A Method for Detecting Atmospheric Lagrangian Coherent Structures Using a Single Fixed-Wing Unmanned Aircraft System

Particle capture in a model chaotic flow

To better understand and optimize the capture of passive scalars (particles, pollutants, greenhouse gases, etc.) in complex geophysical flows, we study capture in the simpler, but still chaotic, time-dependent double-gyre flow model. For a range of model parameters, the domain of the double-gyre flow consists of a chaotic region, characterized by rapid mixing, interspersed with nonmixing islands in which particle trajectories are regular. Capture units placed within the domain remove all particles that cross their perimeters without altering the velocity field. To predict the capture capability of a unit at an arbitrary location, we characterize the trajectories of a uniformly seeded ensemble of particles as chaotic or nonchaotic, and then use them to determine the spatially resolved fraction of time that the flow is chaotic. With this information, we can predict where to best place units for maximum capture. We also examine the time dependence of the capture process, and demonstrate that there can be a trade-off between the amount of material captured and the capture rate.

Gathering Pipeline Methane Emissions in Utica Shale Using an Unmanned Aerial Vehicle and Ground-Based Mobile Sampling

The United States Environmental Protection Agency Greenhouse Gas Inventory only recently updated the emission factors of natural gas gathering pipelines in April 2019 from the previous estimates based on a 1990s study of distribution pipelines. Additional measurements are needed from different basins for more accurate assessments of methane emissions from natural gas midstream industries and hence the overall climate implications of natural gas as the interim major energy source for the next decade. We conducted an unmanned aerial vehicle (UAV) survey and a ground-based vehicle sampling campaign targeting gathering pipeline systems in the Utica Shale from March to April in 2019. Out of 73 km of pipeline systems surveyed, we found no leaks on pipelines and two leaks on an accessory block valve with leak rates of 3.8 ± 0.4 and 7.6 ± 0.8 mg/s. The low leak frequency phenomenon was also observed in the only existing gathering pipeline study in Fayetteville Shale. The UAV sampling system facilitated ease of access, broadened the availability of pipelines for leak detection, and was estimated to detect methane leaks down to 0.07 g/s using Gaussian dispersion modeling. For future UAV surveys adopting similar instrument setup and dispersion models, we recommend arranging controlled release experiments first to understand the system’s detection limit and choosing sampling days with steady and low wind speeds (2 m/s).

Orbit Angular Momentum MIMO with Mode Selection for UAV-Assisted A2G Networks

As an emerging solution for line-of-sight (LOS) wireless communications, in air-to-ground (A2G) channels, the unmanned aerial vehicle (UAV), and allowing the dynamic and flexible network deployments enables the supplement or/and replacement of the terrestrial base stations (BSs). However, in conventional multiple-input-multiple-output (MIMO) systems, high-speed communications are significantly limited by channel crosstalks and spectrum scarcities. An orbit angular momentum (OAM) wireless network, allowing co-existence of multiple physical channels within the same frequency band, offers new degrees of freedom to address this dilemma. In this paper, we investigate the UAV-based A2G radio vortex wireless networks and study its channel model. Then we propose a branch and bound search-based mode selection (BBS-MS) scheme, which uses the spatial distribution characteristics of vortex beams to optimize the spectrum efficiency (SE). Theoretical derivations and numerical results demonstrate that our developed BBS-MS scheme can obtain the optimal performance, which outperforms conventional OAM-based MIMO systems. Also, it possesses a lower complexity compared with exhaustive searches.

Wind Profiling in the Lower Atmosphere from Wind-Induced Perturbations to Multirotor UAS

We present a model-based approach to estimate the vertical profile of horizontal wind velocity components using motion perturbations of a multirotor unmanned aircraft system (UAS) in both hovering and steady ascending flight. The state estimation framework employed for wind estimation was adapted to a set of closed-loop rigid body models identified for an off-the-shelf quadrotor. The quadrotor models used for wind estimation were characterized for hovering and steady ascending flight conditions ranging between 0 and 2 m/s. The closed-loop models were obtained using system identification algorithms to determine model structures and estimate model parameters. The wind measurement method was validated experimentally above the Virginia Tech Kentland Experimental Aircraft Systems Laboratory by comparing quadrotor and independent sensor measurements from a sonic anemometer and two SoDAR instruments. Comparison results demonstrated quadrotor wind estimation in close agreement with the independent wind velocity measurements. However, horizontal wind velocity profiles were difficult to validate using time-synchronized SoDAR measurements. Analysis of the noise intensity and signal-to-noise ratio of the SoDARs proved that close-proximity quadrotor operations can corrupt wind measurement from SoDARs, which has not previously been reported.

A Near-Field Gaussian Plume Inversion Flux Quantification Method, Applied to Unmanned Aerial Vehicle Sampling

The accurate quantification of methane emissions from point sources is required to better quantify emissions for sector-specific reporting and inventory validation. An unmanned aerial vehicle (UAV) serves as a platform to sample plumes near to source. This paper describes a near-field Gaussian plume inversion (NGI) flux technique, adapted for downwind sampling of turbulent plumes, by fitting a plume model to measured flux density in three spatial dimensions. The method was refined and tested using sample data acquired from eight UAV flights, which measured a controlled release of methane gas. Sampling was conducted to a maximum height of 31 m (i.e. above the maximum height of the emission plumes). The method applies a flux inversion to plumes sampled near point sources. To test the method, a series of random walk sampling simulations were used to derive an NGI upper uncertainty bound by quantifying systematic flux bias due to a limited spatial sampling extent typical for short-duration small UAV flights (less than 30 min). The development of the NGI method enables its future use to quantify methane emissions for point sources, facilitating future assessments of emissions from specific source-types and source areas. This allows for atmospheric measurement-based fluxes to be derived using downwind UAV sampling for relatively rapid flux analysis, without the need for access to difficult-to-reach areas.