Multidisciplinary design and flight testing of a remote gas/particle airborne sensor system

The main objective of this paper is to describe the development of a remote sensing airborne air sampling system for Unmanned Aerial Systems (UAS) and provide the capability for the detection of particle and gas concentrations in real time over remote locations. The design of the air sampling methodology started by defining system architecture, and then by selecting and integrating each subsystem. A multifunctional air sampling instrument, with capability for simultaneous measurement of particle and gas concentrations was modified and integrated with ARCAA’s Flamingo UAS platform and communications protocols. As result of the integration process, a system capable of both real time geo-location monitoring and indexed-link sampling was obtained. Wind tunnel tests were conducted in order to evaluate the performance of the air sampling instrument in controlled nonstationary conditions at the typical operational velocities of the UAS platform. Once the remote fully operative air sampling system was obtained, the problem of mission design was analyzed through the simulation of different scenarios. Furthermore, flight tests of the complete air sampling system were then conducted to check the dynamic characteristics of the UAS with the air sampling system and to prove its capability to perform an air sampling mission following a specific flight path

Review article: The use of remotely piloted aircraft systems (RPAS) for natural hazards monitoring and management

The number of scientific studies that consider possible applications of Remotely Piloted Aircraft Systems (RPAS) for the management of natural hazards effects and the identification of occurred damages are strongly increased in last decade. Nowadays, in the scientific community, the use of these systems is not a novelty, but a deeper analysis of literature shows a lack of codified complex methodologies that can be used not only for scientific experiments but also for normal codified emergency operations. RPAS can acquire on-demand ultra-high resolution images that can be used for the identification of active processes like landslides or volcanic activities but also for the definition of effects of earthquakes, wildfires and floods. In this paper, we present a review of published literature that describes experimental methodologiesdeveloped for the study and monitoring of natural hazards

Review article: The use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management

The number of scientific studies that consider possible applications of remotely piloted aircraft systems (RPASs) for the management of natural hazards effects and the identification of occurred damages strongly increased in the last decade. Nowadays, in the scientific community, the use of these systems is not a novelty, but a deeper analysis of the literature shows a lack of codified complex methodologies that can be used not only for scientific experiments but also for normal codified emergency operations. RPASs can acquire on-demand ultra-high-resolution images that can be used for the identification of active processes such as landslides or volcanic activities but can also define the effects of earthquakes, wildfires and floods. In this paper, we present a review of published literature that describes experimental methodologies developed for the study and monitoring of natural hazard

Dynamics of Outgassing and Plume Transport Revealed by Proximal Unmanned Aerial System (UAS) Measurements at Volcán Villarrica, Chile

Volcanic gas emissions are intimately linked to the dynamics of magma ascent and outgassing, and, on geological timescales, constitute an important source of volatiles to the Earth’s atmosphere. Measurements of gas composition and flux are therefore critical to both volcano monitoring and to determining the contribution of volcanoes to global geochemical cycles. However, significant gaps remain in our global inventories of volcanic emissions, (particularly for CO2, which requires proximal sampling of a concentrated plume) for those volcanoes where the near-vent region is hazardous or inaccessible. Unmanned Aerial Systems (UAS) provide a robust and effective solution to proximal sampling of dense volcanic plumes in extreme volcanic environments. Here, we present gas compositional data acquired using a gas sensor payload aboard a UAS flown at Volcán Villarrica, Chile. We compare UAS-derived gas timeseries to simultaneous crater rim multi-GAS data and UV camera imagery to investigate early plume evolution. SO2 concentrations measured in the young proximal plume exhibit periodic variations that are well-correlated with the concentrations of other species. By combining molar gas ratios (CO2/SO2 = 1.48–1.68, H2O/SO2 = 67–75 and H2O/CO2 = 45–51) with the SO2 flux (142 ± 17 t/day) from UV camera images, we derive CO2 and H2O fluxes of ~150 t/day and ~2850 t/day, respectively. We observe good agreement between time-averaged molar gas ratios obtained from simultaneous UAS- and ground-based Multi-GAS acquisitions. However, the UAS measurements made in the young, less diluted plume reveal additional short-term periodic structure that reflects active degassing through discrete, audible gas exhalations.Alfred P. Sloan Foundation; Leverhulme Trus

Numerical fluid dynamics simulation for drones’ chemical detection

The risk associated with chemical, biological, radiological, nuclear, and explosive (CBRNe) threats in the last two decades has grown as a result of easier access to hazardous materials and agents, potentially increasing the chance for dangerous events. Consequently, early detection of a threat following a CBRNe event is a mandatory requirement for the safety and security of human operators involved in the management of the emergency. Drones are nowadays one of the most advanced and versatile tools available, and they have proven to be successfully used in many different application fields. The use of drones equipped with inexpensive and selective detectors could be both a solution to improve the early detection of threats and, at the same time, a solution for human operators to prevent dangerous situations. To maximize the drone’s capability of detecting dangerous volatile substances, fluid dynamics numerical simulations may be used to understand the optimal configuration of the detectors positioned on the drone. This study serves as a first step to investigate how the fluid dynamics of the drone propeller flow and the different sensors position on-board could affect the conditioning and acquisition of data. The first consequence of this approach may lead to optimizing the position of the detectors on the drone based not only on the specific technology of the sensor, but also on the type of chemical agent dispersed in the environment, eventually allowing to define a technological solution to enhance the detection process and ensure the safety and security of first responders

Combining thermal imaging with photogrammetry of an active volcano using UAV : an example from Stromboli, Italy

The authors would like to thank the Istituto Nazionale di Geofisica e Vulcanologia – Sezione di Catania (INGV‐CT) for granting permission to conduct the UAV surveys over the Stromboli volcano. This work was supported by the School for Early Career Researchers at the University of Aberdeen, UK. Dougal Jerram is partly funded through a Norwegian Research Council Centres of Excellence project (project number 223272, CEED). The team would like to thank Angelo Cristaudo for logistical help during the fieldwork efforts on Stromboli.Peer reviewedPostprin

Environmental chemical sensing using small drones: A review

Recent advances in miniaturization of chemical instrumentation and in low-cost small drones are catalyzing exponential growth in the use of such platforms for environmental chemical sensing applications. The versatility of chemically sensitive drones is reflected by their rapid adoption in scientific, industrial, and regulatory domains, such as in atmospheric research studies, industrial emission monitoring, and in enforcement of environmental regulations. As a result of this interdisciplinarity, progress to date has been reported across a broad spread of scientific and non-scientific databases, including scientific journals, press releases, company websites, and field reports. The aim of this paper is to assemble all of these pieces of information into a comprehensive, structured and updated review of the field of chemical sensing using small drones. We exhaustively review current and emerging applications of this technology, as well as sensing platforms and algorithms developed by research groups and companies for tasks such as gas concentration mapping, source localization, and flux estimation. We conclude with a discussion of the most pressing technological and regulatory limitations in current practice, and how these could be addressed by future research

THE GEOMETRY AND TOPOLOGY OF DEFORMATION BAND NETWORKS IN VOLCANICLASTIC ROCKS: A CASE STUDY FROM SHIHTIPING, SOUTH-EASTERN TAIWAN

Deformation bands are tabular strain localization features, common in porous and granular rocks. These structures of millimeter to centimeter thickness can occur as single bands and develop into clusters or networks of bands. Deformation bands have been extensively documented in siliciclastic rocks, whereas fewer studies address deformation bands in porous volcaniclastic rocks. In recent years, volcaniclastic reservoirs have become a hot topic in petroleum- and geothermal exploration, groundwater aquifers and CO2 storage. Deformation bands are generally associated with a permeability reduction from one to three orders of magnitude compared to the host rock and deformation band networks may affect subsurface fluid flow patterns. Knowing the network properties of deformation bands are therefore crucial when predicting their impact on fluid flow. This M.Sc. project quantifies clusters and networks of deformation bands in volcaniclastic rocks from Shihtiping, Eastern Taiwan. A thorough topological analysis of deformation band networks has been carried out, focusing on characterizing the distribution and connectivity of bands within a network. Individual deformation bands were analyzed based on their geometry, including length, density, and intensity, to access the spatial relationship of bands within the networks. The quantitative relation of nodes and branches provides the basis for describing the connectivity in the studied deformation band networks. The deformation band networks are generally dominated by connecting Y-nodes and fully connected (C-C) branches, resulting in high average connectivity. Furthermore, the topological characteristics can be associated with bifurcating, abutting, and splaying bands, and bands are less prone to crosscut one another. The highest connectivity is related to mature deformation band networks and deformation band networks in fully developed faults. This supports the theory that the connectivity of deformation networks develops with time and maturity (strain). The analyses of the deformation bands show that nodal distribution, intensity and connectivity are vulnerable to lithological heterogeneities across the network. This study strengthens our understanding of the development of deformation bands in volcaniclastic rocks and explores the evolution of connectivity in deformation band networks. Quantifying the topological and geometrical characteristics of deformation band networks is essential as it generates parameters used to assess the potential for fluid flow in a reservoir.Masteroppgave i geovitenskapGEOV399MAMN-GEO

Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping

This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up to two in situ metal oxide semiconductor (MOX) gas sensors. Due to its small form-factor, the SNAV is not a hazard for humans, enabling its use in public areas or inside buildings. It can autonomously carry out gas sensing missions of hazardous environments inaccessible to terrestrial robots and bigger drones, for example searching for victims and hazardous gas leaks inside pockets that form within the wreckage of collapsed buildings in the aftermath of an earthquake or explosion. The first contribution of this work is assessing the impact of the nano-propellers on the MOX sensor signals at different distances to a gas source. A second contribution is adapting the 'bout' detection algorithm, proposed by Schmuker et al. (2016) to extract specific features from the derivative of the MOX sensor response, for real-time operation. The third and main contribution is the experimental validation of the SNAV for gas source localization (GSL) and mapping in a large indoor environment (160 m²) with a gas source placed in challenging positions for the drone, for example hidden in the ceiling of the room or inside a power outlet box. Two GSL strategies are compared, one based on the instantaneous gas sensor response and the other one based on the bout frequency. From the measurements collected (in motion) along a predefined sweeping path we built (in less than 3 min) a 3D map of the gas distribution and identified the most likely source location. Using the bout frequency yielded on average a higher localization accuracy than using the instantaneous gas sensor response (1.38 m versus 2.05 m error), however accurate tuning of an additional parameter (the noise threshold) is required in the former case. The main conclusion of this paper is that a nano-drone has the potential to perform gas sensing tasks in complex environments