Fundamental Turbulence Measurement with Unmanned Aerial Vehicles (Invited)

Witte, Brandon M.; Schlagenhauf, Cornelia; Mullen, Jon; Helvey, Jacob; Thamann, Michael A.; Bailey, Sean

doi:10.2514/6.2016-3584

Cited by 6 publications

(9 citation statements)

References 27 publications

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“…Data evaluation, reduction and ABL characterization analyses are conducted by the various sub-task contributors (See Figure 6). Witte, for example, developed a fixed-wing sUAS sensing platform and data reduction to measure and characterize ABL turbulence and validated its performance in comparison to measurements from vertical profiles of a rotary-wing platform, and a portable tower-based sonic anemometer [17].…”

Section: And 2017 Cloud-map Flight Campaign Overviewmentioning

confidence: 99%

Considerations for Atmospheric Measurements with Small Unmanned Aircraft Systems

et al. 2018

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This paper discusses results of the CLOUD-MAP (Collaboration Leading Operational UAS Development for Meteorology and Atmospheric Physics) project dedicated to developing, fielding, and evaluating integrated small unmanned aircraft systems (sUAS) for enhanced atmospheric physics measurements. The project team includes atmospheric scientists, meteorologists, engineers, computer scientists, geographers, and chemists necessary to evaluate the needs and develop the advanced sensing and imaging, robust autonomous navigation, enhanced data communication, and data management capabilities required to use sUAS in atmospheric physics. Annual integrated evaluation of the systems in coordinated field tests are being used to validate sensor performance while integrated into various sUAS platforms. This paper focuses on aspects related to atmospheric sampling of thermodynamic parameters with sUAS, specifically sensor integration and calibration/validation, particularly as it relates to boundary layer profiling. Validation of sensor output is performed by comparing measurements with known values, including instrumented towers, radiosondes, and other validated sUAS platforms. Experiments to determine the impact of sensor location and vehicle operation have been performed, with sensor aspiration a major factor. Measurements are robust provided that instrument packages are properly mounted in locations that provide adequate air flow and proper solar shielding.

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Section: And 2017 Cloud-map Flight Campaign Overviewmentioning

confidence: 99%

Considerations for Atmospheric Measurements with Small Unmanned Aircraft Systems

et al. 2018

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“…During these studies, the probe tubing length was optimized to provide a fre-quency response on the order of 100 Hz. At the typical cruise speed of BLUECAT5, this frequency response translates to a spatial measurement resolution of approximately 0.2 m. Additional wind and water tunnel studies verified that positioning the measurement volume of the probe 18 cm upstream of the nose of the aircraft was sufficient to minimize interference effects from the airframe (Witte et al, 2017).…”

Section: Bluecat5 Uasmentioning

confidence: 85%

“…1a, were constructed from Skywalker X8 airframes, modified to introduce semiautonomous operation by integrating a 3DR Pixhawk autopilot, and ruggedized by strengthening wing spars, skinning the aircraft, adding Kevlar landing skids, and shielding to minimize the ingestion of dirt into the motor. Referred to as the Boundary Layer Unmanned Experiment for the Characterization of Atmospheric Turbulence generation five (BLUECAT5) UASs (Witte et al, 2017), the aircraft had endurance of up to 45 min with 20 m s −1 cruise speeds and were catapult launched and skid landed. The four aircraft used here are identified as BCT5B, BCT5C, BCT5D and BCT5E.…”

Section: Bluecat5 Uasmentioning

confidence: 99%

“…University of Kentucky's research in UAS flight testing evolved over more than 450 flights conducted to evaluate performance of deployable-wing unmanned aircraft (Jacob et al, 2005(Jacob et al, , 2007Thamann et al, 2015), before expanding into atmospheric turbulence (Witte et al, 2016(Witte et al, , 2017, formation and flight control (Mullen et al, 2016;Wellman and Hoagg, 2018;Lippay and Hoagg, 2019), and measurement of atmospheric gas concentrations (Schuyler and Guzman, 2017;Schuyler et al, 2019a, b) as well as participants in the Collaboration Leading Operational UAS Development for Meteorology and Atmospheric Physics (CLOUDMAP) program (Jacob et al, 2018). The CLOUDMAP program focused on the development of UAS technologies for meteorology and atmospheric science and resulted in advancement of capabilities for atmospheric observations with UASs, as demonstrated via multiuniversity flight campaigns (Smith et al, 2017), via validation experiments (Barbieri et al, 2019) and through observations of the surface layer transitions during the 2017 total eclipse (Bailey et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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University of Kentucky measurements of wind, temperature, pressure and humidity in support of LAPSE-RATE using multisite fixed-wing and rotorcraft unmanned aerial systems

Bailey

Sama

Canter

et al. 2020

Earth Syst. Sci. Data

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Abstract. In July 2018, unmanned aerial systems (UASs) were deployed to measure the properties of the lower atmosphere within the San Luis Valley, an elevated valley in Colorado, USA, as part of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). Measurement objectives included detailing boundary layer transition, canyon cold-air drainage and convection initiation within the valley. Details of the contribution to LAPSE-RATE made by the University of Kentucky are provided here, which include measurements by seven different fixed-wing and rotorcraft UASs totaling over 178 flights with validated data. The data from these coordinated UAS flights consist of thermodynamic and kinematic variables (air temperature, humidity, pressure, wind speed and direction) and include vertical profiles up to 900 m above the ground level and horizontal transects up to 1500 m in length. These measurements have been quality controlled and are openly available in the Zenodo LAPSE-RATE community data repository (https://zenodo.org/communities/lapse-rate/, last access: 23 July 2020), with the University of Kentucky data available at https://doi.org/10.5281/zenodo.3701845 (Bailey et al., 2020).

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“…Qu, Xing, & Zhang (2016) used a decomposition of the hovering state equations to estimate the wind, and Qu, Xing, Zhang, & Yu (2017) extended the work using both IMU and a smoothing filter to reduce the effect of sensor noise. Witte et al (2016) used a method based on on-board moving velocity sensors data such as five-hole and hot-wire probes. Benallegue, Mokhtari, & Fridman (2008) built the high-order sliding mode observer as an estimator of the effect of the external disturbances in quadrotors such as wind and noise, using a differential global positioning system, a GPS, and a sonar altimeter.…”

Section: Introductionmentioning

confidence: 99%

Using a quadrotor as wind sensor: time-varying parameter estimation algorithms

Perozzi

Efimov

Biannic

et al. 2020

International Journal of Control

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The objective of this paper is to develop an algorithm for the estimation of time-varying wind parameters by taking into account a detailed quadrotor model. The design objectives include the time convergence optimization, robustness to measurement noises, and a guaranteed convergence of the estimates to the true values under mild applicability conditions. It is supposed that the estimation algorithm can use IMU (accelerometers, gyroscopes) sensors augmented with an earth reference tracking system and rotor rotational velocity sensors. To this end, three time-varying parameter estimation algorithms are introduced, compared and finally merged to estimate the varying wind velocity in on-board quadrotor systems. Final numerical experiments, using a nonlinear quadrotor simulator, are used to validate the proposed approaches.

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Fundamental Turbulence Measurement with Unmanned Aerial Vehicles (Invited)

Cited by 6 publications

References 27 publications

Considerations for Atmospheric Measurements with Small Unmanned Aircraft Systems

Considerations for Atmospheric Measurements with Small Unmanned Aircraft Systems

University of Kentucky measurements of wind, temperature, pressure and humidity in support of LAPSE-RATE using multisite fixed-wing and rotorcraft unmanned aerial systems

Using a quadrotor as wind sensor: time-varying parameter estimation algorithms

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