Articles | Volume 14, issue 2
https://doi.org/10.5194/amt-14-1303-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-1303-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Feb 2021
Research article |
| 18 Feb 2021
| 18 Feb 2021
Towards accurate and practical drone-based wind measurements with an ultrasonic anemometer
William Thielicke
CORRESPONDING AUTHOR
OPTOLUTION Messtechnik GmbH, Gewerbestr. 18, 79539 Lörrach, Germany
Waldemar Hübert
OPTOLUTION Messtechnik GmbH, Gewerbestr. 18, 79539 Lörrach, Germany
Ulrich Müller
OPTOLUTION Messtechnik GmbH, Gewerbestr. 18, 79539 Lörrach, Germany
Michael Eggert
Physikalisch-Technische Bundesanstalt, Bundesallee 100, 38116 Braunschweig, Germany
Paul Wilhelm
Physikalisch-Technische Bundesanstalt, Bundesallee 100, 38116 Braunschweig, Germany
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- Spatially distributed and simultaneous wind measurements with a fleet of small quadrotor UAS T. Wetz & N. Wildmann 10.1088/1742-6596/2265/2/022086
- Distributed wind measurements with multiple quadrotor unmanned aerial vehicles in the atmospheric boundary layer T. Wetz et al. 10.5194/amt-14-3795-2021
- Wind Estimation by Multirotor Drone State Using Machine Learning with Data Rotation and Reduction S. Zimmerman et al. 10.2139/ssrn.4089510
- Validation of wind measurements from a multirotor RPAS-mounted ultrasonic wind sensor using a ground-based LiDAR system L. Scicluna et al. 10.1139/dsa-2022-0034
- An assembled hot wire anemometer design X. Yao et al. 10.21595/jme.2023.23637
- A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al. 10.1016/j.rser.2024.114279
- Experimental Characterization of Propeller-Induced Flow (PIF) below a Multi-Rotor UAV A. Flem et al. 10.3390/atmos15030242
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- Multi-point in situ measurements of turbulent flow in a wind turbine wake and inflow with a fleet of uncrewed aerial systems T. Wetz & N. Wildmann 10.5194/wes-8-515-2023
- Evaluation of an Automatic Meteorological Drone Based on a 6-Month Measurement Campaign M. Hervo et al. 10.3390/atmos14091382
- Wind estimation by multirotor drone state using machine learning with data rotation and reduction S. Zimmerman et al. 10.1016/j.measurement.2022.111491
- Flow Structure around a Multicopter Drone: A Computational Fluid Dynamics Analysis for Sensor Placement Considerations M. Ghirardelli et al. 10.3390/drones7070467
- Towards vertical wind and turbulent flux estimation with multicopter uncrewed aircraft systems N. Wildmann & T. Wetz 10.5194/amt-15-5465-2022
- Experience of Studying the Turbulent Structure of the Atmospheric Boundary Layer Using an Unmanned Aerial Vehicle D. Chechin et al. 10.1134/S0001433821050042
- Maritime Emission Monitoring: Development and Testing of a UAV-Based Real-Time Wind Sensing Mission Planner Module T. Karachalios et al. 10.3390/s24030950
- Enhancing wildfire propagation model predictions using aerial swarm-based real-time wind measurements: A conceptual framework M. Tavakol Sadrabadi & M. Innocente 10.1016/j.apm.2024.03.012
- Design and Optimization of Resonant Cavity Piezoelectric Cantilever Sensor for Wind Velocity and Direction Measurement J. Hu et al. 10.1109/TIM.2022.3178490
- Uncertainty in the Mobile Observation of Wind E. Leibensperger et al. 10.3390/atmos14050765
- Lessons learned from a UAV survey and methane emissions calculation at a UK landfill H. Yong et al. 10.1016/j.wasman.2024.03.025
- Flight Experiments and Numerical Simulations for Investigating Multicopter Flow Field and Structure Deformation F. Pätzold et al. 10.3390/atmos14091336
- A Feedforward Quadrotor Disturbance Rejection Method for Visually Identified Gust Sources Based on Transfer Reinforcement Learning F. Song et al. 10.1109/TAES.2023.3275937
- Measurements and modelling of the three-dimensional near-field dispersion of particulate matter emitted from passenger ships in a port environment M. Haugen et al. 10.1016/j.atmosenv.2022.119384
- Seed Dispersal Models for Natural Regeneration: A Review and Prospects M. Kim et al. 10.3390/f13050659
- Ultra-Light Airborne Measurement System for Investigation of Urban Boundary Layer Dynamics P. Sekula et al. 10.3390/s21092920
25 citations as recorded by crossref.
- Advanced Leak Detection and Quantification of Methane Emissions Using sUAS D. Hollenbeck et al. 10.3390/drones5040117
- Spatially distributed and simultaneous wind measurements with a fleet of small quadrotor UAS T. Wetz & N. Wildmann 10.1088/1742-6596/2265/2/022086
- Distributed wind measurements with multiple quadrotor unmanned aerial vehicles in the atmospheric boundary layer T. Wetz et al. 10.5194/amt-14-3795-2021
- Wind Estimation by Multirotor Drone State Using Machine Learning with Data Rotation and Reduction S. Zimmerman et al. 10.2139/ssrn.4089510
- Validation of wind measurements from a multirotor RPAS-mounted ultrasonic wind sensor using a ground-based LiDAR system L. Scicluna et al. 10.1139/dsa-2022-0034
- An assembled hot wire anemometer design X. Yao et al. 10.21595/jme.2023.23637
- A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes M. Amiri et al. 10.1016/j.rser.2024.114279
- Experimental Characterization of Propeller-Induced Flow (PIF) below a Multi-Rotor UAV A. Flem et al. 10.3390/atmos15030242
- Development and flight-test verification of two-dimensional rotational low-airspeed sensor for small helicopters D. Fujiwara & T. Tonoike 10.1186/s40648-024-00274-2
- Unmanned Aerial Vehicle Communications for Civil Applications: A Review M. Ghamari et al. 10.1109/ACCESS.2022.3208571
- Multi-point in situ measurements of turbulent flow in a wind turbine wake and inflow with a fleet of uncrewed aerial systems T. Wetz & N. Wildmann 10.5194/wes-8-515-2023
- Evaluation of an Automatic Meteorological Drone Based on a 6-Month Measurement Campaign M. Hervo et al. 10.3390/atmos14091382
- Wind estimation by multirotor drone state using machine learning with data rotation and reduction S. Zimmerman et al. 10.1016/j.measurement.2022.111491
- Flow Structure around a Multicopter Drone: A Computational Fluid Dynamics Analysis for Sensor Placement Considerations M. Ghirardelli et al. 10.3390/drones7070467
- Towards vertical wind and turbulent flux estimation with multicopter uncrewed aircraft systems N. Wildmann & T. Wetz 10.5194/amt-15-5465-2022
- Experience of Studying the Turbulent Structure of the Atmospheric Boundary Layer Using an Unmanned Aerial Vehicle D. Chechin et al. 10.1134/S0001433821050042
- Maritime Emission Monitoring: Development and Testing of a UAV-Based Real-Time Wind Sensing Mission Planner Module T. Karachalios et al. 10.3390/s24030950
- Enhancing wildfire propagation model predictions using aerial swarm-based real-time wind measurements: A conceptual framework M. Tavakol Sadrabadi & M. Innocente 10.1016/j.apm.2024.03.012
- Design and Optimization of Resonant Cavity Piezoelectric Cantilever Sensor for Wind Velocity and Direction Measurement J. Hu et al. 10.1109/TIM.2022.3178490
- Uncertainty in the Mobile Observation of Wind E. Leibensperger et al. 10.3390/atmos14050765
- Lessons learned from a UAV survey and methane emissions calculation at a UK landfill H. Yong et al. 10.1016/j.wasman.2024.03.025
- Flight Experiments and Numerical Simulations for Investigating Multicopter Flow Field and Structure Deformation F. Pätzold et al. 10.3390/atmos14091336
- A Feedforward Quadrotor Disturbance Rejection Method for Visually Identified Gust Sources Based on Transfer Reinforcement Learning F. Song et al. 10.1109/TAES.2023.3275937
- Measurements and modelling of the three-dimensional near-field dispersion of particulate matter emitted from passenger ships in a port environment M. Haugen et al. 10.1016/j.atmosenv.2022.119384
- Seed Dispersal Models for Natural Regeneration: A Review and Prospects M. Kim et al. 10.3390/f13050659
1 citations as recorded by crossref.
Latest update: 07 May 2024
Short summary
We developed a wind-measuring drone with exceptional measuring accuracy and a very long flight time. Measurements are extensively validated at different levels. A comparison with a bistatic lidar reveals very small bias and RMSEs. We also present a demonstration measurement in the wake of a wind turbine. We think that our solution is a significant enhancement to existing designs, and other researchers can benefit from the details that we are giving in the paper.
We developed a wind-measuring drone with exceptional measuring accuracy and a very long flight...
