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Gas source localization with a micro-drone using bio-inspired and particle filter-based algorithms
(2013)
Gas source localization (GSL) with mobile robots is a challenging task due to the unpredictable nature of gas dispersion, the limitations of the currents sensing technologies, and the mobility constraints of ground-based robots. This work proposes an integral solution for the GSL task, including source declaration. We present a novel pseudo-gradient-based plume tracking algorithm and a particle filter-based source declaration approach, and apply it on a gas-sensitive micro-drone. We compare the performance of the proposed system in simulations and real-world experiments against two commonly used tracking algorithms adapted for aerial exploration missions.
This article presents the development and validation of an autonomous, gas sensitive microdrone that is capable of estimating the wind vector in real time using only the onboard control unit of the microdrone and performing gas distribution mapping (DM). Two different sampling approaches are suggested to address this problem. On the one hand, a predefined trajectory is used to explore the target area with the microdrone in a real-world gas DM experiment. As an alternative sampling approach, we introduce an adaptive strategy that suggests next sampling points based on an artificial potential field (APF). Initial results in real-world experiments demonstrate the capability of the proposed adaptive sampling strategy for gas DM and its use for gas source localization.
One of the main unsolved issues of CCS is the comprehensive surveillance of CO2 storage areas with reasonable effort and costs. This study presents an approach for distributed subsurface monitoring of gas storage areas. The concept combines different measurement technologies to one multifunctional sensor: membrane based measurement technology for in situ monitoring of gases in soil and fibre optical sensing of temperature and strain (as a measure for structural change). A test field of application-relevant dimensions is built up to validate and optimize the technology.
Carbon capture & storage (CCS) is one of the most promising technologies for greenhouse gas (GHG) management. However, an unsolved issue of CCS is the development of appropriate long-term monitoring systems for leak detection of the stored CO2. To complement already existing monitoring infrastructure for CO2 storage areas, and to increase the granularity of gas concentration measurements, a quickly deployable, mobile measurement device is needed. In this paper, we present an autonomous gas-sensitive micro-drone, which can be used to monitor GHG emissions, more specifically, CO2. Two different measurement strategies are proposed to address this task. First, the use of predefined sensing trajectories is evaluated for the task of gas distribution mapping using the micro-drone. Alternatively, we present an adaptive strategy, which suggests sampling points based on an artificial potential field (APF). The results of real-world experiments demonstrate the feasibility of using gas-sensitive micro-drones for GHG monitoring missions. Thus, we suggest a multi-layered surveillance system for CO2 storage areas.
In this paper, we show that a micro unmanned aerial vehicle (UAV) equipped with commercially available gas sensors can address environmental monitoring and gas source localization (GSL) tasks. To account for the challenges of gas sensing under real-world conditions, we present a probabilistic approach for GSL that is based on a particle filter (PF). Simulation and real-world experiments demonstrate the suitability of this algorithm for micro UAV platforms.
Gas distribution mapping is important to have an accurate understanding of gas concentration levels in hazardous environments. A major problem is that in-situ gas sensors are only able to measure concentrations at their specific location. The gas distribution in-between the sampling locations must therefore be modeled. In this research, we interpret the task of spatial interpolation between sparsely distributed sensors as a task of enhancing an image's resolution, namely super-resolution. Because autoencoders are proven to perform well for this super-resolution task, we trained a convolutional encoder-decoder neural network to map the gas distribution over a spatially sparse sensor network. Due to the difficulty to collect real-world gas distribution data and missing ground truth, we used synthetic data generated with a gas distribution simulator for training and evaluation of the model. Our results show that the neural network was able to learn the behavior of gas plumes and outperforms simpler interpolation techniques.
Remote gas sensors mounted on mobile robots enable the mapping of gas distributions in large or poorly accessible areas. A challenging task however, is the generation of three-dimensional distribution maps from these spatially sparse gas measurements. To obtain high-quality reconstructions, the choice of optimal measuring poses is of great importance. Remote gas sensors, that are commonly used in Robot Assisted Gas Tomography (RAGT), require reflecting surfaces within the sensor’s range, limiting the possible sensing geometries, regardless of whether the robots are ground-based or airborne. By combining ground and aerial robots into a heterogeneous swarm whose agents are equipped with reflectors and remote gas sensors, remote inter-robot gas measurements become available, taking RAGT to the next dimension – releasing those constraints. In this paper, we demonstrate the feasibility of drone-to-drone measurements under realistic conditions and highlight the resulting opportunities.
To better understand the dynamics in hazardous environments, gas distribution mapping aims to map the gas concentration levels of a specified area precisely. Sampling is typically carried out in a spatially sparse manner, either with a mobile robot or a sensor network and concentration values between known data points have to be interpolated. In this paper, we investigate sequential deep learning models that are able to map the gas distribution based on a multiple time step input from a sensor network. We propose a novel hybrid convolutional LSTM - transpose convolutional structure that we train with synthetic gas distribution data. Our results show that learning the spatial and temporal correlation of gas plume patterns outperforms a non-sequential neural network model.
Simulating a Gas Source Localization Algorithm with Gas Dispersion Produced by Recorded Outdoor Wind
(2023)
This paper reports the use of the first gas dispersion simulator capable of introducing large wind fluctuations into simulations. The proposed simulator enables testing of a modification made to a gas source localization algorithm in a realistic scenario in order to study how the change affects it. Gas source localization in an outdoor environment is a challenging task mainly due to the complexity of the gas spread caused by the unpredictable nature of constantly changing wind. Therefore, a novel use of outdoor wind in developing a gas source localization system by simulation is presented in this paper. To consider the characteristic of sudden but large and unpredictable changes in wind direction, we propose to use recorded outdoor wind to simulate a realistic outdoor gas dispersion which has been done for the first time to the best of our knowledge. With the use of this simulator, we have tested a modification to a mobile robot-based gas source localization algorithm. Multiple simulations of the modified and the original particle filter-based algorithm have been done to study the effect of the tested modification. The results showed that a small difference in the algorithm can greatly impact the results. From this study, we show that the use of simulation consisting of the necessary traits to evaluate outdoor gas source localization, has the potential to accelerate the development of a reliable localization system.
This paper presents first advances in the area of aerial chemical trail following. For that purpose, we equipped a palm-size aerial robot, based on the Crazyflie 2.0 quadrocopter, with a small lightweight metal oxide gas sensor for measuring evaporated ethanol from chemical trails. To detect and localize a 5 cm wide chemical trail, a detection criterion was developed that uses only relative changes in the transient phase of the sensor response. The reduction in signal strength dependence improves the robustness of its application. We tested our setup in first crossing-trail experiments showing that our flying ant robot can correlate an odor hit with the chemical trail within 0.14 m. Principally, this could enable aerial chemical trail following in the future.
This paper presents first advances in the area of aerial chemical trail following. For that purpose, we equipped a palm-size aerial robot, based on the Crazyflie 2.0 quadrocopter, with a small lightweight metal oxide gas sensor for measuring evaporated ethanol from chemical trails. To detect and localize the chemical trail, a novel detection criterion was developed that uses only relative changes in the transient phase of the sensor response, making it more robust in its application. We tested our setup in first crossing-trail experiments showing that our flying ant robot is able to correlate an odor hit with the chemical trail within 0.14 m. Principally, this could enable aerial chemical trail following in the future.
Wireless sensor networks provide occupational health experts with valuable information about the distribution of air pollutants in an environment. However, especially low-cost sensors may produce faulty measurements or fail completely. Consequently, not only spatial coverage but also redundancy should be a design criterion for the deployment of a sensor network. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the sensor network can compensate for the outage of sensors. While our results show promising prediction quality of the models, they also indicate the presence of spatially very limited events. We, therefore, conclude that initial measurements with, e.g., mobile units, could help to identify important locations to design redundant sensor networks.
A monitoring method is introduced that creates twodimensional (2D) maps of the soil gas distribution. The method combines linear gas sensing technology for in-situ monitoring of gases in soil with the mapping capabilities of Computed Tomography (CT) to reconstruct spatial and temporal resolved gas distribution maps. A weighted iterative algebraic reconstruction method based on Maximum Likelihood with Expectation Maximization (MLEM) in combination with a source-by-source reconstruction approach is introduced that works with a sparse setup of orthogonally-aligned linear gas sensors. The reconstruction method successfully reduces artifact production, especially when multiple gas sources are present, allowing the discrimination between true and non-existing so-called ghost source locations. Experimental validation by controlled field experiments indicates the high potential of the proposed method for rapid gas leak localization and quantification with respect to Pipeline or underground gas storage issues.
Linear sensor for areal subsurface gas monitoring - calibration routine and validation experiments
(2014)
Membrane based linear gas sensors and fiber optical sensors feature similar geometries and complement each other in quantities to be measured. To the author's best knowledge, it is the first time that these sensors are combined to a multifunctional sensor for distributed measuring of gas concentrations, temperature, and strain. Objective is a comprehensive monitoring of underground gas storage areas. In the presented project a 400 m² test site and a corresponding laboratory system were just built up to characterize, validate, and optimize the combined sensor. Application of the sensor lines in a grid structure should enable spatial resolution of the measurement data and early detection of relevant events, as gas leakage, temperature change, or mechanical impact. A Calibration routine was developed which can be applied subsequent to underground installation. First measurement results indicate the potential of the method, with regard to highly topical energy transport and storage issues.
In this paper, we show that a micro unmanned aerial vehicle (UAV) equipped with commercially available gas sensors can address environmental monitoring and gas source localization (GSL) tasks. To account for the challenges of gas sensing under real-world conditions, we present a probabilistic approach to GSL that is based on a particle filter (PF). Simulation and real-world experiments demonstrate the suitability of this algorithm for micro UAV platforms.
Insect behavior is a common source of inspiration for roboticists and computer scientists when designing gas-sensitive mobile robots. More specifically, tracking airborne odor plumes, and localization of distant gas sources are abilities that suit practical applications such as leak localization and emission monitoring. Gas sensing with mobile robots has been mostly addressed with ground-based platforms and under simplified conditions and thus, there exist a significant gap between the outstanding insect abilities and state-of-the-art robotics systems. As a step toward practical applications, we evaluated the performance of three biologically inspired plume tracking algorithms. The evaluation is carried out not only with computer simulations, but also with real-world experiments in which, a quadrocopter-based micro Unmanned Aerial Vehicle autonomously follows a methane trail toward the emitting source. Compared to ground robots, micro UAVs bring several advantages such as their superior steering capabilities and fewer mobility restrictions in complex terrains. The experimental evaluation shows that, under certain environmental conditions, insect like behavior in gas-sensitive UAVs is feasible in real-world environments.
One of the main unsolved issues of under-ground storages for, e.g., CO2, H2, and natural gas is the comprehensive surveillance of these areas with reasonable effort and costs. Conventional sensors (e.g., soil air probes or borehole probes), however, can only be used for punctual or locally limited measurements; further their application can cause structural influences (invasive application).
In this paper, we describe in detail the setup of a CO2 injection soil test field. This test field will be used to enhance and validate an innovative ap-proach for distributed subsurface monitoring of gas storage areas. To the author’s knowledge, this is the first time that, for this purpose, a test field is built in an application relevant scale.
A 400 m² soil test field with gas injection system was built up, which enables an experimental validation of linear gas sensors for specific applications and gases in an application-relevant scale. Several injection and soil watering experiments with carbon dioxide (CO2) at different days with varying boundary conditions were performed indicating the potential of the method for, e.g., rapid leakage detection with respect to Carbon Capture and Storage (CCS) issues.
A 400 m² soil test field with gas injection system was built up for the purpose of large-scale validation, optimization, and characterization of a novel comprehensive monitoring method for underground gas storage areas. The method combines gas sensing technology with linear form factor for in-situ monitoring of gases in soil with the mapping capabilities of Computed Tomography (CT) to reconstruct time-series of gas distribution maps based on samples of orthogonally-aligned linear gas sensors. Several injection experiments with carbon dioxide (CO2) at different days with varying boundary conditions indicates the potential of the method for, e.g., rapid leakage detection with respect to Carbon Capture and Storage (CCS) issues.
A multifunctional sensor in line shape was developed and introduced in previous work for measuring of gas concentrations, temperature change, and strain. A current field study focuses on a spatially distributed monitoring of subsurface CO2 gas storage sites in near real time.
Mechanical impacts, e.g., caused by construction work, denudation, and seismic activity, can affect the integrity of underground gas storage sites. Thermal or moisture impacts, e.g., caused by weather conditions, can influence the gas distribution behavior. In this paper, we briefly describe the setup of a CO2 injection soil test field. This setup contains actuating elements for the investigation of mechanical and thermal impacts on distributed subsurface gas monitoring. A concept is given for evaluating these impacts and first experimental results are presented.
Gas sensors in linear form based on the measuring principle of gas selective permeability through a membrane were developed and introduced for the detection and quantification of gas concentrations. A current field study focuses on measuring CO2 concentrations for a spatially distributed monitoring of subsurface CO2 gas storage sites in near real time. A 400 m(2) test site and a corresponding laboratory system were built up to characterize, validate, and optimize the sensor. A calibration routine was developed, which can be applied subsequently to underground installation. First measurement results indicate the potential of the method.
A monitoring method is introduced that creates, in near real-time, two-dimensional (2D) maps of the soil gas distribution. The method combines linear gas sensing technology for in-situ monitoring of gases in soil with the mapping capabilities of Computed Tomography (CT) to reconstruct spatial and temporal resolved gas distribution maps. A weighted iterative algebraic reconstruction method based on Maximum Likelihood with Expectation Maximization (MLEM) in combination with a source-by-source reconstruction approach is introduced that works with a sparse setup of orthogonally-aligned linear gas sensors. The reconstruction method successfully reduces artifact production, especially when multiple gas sources are present, allowing the discrimination between true and non-existing so-called ghost source locations. A first experimental test indicates the high potential of the proposed method for, e.g., rapid gas leak localization.
A permanent-magnet synchronous motor (PMSM) servo drive for lightweight robotic platforms that have a high torque demand at low rotational speeds has been developed. First, a current-independent torque controller is derived and cascaded with a speed and position controller, while merely an encoder is used as a sensor device in combination with a speed estimator. Finally, the speed estimator output is compared to gyroscope measurements and the overall functioning is verified on a real system.
In this paper, we introduce a nano aerial robot swarm for indoor air quality monitoring applications such as occupational health and safety of (industrial) workplaces. The concept combines a robotic swarm composing of nano Unmanned Aerial Vehicles (nano UAVs), based on the Crazyflie 2.0 quadrocopter, and small lightweight metal oxide gas sensors for measuring the Total Volatile Organic Compound (TVOC) in ppb and estimating the eCO2 (equivalent calculated carbon-dioxide) concentration in ppm. TVOC is a measure for the indoor air quality. An indoor localization and positioning system will be used to estimate the absolute 3D position of the swarm like GPS. Based on this novel indoor air quality monitoring concept, the development and validation of new algorithms in the field of Mobile Robot Olfaction (MRO) are planned, namely gas source localization and gas distribution mapping. A test scenario will be built up to validate and optimize the gas-sensitive nano aerial robot swarm for the intended applications.
Air pollution within industrial scenarios is a major risk for workers, which is why detailed knowledge about the dispersion of dusts and gases is necessary. This paper introduces a system combining stationary low-cost and high-quality sensors, carried by ground robots and unmanned aerial vehicles. Based on these dense sampling capabilities, detailed distribution maps of dusts and gases will be created. This system enables various research opportunities, especially on the fields of distribution mapping and sensor planning. Standard approaches for distribution mapping can be enhanced with knowledge about the environment’s characteristics, while the effectiveness of new approaches, utilizing neural networks, can be further investigated. The influence of different sensor network setups on the predictive quality of distribution algorithms will be researched and metrics for the quantification of a sensor network’s quality will be investigated.
Concept for investigating mechanical and thermal impacts on distributed subsurface gas monitoring
(2016)
A multifunctional sensor in line shape was developed and introduced in previous work for measuring of gas concentrations, temperature change, and strain. A current field study focuses on a spatially distributed monitoring of subsurface CO 2 gas storage sites in near real time.
Mechanical impacts, e.g., caused by construction work, denudation, and seismic activity, can affect the integrity of underground gas storage sites. Thermal or moisture impacts, e.g., caused by weather conditions, can influence the gas Distribution behavior. In this paper, we briefly describe the setup of a CO 2 injection soil test field. This setup contains actuating elements for the investigation of mechanical and thermal impacts on distributed subsurface gas monitoring. A concept is given for evaluating these impacts and first experimental results are presented.
Gas sensors in linear form based on the measuring principle of gas selective permeability through a membrane were developed and introduced for the detection and quantification of gas concentrations. A current field study focuses on measuring CO2 concentrations for a spatially distributed monitoring of subsurface CO2 gas storage sites in near real time. A 400 m² test site and a corresponding laboratory system were built up to characterize, validate, and optimize the sensor. A calibration routine was developed, which can be applied subsequently to underground installation. First measurement results indicate the potential of the method.
Geogenic gases from natural sources, carbon dioxide (CO2) from a geological repository (carbon capture and storage - CCS) or a leaking gas pipeline can present serious risks in industrial and urban areas. To extend the lead time for risk treatment in such critical regions, reliable detection of gases within the shallow subsurface is required to observe critical gas accumulations before degassing into the atmosphere.
A near real-time monitoring approach is introduced to determine the volumetric expansion of a leakinggas in the subsurface. Considering the pressure relaxation with the ambient air pressure, the approach enables the forecasting of the final size of a pressurized gas body in terms of characteristic lengths. According to theoretical basics, such a characteristic length, which enables us to perform a gas (safety) measurement based on a purely geometrical measure, behaves independently of subsurface properties,i.e., it enables a reliable quantification of the escaping gas irrespective of its heterogeneous or changingflow path distribution. A field test for a 10 l/min pinhole leakage injected into a 10 m long, 0.4 m wide, 0.95 m deep soil-filled trench that was equipped with linear sensors shows the lateral-vertical volumetric gas expansion along these sensors, and demonstrates the applicability of the characteristic length approach.
Real-time wind estimation on a micro unmanned aerial vehicle using its inertial measurement unit
(2015)
This paper presents an approach for a quadrocoper-based micro unmanned aerial vehicle (UAV) that estimates the wind vector (speed and direction) in real-time based on measurement data of its on-board sensors only. This method does not need any additional airspeed sensor or dedicated anemometer, and thus the micro UAV's valuable payload remains free for other sensors. Wind tunnel and field tests were used to evaluate the performance of the approach. In order to quantify its accuracy, experiments are presented where data was collected with an anemometer placed in an open field with the micro UAV in flight following a predefined trajectory around the anemometer and hovering at a defined position close to it.
One challenge associated with navigating a nano aerial robot swarm indoors in unstructured environments is, i.a., the limited air space. To avoid an over-regulation of the available indoor air space (e.g., prohibit copters to fly above each other), a safety region around each copter must be defined. In this paper, we investigate the impact of the downwash of a nano-drone on the stability of another nearby nano-drone. In the experiments, we found out that this downwash has a negative influence of a second nano-drone in a distance of around 1 m vertically and 0.2 m horizontally. Based on the obtained data, we developed a model describing the above-mentioned safety region to enable a safe operation of the swarm in these environments with fewer constraints.
Remote gas sensors mounted on mobile robots enable the mapping of gas distributions in large or hardly accessible areas. A challenging task, however, is the generation of threedimensional distribution maps from these gas measurements.
Suitable reconstruction algorithms can be adapted, for instance, from the field of computed tomography (CT), but both their performance and strategies for selecting optimal measuring poses must be evaluated. For this purpose simulations are used, since, in contrast to field tests, they allow repeatable conditions. Although several simulation tools exist, they lack realistic models of remote gas sensors. Recently, we introduced a model for a Tunable Diode Laser Absorption Spectroscopy (TDLAS) gas sensor taking into account the conical shape of its laser beam. However, the novel model has not yet been validated with experiments. In this paper, we compare our model with a real sensor device and show that the assumptions made hold.
In this paper, we present and validate the concept of an autonomous aerial robot to reconstruct tomographic 2D slices of gas plumes in outdoor environments. Our platform, the so-called Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS), combines a lightweight Tunable Diode Laser Absorption Spectroscopy (TDLAS) gas sensor with a 3-axis aerial stabilization gimbal for aiming at a versatile octocopter. While the TDLAS sensor provides integral gas concentration measurements, it does not measure the distance traveled by the laser diode’s beam nor the distribution of gas along the optical path. Thus, we complement the set-up with a laser rangefinder and apply principles of Computed Tomography (CT) to create a model of the spatial gas distribution from a set of integral concentration measurements. To allow for a fundamental ground truth evaluation of the applied gas tomography algorithm, we set up a unique outdoor test environment based on two 3D ultrasonic anemometers and a distributed array of 10 infrared gas transmitters. We present results showing its performance characteristics and 2D plume reconstruction capabilities under realistic conditions. The proposed system can be deployed in scenarios that cannot be addressed by currently available robots and thus constitutes a significant step forward for the field of Mobile Robot Olfaction (MRO).
In mobile robot applications, some sensors such as open-path gas detectors or laser rangefinders need to be aimed at specific targets in order to get the desired measurements. To do this in a fast and elegant manner, we present a spherical parallel manipulator with three degrees of freedom. Compared to typical serial manipulators, it offers superior dynamics and structural stiffness, which are important parameters for this type of task. We present the mechanical design and derive kinematic equations both to compute set-points for the desired orientation and to estimate the current state of the system. A PID controller is used to generate control signals.
For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback-Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts.
This paper describes a wireless mobile prototype able to perform optical measurements by means of a miniatur-ized spectrometer for low light analysis, e.g. fluorescent sensors. Evaluations, calculations, calibration management and result display are performed by a computer or a standard tablet. The device was designed primarily to detect traces of oil in drinking or ground water and for the analyses of crude oils. However, it can also address a wide range of fluorescent sensors. The fast and user-friendly inspection of water quality or oil properties, as well as the adaptability and mobility, make the device attractive for a variety of users. Further application areas could be easily imple-mented by adapting the optics and the software (database, data processing and calibration plots, etc.)
In this paper, we present an autonomous aerial robot to reconstruct tomographic 2D slices of gas plumes in outdoor environments. Our platform, the so-called Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS) combines a lightweight Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor with a 3-axis aerial stabilization gimbal for aiming on a versatile octocopter. The TDLAS sensor provides integral gas concentration measurements but no information regarding the distance traveled by the laser diode's beam or the distribution of the gas along the optical path. We complemented the set-up with a laser rangefinder and apply principles of Computed Tomography (CT) to create a model of the spatial gas distribution from these integral concentration measurements. To allow for a rudimentary ground truth evaluation of the applied gas tomography algorithm, we set up a unique outdoor test environment based on two 3D ultrasonic anemometers and a distributed array of 10 infrared gas transmitters. We present first results showing the 2D plume reconstruction capabilities of the system under realistic conditions.
A current-independent torque equation for the permanent-magnet synchronous motor (PMSM) aiming at direct-drive servo applications is derived from a first principles model. Instead of measuring currents, all required control parameters are derived from optical incremental encoder measurements. The results are verified on a real system in test series showing the effect of static friction and proving the obtained torque model.
A 400 m2 soil test field with gas injection system was built up, which enables an experimental validation of linear membrane-based gas sensors – a sensor technology which was developed based on the selective permeation of gases through membranes. Several soil watering and injection experiments with carbon dioxide at different days with varying boundary conditions were performed showing that the sensor behaves mostly insensitive to the environmental conditions investigated, i.e., barometric pressure fluctuations, soil temperature, air temperature and humidity, sun duration, and wind speed. Furthermore, depending on water infiltration, a gas phase displacement could be observed in-situ based on the changed measurement signal. The results of the validation experiments highlight the potential of the method for rapid leak detection and localization qualifying the sensor particularly for safety applications, e.g., in underground gas storage areas.
In case of a vehicle fire, an installed LPG (liquefied petroleum gas) tank with a malfunctioning safety device poses severe hazards. To investigate the consequences in case of tank failure, we conducted 16 tests with toroidal shaped LPG vehicle tanks. Three tanks were used for a Hydraulic Burst Test under standard conditions. Another three tanks were equipped with a statutory safety device and were subjected to a gasoline pool fire. The safety device prevented tank failure, as intended. To generate a statistically valid dataset on tank failure, ten tanks without safety devices were exposed to a gasoline pool fire. Five tanks were filled to a level of 20 %; the re-maining five were filled to a level of 100 %. In order to gain information on the heating process, three tem-perature readings at the tank surface, and three nearby flame temperatures were recorded. At distances of l = (7; 9; 11) m to the tank, the overpressure of the shock wave induced by the tank failure and the unsteady tem-peratures were measured. All ten tanks failed within a time of t < 5 min in a BLEVE (boiling liquid expanding vapor explosion). Seven of these resulted directly in a catastrophic failure. The other three resulted in partial failure followed by catastrophic failure. A near field overpressure at a distance of l = 7 m of up to p = 0.27 bar was measured. All ten tests showed massive fragmentation of the tank mantle. In total, 50 fragments were found. These 50 fragments make-up 88.6 % of the original tank mass. Each fragment was georeferenced and weighed. Fragment throwing distances of l > 250 m occurred. For the tanks with a fill level of 20 %, the average number of fragments was twice as high as it was for the tanks that were filled completely.
Commercial, off-the shelf propane cylinders are subjected to high safety regulations. Furthermore, those cylinders are equipped with safety devices like pressure relief valves (PRV). Despite these regulations and safety measures, a failure of the container is possible if exposed to an intense fire. The result of this is severe hazard for users, rescue forces and infrastructure. Within the framework of a destructive test series, 15 identical propane cylinders, without pressure relief devices, were exposed to an intensive fire in horizontal position until failure. Each cylinder was filled with a mass of m =11 kg of liquid propane. Three different fire sources were used (wood, petrol, propane). The experiments revealed the failure of all cylinders in a time period t < 155 s. The failure lead to a fragmentation into several major parts with throwing distances of up to l =262 m. In all trials, the temperature of the cylinder wall (top, side, bottom), of the liquid phase inside and of the surrounding fire (top, side, bottom) was recorded. In addition, the inner cylinder pressure and the induced overpressure of the blast wave after the failure were recorded. Overpressures of up to p=0.27 bar were recorded close to the cylinder (l =5 m). All tests were documented by video from several positions (general view, close-up, high-speed 5000 fps). This test series creates the basis for further experimental studies in the field of alternative fuels for vehicles. The aim of this test series is to assess and analyse the consequences of the failure of gas vessels (for LPG, CNG, CGH2) in the aftermath of severe incidents.
Compressed natural gas (CNG) is a widely used automotive fuel in a variety of countries. In case of a vehicle fire where the safety device also malfunctions, a failure of the CNG automotive cylinder could occur. Such a cylinder failure is associated with severe hazards for the surrounding environment. Firstly, a comprehensive analysis is given below, summarizing various accidents involving CNG automotive cylinders and their consequences. In an extensive experimental program, 21 CNG automotive cylinders with no safety device were tested. Of the 21, burst tests were carried out on 5 Type III and 5 Type IV cylinders. Furthermore, fire tests with 8 Type III and 3 Type IV cylinders were conducted. Apart from cylinder pressure, inner temperature and cylinder mantle temperature, the periphery consequences, such as nearfield blast pressure and fragmentation are documented. The maximum measured overpressure due to a Type III cylinder failure was p = 0.41 bar. Each traceable fragment was georeferenced. All-in-all, fragment throw distances of d > 300 m could be observed. As one key result, it can be stated that the tested Type IV CNG cylinders showed less critical failure behavior then the Type III cylinders under fire impingement.
Gas source localization (GSL) helps mitigate the impact of industrial accidents and natural disasters. While GSL may be dangerous and time-consuming when performed by humans, swarms of agile and inexpensive nano aerial robots may increase the safety and efficiency of gas source localizations. Since the small payloads of nano aerial robots limit the sensing and computing resources, strategies adapted from biological swarms, such as colonies of social insects, are used to coordinate robot swarms. Most swarm GSL strategies are based on the assumption that the maxima of gas concentrations are sufficiently close to the gas sources. However, prior studies have indicated that the occurrence of “bouts”, a metric for the intermittency of gas distributions, may advantageously be used as a more accurate gas source proximity indicator. This paper presents a swarm GSL strategy employing bouts as source proximity indicators and a bio-inspired pheromone system for communication. Nano aerial robots, deployed in this study, act as agents and emit pheromone markers in an artificial environment upon detecting bouts. Leveraging the concept of artificial potential fields, the agents switch between exploiting the knowledge of the swarm by following pheromone gradients and exploring the search space by targeting a random point. The agents are repelled by each other and by walls to avoid collisions. The swarm GSL strategy is implemented into three nano aerial robots and validated in a real-world experiment in an indoor environment with a single gas source. The results indicate that the the swarm GSL strategy presented in this paper is capable of GSL in indoor environments and that the intermittency of gas distributions is a better source proximity indicator than the mean concentration.
Contamination of natural bodies of water or soil with oils and lubricants (or generally, hydrocarbon derivatives such as petrol, fuels, and others) is a commonly found phenomenon around the world due to the extensive production, transfer, and use of fossil fuels. In this work, we develop a simple system for the on-field detection of total petroleum hydrocarbons (TPHs) in water and soil. The test is based on the measurement of the fluorescence signal emitted by the molecular rotor 2-[ethyl[4-[2-(4-nitrophenyl)ethenyl]phenyl]amino]ethanol (4-DNS-OH). This dye is embedded in a hydrophobic polymeric matrix (polyvinylidene fluoride), avoiding interactions with water and providing a robust support for use in a test strip fashion. Together with the strips, an embedded optical system was designed for fluorescence signal read-out, featuring a Bluetooth low-energy connection to a commercial tablet device for data processing and analysis. This system works for the detection and quantification of TPHs in water and soil through a simple extraction protocol using a cycloalkane solvent with a limit of detection of 6 ppm. Assays in surface and sea waters were conclusive, proving the feasibility of the method for in-the-field operation.