Method for airborne measurement of the spatial wind speed distribution above complex terrain

Ingenhorst, Christian; Jacobs, Georg; Stößel, Laura; Schelenz, Ralf; Juretzki, Björn

doi:10.5194/wes-6-427-2021

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…This was true regardless of the separation height above the UAS, thus indicating that 406 mm (5.3 rotor diameters) was sufficient separation to have minimal impact from the rotors on the mean wind speed measurements when positioned between rotors. The results compare well with similar studies, such as Ingenhorst et al [35], which found very good agreement with reference data, including both mean wind speeds and wind directions during positioncontrolled hovering. Though rotor influences were not evaluated in Ingenhorst et al [35], they determined that turbulence intensity estimation was 'reasonably good'.…”

Section: Discussionsupporting

confidence: 91%

“…The results compare well with similar studies, such as Ingenhorst et al [35], which found very good agreement with reference data, including both mean wind speeds and wind directions during positioncontrolled hovering. Though rotor influences were not evaluated in Ingenhorst et al [35], they determined that turbulence intensity estimation was 'reasonably good'. These results are promising preliminary results but requires additional field measurements.…”

Section: Discussionsupporting

confidence: 91%

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Wind Speed Statistics from a Small UAS and Its Sensitivity to Sensor Location

et al. 2022

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With the increase in the use of small uncrewed aircraft systems (UAS) there is a growing need for real-time weather forecasting to improve the safety of low-altitude aircraft operations. This will require integration of measurements with autonomous systems since current available sampling lack sufficient resolution within the atmospheric boundary layer (ABL). Thus, the current work aims to assess the ability to measure wind speeds from a quad-copter UAS and compare the performance with that of a fixed mast. Two laboratory tests were initially performed to assess the spatial variation in the vertically induced flow from the rotors. The horizontal distribution above the rotors was examined in a water tunnel at speeds and rotation rates to simulate nominally full throttle with a relative air speed of 0 or 8 m/s. These results showed that the sensor should be placed between rotor pairs. The vertical distribution was examined from a single rotor test in a large chamber, which suggested that at full throttle the sensor should be about 400 mm above the rotor plane. Field testing was then performed with the sensor positioned in between both pairs of rotors at 406, 508, and 610 mm above the rotor plane. The mean velocity over the given period was within 5.5% of the that measured from a fixed mast over the same period. The variation between the UAS and mast sensors were better correlated with the local mean shear than separation distance, which suggests height mismatch could be the source of error. The fluctuating velocity was quantified with the comparison of higher order statistics as well as the power spectral density, which the mast and UAS spectra were in good agreement regardless of the separation distance. This implies that for the current configuration a separation distance of 5.3 rotor diameters was sufficient to minimize the influence of the rotors.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionsupporting

confidence: 91%

Wind Speed Statistics from a Small UAS and Its Sensitivity to Sensor Location

et al. 2022

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“…For example, in [111], they used in situ wind measurements on sUAS for understanding the atmospheric boundary layer by developing wind profiling measurements using wind-induced perturbations. Mapping wind distributions over complex terrain was explored in [112], where they utilized a Gill WindMaster 3D ultrasonic anemometer mounted on a octocopter (called the WindLocater).…”

Section: Sensors and Equipmentmentioning

confidence: 99%

Advanced Leak Detection and Quantification of Methane Emissions Using sUAS

Hollenbeck

Zulevic

Chen

2021

Drones

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Detecting and quantifying methane emissions is gaining an increasingly vital role in mitigating emissions for the oil and gas industry through early detection and repair and will aide our understanding of how emissions in natural ecosystems are playing a role in the global carbon cycle and its impact on the climate. Traditional methods of measuring and quantifying emissions utilize chamber methods, bagging individual equipment, or require the release of a tracer gas. Advanced leak detection techniques have been developed over the past few years, utilizing technologies, such as optical gas imaging, mobile surveyors equipped with sensitive cavity ring down spectroscopy (CRDS), and manned aircraft and satellite approaches. More recently, sUAS-based approaches have been developed to provide, in some ways, cheaper alternatives that also offer sensing advantages to traditional methods, including not being constrained to roadways and being able to access class G airspace (0–400 ft) where manned aviation cannot travel. This work looks at reviewing methods of quantifying methane emissions that can be, or are, carried out using small unmanned aircraft systems (sUAS) as well as traditional methods to provide a clear comparison for future practitioners. This includes the current limitations, capabilities, assumptions, and survey details. The suggested technique for LDAQ depends on the desired accuracy and is a function of the survey time and survey distance. Based on the complexity and precision, the most promising sUAS methods are the near-field Gaussian plume inversion (NGI) and the vertical flux plane (VFP), which have comparable accuracy to those found in conventional state-of-the-art methods.

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“…Numerous studies have integrated a variety of ow sensors on board multirotor sUAS to measure wind velocity while hovering at one or multiple xed locations. 5,6,13 Some of the ow sensor types considered in these studies include cup, hot-wire, and sonic anemometers; [14][15][16][17] pitot tube and multi-hole probe air data systems; 18 and LiDAR. 19 The effectiveness of these techniques for measuring wind velocity have been found to depend on the payload capacity of the host aircra, and the signicance of measurement error produced by propeller downwash and vehicle motion.…”

Section: Introductionmentioning

confidence: 99%