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274 results for “wakes”
A blind test on wind turbine wake modelling based on wind tunnel experiments: Phase I – The benchmark case
<p>This data set ("Data files.zip") contains the wind tunnel measurement data from Phase I of the Blind test on wind turbine wake modelling based on wind tunnel experiments organised during the TWEET-IE project (www.tweet-ie.eu).</p> <p>This updated version <strong>replaces</strong> the older versions 1.0.0 (https://doi.org/10.5281/zenodo.10566401), 1.1.0 (https://doi.org/10.5281/zenodo.11370112), 2.0 (https://doi.org/10.5281/zenodo.12188194) and 2.1 (https://doi.org/ 10.5281/zenodo.13918935). In comparison to the previous version 2.1 the data documentation has been updated to follow the template of the TWEET-IE project documents, indicating the Grant Agreement Number with the European Union and the Call Topic of the project.</p> <p>All tests were conducted in the closed-loop, low-speed boundary layer wind tunnel of the Chair of Aerodynamics and Fluid Mechanics at Technische Universität München (TUM). The experiments concerned two wind turbines, aligned with the flow, one downstream of the other, at a distance of 5 diameters. For Phase I, no control was applied to the wind turbine models, which were operating at constant RPM. The turbine models, designed and manufactured by TUM, were instrumented with multiple sensors and actuators and had a diameter of 1.1M. Measurements include velocity, power and loads on the turbines. A detailed description of the experimental set up can be found in the accompanying document ("Data documentation.pdf"). </p> <p>File "Submission procedure.zip" includes the format description and the templates of the output data that should be submitted by the participants in the blind test comparison.</p>
Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer
<p>Dataset of the paper "Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer" published in Remote Sensing [1].</p> <p>[1] Brugger P, Fuertes FC, Vahidzadeh M, Markfort CD, Porté-Agel F. Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer. <em>Remote Sensing</em>. 2019; 11(19):2247. https://doi.org/10.3390/rs11192247.</p>
Field measurements of wake meandering at a utility-scale wind turbine with nacelle-mounted Doppler lidars
<p>Dataset of the paper " Dataset of the paper "Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer" published in Remote Sensing [1]. " published in Wind Energy Science [1].</p> <p>[1] Brugger, P., Markfort, C., and Porté-Agel, F.: Field measurements of wake meandering at a utility-scale wind turbine with nacelle-mounted Doppler lidars, Wind Energ. Sci., 7, 185–199, https://doi.org/10.5194/wes-7-185-2022, 2022.</p>
Dataset for the paper "Aircraft wake vortices affecting airport wind measurements"
<p>Dataset in support of the paper "Aircraft wake vortices affecting airport wind measurements". The dataset contains the results of the manual classification as discussed in section 2 and 3 of the paper, details can be found there.</p><p>For each take-off, one row exists in the dataset. The columns are:</p><ul><li><i>takeoff_no</i>: int, Incrementing integer</li><li><i>timestamp</i>: string, UTC time the flight passes by the anemometer</li><li><i>flight_id</i>: string, Unique identifier for the flight</li><li><i>typecode</i>: string, ICAO aircraft typecode of the flight</li><li><i>wtc</i>: string: ICAO wake turbulence category of the flight</li><li><i>groundspeed_kts</i>: float, Groundspeed [kts] at the moment of passing by the anemometer</li><li><i>alt_above_thr_m</i>: float, Altitude above runway threshold [m] at the moment of passing by the anemometer</li><li><i>wind_speed_kts</i>: float, Wind speed [kts]. Computed as a mean of the sensor values for a 2min window ending at the crossing timestamp</li><li><i>wind_dir_deg</i>: float, Wind direction [°]. Computed as a mean of the sensor values for a 2min window ending at the crossing timestamp</li><li><i>is_event_visual_assessor_1</i>: int, Classification of assessor 1 of wheather the flight caused a wake that hit the anemometer</li><li><i>is_event_visual_assessor_2</i>: int, Classification of assessor 2 of wheather the flight caused a wake that hit the anemometer</li><li><i>is_event_visual_assessor_3</i>: int, Classification of assessor 3 of wheather the flight caused a wake that hit the anemometer</li><li><i>is_event_visual_sum</i>: int, Sum of classifications of 3 assessors (0 to 3)</li><li><i>is_event_wake_model</i>: float, Classification of wheather the flight caused a wake that hit the anemometer based on P2P wake model output (only applied to flights with a sum of classifications of 2 and more)</li><li><i>is_event</i>: int, Final classification of wheather the flight caused a wake that hit the anemometer</li></ul><p> </p>
Virtual RHI lidar scans retrieved in high-fidelity wake vortex simulations of landing aircraft under turbulent crosswind conditions - LES Lidar Simulator (LLS)
<p>This dataset contains two types of virtual measurements of multiple pulsed lidar integrated into high-fidelity hybrid RANS-LES wake vortex simulations of a landing Airbus A340 aircraft. Simulations have been performed for four different atmospheric conditions, varying in crosswind and therefore turbulence in the atmosphere.</p> <p>The two lidar simulator types are:</p> <ul> <li>LLS: LES Lidar Simulator (no noise, Signal to Noise Ratio >> 1). The analysis is based on the Range Gate Weighting function (RWF) [based on formulations in for example [1]]. Given the assumption of no instrument noise, no spectral analysis in the frequency domain is required.</li> <li>LLSn: LES Lidar Simulator with noise (realisitic Signal to Noise Ratio). Based on formulations in [2], where non-linear low-pass spatial filters accurately model real field measurements. Both background noise and aerosol noise contained in real lidar measurements are modeled. </li> </ul> <p>Provided are the raw lidar scans in RHI format, with the position and strength of the wake vortices within each lidar scan given by a pressure-vorticity tracking algorithm from the wake vortex simulation (simulation truth, ST). Misidentifications have been removed from the provided dataset. In addition to the labels for each wake vortex lidar scan, also wind background scans are provided for the crosswind simulation cases. These background scans give a better understanding of the prevailing atmospheric condition within which the aircraft lands. </p> <p>Furthermore the evaluation of the LLS scans using the Method of Radial Velocities (RV method) [3], a state-of-the-art wake vortex characterization method for lidar scans is provided for a subset of the LLS dataset. For analysing the impact of the RWF for LLS scans, selected lidar scans are also given in a 'naive' fashion (lidar scans assuming point measurements are possible).</p> <p><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></p> <h3><strong>Overview</strong></h3> <p><strong>In total each dataset (LLS and LLSn) contains 8 Aircraft landing simulation with associated </strong><strong>virtual lidar RHI scans:</strong></p> <ul> <li>2x no wind</li> <li>6x crosswind (specified at height b_0)</li> <li>0.5w_0 from port direction</li> <li>0.5w_0 from starboard direction</li> <li>1.0w_0 from port direction</li> <li>1.0w_0 from starboard direction</li> <li>2.0w_0 from port direction</li> <li>2.0w_0 from starboard direction</li> </ul> <p><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></p> <h3><strong>The dataset has 13 folders:</strong></h3> <div> <p><strong>POS1_POS2_POS8_scans = Individual scans of various simulations as well as background wind scans (no wake vortices). </strong></p> <p>POS1_POS2: 0_0, 0_5, 1_0, 2_0 corresponds to the wind, same as POS1_POS2 below</p> <p>POS8: LLS or LLSn, corresponding to the type of lidar simulator (without or with instrument noise, respectively)</p> </div> <div> </div> <div>Scan naming convention: POS1_POS2_POS3_POS4_POS5_POS6_POS7.csv</div> <div> </div> <div>Example: 0_5_D_248_8_161.2201878198302_168.4201878198237.csv</div> <div> </div> <div> <ul> <li>POS1_POS2: Together they form a factor which is multiplied with the initial descend speed of the wake vortex pair, w_0. The definition for w_0 can be found in [4]. It is common for the crosswind speed to be set according to the multiples of w_0. Due to the landing of the A340 aircraft, and the logarithmic nature of the wind simulation, we set the crosswind at the b_0 altitude of the simulation. For a definition of b_0, also see [4]. For the above example, 0.5w_0 is the crosswind speed. Note that every 26 lidar positions (POS4), the direction of the crosswind changes, if there is a crosswind.</li> </ul> </div> <div> - Crosswind approaches from port side: POS4: 0-25, 52-77, 104-129, 156-181, 208-233, 260-285</div> <div> - Crosswind approaches from starboard side: POS4: 26-51, 78-103, 130-155, 182-207, 234-259, 286-311</div> <div> <ul> <li>POS3: Label indicating which part of the numerical simulation this scan belongs to (a full landing simulation consists of A-D). A: Hybrid RANS-LES, B,C,D: temporal LES (in order). For the above example, D indicates that the lidar scan was recorded during the last part of the simulation. W: Prior to the wake vortex simulations with crosswind, a background wind scan for each lidar position is recorded.</li> </ul> </div> <div> <ul> <li>POS4: Specifies the number of the lidar, implications are the longitudinal position along the glide path of the aircraft. In the packs of lidar positions described in the description of POS1_POS2, the last lidar position is the closest to the touchdown point of the aircraft, smaller lidar positions within this pack are further away (in order) - also see the lid_plane_info directory. Note that the lidar position also adjusts the spectrum of the elevation angles used (and thus the size of the lidar scan). For that see the associated virtual lidar scan raw data. In the above example we have lidar position 248, thus crosswind approaching from starboard and rather mid-way of the longitudinal glide path direction.</li> </ul> </div> <div> <ul> <li>POS5: Specifies the number of the scan for this lidar position (POS4) and simulation (POS1__POS2). In the above example this is scan number 8.</li> </ul> </div> <div> <ul> <li>POS6_POS7: Specifies the simulation time within which the scan was measured during the aircraft landing simulation (POS1__POS2). In the above example this is 161.2201878198302 s to 168.4201878198237 s.</li> </ul> </div> <div>The scans purely with wind, an no wake vortices are stored in an additional directory within conv_scans. The case '0_0' (no wind) does not require wind scans. </div> <div> </div> <div>Within the scan files, we have a four (LLS) or five (LLSn) columns:</div> <div> <ul> <li>t: Simulation time</li> <li>ELE: Elevation angle [deg] of the lidar beam</li> <li>R: Range from lidar [m]</li> <li>v_r: LOS velocity (radial velocity) along lidar beam</li> <li>snr: Signal to Noise Ratio </li> </ul> <p> </p> <p><strong>wind_POS8_scans = Simulated lidar scans of the background wind for LLS and LLSn.</strong></p> <p>POS8: LLS or LLSn, corresponding to the type of lidar simulator (without or with instrument noise, respectively)</p> <p>Scans simulated here are in the same format as <em>POS1_POS2_POS8_scans</em>. Wind scans for point measurement scans are found in <em>naive_scan_subset </em>- the scans with POS4 = 0 should be used if more are available.</p> <p> </p> <p><strong>naive_scan_subset = Simulated lidar scans simulating velocity point measurements.</strong></p> </div> <div> <p>The format is the same as for <em>POS1_POS2_POS8_scans</em>, with the difference of being sorted first by wind subdirectories and then lidar number (LID).</p> <p> </p> <p><strong>labels = Labels of wake vortices for the above wake vortex scans. </strong></p> </div> <div>We have 4 main files, where each file represents the targets for one wind strength 0.0w_0, 0.5w_0, 1.0w_0, 2.0w_0.</div> <div>Within the labels files, we have a multitude of columns with different data:</div> <div> <ul> <li>#Time: Simulation (not scan) time.</li> <li>y_uw : lateral position in simulation domain of upwind (port) vortex.</li> <li>z_uw : height position in simulation domain of upwind (port) vortex.</li> <li>y_dw : lateral position in simulation domain of downwind (starboard) vortex.</li> <li>z_dw : height position in simulation domain of downwind (starboard) vortex.</li> <li>G_515_uw: Gamma 515 Circulation strength (see additional notes) of upwind (port) vortex [m^2/s].</li> <li>G_515_dw: Gamma 515 Circulation strength (see additional notes) of downwind (starboard) vortex [m^2/s].</li> <li>cr_uw: Core radius of upwind (port) vortex (in meters).</li> <li>cr_dw: Core radius of downwind (starboard) vortex (in meters).</li> <li>uw distance from lidar [m]: Horizontal distance of upwind (port) vortex from respective lidar.</li> <li>dw distance from lidar [m]: Horizontal distance of downwind (starboard) vortex from respective lidar.</li> <li>uw height [m]: Vertical distance of upwind (port) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m).</li> <li>dw height [m]: Vertical distance of downwind (starboard) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m).</li> <li>vortex age uw [s]: Age of upwind (port) vortex (after first generated by aircraft at respective measurement plane of lidar).</li> <li>vortex age dw [s]: Age of downwind (starboard) vortex (after first generated by aircraft at respective measurement plane of lidar).</li> <li>scan: Associated scan.</li> <li>LID: Associated lidar number.</li> </ul> <p>On top of the summarizing files for each simulation, in the subdirectory <em>individual</em>, the label for each scan is given in a separate file.</p> <p>The above gives information on the simulation truth, furthermore the file <em>labels_with_rv.csv</em> can be found here, where the following extra label columns are given for a subset of scans: [in the following Conv and Naive refer to LLS scans and point measurement scans, respectively]</p> <ul> <li>wind: Strength of crosswind, same as POS1_POS2 in <em>POS1_POS2_scans. </em>The sign corresponds to the wind direction. Negative is a crosswind from the port side of the aircraft, positive is a crosswind from the starboard side of the aircraft.</li> <li>Conv RV: 1 indicates the RV method has been evaluated for LLS scans, 0 if not.</li> <li>Naive RV: 1 indicates the RV method has been evaluated for point measurement scans, 0 if not.</li> <li>conv rv G_515_uw: Gamma 515 Circulation strength (see additional notes) of upwind (port) vortex computed using the RV method on LLS scans [m^2/s].</li> <li>conv rv G_515_dw: Gamma 515 Circulation strength (see additional notes) of downwind (starboard) vortex computed using the RV method on LLS scans [m^2/s].</li> <li>conv rv uw distance from lidar [m]: Horizontal distance of upwind (port) vortex from respective lidar using the RV method on LLS scans.</li> <li>conv rv dw distance from lidar [m]: Horizontal distance of downwind (starboard) vortex from respective lidar using the RV method on LLS scans.</li> <li>conv rv uw height [m]: Vertical distance of upwind (port) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m) using the RV method on LLS scans.</li> <li>conv rv dw height [m]: Vertical distance of downwind (starboard) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m) using the RV method on LLS scans.</li> <li>naive rv G_515_uw: Gamma 515 Circulation strength (see additional notes) of upwind (port) vortex computed using the RV method on point measurement scans [m^2/s].</li> <li>naive rv G_515_dw: Gamma 515 Circulation strength (see additional notes) of downwind (starboard) vortex computed using the RV method on point measurement scans [m^2/s].</li> <li>naive rv uw distance from lidar [m]: Horizontal distance of upwind (port) vortex from respective lidar using the RV method on point measurement scans.</li> <li>naive rv dw distance from lidar [m]: Horizontal distance of downwind (starboard) vortex from respective lidar using the RV method on point measurement scans.</li> <li>naive rv uw height [m]: Vertical distance of upwind (port) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m) using the RV method on point measurement scans.</li> <li>naive rv dw height [m]: Vertical distance of downwind (starboard) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m) using the RV method on point measurement scans.</li> <li>uw phi [deg]: Elevation angle to the center of the upwind (port) vortex from the simulation truth.</li> <li>uw range [m]: Range from the lidar to the center of the upwind (port) vortex from the simulation truth.</li> <li>dw phi [deg]: Elevation angle to the center of the downwind (starboard) vortex from the simulation truth.</li> <li>dw range [m]: Range from the lidar to the center of the downwind (starboard) vortex from the simulation truth.</li> <li>conv rv uw phi [deg]: Elevation angle to the center of the upwind (port) vortex using the RV method on LLS scans.</li> <li>conv rv uw range [m]: Range from the lidar to the center of the upwind (port) vortex using the RV method on LLS scans.</li> <li>conv rv dw phi [deg]: Elevation angle to the center of the downwind (starboard) vortex using the RV method on LLS scans.</li> <li>conv rv dw range [m]: Range from the lidar to the center of the downwind (starboard) vortex using the RV method on LLS scans.</li> <li>naive rv uw phi [deg]: Elevation angle to the center of the upwind (port) vortex using the RV method on point measurement scans.</li> <li>naive rv uw range [m]: Range from the lidar to the center of the upwind (port) vortex using the RV method on point measurement scans.</li> <li>naive rv dw phi [deg]: Elevation angle to the center of the downwind (starboard) vortex using the RV method on point measurement scans.</li> <li>naive rv dw range [m]: Range from the lidar to the center of the downwind (starboard) vortex using the RV method on point measurement scans.</li> <li>y err conv uw [m]: Cartesian localization error (RV minus ST) in y-direction (lateral) between simulation truth and RV method of upwind (port) vortex center on LLS scans.</li> <li>y err conv dw [m]: Cartesian localization error (RV minus ST) in y-direction (lateral) between simulation truth and RV method of downwind (starboard) vortex center on LLS scans.</li> <li>z err conv uw [m]: Cartesian localization error (RV minus ST) in z-direction (vertical) between simulation truth and RV method of upwind (port) vortex center on LLS scans.</li> <li>z err conv dw [m]: Cartesian localization error (RV minus ST) in z-direction (vertical) between simulation truth and RV method of downwind (starboard) vortex center on LLS scans.</li> <li>y err naive uw [m]: Cartesian localization error (RV minus ST) in y-direction (lateral) between simulation truth and RV method of upwind (port) vortex center on point measurement scans.</li> <li>y err naive dw [m]: Cartesian localization error (RV minus ST) in y-direction (lateral) between simulation truth and RV method of downwind (starboard) vortex center on point measurement scans.</li> <li>z err naive uw [m]: Cartesian localization error (RV minus ST) in z-direction (vertical) between simulation truth and RV method of upwind (port) vortex center on point measurement scans.</li> <li>z err naive dw [m]: Cartesian localization error (RV minus ST) in z-direction (vertical) between simulation truth and RV method of downwind (starboard) vortex center on point measurement scans.</li> <li>D err conv uw [m]: Euclidean distance error for the Cartesian coordinates to the vortex center between simulation truth and RV method of upwind (port) vortex center on LLS scans.</li> <li>D err conv dw [m]: Euclidean distance for the Cartesian coordinates to the vortex center between simulation truth and RV method of downwind (starboard) vortex center on LLS scans.</li> <li>D err naive uw [m]: Euclidean distance error for the Cartesian coordinates to the vortex center between simulation truth and RV method of upwind (port) vortex center on point measurement scans.</li> <li>D err naive dw [m]: Euclidean distance for the Cartesian coordinates to the vortex center between simulation truth and RV method of downwind (starboard) vortex center on point measurement scans.</li> <li>plus_minus_phi: Indicates whether a RHI lidar scan features a positive lidar scanning rate (plus), or a negative one (negative).</li> <li>conv err G_515_uw: Circulation error (RV minus ST) between simulation truth and RV method of upwind (port) on LLS scans [m^2/s]. </li> <li>conv err G_515_dw: Circulation error (RV minus ST) between simulation truth and RV method of downwind (starboard) on LLS scans [m^2/s]. </li> <li>naive err G_515_uw: Circulation error (RV minus ST) between simulation truth and RV method of upwind (port) on point measurement scans [m^2/s]. </li> <li>naive err G_515_dw: Circulation error (RV minus ST) between simulation truth and RV method of downwind (starboard) on point measurement scans [m^2/s]. </li> <li>conv err uw phi: Elevation angle error (RV minus ST) between simulation truth and RV method of upwind (port) vortex center on LLS scans [deg].</li> <li>conv err dw phi: Elevation angle error (RV minus ST) between simulation truth and RV method of downwind (starboard) vortex center on LLS scans [deg].</li> <li>naive err uw phi: Elevation angle error (RV minus ST) between simulation truth and RV method of upwind (port) vortex center on LLS scans [deg].</li> <li>naive err dw phi: Elevation angle error (RV minus ST) between simulation truth and RV method of downwind (starboard) vortex center on point measurement scans [deg].</li> <li>conv err uw range: Range from lidar error (RV minus ST) between simulation truth and RV method of upwind (port) vortex center on LLS scans [m].</li> <li>conv err dw range: Range from lidar error (RV minus ST) between simulation truth and RV method of downwind (starboard) vortex center on LLS scans [m].</li> <li>naive err uw range: Range from lidar error (RV minus ST) between simulation truth and RV method of upwind (port) vortex center on point measurement scans [m].</li> <li>naive err dw range: Range from lidar error (RV minus ST) between simulation truth and RV method of downwind (starboard) vortex center on point measurement scans [m].</li> </ul> <p><em>0_0_movement.csv</em> gives insight on the vertical movement on selected vortices in selected lidar scans of the 0_0 wind case. </p> <p>The following data columns exist within the file (note csv file with ; separator):</p> <ul> <li>Str_dw: Vertical movement of the downwind (starboard) vortex, 0 is neutral, -1 is downward, and 1 is upward.</li> <li>Prt_uw: Vertical movement of the upwind (port) vortex, 0 is neutral, -1 is downward, and 1 is upward.</li> <li>scan_code: Identfies associated lidar scan with POS3_POS4_POS5 as in <em>POS1_POS2_scans.</em></li> </ul> <p> </p> <p><strong>lid_plane_info = Additional guidance on the simulated lidars.</strong></p> <p>Each simulation case has a separate file due to minimal ground speed changes of the aircraft. </p> <p>The following data columns exist within each file:</p> <ul> <li>Index: Gives the lidar number corresponding to POS4 and LID.</li> <li>Plane Pos [x]: Gives the longitudinal position of the lidar with respect to the glide path of the aircraft in meters.</li> <li>time plane passed [s]: Gives the simulation time when the aircraft first passes the measurement plane (related to the previous column.</li> <li>height plane passed [m]: Aircraft altitude at the respective lidar positon.</li> <li>lateral position lidar from GP [m]: Lateral position of the lidar from the glide path of the aircraft.</li> </ul> <p><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></p> </div> <h3>Additional Notes: </h3> <div> <ul> <li>Gamma 515 circulation [4]: The Gamma 515 circulation is the averaged circulation of a vortex evaluated at radii 5-15m from the vortex center. </li> </ul> </div> <div> <div> <ul> <li>Lidar scans may contain 2 or 1 vortex. In the case of 1 vortex, the other has G_515_?? set to nan. Note that the position may still available in the labels data sets, but only vortices with available G_515_?? values should be used.</li> <li>The upwind and downwind definitions should purely be taken as names, rather than physical meaning. The names are derived from the simulation, due to mirroring and other post-processing these can be misleading however.</li> </ul> </div> </div> <div><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></div> <div> <p><strong>Funding</strong>: This dataset was generated within the mFUND <a href="https://bmdv.bund.de/SharedDocs/DE/Artikel/DG/mfund-projekte/kiwi.html">KIWI project</a> funded by the Federal Ministry for Digital and Transportation Germany and the DLR undertaking "Wetter und Disruptive Ereignisse".</p> <p><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></p> <p><strong>Acknowledgements:</strong></p> <p>The necessary RANS simulations were performed as part of the EU-funded AWIATOR by DLR's Institute of Aerodynamics and Flow Technology.</p> <p>We acknowledge Airbus for the allowance to use the RANS data.</p> <p>The LES was performed with the incompressible Navier-Stokes code MGLET [5] and kindly provided by the Technical University of Munich, Hydromechanics.</p> <p>The wake vortex simulations were computed on the high performance computer SuperMUC-NG by Leibniz-Rechenzentrum (LRZ).</p> </div> <div><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></div> <div> </div> <div><strong>References</strong>: </div> <div> </div> <div>[1] Robey, Rachel, and Julie K. Lundquist. "Behavior and mechanisms of Doppler wind lidar error in varying stability regimes." <em>Atmospheric Measurement Techniques</em> 15.15 (2022): 4585-4622.</div> <div> </div> <div>[2] Stephan, Anton, Norman Wildmann, and Igor Smalikho. "Effectiveness of the MFAS Method for Determining the Wind Velocity Vector from Windcube 200s Lidar Measurements." <em>Atmospheric and Oceanic Optics</em> 32.5 (2019): 555-563.</div> <div> </div> <div>[3] Smalikho, Igor., et al. "Method of radial velocities for the estimation of aircraft wake vortex parameters from data measured by coherent Doppler lidar." <em>Optics Express</em> 23.19 (2015): A1194-A1207.</div> <div> </div> <div>[4] Gerz, Thomas, Frank Holzäpfel, and Denis Darracq. "Commercial aircraft wake vortices." <em>Progress in Aerospace Sciences</em> 38.3 (2002): 181-208. </div> <div> </div> <div>[5] Manhart, Michael. "A zonal grid algorithm for DNS of turbulent boundary layers." <em>Computers & fluids</em> 33.3 (2004): 435-461.</div>
Maintenance of Wakefulness Test (MWT) recordings
<p>Each file contains a MWT trial (first trial after noon) recording of a patient. The data contains occipital EEG and EOG data. All signals were bandpass filtered between 0.5-45 Hz.</p> <p>In each file, the data is structured as the following:</p> <ul> <li>fs: sampling rate.</li> <li>eeg_O1: EEG channel O1-M2 where M2 is the mastoid electrode on the opposite side.</li> <li>eeg_O2: EEG channel O2-M1 where M1 is the mastoid electrode on the opposite side.</li> <li>E1 and E2: EOG channels for left and right eye, both referenced to M1.</li> <li>labels_O1 and labels_O2: arrays with expert scoring (0-wake, 1-MSE, 2-MSEc, 3-ED, according to the BERN scoring criteria published in Hertig-Godeschalk et al. doi:10.1093/sleep/zsz163.); length of the arrays is the same as for other signals, i.e. there is a label per sample.</li> <li>prec: amount of signal samples per label, in this case it is 1. variables prec and half_prec were not used.</li> <li>num_Labels: length of the signal in samples.</li> </ul> <p>Further descriptions, details, and outcomes can be found in the related studies. The published studies which are based on this data and address the borderland between wakefulness and sleep, i.e. microsleep episodes, are listed under related/alternative identifiers.</p>
MSSWD - Multi-Spectral Ship Wake Dataset
<p>The <strong>Multi-Spectral Ship Wake Dataset (MSSWD)</strong> is a dataset designed for ship wake detection in multi-spectral satellite imagery. It is structured as follows:</p> <p>- <strong>Source</strong>: 661 image chips derived from 50 Sentinel-2 images, captured by the Multi-Spectral Instrument (MSI) at 10-meter resolution across the visible, near-infrared (VNIR), and short-wave infrared (SWIR) spectral bands. The chips come already pre-processed to highlight sea surface features by using a Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. <br> <br>- <strong>Content</strong>: The dataset includes 1059 ship wakes, with various configurations such as:<br> - Single ship wakes<br> - Multiple ship wakes<br> - False wakes (e.g., airplane wakes, sea crests)<br> - Sea clutter with no visible wakes</p> <p>- <strong>Wake Characteristics</strong>: Diverse patterns of ship wakes are captured, including:<br> - Vertical, horizontal, and tilted wakes<br> - Cluttered sea scenes<br> - Partial occlusions due to cloud cover</p> <p>- <strong>Data Quality</strong>: Focused on <em>quality over quantity</em>, MSSWD reflects real-world complexity by collecting data in congested, crowded maritime environments.</p> <p>- <strong>Data Labelling</strong>: Manually annotated using polygonal annotations to delineate wake contours, which allows:<br> - Instance segmentation<br> - Enhanced refinement during data augmentation</p>
Twin Test 2: Wake interactions of a cluster of turbines and wake steering techniques. Wind tunnel data.
<p>The aerodynamic performance of two identical wind turbine models was characterized under various static and dynamic conditions in a synchronous configuration within the wind tunnel test section. Two experimental campaigns were performed at Technische Universität München (TUM) and at the National Technical University of Athens (NTUA) to investigate wake flow control techniques. This document contains the necessary information to understand the performed experiments and to access and use the available data. While both experimental set ups are detailed, only data from the TUM campaign are available at the time of writing, as the NTUA campaign results will form Phase II of an ongoing blind test campaign and cannot be published.</p>
LiDAR Cluster Statistic of Wind Turbine Wakes
<p>Mean and standard deviation of the wake velocity field generated by utility-scale wind turbines for different turbulence intensity of the incoming wind and rotor thrust coefficient. Statistics are retrieved from wind LiDAR measurements. More details in this paper https://onlinelibrary.wiley.com/doi/full/10.1002/we.2430 </p>
OWA Wake Modelling Challenge Dataset
<p>This repository collects input and simulation datasets from the Offshore Wind Accelerator (OWA) Wake Modelling Challenge, whose objective is to improve confidence in wake models in the prediction of array efficiency. The data is meant to be used together with the open-source model evaluation scripts available in the following github repository: <a href="https://github.com/CENER-EPR/OWAbench">https://github.com/CENER-EPR/OWAbench</a></p> <p>The results of the challenge are summarized in the following paper:</p> <p>Sanz Rodrigo J, Borbón Guillén F, Fernandes Correia P M, García Hevia B, Schlez W, Schmidt S, Basu S, Li B, Nielsen P, Cathelain M, Dall’Ozzo C, Grignon L, Pullinger D (2020) Validation of Meso-Wake Models for Array Efficiency Prediction Using Operational Data from Five Offshore Wind Farms. J. Phys.: Conf. Ser., under review</p>
FarmConners_Single_Full_Wake
<p>This dataset is a subset of the TotalControl windfarm dataset compiled in the context of FarmConners Wind Farm Control Benchmark project for code comparison. </p> <p>This case aims to investigate the quantities of interest in the single wake behind a normally operating turbine, for a <strong>fully aligned 2-turbine configuration</strong>. It should be noted that the configuration is a subset of a wind farm simulation, <em>i.e.</em> there are additional blockage effects.</p> <p>The python files "plot_cross_sections.py" and "plot_performance.py" are to be used for loading and visualizing the velocity field through turbines stored in "cross_sections_timeseries.h5" and the turbine performance in "turb_performance.h5" respectively. </p> <p>The inflow in this case is a Conventionally Neutral Boundary Layer (CNBL) with a capping inversion of 4K strength at a height of 250 m. The precursor data of the atmospheric boundary layer simulations without any turbines is publicly available in the online zenodo repository at <a href="https://zenodo.org/communities/totalcontrolflowdatabase/">https://zenodo.org/communities/totalcontrolflowdatabase/</a>. The complete datasets for the TotalControl windfarm simulations area available at <a href="https://zenodo.org/communities/totalcontrolwindfarmdatabase/">https://zenodo.org/communities/totalcontrolwindfarmdatabase/</a></p>
Supplementary Material: A Large-Eddy Simulation Study of Vertical Axis Wind Turbine Wakes in the Atmospheric Boundary Layer
<p>Supplementary material for <em>Energies</em> <strong>2016</strong>, <em>9</em>, 366; doi:10.3390/en9050366:</p> <p><strong>Video S1:</strong> Normalized instantaneous streamwise velocity field both on a vertical plane (<em>x</em>-<em>z</em>) going through the center of the turbine and on a horizontal plane at the equator height of the turbine (Note: the physical time corresponding to this video is 1 minute and 17 seconds, and the size of the blades is magnified for illustration purposes).</p> <p><strong>Video S2:</strong> Normalized instantaneous streamwise velocity field on a horizontal plane at the equator height of the turbine for two cases: when the turbine starts to operate (top) and when the flow has reached statistically steady condition (bottom) (Note: the physical time corresponding to both videos is 1 minute and 17 seconds, and the size of the blades is magnified for illustration purposes).</p>
Figure 3. – Mulloidichthys flavolineatus flavolineatus from Wake Island. A in Junior synonymy of Mulloides armatus and intraspecific comparisons of the yellowstripe goatfish Mulloidichthys flavolineatus (Mullidae) using a comprehensive alpha-taxonomy approach
Figure 3. – Mulloidichthys flavolineatus flavolineatus from Wake Island. A: BPBM 4089, 286 mm SL (Loreen O'Hara); B: Adult (Phillip & Lisa Lobel); C: Several adults or subadults; one of the four fish at top right has a yellow caudal fin (Phillip & Lisa Lobel). Scale bar = 4 cm.
Figures and data: Combining wake redirection and derating strategies in a load-constrained wind farm power maximization
<p><strong>Figures from the publication <em>Combining wake redirection and derating strategies in a load-constrained wind farm power maximization.</em></strong></p> <p> </p> <p>*.fig files can be opened in <code>Matlab</code></p> <p>*.csv files can be opened through a standard text editor (e.g.,<code> Notepad++</code>), imported and visualized in <code>Matlab</code> through the functions <code>>>readmatrix()</code> and <code>>>readtable()</code></p>
First Results on Wake Detection in SAR Images by Deep Learning
<p>Supplementary Materials of the paper "First Results on Wake Detection in SAR Images by Deep Learning" published on MDPI - Remote Sensing</p>
Widespread ripples synchronize human cortical activity during sleep, waking, and memory recall
<p>These are the data and code for the article 'Widespread ripples synchronize human cortical activity during sleep, waking, and memory recall.' Please cite this article when using these data or code.</p>
Sharing First Law and Wake Up the Snake
<p>Professor Anne Poelina talks about the importance of First Law and of protecting the Martuwarra, Fitzroy River, a Living Ancestral Being, in a Zoom interview with Dr Cristy Clark (23rd May 2022).</p> <p>Poelina, A., & Clark, C. (2022, May 23rd). <em>Sharing First Law and Wake Up the Snake: An Interview with Professor Anne Poelina</em>. Available at: <a href="https://vimeo.com/714059501">https://vimeo.com/714059501</a></p>
A physically interpretable data-driven surrogate model for wake steering
<p>PALM input files for the simulations performed in the study "A physically interpretable data-driven surrogate model for wake steering" by Sengers et al. (2022). </p> <p>The PALM code is available at <a href="https://palm.muk.uni-hannover.de/">https://palm.muk.uni-hannover.de</a><br> Additional information to the input files is given in the README file</p> <p>Cite this as:<br> B.A.M. Sengers (2022). Dataset: A physically interpretable data-driven surrogate model for wake steering. https://doi.org/10.5281/zenodo.6821164</p>
"A physics-based model for wind turbine wake expansion in the atmospheric boundary layer"
<p>Vahidi, Dara, and Fernando Porté-Agel. "A physics-based model for wind turbine wake expansion in the atmospheric boundary layer." <em>Journal of Fluid Mechanics</em> 943 (2022).</p>
Large eddy simulation of a quadcopter in forward flight: aeroloads and wake
<p>This dataset contains model information and results of large eddy simulation of a quadcopter in forward flight. The simulations were performed using the Vortex Particle-Mesh method. The study uses a generic model with a blade and airframe geometry inspired from a DJI drone. One flight condition is considered, with the vehicle at a forward speed of 10 m/s and at a pitch angle of 13 degrees. The results of two cases are available: 1) a simulation of the full model (comprising 4 rotors and the airframe); 2) a simulation of the rotors only (no airframe included).</p> <p>The data contains a description of the airframe (CAD files), a description of the blades, the resulting aeroloads on the blades and airframe (for each case), the time-resolved velocity and vorticity field in a cross-section of the wake (for each case), and supporting material for the companion article:</p> <blockquote> <p>D.-G. Caprace, A. Ning, P. Chatelain, G. Winckelmans, Effects of rotor-airframe interaction on the aeromechanics and wakes of a quadcopter in forward flight, 2022</p> </blockquote>
ScienceDex guides
Understand access before you commit
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.