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395 results for “aircraft”
Analysis of two Methods for Aircraft Fuel Requirement Calculations in the Context of a novel Methodological Framework for LCA of Sustainable Aviation
<p>This Microsoft Excel file contains equations to compare different approaches to calculate fuel efficiency ("energy use" in [MJ/t*km]) of aircraft over a specific distance at a specific payload. </p> <p>Two approaches are compared: A novel approach by <a href="10.1016/j.scitotenv.2023.163881" target="_blank" rel="noopener">Su-ungkavatin et al.</a> and the more established approach well documented by eg. <a href="https://www.fzt.haw-hamburg.de/pers/Scholz/arbeiten/TextBurzlaff.pdf" target="_blank" rel="noopener">Burzlaff</a> or <a href="http://www.aircraftmonitor.com/uploads/1/5/9/9/15993320/aircraft_payload_range_analysis_for_financiers___v2.pdf" target="_blank" rel="noopener">Ackert</a>.</p> <p>This work augments a Letter to the Editor we submitted to the journal <a href="https://www.sciencedirect.com/journal/science-of-the-total-environment" target="_blank" rel="noopener">Science of the Total Environment</a>.</p>
Black Swift Technologies S1 Unmanned Aircraft System Observations from LAPSE-RATE
<p>This dataset contains meteorological data collected by Black Swift Technologies' S1 unmanned aircraft system during the 2018 LAPSE-RATE (Lower Atmospheric Profiling Studies at Elevation - a Remotely-piloted Aircraft Team Experiment) field campaign. Questions about the dataset should be addressed to Jack Elston (elstonj@bst.aero).</p>
Auralization of aircraft
<p>This dataset consists of 4 files, and are a preliminary result. </p> <ul> <li>recording.wav is a recording of an Airbus A320 taken nearby Zurich airport,</li> <li>reverted.wav is the signal that is obtained after backpropagating in time-domain,</li> <li>synthesis.wav is a signal created from features (tones/noise) obtained through an automatic analysis of the backpropagated signal,</li> <li>auralization.wav is what one would hear at the receiver after propagating again from source to receiver. Ideally, this signal matches with the recording.</li> </ul> <p> </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>
Aircraft Marshaling Signals Dataset of FMCW Radar and Event-Based Camera for Sensor Fusion
<p><strong>Dataset Introduction</strong></p><p>The advent of neural networks capable of learning salient features from variance in the radar data has expanded the breadth of radar applications, often as an alternative sensor or a complementary modality to camera vision. Gesture recognition for command control is arguably the most commonly explored application. Nevertheless, more suitable benchmarking datasets than currently available are needed to assess and compare the merits of the different proposed solutions and explore a broader range of scenarios than simple hand-gesturing a few centimeters away from a radar transmitter/receiver. Most current publicly available radar datasets used in gesture recognition provide limited diversity, do not provide access to raw ADC data, and are not significantly challenging. To address these shortcomings, we created and make available a new dataset that combines FMCW radar and dynamic vision camera of 10 aircraft marshalling signals (whole body) at several distances and angles from the sensors, recorded from 13 people. The two modalities are hardware synchronized using the radar's PRI signal. Moreover, in the supporting publication we propose a sparse encoding of the time domain (ADC) signals that achieve a dramatic data rate reduction (>76%) while retaining the efficacy of the downstream FFT processing (<2% accuracy loss on recognition tasks), and can be used to create an sparse event-based representation of the radar data. In this way the dataset can be used as a two-modality neuromorphic dataset.</p><p><strong>Synchronization of the two modalities</strong></p><p>The PRI pulses from the radar have been hard-wired to the event stream of the DVS sensor, and timestamped using the DVS clock. Based on this signal the DVS event stream has been segmented such that groups of events (time-bins) of the DVS are mapped with individual radar pulses (chirps).</p><p><strong>Data storage</strong></p><p>DVS events (x,y coords and timestamps) are stored in structured arrays, and one such structured array object is associated with the data of a radar transmission (pulse/chirp). A radar transmission is a vector of 512 ADC levels that correspond to sampling points of chirping signal (FMCW radar) that lasts about ~1.3ms. Every 192 radar transmissions are stacked in a matrix called a radar frame (each transmission is a row in that matrix). A data capture (recording) consisting of some thousands of continuous radar transmissions is therefore segmented in a number of radar frames. Finally radar frames and the corresponding DVS structured arrays are stored in separate containers in a custom-made multi-container file format (extension .rad). We provide a (rad file) parser for extracting the data out of these files. There is one file per capture of continuous gesture recording of about 10s.</p><p>Note the number of 192 transmissions per radar frame is an ad-hoc segmentation that suits the purpose of obtaining sufficient signal resolution in a 2D FFT typical in radar signal processing, for the range resolution of the specific radar. It also served the purpose of fast streaming storing of the data during capture. For extracting individual data points for the dataset however, one can pool together (concat) all the radar frames from a single capture file and re-segment them according to liking. The data loader that we provide offers this, with a default of re-segmenting every 769 transmissions (about 1s of gesturing).</p><p><strong>Data captures directory organization (</strong><a href="https://zenodo.org/api/records/10359770/draft/files/radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z/content">radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z</a><strong>)</strong></p><p>The dataset captures (recordings) are organized in a common directory structure which encompasses additional metadata information about the captures.</p><p>dataset_dir/<stage>/<room>/<person>-<gesture>-<distance>/ofxRadar8Ghz_yyyy-mm-dd_HH-MM-SS.rad</p><p>Identifiers</p><ul><li>stage [train, test].</li><li>room: [conference_room, foyer, open_space].</li><li>subject: [0-9]. Note that 0 stands for no person, and 1 for an unlabeled, random person (only present in test).</li><li>gesture: ['none', 'emergency_stop', 'move_ahead', 'move_back_v1', 'move_back_v2', 'slow_down' 'start_engines', 'stop_engines', 'straight_ahead', 'turn_left', 'turn_right'].</li><li>distance: ['xxx', '100', '150', '200', '250', '300', '350', '400', '450'] (in cm). Note that xxx is used for none gestures when there is no person present in front of the radar (i.e. background samples), or when a person is walking in front of the radar with varying distances but performing no gesture.</li></ul><p>The test data captures contain both subjects that appear in the train data as well as previously <i>unseen</i> subjects. Similarly the test data contain captures from the spaces that train data were recorded at, as well as from a new <i>unseen</i> open space.</p><p><strong>Files List</strong></p><p><a href="https://zenodo.org/api/records/10359770/draft/files/radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z/content">radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z</a></p><p>This is the actual archive bundle with the data captures (recordings).</p><p><a href="https://zenodo.org/api/records/10359770/draft/files/rad_file_parser_2.py/content">rad_file_parser_2.py</a></p><p>Parser for individual .rad files, which contain capture data.</p><p><a href="https://zenodo.org/api/records/10359770/draft/files/loader.py/content">loader.py</a></p><p>A convenience PyTorch Dataset loader (partly Tonic compatible). You practically only need this to quick-start if you don't want to delve too much into code reading. When you init a DvsRadarAircraftMarshallingSignals class object it automatically downloads the dataset archive and the .rad file parser, unpacks the archive, and imports the .rad parser to load the data. One can then <i>request from it </i>a training set, a validation set and a test set as torch.Datasets to work with<i>.</i> </p><p><a href="https://zenodo.org/api/records/10359770/draft/files/aircraft_marshalling_signals_howto.ipynb/content">aircraft_marshalling_signals_howto.ipynb</a></p><p>Jupyter notebook for exemplary basic use of loader.py</p><p><strong>Contact</strong></p><p>For further information or questions try contacting first M. Sifalakis or F. Corradi.</p><p> </p>
Data supporting: Improved Tangential Interpolation-based Multi-input Multi-output Modal Analysis of a Full Aircraft
Open the record for dataset details and reuse information.
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>
pCRM9 aeroelastic aircraft wing model for NASTRAN without RBE2
<p><strong>pCRM9 aeroelastic aircraft wing model for NASTRAN without RBE2</strong></p> <p>Based on the model published by Paul LANCELOT from Delft University of Technology, Faculty of Aerospace Engineering, Department of Aerospace Structures and Materials</p> <blockquote> <p>Lancelot, Paul (2021): pCRM9 aeroelastic aircraft wing model for NASTRAN. 4TU.ResearchData. Dataset. DOI: <a href="https://doi.org/10.4121/16834387.v1 ">10.4121/16834387.v1 </a></p> </blockquote> <p> </p> <p>Modified by: Saullo G. P. Castro removing RBE2 elements and the input related to the aeroelastic analysis.</p> <p>Delft University of Technology, Faculty of Aerospace Engineering, Department of Aerospace Structures and Materials</p> <p> </p> <p>Contact Information:</p> <p>s.g.p.castro@tudelft.nl</p> <p>Delft University of Technology - Faculty of Aerospace Engineering</p> <p>Kluyverweg 1, 2629HS Delft, The Netherlands</p> <p> </p> <p><strong>General Introduction</strong></p> <p><br> This is the pCRM9 aeroelastic finite element model.</p> <p>It is based on the University of Michigan undeflected CRM geometry:</p> <blockquote> <p>Brooks, Timothy; Kenway, Gaetan G.W.; Martins, Joaquim (2019), “uCRM: undeflected Common Research Model”, Mendeley Data, V1, DOI: <a href="https://doi.org/10.17632/gpk4zn73xn.1">10.17632/gpk4zn73xn.1</a></p> </blockquote> <p> </p> <p>This FEM model is tailored for optimisation and load analysis using NASTRAN.</p> <p><strong>Content</strong></p> <p>pCRM9_103_MAIN_FILE.bdf is the main file to run the analysis with NASTRAN SOL103, natural frequency solution.<br> pCRM9_CONM2_MTOW.dat contains the weigth and balance informations of the model.<br> pCRM9_mat.dat contains the material informtions of the model (in this case aluminium).<br> pCRM9_model_2.dat contains the finite elements informations of the model (GRID, CQUAD, RBE cards etc.).<br> pCRM9_PSHELL.dat contains the mechanical propeties for each elements of the model.<br> pCRM9_ribs_fem.dat contains the finite elements informations relative to the ribs of the wingbox.<br> pCRM9_FEM_model.PNG is an illustration of the structural model.</p> <p> </p>
Components of spore capture device in "A simple mechanism for uncrewed aircraft bioaerosol sampling in the lower atmosphere"
<p>These STL files enable the 3D printing of the referenced spore capture device. The complete device can be assembled following printing of the: (1) petri dish holder base; (2) lid; and (3) flange. The STL file extension stands for stereolithography, colloquially referred to as Standard Triangle Language or Standard Tessellation Language, and is a popular file format for 3D printing. The 3D models were created, and can be viewed, with CAD software.</p>
Data for "A simple mechanism for uncrewed aircraft bioaerosol sampling in the lower atmosphere"
<p>Colony count data collected from Petri dishes, as described in "A simple mechanism for uncrewed aircraft bioaerosol sampling in the lower atmosphere." See the associated article for more information.</p>
Hydrogen peroxide in the upper tropical troposphere over the Atlantic Ocean and western Africa during the CAFE-Africa aircraft campaign
<p>We provide here the supporting dataset for our study on airborne measurements of oh hydrogen peroxide in the upper tropical troposphere over the Atlantic Ocean and western Africa during the CAFE-Africa aircraft campaign in 2018.</p> <p> </p>
Prospective Life Cycle Inventory Datasets for conventional and hybrid electric aircraft technologies
<p><strong><em>Supplementary Material - Filled LCI data collection schemes </em></strong>from the publication <em><strong>"Prospective Life Cycle Inventory Datasets for conventional and hybrid electric aircraft technologies"</strong></em>. This repository includes LCI data for three time horizons; short-term, medium-term, and long-term.</p> <p>In the <strong>short-term time horizon</strong>, LCI data for the following technologies distinguished according to two different configurations (conventional and GT-bat) are covered in this repository:</p> <ul> <li>Airframe conventional (GENESIS_LCI_airframe_short-term_conventional_v01.xlsx)</li> <li>Airframe GT-bat (GENESIS_LCI_airframe_short-term_GT-bat_v01.xlsx)</li> <li>Airport (GENESIS_LCI_airport_short-term_v01.xlsx)</li> <li>Battery EOL (GENESIS_LCI_battery_EoL_Li-ion_short-term_GT-bat_v01.xlsx)</li> <li>Battery Li-ion (GENESIS_LCI_battery_Li-ion_short-term_GT-bat_v01.xlsx)</li> <li>Battery charging station (GENESIS_LCI_battery-charging-station_short-term_v01.xlsx)</li> <li>Power electronics and drives (GENESIS_LCI_power_elec_drives_short-term_v01.xlsx)</li> <li>Powerplant conventional (GENESIS_LCI_powerplant_short-term_conventional_v01.xlsx)</li> <li>Powerplant GT-bat (GENESIS_LCI_powerplant_short-term_GT-bat_v01.xlsx)</li> <li>SAF (GENESIS_LCI_SAF_short-term_v01.xlsx)</li> </ul> <p>In the <strong>medium-term time horizon</strong>, LCI data for the following technologies distinguished according to three different configurations (conventional, GT-bat, and PEMFC-bat) are covered in this repository:</p> <ul> <li>Airframe conventional (GENESIS_LCI_airframe_medium-term_conventional_v01.xlsx)</li> <li>Airframe GT-bat (GENESIS_LCI_airframe_medium-term_conventional_v01.xlsx)</li> <li>Airframe PEMFC-bat (GENESIS_LCI_airframe_medium-term_PEMFC-bat_v01.xlsx)</li> <li>Airport (GENESIS_LCI_airport_medium-term_v01.xlsx)</li> <li>Battery EOL Li-S GT-bat (GENESIS_LCI_battery_EoL_Li-S_medium-term_GT-bat_v01.xlsx)</li> <li>Battery EOL Li-S PEMFC-bat (GENESIS_LCI_battery_EoL_Li-S_medium-term_PEMFC-bat_v01.xlsx)</li> <li>Battery Li-S GT-bat (GENESIS_LCI_battery_Li-S_medium-term_GT-bat_v01.xlsx)</li> <li>Battery Li-S PEMFC-bat (GENESIS_LCI_battery_Li-S_medium-term_PEMFC-bat_v01.xlsx)</li> <li>Battery charging station (GENESIS_LCI_battery-charging-station_medium-term_v01.xlsx)</li> <li>Fuel cell PEM (GENESIS_LCI_fuel cell_PEM_medium-term_PEMFC-bat_v01.xlsx)</li> <li>H<sub>2</sub> onboard storage (GENESIS_LCI_H2_onboard_storage_medium-term_PEMFC_v01.xlsx)</li> <li>Power electronics and drives GT-bat (GENESIS_LCI_power_elec_drives_medium-term_GT-bat_v01.xlsx)</li> <li>Power electronics and drives PEMFC-bat (GENESIS_LCI_power_elec_drives_medium-term_PEMFC-bat_v01.xlsx)</li> <li>Powerplant conventional (GENESIS_LCI_powerplant_medium-term_conventional_v01.xlsx)</li> <li>Powerplant GT-bat (GENESIS_LCI_powerplant_medium-term_GT-bat_v01.xlsx)</li> <li>Powerplant PEMFC-bat (GENESIS_LCI_powerplant_medium-term_PEMFC-bat_v01.xlsx)</li> </ul> <p>In the <strong>long-term time horizon</strong>, LCI data for the following technologies distinguished according to three different configurations (conventional, PEMFC-bat, and SOFC-bat) are covered in this repository:</p> <ul> <li>Airframe conventional (GENESIS_LCI_airframe_long-term_conventional_v01.xlsx)</li> <li>Airframe PEMFC-bat (GENESIS_LCI_airframe_long-term_PEMFC-bat_v01.xlsx)</li> <li>Airframe SOFC-bat (GENESIS_LCI_airframe_long-term_SOFC-bat_v01.xlsx)</li> <li>Airport (GENESIS_LCI_airport_long-term_v01.xlsx)</li> <li>Battery EOL Li-Air PEMFC-bat (GENESIS_LCI_battery_EoL_Li-air_long-term_PEMFC-bat_v01.xlsx)</li> <li>Battery EOL Li-Air SOFC-bat (GENESIS_LCI_battery_EoL_Li-air_long-term_SOFC-bat_v01.xlsx)</li> <li>Battery Li-Air PEMFC-bat (GENESIS_LCI_battery_Li-air_long-term_PEMFC-bat_v01.xlsx)</li> <li>Battery Li-Air SOFC-bat (GENESIS_LCI_battery_Li-air_long-term_SOFC-bat_v01.xlsx)</li> <li>Battery charging station (GENESIS_LCI_battery-charging-station_long-term_v01.xlsx)</li> <li>Fuel cell PEM (GENESIS_LCI_fuel cell_PEM_long-term_PEMFC-bat_v01.xlsx)</li> <li>Fuel cell SO (GENESIS_LCI_fuel cell_SO_long-term_SOFC-bat_v01.xlsx)</li> <li>H<sub>2</sub> onboard storage PEMFC-bat (GENESIS_LCI_H2_onboard_storage_long-term_PEMFC-bat_v01.xlsx)</li> <li>H<sub>2</sub> onboard storage SOFC-bat (GENESIS_LCI_H2_onboard_storage_long-term_SOFC-bat_v01.xlsx)</li> <li>Power electronics and drives PEMFC-bat (GENESIS_LCI_power_elec_drives_long-term_PEMFC-bat_v01.xlsx)</li> <li>Power electronics and drives SOFC-bat (GENESIS_LCI_power_elec_drives_long-term_SOFC-bat_v01.xlsx)</li> <li>Powerplant conventional (GENESIS_LCI_powerplant_long-term_conventional_v01.xlsx)</li> <li>Powerplant PEMFC-bat (GENESIS_LCI_powerplant_long-term_PEMFC-bat_v01.xlsx)</li> <li>Powerplant SOFC-bat (GENESIS_LCI_powerplant_long-term_SOFC-bat_v01.xlsx)</li> </ul> <p>Additionally, the following file is used for <strong>all time horizons</strong>:</p> <ul> <li>H<sub>2</sub> production and supply (GENESIS_LCI_H2_production_&_supply_v01.xlsx)</li> </ul>
Observations of trace gases in the lowermost stratosphere and upper troposphere from the SPURT aircraft measurement program
<p><strong>Introduction</strong></p> <p>SPURT (Spurenstofftransport in der Tropopausenregion, trace gas transport in the tropopause region) was an aircraft measurement program funded by the AFO 2000 programme of the German Ministry for Education and Research (BMBF). Eight campaigns (36 flights in total) were conducted between November 2001 and July 2003 to investigate trace gas transport in the extratropical upper troposphere and lowermost stratosphere in all seasons. A wide range of trace gases with different lifetimes and sink/source characteristics were measured in-situ from a Learjet 35A aircraft flying at altitudes up to 13.7 km. The data set is well suited for studies of atmospheric transport, for model validation, and for investigations of seasonal changes in the upper troposphere and lowermost stratosphere as demonstrated in numerous accompanying studies.</p> <p><strong>Dataset content</strong></p> <ol> <li>In-situ measurements of N<sub>2</sub>O, CH<sub>4</sub>, CO, CO<sub>2</sub>, CFC12, H<sub>2</sub>, SF<sub>6</sub>, NO, NO<sub>y</sub>, O<sub>3</sub> and H<sub>2</sub>O along all flight tracks.</li> <li>Position and meteorological quantities recorded by the aircraft along all flight tracks.</li> <li>Meteorological data from ECMWF analysis fields and derived products such as potential vorticity and equivalent latitude interpolated to all flight tracks.</li> <li>Merge files (extension .mrg) of all observations, aircraft positions and other data merged into one single file per flight at 5 sec temporal resolution. Due to different instrument response times or computer clocks, the individual measurements were typically shifted by several seconds relative to each other. These time shifts are corrected for in the merge files.</li> <li>Ten day backward trajectories started every 12 minutes along the flight tracks computed with <em>Lagranto</em> (<a href="https://dx.doi.org/10.5194/gmd-8-2569-2015">doi:10.5194/gmd-8-2569-2015</a>) based on 3-hourly ECMWF IFS analysis/forecast fields.</li> <li>Further information such as flight quicklooks, flight protocols, meteorological reports, etc.</li> </ol> <p>All measurement data are provided in NASA/Ames format (<a href="https://espo.nasa.gov/content/Ames_Format_Specification_v20">https://espo.nasa.gov/content/Ames_Format_Specification_v20</a>), which is a self-explaining ASCII format that can conveniently be read by many software packages, e.g. the nappy library for python.</p> <p><strong>Quick start guide</strong></p> <ol> <li>Download and unpack the gzip compressed tar file (unpacking generates the two directories <em>images </em>and <em>data</em>)</li> <li>Change to the directory <em>data </em>and open the file index.html with a web browser. This will open a web page providing an overview of the eight campaigns and associated data.</li> <li>For most purposes it will be sufficient to work with the merge files: Change to the <em>data</em> directory and list all merge files by typing "ls */*/*.mrg" (Linux) or "dir *\*\*.mrg" (Windows).</li> </ol> <p> </p> <p><strong>References:</strong></p> <p>The reference journal article for the SPURT project is</p> <p><em>Engel, A., Bönisch, H., Brunner, D., Fischer, H., Franke, H., Günther, G., Gurk, C., Hegglin, M., Hoor, P., Königstedt, R., Krebsbach, M., Maser, R., Parchatka, U., Peter, T., Schell, D., Schiller, C., Schmidt, U., Spelten, N., Szabo, T., Weers, U., Wernli, H., Wetter, T., and Wirth, V.: Highly resolved observations of trace gases in the lowermost stratosphere and upper troposphere from the Spurt project: an overview, Atmos. Chem. Phys., 6, 283–301, https://doi.org/10.5194/acp-6-283-2006, 2006. </em></p> <p>Many more scientific publications emerged from the project (see reference list and object identifiers).</p>
Zonal mean of atmospheric water vapour and water vapour perturbation by emitted trace gases of hypersonic aircraft
<p>This short movie (no sound) shows two figures with time steps of five days over a period of fourteen years (2000-2014). On the left the atmospheric mixing ratio of water vapour is presented in parts per million. On the right the perturbation of stratospheric water vapour is depicted in parts per million. The perturbation is created by emitted water vapour of hypersonic aircraft flying at high altitudes (35 km). Over the years the accumulation of water vapour up to equilibrium is shown.</p>
World map presentation of water vapour perturbation emitted by hypersonic aircraft at 32-38 km altitude
<p>World map presentation of water vapour perturbation by hypersonic aircraft in a layer at 32-38 km altitude. In this movie emitted water vapour accumulates for one month in one-hour steps (interpolated from data with five-hour time intervals for a more fluid film). Transport of perturbation dominated by horizontal movement through atmospheric air mass is visible.</p>
PROCRAFT Final Meeting - Material study on the aircraft of the Deutsches Museum by Marisa Pamplona – Deutsches - Museum
Open the record for dataset details and reuse information.
PROCRAFT Final Meeting - Educational activities on WWII aircraft at the Montreal Aviation Museum by Jean Desbiens – Montreal Museum
Open the record for dataset details and reuse information.
PROCRAFT Final Meeting - Choice of restoration or renovation of WWII aircraft paintwork, illustrated by two case studies Bristol Bolingbroke and Messerschmitt 163B Komet by Thilo Bürgel, National Museum of Flight
Open the record for dataset details and reuse information.
Improving environmental performances of integrated bladed rotors for aircraft
<p>Supplementary Materials for publication Rupcic et al. 2022, Improving environmental performances of integrated bladed rotors for aircraft</p>
The realization of autonomous, aircraft-based, real-time aerosol mass spectrometry in the upper troposphere and lower stratosphere (dataset)
<p>Dataset accompanying the journal article titled "The realization of autonomous, aircraft-based, real-time aerosol mass spectrometry in the upper troposphere and lower stratosphere". Preprint: doi.org/10.5194/egusphere-2022-33</p>
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.