This paper discusses a method of determining the minimum safe altitude of an uncrewed aerial vehicle (UAV) at any point within a designated airspace by conducting a glide reachability analysis. Recently, fixed-wing UAVs are more regularly deployed near population centers and in extreme environments, requiring increasingly robust emergency systems and planning. The long-ranges and adverse terrain associated with monitoring the Volcan de Fuego in Guatemala by a team from the University of Bristol (UoB) increases the likelihood that motor failure would result in the aircraft being unable to Return To Home (RTH) and impossible to retrieve. A method for delineating a boundary representing the minimum safe altitude required for the aircraft to safely glide to the airfield in the event of a motor failure was developed within MATLAB, defined by the UAV's minimum glide angle in wind. This model was subsequently compared with flight data from UoB missions around Fuego to better improve its accuracy and analyze the limitations of the missions.

The Open Aircraft Project is an initiative by the UK Complex Autonomous Aircraft Systems Configuration, Analysis, and Design Exploratory (CASCADE) Programme Grant team to create small unoccupied aerial system (SUAS) designs which are freely available to all. Starting with a representative but challenging set of mission requirements, two individual teams have designed, built and test flown competing vertical take-off and landing (VTOL) configurations. This paper describes the contribution made to this project by the University of Bristol. A tail-sitter configuration was explored in order to achieve launch and recovery within restricted spaces. The wing design was chosen to achieve a compromise between low-speed, range and endurance objectives. The resulting aircraft show improved performance over existing designs that will be invaluable in facilitating volcanic survey flights at ranges and payloads greater than currently achievable. Results are given for both a scaled version and the final 2.6m span aircraft.

View Video Presentation: https://doi.org/10.2514/6.2021-0811.vid This paper presents a method for coordinating volcanic plume interception using a fixed wing Unmanned Aerial Vehicle (UAV), developed for practical use over Volcan de Feugo, Guatemala. A series of techniques have previously been developed for operating in this area, spanning 60 flights since February 2017, targeting the collection of ash from within plumes in order to analyse the ash Particle Size Distribution (PSD). Collecting multiple ash samples per flight allows comparison of ash PSD between the plume immediately after eruption and after some significant time has elapsed (i.e. 5 - 25 minutes). A Coordinated Plume Interception (CPI) scheme is presented here which automates these interceptions within the context and limitations of operating around Volcan de Fuego, taking into consideration the Beyond Visual Line of Sight (BVLOS) flight zone limits. Five methods are presented, compared in terms of efficiency and robustness, and assessed for flight zone compatibility. The methods are tested using software in the loop, and a tiered scheme is suggested for future flight testing and use.

This paper presents a series of Unmanned Aerial Vehicle flights that came about as the culmination of a method development project, collecting ash from volcanic plumes. Ash sampling from within plumes provides vital knowledge to improve volcanic ash-dispersion models, outputs that are used in hazard assessments including air traffic control. Ash was collected from numerous erupting plumes obtained from 17 flights around Fuego volcano, Guatemala, in March 2019. Fuego is a highly active volcano and regularly causes disruption to air traffic and also multiple hazards for the local population. Ash sampling from within eruption plumes is therefore a unique engineering challenge. Plumes were intersected at various stages of dispersal, including dense turbulent plumes from very recent eruptions close to the crater. The methods presented in this paper report the successful operation of UAVs in a hostile environment, flying sorties over 9km from launch with altitudes of 5400m above mean sea level in a safe and reliable manner. This paper will present data that details key factors found to influence mission efficiency and performance, and discuss areas in which future efforts might focus in order to further increase operational efficiency.

A team from the University of Bristol have developed a method of operating fixed wing Unmanned Aerial Vehicles (UAVs) at long-range and high-altitude over Volcán de Fuego in Guatemala for the purposes of volcanic monitoring and ash-sampling. Conventionally, the mission plans must be carefully designed prior to flight, to cope with altitude gains in excess of 3000 m, reaching 9 km from the ground control station and 4500 m above mean sea level. This means the climb route cannot be modified mid-flight. At these scales, atmospheric conditions change over the course of a flight and so a real-time trajectory planner (RTTP) is desirable, calculating a route on-board the aircraft. This paper presents an RTTP based around a genetic algorithm optimisation running on a Raspberry Pi 3 B+, the first of its kind to be flown on-board a UAV. Four flights are presented, each having calculated a new and valid trajectory on-board, from the ground control station to the summit region of Volcań de Fuego. The RTTP flights are shown to have approximately equivalent efficiency characteristics to conventionally planned missions. This technology is promising for the future of long-range UAV operations and further development is likely to see significant energy and efficiency savings.

This paper describes a series of proof-of-concept Beyond Visual Line Of Sight unmanned aerial vehicle flights which reached a range of up to 9 km and an altitude of 4,410 m Above Mean Sea Level over Volcan de Fuego in Guatemala, interacting with the volcanic plume on multiple occasions across a range of different conditions. Volcan de Fuego is an active volcano which emits gas and ash regularly, causing disruption to airlines operating from the international airport 50 km away and impacting the lives of the local population. Collection of data from within the plume develops scientists' understanding of the composition of the volcano's output and is of use to scientists, aviation, and hazard management groups alike. This paper presents preliminary results of multiple plume interceptions with multiple aircraft, carrying a variety of sensors. A plume-detection metric is introduced, which uses a combination of flight data and atmospheric sensor data to identify flight through a volcanic plume. Future work will develop the automation of plume tracking such that reliable scientific data sets can be gathered in a robust manner.

The Cityflocks project has been developing and deploying lightweight, fast-response meteorological sensors to be carried by bird species in urban areas to address the paucity of measurements in the region a few hundred metres above the urban rooftops. Accurate and fast routine temperature measurements in this region will help further our understanding of the above-canopy internal boundary layer structure and the spatial variability of Urban Boundary Layers (Barlow 2014). Data implications include improving forecasting of Urban Heat Island events and urban weather, improving air pollution modelling and informing sustainable urban planning. Other methods of making such measurements routinely, such as using manned aircraft or Unmanned Aerial Vehicles (UAVs), can be prohibitively expensive or require onerous permissions and logistics to achieve.

(1) University of Bristol, Department of Aerospace Engineering, United Kingdom, (2) University of Bristol, Department of Earth Sciences, United Kingdom, (3) University of Cambridge, Department of Earth Sciences, Cambridge, United Kingdom, (4) University of Bristol, School of Geographical Sciences, United Kingdom, (5) University of Birmingham, School of Geography, Earth and Environmental Sciences, United Kingdom, (6) INSIVUMEH, Guatemala