Sensing atmospheric flows in aquatic environments using a multirotor small uncrewed aircraft system (sUAS)

González-Rocha, Javier; Bilyeu, Landon; Ross, Shane D.; Foroutan, Hosein; Jacquemin, Stephen J.; Ault, Andrew P.; Schmale, David G.

doi:10.1039/d2ea00042c

Cited by 4 publications

(5 citation statements)

References 38 publications

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“…Wind speed and direction measurements from 8/5/2019 to 8/9/2019 were taken from Weather Underground, station KOHCELIN4, which is located less than 5 km west from the WSU-LC on the western shore of GLSM. Meteorological data from the station was confirmed via cross-checking with observational notes from the field, with specific focus on a high wind speed and storm event (8/6/2019) . Wind speed and direction measurements were recorded with 5 min time resolution and were averaged into 60 min intervals.…”

Section: Methodsmentioning

confidence: 99%

“…Meteorological data from the station was confirmed via crosschecking with observational notes from the field, with specific focus on a high wind speed and storm event (8/6/2019). 63 Wind speed and direction measurements were recorded with 5 min time resolution and were averaged into 60 min intervals. While the wind speed measurements were not taken at the sampling locations, which could potentially be influenced by the topography (though the region is very flat), these data are consistent with trends in noncontinuous data from Bilyeu et al 64 and Gonzaĺez-Rocha et al 63 collected on the shoreline of the WSU-LC.…”

Section: ■ Introductionmentioning

confidence: 99%

“…63 Wind speed and direction measurements were recorded with 5 min time resolution and were averaged into 60 min intervals. While the wind speed measurements were not taken at the sampling locations, which could potentially be influenced by the topography (though the region is very flat), these data are consistent with trends in noncontinuous data from Bilyeu et al 64 and Gonzaĺez-Rocha et al 63 collected on the shoreline of the WSU-LC. Satellite imagery is from the 2019 Ohio National Agriculture Imagery Program and retrieved from the NOAA Office of Coastal Management (https://naip-usdaonline.hub.…”

Section: ■ Introductionmentioning

confidence: 99%

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Aerosolized Cyanobacterial Harmful Algal Bloom Toxins: Microcystin Congeners Quantified in the Atmosphere

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Olson,

Birbeck

et al. 2023

Environ. Sci. Technol.

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Section: Methodsmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

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Aerosolized Cyanobacterial Harmful Algal Bloom Toxins: Microcystin Congeners Quantified in the Atmosphere

Shi,

Olson,

Birbeck

et al. 2023

Environ. Sci. Technol.

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“…Large and expensive UAVs capable of flying at high altitudes should bridge the gap between satellite data [15] and measurements obtained from ground-based networks in global NWP. At the same time, small and inexpensive UAVs can fill the gaps in obtaining data on profiles of the turbulent atmospheric boundary layer, especially in hard-to-reach or dangerous places [16][17][18][19] for short-term forecasting in local territories.…”

Section: Introductionmentioning

confidence: 99%

“…A possible solution to this problem is the use of the UAV itself as a detector of the state of the atmosphere. The use of a UAV as a detector makes it possible to obtain information about the wind velocity in a turbulent atmosphere from autopilot data of hovering UAVs [17,19,[23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Profiling of Atmospheric Turbulence Using UAV Autopilot Data

Shelekhov

Afanasiev

Shelekhova

et al. 2023

Drones

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The capabilities of hovering unmanned aerial vehicles (UAVs) in low-altitude sensing of atmospheric turbulence with high spatial resolution are studied experimentally. The vertical profile of atmospheric turbulence was measured at the Basic Experimental Observatory (Tomsk, Russian Federation) with three quadcopters hovering at altitudes of 4, 10, and 27 m in close proximity (~5 m) to anemometers installed on weather towers. The behavior of the longitudinal and lateral wind velocity components in the 0–10 Hz frequency band is analyzed. In addition, the obtained wind velocity components were smoothed over 1 min by the moving average method to describe long turbulent wind gusts. The discrepancy between the UAV and anemometer data is examined. It is found that after smoothing, the discrepancy does not exceed 0.5 m/s in 95% of cases. This accuracy is generally sufficient for measurements of the horizontal wind in the atmosphere. The spectral and correlation analysis of the UAV and anemometer measurements is carried out. The profiles of the longitudinal and lateral scales of turbulence determined from turbulence spectra and autocorrelation functions are studied based on the UAV and anemometer data.

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Rotary-wing drone-induced flow – comparison of simulations with lidar measurements

Jin,

Ghirardelli,

Mann

et al. 2024

Atmos. Meas. Tech.

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Abstract. Ultrasonic anemometers mounted on rotary-wing drones have the potential to provide a cost-efficient alternative to the classical meteorological mast-mounted counterpart for atmospheric boundary layer research. However, the propeller-induced flow may degrade the accuracy of free-stream wind velocity measurements by wind sensors mounted on drones – a fact that needs to be investigated for optimal sensor placement. Computational fluid dynamics (CFD) simulations are an alternative to experiments for studying characteristics of the propeller-induced flow but require validation. Therefore, we performed an experiment using three short-range continuous-wave Doppler lidars (light detection and ranging; DTU WindScanners) to measure the complex and turbulent three-dimensional wind field around a hovering drone at low ambient wind speeds. Good agreement is found between experimental results and those obtained using CFD simulations under similar conditions. Both methods conclude that the disturbance zone (defined as a relative deviation from the mean free-stream velocity by more than 1 %) on a horizontal plane located at 1 D (rotor diameter D of 0.71 m) below the drone extends about 2.8 D upstream from the drone center for the horizontal wind velocity and more than 7 D for the vertical wind velocity. By comparing wind velocities along horizontal lines in the upstream direction, we find that the velocity difference between the two methods is ≤ 0.1 m s−1 (less than a 4 % difference relative to the free-stream velocity) in most cases. Both the plane and line scan results validate the reliability of the simulations. Furthermore, simulations of flow patterns in a vertical plane at the ambient speed of 1.3 m s−1 indicate that it is difficult to accurately measure the vertical wind component with less than a 1 % distortion using drone-mounted sonic anemometers.

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Sensing atmospheric flows in aquatic environments using a multirotor small uncrewed aircraft system (sUAS)

Abstract: Multirotor sUAS wind sensing capabilities are effective for resolving onshore and offshore atmospheric flow variations in aquatic environments where airborne hazardous agents threaten downwind communities.

Cited by 4 publications

References 38 publications

Aerosolized Cyanobacterial Harmful Algal Bloom Toxins: Microcystin Congeners Quantified in the Atmosphere

Aerosolized Cyanobacterial Harmful Algal Bloom Toxins: Microcystin Congeners Quantified in the Atmosphere

High-Resolution Profiling of Atmospheric Turbulence Using UAV Autopilot Data

Rotary-wing drone-induced flow – comparison of simulations with lidar measurements

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