8.1 Sensorik, mess- und prüftechnische Verfahren
Filtern
Dokumenttyp
- Vortrag (120)
- Beitrag zu einem Tagungsband (74)
- Posterpräsentation (65)
- Zeitschriftenartikel (47)
- Sonstiges (6)
- Dissertation (4)
- Buchkapitel (2)
- Beitrag zu einem Sammelband (1)
- Forschungsbericht (1)
Sprache
- Englisch (233)
- Deutsch (83)
- Mehrsprachig (3)
- Polnisch (1)
Schlagworte
- Mobile Robot Olfaction (29)
- Nano aerial robot (19)
- TDLAS (14)
- H2Safety@BAM (12)
- Sensor network (12)
- Swarm (12)
- Environmental monitoring (11)
- Aerial robot (10)
- Drop test (10)
- Finite element method (10)
Organisationseinheit der BAM
- 8 Zerstörungsfreie Prüfung (320)
- 8.1 Sensorik, mess- und prüftechnische Verfahren (320)
- 2 Prozess- und Anlagensicherheit (34)
- 1 Analytische Chemie; Referenzmaterialien (32)
- 3 Gefahrgutumschließungen; Energiespeicher (32)
- 2.1 Sicherheit von Energieträgern (30)
- 8.6 Faseroptische Sensorik (28)
- 9 Komponentensicherheit (26)
- 7 Bauwerkssicherheit (23)
- 8.2 Zerstörungsfreie Prüfmethoden für das Bauwesen (20)
Paper des Monats
- ja (4)
Communities worldwide face significant threats from Explosive Remnants of War (ERW), which endanger lives and restrict land usage. From forest fires due to ERWs or in ERW-contaminated areas (e.g., in Jüterbog, Germany) to broader global challenges (e.g., the Ukrainian conflict), the need for efficient detection and removal of these remnants, especially for humanitarian demining, is paramount. Traditional methods, like manual demining, have severe limitations in safety and efficiency. Here, we introduce an innovative solution to these challenges: “Chemosensing Smart Dust.” This technology uses chemoselective dyes that change their fluorescence properties when exposed to explosives like 2,4,6-trinitrotoluene (TNT). Fluorescence-based detection offers superior sensitivity, reduced likelihood of false positives, and enhanced accuracy of explosive detection. Drones, equipped with excitation lasers or LEDs, deploy the Chemosensing Smart Dust over areas of interest and actively detect the fluorescence changes using high-resolution cameras, offering a rapid, safe, and adaptable detection method. Beyond demining, this innovative approach has potential applications in monitoring polluted areas, homeland security, and emergency response.
With the reduction of large oil spills because of stricter regulations and safety measures, the question of how to manage smaller oil spills arises. Few on-site analytical tools are available for first responders or other law enforcement personnel to rapidly test for crude oil in the early management of localized polluted areas. The approach reported here relies on well-described computer-assisted multivariate data analysis of the intrinsic fluorescence fingerprints of crude oils to build a multivariate model for the rapid classification of crude oils and the prediction of their properties. Thanks to a dedicated robust portable reader, the method allowed classification and accurate prediction of various properties of crude oil samples like density (according to API, the American Petroleum Institute and viscosity as well as composition parameters such as volume fractions of paraffins or aromatics. In this way, autonomous operation in on-site or in-the-field applications becomes possible based on the direct (undiluted and untreated) measurement of samples and a rapid, tablet-operated readout system to yield a robust and simple analytical test with superior performance. Testing in real-life scenarios allowed the successful classification and prediction of a number of oil spill samples as well as weathered samples that closely resemble samples collected by first responders.
Microalloying elements such as Nb and Ti play a decisive function in achieving the desired mechanical strength of quenched and tempered, high-strength fine-grain structural steels with a nominal yield strength ≥ 690 MPa. The current specifications for the chemical composition only provide manufacturers with upper limits. However, even minor deviations in the alloy concept can have a significant impact on the mechanical properties. Consequently, accurate prediction of weldability and the integrity of welded joints becomes difficult or even impossible due to differences in composition and the resulting microstructures. Undesirable consequences include a possible softening of the heat-affected zone (HAZ) or, conversely, hardening effects. In view of these challenges, various microalloying strategies with different Ti and Nb contents are being systematically investigated for the first time using specially developed laboratory casting alloys. Each alloying route is based on the common S690QL, maintaining both the chemical composition and the heat treatment parameters. To analyse the weldability, three-layer welds were performed using gas metal arc welding (GMAW) and critical microstructures, such as areas of the heat-affected zone (HAZ) that exhibit significant softening or hardening, were identified. The effect of the softened HAZ region on failure was evaluated using transverse tensile specimens. Digital image correlation (DIC) is used to image changes in local strains in different HAZ regions in situ. Using a specially developed mirror system, the local strains of the microstructure zones on the top and bottom of the weld are recorded simultaneously. This makes it possible to analyse how the weld seam geometry (e.g., V-seam) influences the strain gradients. In addition, the analysis of the local deformation helps to understand the effects of the softened HAZ on the global strain, the fracture constriction, the fracture position, and the overall fracture behavior.
Mikrolegierungselemente wie Nb und Ti sind entscheidend für die erwünschte mechanische Festigkeit von vergüteten Feinkornbaustählen mit einer Nennstreckgrenze von ≥ 690 MPa. Aktuelle Spezifikationen geben lediglich Obergrenzen für die chemische Zusammensetzung vor, allerdings können geringfügige Abweichungen erhebliche Auswirkungen auf die mechanischen Eigenschaften haben. Die Vorhersage der Schweißbarkeit und Integrität von Schweißverbindungen stellt aufgrund der variierenden Zusammensetzung und Mikrostrukturen Herausforderungen dar. Unerwünschte Effekte wie Erweichung der Wärmeeinflusszone (WEZ) oder Verfestigung können auftreten. Um dies zu untersuchen, wurden verschiedene Mikrolegierungsrouten mit variierenden Ti- und Nb-Gehalten an Laborschmelzen erforscht. Die Basis jeder Route entsprach der üblichen S690QL in Zusammensetzung und Wärmebehandlung. Dreilagenschweißungen wurden mittels Metallaktivgasschweißens (MAG) durchgeführt, um kritische Gefügebereiche zu identifizieren. Die Analyse konzentrierte sich auf Phasenumwandlungen während der Abkühlung und metallurgisches Ausscheidungsverhalten. Die mechanischen Eigenschaften der Schweißnähte wurden durch Zugversuche ermittelt. Die Ergebnisse zeigen einen bedeutenden Einfluss der Mikrolegierungsroute und Schweißwärmezufuhr auf die Ausscheidungskinetik, trotz insgesamt guter Schweißbarkeit der Werkstoffe.
Ziel des FuE-Vorhabens ist die Entwicklung, Aufbau und Validierung eines gasto-mographischen Messsystems basierend auf stationärer und mobiler Sensorik zum Emissionsmonitoring in kommunalen Kläranlagen. Die Gastomographie basiert hierbei im Wesentlichen auf, aus unterschiedlichen Sichtwinkeln aufgenommenen, integralen Konzentrationsmessungen, anhand derer eine Rekonstruktion der Gasverteilung erfolgt. Das FuE-Projekt kombiniert hierfür stationäre Sensornetzwerke mit luftgestützten und bodengebundenen Robotersystemen in einem System. Letztere werden mit Reflektoren und/oder Gasdetektoren ausgestattet. Hierfür werden Open-Path-Gasmesstechnik basierend auf Tunable Diode Laser Ab-sorption Spectroscopy (TDLAS, primär CH4 in ppm·m) und In-situ-Gassensorik (CH4, ppm) auf Basis von z.B. Infrarot eingesetzt. Dies sind zwei komplementäre Methoden der Gasmessung: Die In-situ-Gassensorik ermöglicht Punktmessungen, wohingegen TDLAS-Sensoren Gaskonzentrationen integral über offene Messstre-cken von bis zu 60 m gegen Oberflächen messen. Die In-situ-Gassensorik wird für Referenzmessungen und ggf. zur Erhöhung der Rekonstruktionsqualität zum Ein-satz kommen. Die wesentlichen Neuheiten des Projekts sind:
• 2D-Gastomographie auf Basis eines mit TDLAS-basierter Open-Path-Gasmesstechnik und Reflektoren ausgestatteten heterogenen Messsystems.
• CT-basierte Rekonstruktionsalgorithmen, adaptiert an die komplexen Rahmenbedingungen.
• Validierung des Messsystems anhand realer Messungen auf Kläranlagen.
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.
A steel pipeline segment of 2.5 m length was subjected to quasi-static four-point bending load in three steps for studying the initial cracking and damage accumulation based on the Acoustic Emission (AE) technique and by the direct current potential drop (DCPD) technique. For the latter, a new post-test analysis method was established.
AE is found more sensitive to crack initiation than DCPD. Formation of mesoscopic and macroscopic cracks as well as their closure and the resulting friction generate weighted peak frequencies below 400 kHz, whereas microscopic cracking produces AE with broad band spectra identifiable by weighted peak frequencies above 400 kHz. Critical states alike the maximum load level and the leak opening were accompanied by peak amplitudes above 85 dBAE. This rather fundamental study provides a data base for possibly developing advanced strategies of detection and alarm systems based on acoustic monitoring of pipelines, or at least, steel structures.
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.