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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 einer Großversuchsserie wurden auf dem BAM-TTS 15 11-kg-Propangasflaschen ohne Sicherheitseinrichtung mit drei verschiedenen Methoden (Holz, Benzinpool, Gas) unterfeuert. Der Beitrag beschreibt die dokumentierten Auswirkungen (z. B. Fragmentierung, Überdruck), die Art des Versagens sowie den Behälterzustand zum Zeitpunkt des Versagens.
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.
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.
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.
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.
A set of equations is derived to estimate the 3D wind vector with a multirotor aircraft using the aircraft itself as a flying anemometer. Since the thrust component is required to compute the wind vector, the PWM signal controlling the motors of the aircraft is measured and a transfer function describing the relation between the PWM signal and the rotational speed of the motors is derived.
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). AM tests were documented hy 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.
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.
In the KonSens Project, sensor systems are developed, validated, and operated in form of functional models for the application areas Structure Integrated Sensors and Mobile Multi-gas Sensors. Key aspects are the detection and evaluation of corrosion processes in reinforced concrete structures as well as the detection and quantification of very low concentrations of toxic gases in air. The adaption of sensor principles from the lab into real-life application including appropriate communication techniques is a major task.
In recent years, Structural Health Monitoring have gained in importance, since growing age of buildings and infrastructure as well as increasing load requirements demand for reliable surveillance methods. In this regard, the project follows two strategies: First, the development and implementation of completely embedded sensor systems consisting of RFID-tag and in situ sensors, and their further application potential (e.g. for precast concrete elements, roadways, wind power plants, and maritime structures). Secondly, the development of a long-term stable, miniaturized, fiber optic sensor for a ratiometric and referenced measurement of the pH-value in concrete based on fluorescence detection as an indicator for carbonation and corrosion.
Environmental pollution through emission of toxic gases becomes an increasing problem not only in agriculture (e.g. biogas plants) and industry but also in urban areas. This leads to increasing demand to monitor environmental emissions as well as ambient air and industrial air components in many scenarios and in even lower concentrations than nowadays. The selectivity of luminescence-based sensors is enabled by the combination of the sensing dye and the material, which is used as accumulation medium for concentration of the analyte. This principle allows for developing gas sensors with high selectivity and sensitivity of defined substances. Additional benefits, particularly of fluorescence-based sensors, are their capability for miniaturization and potential multiplex mode. Objective is the development and implementation of sensors based on fluorescence detection for defined toxic gases (ammonia, hydrogen sulfide, ozone, and benzene) with sensitivity in the low ppm or even ppb range. Additionally, the integration of such sensors in mobile sensor devices is addressed.