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Organisationseinheit der BAM
- 8.1 Sensorik, mess- und prüftechnische Verfahren (71) (entfernen)
The project "SealWasteSafe" of the Bundesanstalt für Materialforschung und -prüfung (BAM, Berlin) deals with sealing structures applied for underground disposal of nuclear waste from two perspectives: (1) material improvement for application in sealing constructions and (2) feasibility study regarding multi-sensor approaches to ensure quality assurance and long-term monitoring.
One specimen of 150 l made of alkali-activated material, which was found innovative and suitable for sealing constructions based on preliminary laboratory studies, and, for comparison purpose, another one made of salt concrete, are manufactured with an integrated multi-sensory setup for quality assurance and long-term-monitoring. The specimens were left in their cast form and additionally thermally insulated to simulate the situation in the repository. The multi-sensory concept comprises RFID technology embedded in the specimens suppling material temperature and moisture measurements, integrated fibre optic sensing allowing strain measurement and acoustic emission testing for monitoring possible crack formation. Overall, the suitability and the functionality of the sensors embedded into and attached to strongly alkaline (pH > 13 for the AAM) and salt corrosive (NaCl) environment was proven for the first 672 h.
First temperature measurement based on RFID succeeded after 626 h for the alkali-activated material and after 192 h for the conventional salt concrete. Strain measurement based on distributed fibre optic sensing turned out the alkali-activated material with > 1 mm m-1 undergoing approximately twice the compression strain as the salt concrete with strains < 0.5 mm m-1. In contrast, the acoustic emission first and single hits representing crack formation in numbers, was found for alkali-activated material half of that detected at the salt concrete.
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.
In this work, we demonstrate the ability of an electronic nose system based on an array of six-semiconductor gas sensors for outdoor air quality monitoring over a day at a traffic road in downtown of Meknes city (Morocco). The response of the sensor array reaches its maximum in the evening of the investigated day which may due to high vehicular traffic or/and human habits resulting in elevated concentrations of pollutants. Dataset treatment by Principal Component Analysis and Discriminant Function Analysis shows a good discrimination between samples collected at different times of the day. Moreover, Support Vector Machines were used and reached a classification success rate of 97.5 %. Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS) technique was used to validate the developed e-nose system by identifying the composition of the analyzed air samples. The discrimination obtained by e-nose system was in good agreement with the TD-GC-MS results. This study demonstrates the usefulness of TD-GC-MS and e-nose, providing high accuracy in discriminating outdoor air samples collected at different times. This demonstrates the potential of using the e-nose as a rapid, easy to use and inexpensive environmental monitoring system.
Vor allem in den letzten Jahren ist das Interesse der Industrie an der additiven Fertigung deutlich gestiegen. Die Vorteile dieser Verfahren sind zahlreich und ermöglichen eine ressourcenschonende, kundenorientierte Fertigung von Bauteilen, welche zur stetigen Entwicklung neue Anwendungsbereiche und Werkstoffe führen. Aufgrund der steigenden Anwendungsfälle, nimmt auch der Wunsch nach Betriebssicherheit unabhängig von anschließenden kostenintensiven zerstörenden und zerstörungsfreien Prüfverfahren zu. Zu diesem Zweck werden im Rahmen des von der BAM durchgeführten Themenfeldprojektes „Prozessmonitoring in Additive Manufacturing“ verschiedenste Verfahren auf ihre Tauglichkeit für den in-situ Einsatz bei der Prozessüberwachung in der additiven Fertigung untersucht. Hier werden drei dieser in-situ Verfahren, die Thermografie, die optische Emissionsspektroskopie und die Schallmissionsanalyse für den Einsatz beim Laser-Pulver-Auftragschweißen betrachtet.
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.
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.
The best-known discretization methods for solving engineering problems formulated as partial differential equations are finite difference method (FDM), finite element method (FEM) and finite volume method (FVM). While the finite volume method is used in fluid mechanics, the finite element method is predominant in solid state mechanics. At first glance, FVM and FEM are two highly specialized methods. However, both methods can solve problems of both solid mechanics and fluid mechanics well. Since experimental mechanics deals not only with solid state physics but also with fluid mechanics problems, we want to understand FVM in the sense of FEM in this work. In the long term, we want to use the variational calculus to unify many important numerical methods in engineering science into a common framework. In this way, we expect that experiences can be better exchanged between different engineering sciences and thus innovations in the field of experimental mechanics can be advanced. But in this work, we limit ourselves to the understanding of the FVM with the help of the variational calculus already known in FEM. We use a simple 1D Poisson equation to clarify the point. First, we briefly summarize the FVM and FEM. Then we will deal with the actual topic of this paper, as we establish the FEM and the FVM on a common basis by variation formulation. It is shown here that the FVM can be understood in terms of the finite element method with the so-called Galerkin-Petrov approach.
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.
Von den Zellulosefibrillen über die Zellwand und die Anordnung der Früh- und Spätholzzellen in einem Jahrring bis hin zum Balken von Natur aus ist Holz ein optimierter Hochleistungswerkstoff, der Handwerker, Architekten, Ingenieure und Wissenschaftler fasziniert und begeistert. Im digitalen Zeitalter werden neben Standard-Laborexperimenten zunehmend Modelle und Simulationen eingesetzt, um das Verhalten des Materials unter verschiedenen Belastungen besser zu verstehen und vorherzusagen. Dazu werden Zahlenwerte von Verformungen im subzellulären Maßstab benötigt, die nun in Experimenten an der TOMCAT-Beamline (TOmographic Microscopy and Coherent rAdiology ExperimenTs) der SLS (Swiss Light Source) gewonnen werden konnten: Holzproben aus Fichte ( Picea abies Karst.) mit einem Prüfquerschnitt von mindestens 1 mm² auf Zug oder Druck beansprucht. Die strukturellen Veränderungen auf Zellebene wurden mittels Computertomographie erfasst. Für die nachträgliche Analyse der 3D-Mikrostruktur von Holz wurde ein Ansatz entwickelt, der es ermöglicht, einzelne Zellen, die in mehreren Tomogrammen unterschiedlicher Belastungszustände erfasst wurden, zu verfolgen. Dabei wurden die Zellgeometrien und das subzelluläre Deformationsverhalten quantifiziert. Unter Zugbelastung beispielsweise verengt sich die Zellwanddicke um ca. 0,8%, das sind bei der gemessenen mittleren Zellwanddicke von 3,5 µm ca. 28 nm. Diese und andere Erkenntnisse liefern nun einen direkten numerischen Zusammenhang zwischen Verformungen der Holzmikrostruktur und dem daraus resultierenden makroskopischen Verhalten.