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95 results for “Aerodynamics”
Unsteady Aerodynamics Open Data Set
<p>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.<br> The four experiments and selected data are:</p> <ul> <li>University of Glasgow dynamic stall experiments: NACA0015 and NACA0030 airfoils tested at sinusoidal type motion of the pitch.</li> <li>NREL OSU experiments: LS(1)0417MOD, NACA4415 and S809 airfoils tested at sinusoidal type motion of the pitch.</li> <li>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.</li> <li>ForWind airfoil tests under tailored inflow turbulence: DU00W212 airfoil with laminar flow, open grid condition and one sinusoidal dynamic grid condition.</li> </ul>
Wind tunnel test data for the evaluation of the aerodynamic coefficients of an antenna mast with ancillaries.
<p>This dataset comprises measured data and results from static wind tunnel tests conducted in April 2024 at the Giovanni Solari Wind Tunnel Facility (GS-WinDyn). The tests aim to assess the drag, lift, and moment coefficients <span>of an antenna mast designed as a triangular lattice tower, equipped with both linear and discrete ancillary components.</span> The wind tunnel experiments are carried out under both smooth and turbulent flow conditions using a scaled 3D model of the antenna mast. Five ancillary configurations, based on predominant patterns observed, are tested. Drag forces, lift forces and moments are measured using two six-component force balances attached to the ends of the model, while downstream three-component velocity data is captured by a Cobra probe. For each configuration, aerodynamic coefficients are determined for angles of attack ranging from 0° to 360°, with increments of up to 10°. The dataset provides the measured data and the obtained aerodynamic coefficients and it has significant reuse potential in several applications: comparison with experimental wind tunnel data, validation of analytical and numerical CFD models with similar configurations, estimation of wind loads due to ancillary structures, and characterization of wake effects.</p>
Correlations for aerodynamic force coefficients of non-spherical particles in compressible flows
<div> <div><span># Data repository for the paper</span></div> <br> <div><span># </span><span>_Correlations for aerodynamic force coefficients of non-spherical particles in compressible flows_</span></div> <br> <div><span>Corresponding author:</span></div> <div><span>Berend.van.Wachem@multiflow.org</span></div> <br> <div><span>This repository consists of the data and exemplary python scripts for the paper "Correlations for aerodynamic force coefficients of non-spherical particles in compressible flows" by Christian Gorges, Victor Chéron, Anjali Chopra, Fabian Denner and Berend van Wachem. The data stored in this repository have the following data format:</span></div> <br> <div><span>-</span><span> .csv files consisting the raw data of the simulations used for the coefficient plots in the results' chapter of the paper</span></div> <div><span> </span></div> <div><span>-</span><span> .py files containing python scripts serving as examples on how to use and plot the raw data of the .csv files and the correlations</span></div> <br> <div><span>The main folders of this repository are named as the non-spherical particle shapes (Oblate, Prolate, Rod-like) and a folder with the data on which the correlations are based.</span></div> <br> <div><span>The folders named after the non-spherical particle shapes contain the raw simulation data. For instance, the Oblate folder contains the individual .csv files of all simulations of the oblate spheroid for all Reynolds numbers, Mach numbers, and angles of attack.</span></div> <br> <div><span>The folder Correlations/ consists of the temporally averaged drag, lift and torque coefficients, which are written in .csv files and stored in the folder ResultsCoefficients/, as well as Python scripts for plotting the correlations. </span></div> <br> <div><span>The naming style of the raw data files and the subfolders for each section is explained in the following:</span></div> <br> <div><span>The file names of the .csv files within the particle shape folders consist of the Reynolds number, followed by the Mach number and the angle of attack. For example "log_Re100M2_0_alpha_90.csv" consists of the data for a Reynolds number of 100, a Mach number of 2.0 and an angle of attack of 90 degrees. The content in the .csv files is given as: "%f,%f,%f,%f\n" which corresponds to "Physical time, drag coefficient, lift coefficient, torque coefficient". The first row in each file gives the headers of each column.</span></div> <br> <div><span>The .csv files in the folder Correlations/ResultsCoefficients/ are split per coefficient, shape, and particle Reynolds numbers, which can be identified by the name of the .csv file. For instance, the results obtained for the lift coefficient of</span></div> <div><span>the prolate spheroid particle for at a particle Reynolds numbers 100 for all orientation angles and Mach numbers are given in the file:</span></div> <div><span>"Prolate_100_CL.csv". In these files, the results are ordered per orientation angle (rows) and Mach</span></div> <div><span>number (column). </span></div> <br> <div><span>The python scripts have been tested with Python 3.11.5.</span></div> <br> <div><span>PlotCoefficients.py is an example python script to read the .csv files and plot the aerodynamic force coefficients as it is done in the results section of the paper.</span></div> <br> <div><span>The python scripts in the directory Correlations/ are split in three main functions in two files:</span></div> <div><span>-</span><span> Getter.py (read the .csv files storing the coefficients - separate functions</span></div> <div><span> for the drag, lift and torque coefficients)</span></div> <div><span>-</span><span> ManuscriptCorrelation.py with all the correlations derived in this work for an</span></div> <div><span> effective implementation in any solver, and a plotting function to have visual</span></div> <div><span> representation of the correlations.</span></div> <div><span>-</span><span> generalmain.py (calls Getter and Plotter)</span></div> <br> <div><span>The Getter is called from the generalmain.py file. (run python3 generalmain.py) so that all coefficients can be gathered in a 3D array.</span></div> <div><span>First dimension : Reynolds number</span></div> <div><span>Second dimension : Orientation angle</span></div> <div><span>Third dimension : Mach number</span></div> <div><span>The user just needs to give the absolute path to the folder ResultsCoefficients/.</span></div> <br> <div><span>This project has received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), grant number 447633787.</span></div> </div>
Aerodynamic model comparison for an X-shaped vertical-axis wind turbine
<p>This repository can be used to reproduce the power, thrust, blade forces, and vertical induction from the journal paper 'Aerodynamic model comparison for an X-shaped vertical-axis wind turbine (https://doi.org/10.5194/wes-2023-115)'. The processing and plotting files are in MATLAB format (*.m). As an alternative to MATLAB, Octave can be used to run these files as well.</p>
Enhanced Field Rotor Aerodynamics - ECN
<p>In these full scale aerodynamic test program local aerodynamic quantities (forces, inflow velocities, inflow angles) are measured at several radial positions along the blade.<br>The aerodynamic experiments at ECN were performed in two different phases:</p> <ul> <li><strong>The non-rotating phase (1993-1994).</strong> In this phase the blade was mounted vertically on a non-rotating facility in the free stream. This test aimed at acquiring data at free stream conditions without rotational effects. Furthermore the instrumentation could be tested relatively easy in order to prepare for the next phase.</li> <li><strong>The rotating phase (1995-1997).</strong> In this phase the blade was mounted on the HAT-25 experimental wind turbine and data were acquired under rotation. Furthermore some non-rotating measurements have been repeated. In this report the measurements from the rotating phase are reported only.</li> </ul> <p>The HAT-25 wind turbine is located at ECN near Petten in the Netherlands. The prevailing wind direction is from South-West, at which most of the measurements are taken. In this direction, the terrain upstream of the turbine is obscured by dunes over around 600 m. As a result, the turbulence intensities at the site may depend strongly on the conditions. Generally, the values of the turbulence intensities are between 5% and 20%.<br>Time series measurements are supplied, in which the angle of attack ranges from negative values to deep stall values. Also measurements at yaw misalignment and at stand still have been supplied. Also sectional profile coefficients under rotating and 2D conditions are stored. </p> <p>The SourceDatabase.zip archive contains original database with data file formats and conventions harmonized (across all IEA Task 18: Enhanced Field Rotor Aerodynamics experiments) in order to make the full database easy accessible (see "<strong>Related works</strong>"). The rotating phase measurements have been merged into two files: "rottim_prof_merged.csv" (containing profile, SCADA and SHM data) and "rottim_p_merged.csv" (containing aerodynamic pressure measurements) to facilitate import of all the data. </p>
Global aerodynamic parameters in 1982-2017 based on GLASS LAI (v1.1 update to 2020)
<p>The bulk surface properties, including canopy height (ht), zero-plane displacement height (d), and roughness length (z0) are crucial biophysical parameters that influence momentum, energy, and mass exchanges at the land-atmosphere interface. Here we provide a global estimate of bulk aerodynamic parameters during 1982-2017 at 0.05-degree spatial resolution and monthly temporal interval, using remote-sensed leaf area index, canopy height, and a turbulent transfer model. Two separate datasets are generated using GLASS LAI and GIMMS LAI. This dataset includes the products based on GLASS LAI. </p> <p>Details can be found: </p> <p>Liu, Y., Guo, W., Huang, H., Ge, J., Qiu, B., 2021. Estimating global aerodynamic parameters in 1982–2017 using remote-sensing data and a turbulent transfer model. Remote Sens. Environ. 260, 112428. https://doi.org/https://doi.org/10.1016/j.rse.2021.112428</p>
A method to determine local aerodynamic force coefficients from fiber-resolved 3D flow simulations around a staple fiber yarn: simulation data
<p>This data set contains all set-up files and necessary scripts to run the simulations performed in the publication <a href="https://doi.org/10.1007/s11044-024-09992-2" target="_blank" rel="noopener">"A method to determine local aerodynamic force coefficients from fiber-resolved 3D flow simulations around a staple fiber yarn"</a>, published in Multibody System Dynamics.</p>
Data to the paper "Drop impact onto a moving substrate: Aerodynamic rebound"
<p>These are the data used in the paper :<br>"Drop Impact onto a Moving Substrate: Aerodynamic Rebound."</p>
Aerodynamic Roughness Controlled by Wind Direction, with Implications for Glacial Surface Energy Balance and Melt Rate
<p>This repository includes raw datasets, Python scripts, and output data products associated with the MRes project '<span>Aerodynamic Roughness Controlled by Wind Direction, with Implications for Glacial Surface Energy Balance and Melt Rate</span>', by Josh Abrahams, University of Leeds. </p>
Dataset: Benchmark Problems for Simulating Hyperloop Aerodynamics
<p>Dataset for the results contained within 'Benchmarks problems for Simulating Hyperloop Aerodynamics', Lang et al., Phys. Fluids 36 (2024). doi.org/10.1063/5.0229914</p> <p>In this study, 3 benchmark problems for simulating the aerodynamics of a Hyperloop system are proposed. This dataset gives the raw data used to generate the figures and also coordinates of the geometries used in the simulations.</p>
Aerodynamic characterisation of porous fairings : pressure drop and Laser Doppler Velocimetry measurements
<p>Aviation has become a mass transportation industry, and all prospective studies foresee growth in this sector. Among the challenges, noise in the vicinity of airports has gone from a marginal annoyance to a real public health concern. To address this problem, as well as others such as fuel consumption, aircraft manufacturers are considering radically new aircraft architectures that could enter service quickly. In the meantime, however, the noise of traditional aircraft must be reduced significantly. Aircraft noise, during takeoff and landing, results primarily from a combination of (i) engine noise, which is generated by the fan and jet, and (ii) airframe noise, primarily due to the landing gear (LG) and high lift devices (HLD), the latter including slats and trailing edge flaps, which are deployed at low speeds to increase lift. During takeoff, engine noise remains dominant, while on approach and landing, engines operate at low speeds (typically 50% of N1), and airframe noise becomes a significant contributor, especially for newer aircraft equipped with latest generation turbofans. Its mitigation is therefore of primary interest.<br> However, due to the strong integration constraints imposed by other disciplines than acoustics on components such as LGs and HLDs, the development of noise reduction technologies (NRT) on these airframe components has been limited. This lack of breakthroughs is also due to the complexity of flow physics, and thus our still limited knowledge of airframe noise generation mechanisms. The noise of the landing gear, slats and flaps has been studied on a real and reduced scale, mainly on the basis of experimental means. The maturity of numerical simulations now allows to study the mechanisms of the noise sources on various complex configurations. Moreover, numerical simulation methods can be sufficiently accurate to predict the noise generated by such configurations. In order to take the next step in the maturity of numerical prediction, these NIRs must be accurately evaluated and modeled. Experimental data based on academic configurations are therefore needed to validate the new tools and numerical models. One promising NRT is the use of a fairing in front of the landing gear to reduce the noise of this system. The present study aims at collecting an experimental database (pressure drop and turbulence characteristics) of several fairing solutions in order to have validation test cases for CFD simulation and thus develop new models for such complex geometries. The fairing samples are thus tested on the "Acoustic and Aerothermal Bench" (B2A), by measuring the pressure drop of each sample and the flow field by Laser Doppler Velocimetry (LDV). The experimental methodology will be presented first. The database will then be described. Some technical validations will also be proposed on the basis of a comparison with the literature.</p>
Aerodynamics of a Floating Wind Turbine Scale Model with Active Control
<p>This dataset is about the aerodynamic response of a wind turbine scale model subjected to prescribed platform pitch motion, as it would occur during normal operation of floating wind turbines. The wind turbine has active control functionalities representative of those of utility-scale turbines. The turbine controller is the Reference Open Source Controller (ROSCO), which has been implemented in MATLAB Simulink for wind tunnel testing and co-simulation with OpenFAST. The dataset contains an OpenFAST model of the scaled turbine and its controller. </p>
Dataset in support of "Laboratory wave and stress measurements quantify the aerodynamic sheltering in extreme winds" by Tan et al. (2023, JGR: Oceans)
<p><strong>Data introduction:</strong></p> <p> There are three datasets used in this research: dataset 1 from Wind-Only (WO) experiment, dataset 2 from JONSWAP experiment with 10-cm significant wave height (J10), and dataset 3 from monochromatic wave experiment with 7.5-cm amplitude (M7.5).</p> <p> Each dataset contains quality-controlled data of the respective experiment mentioned above. The data files are in the mat (MATLAB) format. There are 9 mat files in each dataset, and each file represents data collected under a specific wind forcing condition, with the fan frequency in the 10-50 Hz range with 5 Hz interval.</p> <p> Each file contains four variables: <em>seg</em> (water elevation time series collected by the wave-wire with the units of <em>m</em>, demeaned and detrended), <em>U</em> (along-tank, downwind component of wind sampled by the IRGASON anemometer with the units of <em>m/s</em>), <em>V</em> (cross-tank component of wind sampled by the IRGASON anemometer with the units of <em>m/s</em>), and <em>W</em> (vertical component of wind collected by the IRGASON anemometer with the units of m/s). All four variables were collected at a sampling frequency of 20 Hz.</p>
Figures: Vortex model of the aerodynamic wake of airborne wind energy systems
<p>Figures in .pdf, .png and .fig format.</p><p>Figures in .fig format can be opened with MATLAB or other open source programming languages (e.g., Python thought the command scipy.io.loadmat or Octave)</p><p>Figures were updated after: Trevisi, F., Croce, A., and Riboldi, C. E. D.: Corrigendum to "Vortex model of the aerodynamic wake of airborne wind energy systems", published in Wind Energ. Sci., 8, 999–1016, 2023, https://doi.org/10.5194/wes-8-999-2023-corrigendum"</p>
Data and code from: Body oscillations couple with wing flapping to reduce aerodynamic power in wild silkmoth flight
Open the record for dataset details and reuse information.
Aerodynamics code used in Wind Energy Science paper "Comparison of a coupled near- and far-wake model with a free-wake vortex code"
<p>This research code has been developed from the start of my PhD as a first step before the HAWC2 implementation of the near wake model.</p> <p>It can be used to make aerodynamic computations of a stiff wind turbine rotor, and it includes</p> <ul> <li>A BEM and far wake model implementation based on the one in HAWC2</li> <li>An attached flow unsteady airfoil aerodynamics model including the modifications described in the WES article</li> <li>Most importantly a near wake model implementation including all major modifications except the recent stand still extension presented at TORQUE 2016</li> </ul> <p>All the data files need to be in a subfolder 'NREL_5MW' located in the same folder as the compiled source code.</p> <p>With the present (hardcoded) settings, the program will simulate the NREL 5 MW reference turbine for 650 seconds, with blade vibrations according to different prescribed mode shapes after steady state is reached. The aerodynamics model is a coupled near and far wake model. The integrated aerodynamic work during 1 period of the different prescribed vibrations will be output in the file 'aerowork.out' .</p> <p>The NREL 5 MW turbine is described in:</p> <p>Jonkman, J., Butterfield, S., Musial,W., and Scott, G.: Definition of a 5-MW Reference Wind Turbine for Offshore System Development, National Renewable Energy Laboratory, 2009.</p>
Datasets and code of the manuscript 'Insights into the Aerodynamic versus Radiometric Surface Temperature Debate in Thermal-based Evaporation Modeling'
<p>This contains the datasets and codes that were used to generate the results and discussions in the manuscript</p>
RETALT1 – AERODYNAMIC DATA BASE 2.0
<p>The excel file contains the Aerodynamic Data Base for the RETALT1 configuration. The pdf file contains the necessary information to use this data base</p>
RETALT2 – AERODYNAMIC DATA BASE 2.0
<p>The excel file contains the Aerodynamic Data Base for the RETALT2 configuration. The pdf file contains the necessary information to use this data base. The CFD Excel sheet contains the results of CFD simulations for different conditions and control surface deflections</p>
Aerodynamic data of WiRE-01 Blade
<p>Tabular lift and drag coefficients of WiRE-01 blade at Reynolds number from 4000 to 10000, obtained from wall-resolved LES in OpenFOAM.</p> <p>For more details about the simulation setup, see Ref [1]. For more details about the blade design, see Ref. [2]. </p> <p>[1] Revaz, T., Lin, M., & Porté-Agel, F. (2020). Numerical framework for aerodynamic characterization of wind turbine airfoils: Application to miniature wind turbine WiRE-01. <em>Energies</em>, <em>13</em>(21), 5612.</p> <p>[2] Bastankhah, M., & Porté-Agel, F. (2017). A new miniature wind turbine for wind tunnel experiments. Part i: Design and performance. <em>Energies</em>, <em>10</em>(7), 908.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.