Browsing by Author "Gonzalez-Rocha, Javier"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Sensing atmospheric flows in aquatic environments using a multirotor small uncrewed aircraft system (sUAS)
    Gonzalez-Rocha, Javier; Bilyeu, Landon; Ross, Shane D.; Foroutan, Hosein; Jacquemin, Stephen J.; Ault, Andrew P.; Schmale, David G. III (Royal Society Chemistry, 2023-01)
    New wind sensing technologies are needed to measure atmospheric flows in aquatic environments where hazardous agents may be present and conventional atmospheric sensors are difficult to deploy. Here, we present the application of model-based multirotor sUAS (small uncrewed aircraft system) wind estimation to measure atmospheric flow variations in aquatic environments. Thirty-two sUAS flights were conducted at Grand Lake St. Marys (GLSM), Ohio in August, 2019 to characterize differences in wind profiles (wind speed and wind direction) across onshore and offshore (over the lake) locations 80 m apart. A harmful algal bloom was present in GLSM during the experiment. Fourteen calibration flights were conducted at the same site to validate multirotor sUAS wind estimates hovering next to a sonic anemometer (SA) installed 13 m above ground level. Forty-seven calibration profiles were performed in Blacksburg, Virginia on June 30th, 2020 to validate multirotor sUAS wind estimates obtained in steady ascending vertical flight next to a SoDAR wind profiler. Differences between onshore and offshore wind speed measurements at GLSM increased from morning to afternoon on each day of experiments. Flights performed next to SA and SoDAR instruments also demonstrated multirotor sUAS estimates of wind velocity components u and v to have mean absolute error values of 0.4 m s(-1) and 0.3 m s(-1) (hovering) and 1.2 m s(-1) and 1.5 m s(-1) (ascending), respectively. Overall, our findings support further development of multirotor sUAS capabilities for resolving atmospheric flows in aquatic environments.
  • Thumbnail Image
    Sensing Atmospheric Winds from Quadrotor Motion
    Gonzalez-Rocha, Javier (Virginia Tech, 2020-06-01)
    Wind observations that are critical for understanding meteorological processes occurring inside of the Earth's atmospheric boundary layer (ABL) are sparse due to limitations of conventional atmospheric sensors. In this dissertation, dynamic systems and estimation theory are combined with experimental methods to exploit the flight envelope of multirotor UAS for wind sensing. The parameters of three quadrotor motion models, consisting of a kinematic particle, a dynamic particle, and a dynamic rigid body models are developed to measure wind velocity in hovering flight. Wind tunnel and steady level flight tests are used to characterize kinematic and dynamic particle models. System identification stepwise regression and output error algorithms are used to determine the model structure and parameter estimates of rigid body models. The comparison of all three models demonstrates the rigid body model to have higher performance resolving slow-varying winds based on a frequency response analysis and field experiments conducted next to a 3-D sonic anemometer. The dissertation also presents an extension of the rigid body wind estimation framework to profile the horizontal components of wind velocity in vertical steady ascending flight. The extension employed system identification to characterize five rigid body models for steady-ascending flight speeds increasing from 0 to 2 m/s in intervals of 0.5~m/s. State observers for wind profiling were synthesized using all five rigid body models. Performance assessments employing wind observations from in situ and remote sensors demonstrated model-based wind profiling results to be be in close agreement with ground-truth wind observations. Finally, the rigid body wind sensing framework developed in this dissertations for multirotor UAS is employed to support science objectives for the Advanced Lagrangian Predictions for Hazards Assessment Project. Quadrotor wind measurements sampled at 10 m above sea level were used to characterize the leeway of a person in water for search and rescue scenarios. Leeway values determined from quadrotor wind measurements were found to be in close to leeway parameters previous published in the literature. This results demonstrates the utility of model-based wind sensing for multirotor UAS for providing wind velocity observations in complex environments where conventional wind observations are not readily available.