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22 results for “DLR”
Spherical harmonic models of the shape of asteroid (4) Vesta [DLR SPG]
<p>This archive contains four spherical harmonic models of the shape of asteroid 4 Vesta, truncated at different maximum spherical harmonic degrees. The highest resolution model has a maximum spherical harmonic degree of 5759, which was generated from a shape model sampled at 64 pixels per degree.</p> <p>The data used to generate these models are from a DLR stereo photogrammetric shape model based on Dawn high altitude mapping orbit framing camera images, as found in the file <code>VE_HAMO_G_00N_330E_EQU_DTM.IMG</code> on <a href="https://sbnarchive.psi.edu/pds3/dawn/fc/DWNVSPG_2/DATA/">NASA's PDS website</a>. This image file was first converted to netcdf format using the <a href="https://www.generic-mapping-tools.org/">generic-mapping-tools</a> function <code>xyz2grd</code>, and it was then converted to a gridline registration using the function <code>grdsample</code>. The grid was then shifted such that the frist column corresponded to 0 E longitude using the function <code>grdedit</code>, and the resulting netcdf file was read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software and expanded into spherical harmonics using the function <code>SHCoeffs.expand()</code>. The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The units of the coefficients are meters.</p> <p>The four files in this archive are</p> <ul> <li>Vesta_DLR_SPG_shape_5759.bshc.gz</li> <li>Vesta_DLR_SPG_shape_2879.bshc.gz</li> <li>Vesta_DLR_SPG_shape_1439.bshc.gz</li> <li>Vesta_DLR_SPG_shape_719.bshc.gz</li> </ul> <p>The numbers 5759, 2879, 1439, and 719 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of 64, 32, 16, and 8 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip. The lower resolution models were generated by truncating the spherical harmonic coefficients of the highest resolution model.</p>
Spherical harmonic models of the shape of asteroid (1) Ceres [DLR SPG]
<p>This archive contains four spherical harmonic models of the shape of asteroid (1) Ceres, truncated at different maximum spherical harmonic degrees. The highest resolution model has a maximum spherical harmonic degree of 5399, which was generated from a shape model sampled at 60 pixels per degree.</p> <p>The data used to generate these models are from a DLR stereo photogrammetric shape model based on Dawn high altitude mapping orbit framing camera images, as found in the file <a href="https://sbnarchive.psi.edu/pds3/dawn/fc/DWNCHSPG_2/DATA/"><code>CE_HAMO_G_00N_180E_EQU_DTM.IMG</code></a> on <a href="https://sbnarchive.psi.edu/pds3/dawn/fc/DWNCHSPG_2/DATA/">NASA's PDS website</a>. This image file was first converted to netcdf format using the <a href="https://www.generic-mapping-tools.org/">generic-mapping-tools</a> function <code>xyz2grd</code>, it was then converted to a gridline registration using the function <code>grdsample</code>, and the resulting netcdf file was read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software and expanded into spherical harmonics using the function <code>SHCoeffs.expand()</code>. The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The units of the coefficients are meters.</p> <p>The four files in this archive are</p> <ul> <li>Ceres_DLR_SPG_shape_5399.bshc.gz</li> <li>Ceres_DLR_SPG_shape_2879.bshc.gz</li> <li>Ceres_DLR_SPG_shape_1439.bshc.gz</li> <li>Ceres_DLR_SPG_shape_719.bshc.gz</li> </ul> <p>The numbers 5399, 2879, 1439, and 719 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of 60, 32, 16, and 8 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip. The lower resolution models were generated by truncating the spherical harmonic coefficients of the highest resolution model.</p>
Heliostat and receiver efficiency calculated with DLR's software HFLCAL
<p>The heliostat field and the receiver design efficiency was estimated for the design point by optimizing for lowest LCOE.</p> <p>The datasets could help other people design a heliostat field.</p> <p>For detailed analysis, please refer to Deliverable 1.2 (Process Parameters of Solar Particle Cycle) to be downloaded at: <a href="https://www.compassco2.eu/wp-content/uploads/2022/03/Deliverable1.2_COMPASsCO2_V2.pdf">https://www.compassco2.eu/wp-content/uploads/2022/03/Deliverable1.2_COMPASsCO2_V2.pdf</a></p>
Heliostat field positions after optimization with DLR's software HFLCAL
<p>The dataset provides the design of the heliostat field layout, in terms of number and positions. The filed layout is optimized by minimizing the estimated LCOE of the system, using the DLR tool Visual HFLCAL software</p> <p>The datasets could help other people design a heliostat field.</p> <p>For detailed analysis, please refer to Deliverable 1.2 (Process Parameters of Solar Particle Cycle) to be downloaded at: <a href="https://www.compassco2.eu/wp-content/uploads/2022/03/Deliverable1.2_COMPASsCO2_V2.pdf">https://www.compassco2.eu/wp-content/uploads/2022/03/Deliverable1.2_COMPASsCO2_V2.pdf</a></p>
H2020 ENODISE: DLR Analytical Aeroacoustic Database BLI Configuration A1 and A2
<p>This database contains the acoustic prediction results of DLR for a single operating point of the A1 and A2 configurations investigated in the framework of the European project ENODISE. In these configurations, a single two-bladed propeller is immersed in a boundary layer. The present results can be compared to the measurements carried out by the University of Bristol and the University of Twente for different boundary layer characteristics. The experimental results are saved elsewhere on the ZENODO repository.</p> <p>The investigated operating point as calculated in the prediction is:</p> <ul> <li>Uinf = 33 m/s, 6500 rpm, advance ratio J=1.</li> </ul> <p>The experimental measurements were conducted at a slightly lower freestream velocity.</p> <p>The DLR prediction results were obtained using the analytical approach implemented in the DLR in-house program PropNoise coupled to the blade element momentum theory. The calculations were informed by the hot-wire measurements carried out in the boundary-layer as the propeller was removed.</p> <p>Refer to the two references cited below to obtain more information about the theory that was applied to obtain the results: </p> <ol> <li> S. Guérin, T. Lade, L. Castelucci, I. Zaman, Tonal noise emission by a low-Mach low-Reynolds number propeller ingesting a boundary layer, 29th International Congress on Sound and Vibration, 10-13 July 2023, Prague (CZ).</li> <li> S. Guérin, T. Lade, L. Castelucci, I. Zaman, Broadban noise emission by a low-Mach low-Reynolds number propeller ingesting a boundary layer, Inter-Noise 2023, 20-23 August 2023, Chiba (Great Tokyo), Japan.</li> </ol> <p>The results for tonal and broadband noise are saved separately. The DLR results are saved into an h5 file, which can be read with e.g. python.</p> <p>Further details can be found in the file <em>DLR_documentation_A1_A2.pptx</em></p>
H2020 ENODISE: DLR Analytical Aeroacoustic Database Configuration B1
<p>This database contains the acoustic prediction results of DLR for a single operating point of the B1 configuration investigated in the framework of the European project ENODISE. In this configuration, three identical propellers with 6 blades are mounted at leading-edge of a wing (puller configuration). The results presented here can be compared to the measurements carried out by the Technical University of Delft, when these are available. The experimental results should be also saved on the ZENODO repository (use the key word ENODISE).</p> <p>The investigated operating point as calculated in the prediction is:</p> <ul> <li>Uinf = 30 m/s, advance ratio J=0.8.</li> </ul> <p>The DLR prediction results were obtained using the analytical approach implemented in the DLR in-house program PropNoise coupled to the blade element momentum theory.</p> <p>The interaction with the wing is accounted for in a simplistic way as explained in the detailed documentation <em>B_documentation.pptx.</em></p> <p>Only, the results for tonal noise are available in this database. Three cases can be investigated separately: a single isolated propeller, 3 distributed propellers, three distributed propellers interacting with the wing.</p> <p> </p> <p>The DLR results are saved in h5 files, which can be read with e.g. Python.</p>
Brainport, Automated valet parking, dropoff scenario, DLR vehicle
<p><strong>Scenario description</strong>:</p> <p>The AD-vehicle receives parking command message (AutoPilot.VehicleCommand) containing the destination parking spot and the free obstacle route and drives from the drop-off position and parks to the destination parking spot. During the parking process the vehicle send two type of messages (AutoPilot.PositionEstimate and AutoPilot.VehicleAVPStatus)</p> <p><strong>Session description</strong>:</p> <p>The dropoff scenario with DLR vehicle. DLR vehicle parks autonomously from the dropoff location to the selected parking spot at the parking area on DLR test area in Brunswick.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Brainport, Automated valet parking, pickup DLR automated vehicle
<p><strong>Scenario description</strong>:</p> <p>The AD-vehicle receives parking command message (AutoPilot.VehicleCommand) containing the destination pickup spot and the free obstacle route and drives from the parking spot and parks to the destination pickup spot. During the collection process the vehicle send two type of messages (AutoPilot.PositionEstimate and AutoPilot.VehicleAVPStatus)</p> <p><strong>Session description</strong>:</p> <p>The pickup scenario with DLR vehicle. DLR vehicle parks autonomously from the parking spot to the destination pickup spot at the parking area on DLR test site in Brunswick.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Brainport, Automated valet parking, dropoff automated DLR vehicle
<p><strong>Scenario description</strong>:</p> <p>The AD-vehicle receives parking command message (AutoPilot.VehicleCommand) containing the destination parking spot and the free obstacle route and drives from the drop-off position and parks to the destination parking spot. During the parking process the vehicle send two type of messages ( AutoPilot.PositionEstimate and AutoPilot.VehicleAVPStatus)</p> <p><strong>Session description</strong>:</p> <p>The dropoff scenario with DLR vehicle. DLR vehicle parks autonomously from the dropoff location to the selected parking spot at the parking area on DLR test area in Brunswick.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Analysis of the DLR Knowledge Exchange Workshop Series on Software Engineering
<p>This repository is used to analyze the workshops of the DLR internal workshop series on software<br> engineering. These workshops are two-day events of the DLR software engineering community and<br> focus on different main topics every year.</p>
H2020 ENODISE: DLR Configuration B, Analytical
<p>A varitiation of phase shift and tip gap between adjacent propellers of configuration B1 has been carried out and acoustically investigated. For further description please read <em>D5-7_B1.pdf.</em></p>
Brainport, Automated valet parking, pickup scenario with DLR vehicle
<p><strong>Scenario description</strong>:</p> <p>The AD-vehicle receives parking command message (AutoPilot.VehicleCommand) containing the destination pickup spot and the free obstacle route and drives from the parking spot and parks to the destination pickup spot.During the collection process the vehicle send two type of messages (AutoPilot.PositionEstimate and AutoPilot.VehicleAVPStatus)</p> <p><strong>Session description</strong>:</p> <p>The pickup scenario with DLR vehicle. DLR vehicle parks autonomously from the parking spot to the destination pickup spot at the parking area on DLR test site in Brunswick.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Role and practice of research software development at DLR
<p>The deposit contains the results of a survey concerning the role and practice of research software development at the German Aerospace Center (DLR). The survey started end of November 2018 and ended mid-February 2019. We received 773 answers which gave us interesting insights concerning developer demographics, tool usage, documentation, testing as well as software citation at DLR.</p>
DLR AS TEA Configuration B1 Numerical Simulation Database
<p>This database contains the results of the numerical simulations conducted in the framework of WP5.4 by DLR AS TEA. See README file for the description of the cases simulated.</p>
DLR AS TEA Configuration A1 and A2 Numerical Simulation Database
<p>This database contains the results of the numerical simulations conducted in the framework of WP5.2 by DLR AS TEA. See README file for the description of the cases simulated.</p>
DLR Urban Traffic dataset (DLR UT)
<p>The dataset includes both raw and metadata from the <a href="https://www.dlr.de/en/ts/research-transfer/research-infrastructure/test-areas/recording-technology/research-intersection-a-hub-for-data-collection-in-the-field" target="_blank" rel="noopener">Application Platform Intelligent Mobility (AIM) Research Intersection</a> in Braunschweig, Germany.</p> <p>The raw data comprises trajectory data of traffic participants, as well as traffic light data, local weather data, air quality data, and road condition data. The trajectory data is indexed by object ID and timestamps, including detailed information about the position, speed, acceleration, dimensions, and classification of each object. The dataset contains 32,296 trajectories, covering both motorized and vulnerable road users. The traffic light data captures the current state of all 30 traffic lights at the intersection. The weather data provides information on wind, sunlight, precipitation, visibility, and more. The air quality data represent concentrations of five different gases and fine particle concentrations in the atmosphere. The road condition data provide information on surface temperature, water layer thickness, and more. </p> <p>The metadata contains data extracted from the raw trajectory data, specifically traffic volume data, and 4 OpenSCENARIO files that represent the trajectory data using <a href="https://www.asam.net/static_downloads/ASAM_OpenSCENARIO_V1.2.0_Model_Documentation/modelDocumentation/content/FollowTrajectoryAction.html">FollowTrajectoryActions</a>.</p> <p><em>For any questions regarding the dataset, please contact <a title="mailto:opendata-ts@dlr.de" href="mailto:opendata-ts@dlr.de" target="_blank" rel="noopener">opendata-ts@dlr.de</a>. For citation, please refer to the <a href="https://ieeexplore.ieee.org/document/11097400/" target="_blank" rel="noopener">publication</a>.<br></em></p> <p><em>Take a look at the <a href="https://doi.org/10.5281/zenodo.14012005" target="_blank" rel="noopener">DLR Highway Traffic dataset</a> if you're interested in trajectories up to 6 km long from a German highway.</em></p>
Flask sampling data obtained during the CoMet campaign in summer 2018 over Silesia on DLR Cessna
Open the record for dataset details and reuse information.
DC3 In-Situ DLR Falcon Meteorological and Navigational Data
DC3_MetNav_AircraftInSitu_DLR-Falcon_Data are meteorological and navigational data collected onboard the DLR Falcon aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. Data collection for this product is complete.The Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.DC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.In addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.
DC3 In-Situ DLR-Falcon Trace Gas Data
DC3_TraceGas_AircraftInSitu_DLR-Falcon_Data are in-situ trace gas data collected onboard the DLR Falcon aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. Data collection for this product is complete.The Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.DC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.In addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.
DC3 In-Situ DLR-Falcon Aerosol Data
DC3_Aerosol_AircraftInSitu_DLR-Falcon_Data are in-situ aerosol data collected onboard the DLR Falcon aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. Data collection for this product is complete.The Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.DC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.In addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.
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