files beginning with 'aerofoil_ns167' since the elements have a Q ratio of 1.67.All aerofoils were tested at 4 angles of attack (0, 4, 8, 12) and 5 freestream velocities (10, 15, 20, 25, 30). This is in the relevant file name.Calibration data for the hot-wire is included. Use these values to convert from voltage to velocity. Use the morning calibration for the nonns aerofoil and afternoon calibration for the ns167 aerofoil.v1 and v1_m is voltage. For acquiring data, Step_2_acquisition.m was usedFor acquiring data, Step_1_calibration.m was used

ABSTRACT: Aiming to mitigate the aerodynamic heating during hypersonic re-entry, the aerothermodynamic optimization of aerospace plane airfoil leading edge is conducted. Lift-to-drag ratio at landing condition is taken as a constraint to ensure the landing aerodynamic performance. First, airfoil profile is parametrically described to be more advantageous during the optimization process, and the Hicks-Henne type function is improved considering its application on the airfoil leading edge. Computational Fluid Dynamics models at hypersonic as well as landing conditions are then established and discussed. Design of Experiment technique is utilized to establish the surrogate model. Afterwards, the previously mentioned surrogate model is employed in combination with the Multi-Island Genetic Algorithm to perform the optimization procedure. NACA 0012 is taken as the baseline airfoil for case study. The results show that the peak heat flux of the optimal airfoil during hypersonic flight is reduced by 7.61% at the stagnation point, while the lift-to-drag remains almost unchanged under landing condition.