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128 results for “Vortex”
Coherent vortex dynamics in a strongly-interacting superfluid on a silicon chip: Experimental and simulation data sets
<p>This data set collates the experimental and simulation data for the research paper "Coherent vortex dynamics in a strongly-interacting superfluid on a silicon chip".</p>
Vortex Catalog from Jovian Vortex Hunter Zooniverse citizen science project
<p>This dataset contains the aggregated results from the <a href="https://www.zooniverse.org/projects/ramanakumars/jovian-vortex-hunter/" target="_blank" rel="noopener">Jovian Vortex Hunter citizen science project</a>, where citizen science volunteers labeled images from the JunoCam instrument and determined locations and sizes of vortices.</p> <p>The CSV file contains results from the first workflow and details the consensus from volunteers on different features in each image (given by the Zooniverse Subject ID). There are five categories to choose from:</p> <ol> <li>Vortex</li> <li>Turbulent (i.e. Folded Filamentary Regions: FFRs)</li> <li>Cloud bands</li> <li>Blurry (i.e., image issues)</li> <li>Featureless (there is no discernable feature in the image)</li> </ol> <p>The CSV file also contains an additional column defining the number of classifications of the subject.</p> <p>The JSON file contains the aggregated catalog of vortices and their properties from the second workflow. The format of the JSON file is as below:</p> <p>Each entry contains a dictionary of vortex properties, as shown below. The entry is determined by aggregating the vortex properties across multiple Zooniverse images which share the vortex, and building a consensus from multiple volunteer responses.</p> <pre><code>{ "subject_ids": [array of Zooniverse subject ID for each vortex], "perijove": the perijove corresponding to the image from this vortex was determined "color": the aggregated color of the vortex determined from volunteer responses, "lon": System III planetographic longitude [degree], "lat": planetographic latitude [degree], "x0", "y0": reference coordinate on the Zooniverse crop image for longitude/latitude, "x", "y": coordinate of the vortex center on the Zooniverse crop image "rx", "ry": radius of the vortex in pixel coordinates on the Zooniverse crop image "angle": angle in degree from horizontal of the orientation of the vortex in the Zooniverse crop image, "sigma": 1sigma error in the scale of the vortex, "angular_width": width of the vortex in degrees on the planet, "angular_height": height of the vortex in degrees on the planet, "physical_width", "physical width": width/height of the vortex in km, "extracts": [ array of dictionaries consisting of individual ellipses that make up the vortex consensus ] "colors": { "brown": consensus on the vortex being brown [0-1], "red": consensus on the vortex being red [0-1], "dark": consensus on the vortex being dark [0-1], "white": consensus on the vortex being white [0-1], "white-brown": consensus on the vortex being white and brown [0-1], "white-red": consensus on the vortex being white and red [0-1], "red-brown": consensus on the vortex being red and brown [0-1], } }</code></pre> <p>Each extract is a dictionary containing the following properties. An extract is a single aggregated ellipse on a single Zooniverse image.</p> <pre><code>{ "subject_id": Zooniverse subject ID, "perijove": the perijove when the JunoCam image was taken, "color": the color of the vortex with the highest vote fraction, "lon": System III longitude of the vortex [degree], "lat": planetographic latitude of the vortex [degree], "x0", "y0": reference coordinate on the Zooniverse crop image for longitude/latitude, "x", "y": coordinate of the vortex center on the Zooniverse crop image "rx", "ry": radius of the vortex in pixel coordinates on the Zooniverse crop image "angle": angle in degree from horizontal of the orientation of the vortex in the Zooniverse crop image, "probability": the 1sigma error in the scale of the vortex, "angular_width": width of the vortex in degrees on the planet, "angular_height": height of the vortex in degrees on the planet, "physical_width", "physical width": width/height of the vortex in km, }</code></pre>
Bounding box coordinates of von Karman vortex street
<p>Those files contain bounding box coordinates of von Karman vortex street annotated by VoTT. The von Karman vortex street is annotated as one object in vortex_street.tar.gz, while each vortex in the von Karman vortex street is annotated as one object in vortices.tar.gz. The original video file is from https://doi.org/10.1063/1.4921683.1 to 1.4921683.9. See also the reference.</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>
Vortices and vortex stripes in a dipolar Bose-Einstein condensate
<p>Quantized vortices are a prototypical feature of superfluidity that have been observed in multiple quantum gas experiments. But the occurrence of vortices in dipolar quantum gases — a class of ultracold gases characterized by long-range anisotropic interactions — has not been reported yet. Here, we exploit the anisotropic nature of the dipole-dipole interaction of a dysprosium Bose-Einstein condensate to induce angular symmetry breaking in an otherwise cylindrically symmetric pancake-shaped trap. Tilting the magnetic field towards the radial plane deforms the cloud into an ellipsoid, which is then set into rotation. At stirring frequencies approaching the radial trap frequency, we observe the generation of dynamically unstable surface excitations, which cause angular momentum to be pumped into the system through vortices. Under continuous rotation, the vortices arrange into a stripe configuration along the field, in close agreement with numerical simulations.</p>
Aeroacoustic investigations of streamwise vortex generators for boundary layer separation control.
<p>This project contains the data obtained as a result of the Preludium Grant no 2022/45/N/ST8/01425 of the Polish National Science Centre fundings. Within the "Aeroacoustic investigations of streamwise vortex generators for boundary layer separation control" project, two main research tasks were defined:<br> 1. Implementation of porous FW-H analogy into the developed aeroacoustic code.<br> 2. Validation of the porous FW-H analogy implementation against analytical solutions for elementary sources.</p> <p>The resutls from these tasks are uploaded here. The details of the data are included in the EOP_medata_2.docx document uploaded. </p>
Vortex input files - Carroll et al. Scientific Reports
<p>Vortex input files associated with the manuscript by Carroll et al. "Biological and sociopolitical sources of uncertainty in population viability analysis for endangered species recovery planning", published in Scientific Reports http://doi.org/10.1038/s41598-019-45032-2</p> <p>v2013nodd.xml: Scenario without density-dependent reproduction adapted from 2013 Mexican wolf PVA, as presented initially in Carroll et al. 2014. https://doi.org/10.1111/cobi.12156<br> v2013dd.xml: Scenario with density-dependent reproduction adapted from 2013 Mexican wolf PVA.<br> v2017.xml: Scenario adapted from 2017 Mexican wolf PVA (Miller 2017). <br> MXWlivNoMex.txt: Pedigree input file for above scenarios.</p>
Vortex input files -- McHugh et al. Staying Alive: Long-term success of small cetacean interventions in southwest Florida
<p>Vortex input file used to generate population level impacts in the manuscript "Staying Alive: Long-term success of small cetacean interventions in southwest Florida", by K. A. McHugh, et al. </p>
Observation and modeling of high-7Be events in Northern Europe associated with the instability of the Arctic polar vortex in early 2003
<p>Data repository for paper: "Observation and modeling of high-7Be events in Northern Europe associated with the instability of the Arctic polar vortex in early 2003"</p> <p>Erika Brattich; Hongyu Liu; Bo Zhang; Miguel Angel Hernandez-Ceballos; Jussi Paatero; Darko Sarvan; Vladimir Djurdjevic; Laura Tositti; Jelena Ajtić, submitted to Atmospheric Chemistry and Physics, October 2020</p> <p>Created by Erika Brattich (erika.brattich@unibo.it), October 2020</p> <p>-> Data</p> <p>- Pb210_2sites.csv contains daily 210Pb observations at Helsinki and Sodankyla in the period January-March 2003. Dates are in "m/d/yyyy" format and 210Pb measurements are in µBq/m³.</p> <p>Please refer to Mattson et al. (1996) for details.</p> <p>-> Model output</p> <p>- trac_avg.merra2_2x25_RnPbBe.200301-200303-monthly-means.bpch.gz: contains model output of monthly mean 210Pb and 7Be concentrations for January, February, and March 2003.</p> <p>- ts_1h.200301.bpch.tar: hourly model output for January 2003</p> <p>- ts_1h.200302.bpch.tar: hourly model output for February 2003</p> <p>- ts_1h.200303.bpch.tar: hourly model output for March 2003</p> <p>- diaginfo.dat, tracerinfo.dat: information data files needed for the GAMAP package (in IDL) used to read and process the model output. GAMAP is publicly available at: <a href="http://acmg.seas.harvard.edu/gamap/">http://acmg.seas.harvard.edu/gamap/</a> </p> <p>References:</p> <p>Mattsson, R., Paatero, J., and Hatakka, J.: Automatic Alpha/Beta Analyser for Air Filter Samples - Absolute Determination of Radon Progeny by Pseudo-coincidence Techniques, Radiat. Prot. Dosim., 63, 133-139, doi:10.1093/oxfordjournals.rpd.a031520, 1996.</p> <p> </p> <p> </p>
Vortex input files -- Ashe et al., "Minding the data-gap trap: predicting the dynamics of abundant dolphin species under uncertainty"
<p>Vortex input file used for analyses presented in:<br> "Minding the data-gap trap: predicting the dynamics of abundant dolphin species under uncertainty", <br> by Erin Ashe, Rob Williams, Christopher Clark, Christine Erbe, Leah Gerber, Ailsa Hall, Philip Hammond, Robert C. Lacy, Randall Reeves, & Nicole Vollmer<br> </p>
Dataset from "Constraining Martian regolith and vortex parameters from combined seismic and meteorological measurements"
<p>The table provided below (in CSV format) includes derived data obtained from the raw data of the InSight SEIS and APSS experiments. For the raw data, we acknowledge:</p> <p>InSight Mars SEIS Data Service. (2019). SEIS raw data, Insight Mission. IPGP, JPL, CNES, ETHZ, ICL, MPS, ISAE-Supaero, LPG, MFSC. https://doi.org/10.18715/SEIS.INSIGHT.XB_2016</p> <p>The dataset in this table was used to produce Figures 6, 7, 11 and 12 of the following paper:</p> <p>N. Murdoch, A. Spiga, R. Lorenz, R.F. Garcia, C. Perrin, R. Widmer-Schnidrig, S. Rodriguez, N. Compaire, N. H. Warner, D. Mimoun, D. Banfield, P. Lognonné and W.B. Banerdt. Constraining Martian regolith and vortex parameters from combined seismic and meteorological measurements. Journal of Geophysical Research: Planets.</p> <p>The table provides derived vortex parameters of all vortices studied in this paper. The columns of the table contain the following properties of every vortex event: Sol, UTC date and time, Local Mean Solar Time (LMST), Observed pressure deficit <span class="math-tex">\(\Delta P_{obs}\)</span> (determined from the fit to the Ellehoj et al. (2010) model after filtering in the 0.02 - 0.3 Hz frequency band), Observed pressure drop encounter duration <span class="math-tex">\(\tau\)</span> (FWHM determined from the fit to the Ellehoj et al. (2010) model after filtering in the 0.02 - 0.3 Hz frequency band), the Ellehoj et al. (2010) model goodness of fit to the vortex pressure data (after filtering in the 0.02 - 0.3 Hz frequency band), Mean background wind speed <span class="math-tex">\(v\)</span>, Standard deviation of background wind speed <span class="math-tex">\(\sigma_v\)</span>, Maximum radial tilt <span class="math-tex">\(\theta_{obs}\)</span>, Azimuth at maximum radial tilt (i.e. at closest approach) <span class="math-tex">\(\alpha_{obs}\)</span>, Mean miss distance <span class="math-tex">\(x\)</span> (i.e. when <span class="math-tex">\(S = v\)</span>), <span class="math-tex">\(\eta \)</span> (defined as <span class="math-tex">\(E/(1-\nu^2) \)</span>), Mean <span class="math-tex">\(\zeta\)</span> (i.e. when <span class="math-tex">\(S = v\)</span>). For further details about these parameters please see the paper cited above.</p>
Data from: How density dependence, genetic erosion, and the extinction vortex impact evolutionary rescue
<p>Following severe environmental change that reduces mean population fitness below replacement, populations must adapt to avoid eventual extinction, a process called evolutionary rescue. Models of evolutionary rescue demonstrate that initial size, genetic variation, and degree of maladaptation influence population fates. However, many models feature populations that grow without negative density dependence or with constant genetic diversity despite precipitous population decline, assumptions likely to be violated in conservation settings. We examined the simultaneous influences of density-dependent growth and erosion of genetic diversity on populations adapting to novel environmental change using stochastic, individual-based simulations. Density dependence decreased the probability of rescue and increased the probability of extinction, especially in large and initially well-adapted populations that previously have been predicted to be at low risk. Increased extinction occurred shortly following environmental change, as populations under density dependence experienced more rapid decline and reached smaller sizes. Populations that experienced evolutionary rescue lost genetic diversity through drift and adaptation, particularly under density dependence. Populations that declined to extinction entered an extinction vortex, where small size increased drift, loss of genetic diversity, and the fixation of maladaptive alleles, hindered adaptation, and kept populations at small densities where they were vulnerable to extinction via demographic stochasticity.</p>
Datasets used for analysis and plotting in the study by Zhou et al. "Antarctic vortex dehydration in 2023 as a substantial removal pathway for Hunga Tonga-Hunga Ha'apai water vapour"
<p>These data are model simulated water vapour (H2O) and ozone (O3) model mixing ratios between 2022 and 2023 that were used to create figures for the study by Zhou et al. "Antarctic vortex dehydration in 2023 as a substantial removal pathway for Hunga Tonga-Hunga Ha'apai water vapour".</p><p>We use the TOMCAT/SLIMCAT 3-D off-line chemical transport model (Chipperfield, 2006) to represent the Hunga Tonga-Hunga Ha'apai (HTHH) H2O plume and quantify its longevity and ozone impacts. The model was run at a horizontal resolution of 2.8 degrees and 32 levels from the surface to about 60 km forced with ECMWF ERA5 meteorology. </p><p>A control simulation (file name with "MPC741") without treatment of HTHH was integrated from 1980 to October 2023. Output from run control for January 1st, 2022 was used to intialise a HTHH H2O perturbed run (run HT, file name with "MPC744") until October 2023 with the injection of 150 Tg of H2O into the low-mid stratosphere at southern subtropical latitudes. To test the possible future evolution of the HTHH H2O three further model runs were performed. These were integrated from January 1st, 2023 until 2030 using repeating ERA-5 meteorology for 2022. Run Con_2022 (file name with "MPC741__2022pd") was an extension of run control; run HT2022 was an extension of run HT (file name with "MPC744_2022pd")<i>, </i>and run HT2022ns (file name with "MPC744_2022pdns") was the same as run HT_2022 but had sedimentation of PSC particles turned off. Please see our paper for more information about the simulations.</p>
Revealing the microscopic mechanism of vortex pinning in superconductors
<p>Vortex pinning is a crucial factor that determines the critical current of practical superconductors, but its microscopic mechanism has long been elusive. Here using high-resolution scanning tunneling microscopy, we studied the pinning of single vortex by point defect in high-Tc FeSe-based superconductors. We found the defect-vortex interaction drives low-energy vortex bound states away from EF, which effectively lowered the energy of vortex and caused the pinning. It indicates the local pairing near the pinned vortex core is actually enhanced, which is in sharp contrast to the traditional understanding that non-superconducting regions pin the vortex, but is well captured by our microscopic quantum model. Furthermore, we directly obtained the elementary pinning energy from tunneling spectrum and estimated the pinning force via the spatial gradient of pinning energy. The obtained value aligns with the bulk critical current measurement. Our study thus established a general microscopic mechanism of vortex pinning in superconductors.</p>
VORTEX-2 2009-2010 radar data from NOAA X-band dual Polarimetric radar (NOXP)
<p>This dataset provides level 2 radar data collected by NOAA X-band dual Polarimetric radar (NOXP) during <span>Verification of the Origins of Rotation in Tornadoes Experiment</span> (VORTEX-2) in 2009-2010.</p> <p>Archive file names have form YYYY.NOX.sweep.MMDD.tar.gz, where YYYY denotes year, MMDD denotes month and day. Extracting the archive with a unix command like</p> <p>tar zxf YYYY.NOX.sweep.MMDD.tar.gz</p> <p>produces a directory tree in the current working directory. Data files are in paths of form:</p> <p>YYYY/NOX/sweep/MMDD/NOXYYMMDDHHMMSS.RAW????/swp.YYYMMDDHHMMSS.NOXPRVP.d.dd.0_PPI_v1</p> <p>where YYYY denotes year, MMDD denotes month and day, NOXYYMMDDHHMMSS.RAW???? identifies the radar volume (cycle of antenna pointing angles), and swp.YYYMMDDHHMMSS.NOXPRVP.d.dd.0_PPI_v1 denotes a file with data for one sweep (cycle of antenna pointing angles with the same elevation (PPI) or azimuth (RHI)). YYY is number of years since 1900. d.dd is the sweep angle.</p> <p>The sweep files are DORADE format, documented at https://www.eol.ucar.edu/sites/default/files/files_live/private/files/field_project/EMEX/DoradeDoc.pdf . RadxConvert, which is part of lrose (https://ncar.github.io/lrose-core), can rewrite the files in other formats as needed by other software.</p>
FIB cleaned and reshaped Electrostatic electron vortex beam generators - dataset2
<p>Here is a second set of SEM images of Electrostatic electron vortex beam generators fabricated at the CNR-IMM in Bologna and FIB cleaned and reshaped at the CNR-NANO in Modena. The difference between this batch of devices and the first one is in the thickness of the electrode (the z-dimension), this batch has a electrode thickness of 30um, while the first batch is 10um thick.</p>
FIB cleaned an reshaped Electrostatic Electron Vortex Beam Generators
<p>Here are collected the SEM images (top-view and tilted-view) of the Electrostatic Electron Vortex Beam Generators fabricated at the CNR-IMM in Bologna and then cleaned and reshaped by FIB milling at the CNR-NANO in Modena. Different shapes of the main paired needles have been fabricated to try to optimize the reproduction the ideal phase profile as reported in https://doi.org/10.1103/PhysRevResearch.2.013185</p>
Vortex shedding topology for oscillating heavy spherical pendulums underwater
<p>Relevant data of vortex shedding topology for oscillating heavy spherical pendulums underwater to reproduce the major findings of the article "Dynamics of heavy subaqueous spherical pendulums" published in Journal of Fluid Mechanics. The article has been published as open access: <a href="https://doi.org/10.1017/jfm.2023.170">https://doi.org/10.1063/5.0086557</a></p>
BRAIN Journal-On the Idea of a New Artificial Intelligence Based Optimization Algorithm Inspired From the Nature of Vortex-Figure 1. Working mechanism of the VOA.
<p>As it can be seen from the algorithm steps, the VOA employs simple equations. It is an<br> advantage that the algorithm can be formed and applied within optimization problems whereas<br> alternative algorithms may contain some complex solution steps (This situation may be also an<br> disadvantage for the VOA when it is applied in more difficult optimization problems but while the<br> world is transformed into a ‘strong simplicity’, the VOA may be a practical solution approach).<br> The working mechanism of the VOA can be visualized briefly as like in Figure 1.</p>
Giant vortex clusters in a two-dimensional quantum fluid: Data sets
<p>This data set collates the experimental and simulation data for the paper "Giant vortex clusters in a two-dimensional quantum fluid."</p> <p><strong>Database S1: Data_Excel_Sheet.xlsx </strong>contains the data shown in Figs. 1, 3, and 4 of the paper.</p> <p><strong>Database S2: Exp_Vortex_Location_Data.zip</strong> contains the experimental vortex positions.</p> <p><strong>Database S3: 2DGPE.zip</strong> contains the outputs of the 2D GPE simulations, along with the generating scripts.</p> <p><strong>Database S4: MonteCarlo_raw.zip</strong> contains the Monte Carlo outputs used to generate the shown in Fig. 1. </p> <p>Additional scripts to reproduce the MC data, GPE data, and reproduce the figures may be found at <a href="https://github.com/UQBEC/GiantVortices">https://github.com/UQBEC/GiantVortices</a>.</p> <p> </p>
ScienceDex guides
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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.