8.1 Sensorik, mess- und prüftechnische Verfahren
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- 8.1 Sensorik, mess- und prüftechnische Verfahren (240)
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- 4.3 Schadstofftransfer und Umwelttechnologien (2)
- 5 Werkstofftechnik (2)
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Paper des Monats
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Eingeladener Vortrag
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We introduce a passive smart dust concept as a novel solution for environmental monitoring. Utilizing chemical reagents like colorimetric indicators and other chemosensors, these particles detect varying environmental conditions. We developed paper-based sensors that are both cost-effective and eco-friendly. In practical tests, these sensors, dispersed over a designated area, successfully identified hazardous substances by changing their color when exposed to acids or bases. This color change was remotely detectable using a drone-mounted color camera. The data thus obtained was processed through specialized software, accurately pinpointing areas of contamination. This method proves the efficacy and scalability of passive smart dust technology for real-time, environmentally sustainable remote sensing of hazardous materials
Use/distribution of cost-effective and biocompatible pieces of cellulose paper as passive optical chemosensors and readout by the camera system.
Modification of the paper surface using various chemically sensitive dyes (indicators) provides an optically detectable reaction and conclusions about the target substances and precise location.
This paper outlines significant advancements in our previously developed aerial gas tomography system, now optimized to reconstruct 2D tomographic slices of gas plumes with enhanced precision in outdoor environments. The core of our system is an aerial robot equipped with a custom-built 3-axis aerial gimbal, a Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor for CH4 measurements, a laser rangefinder, and a wide-angle camera, combined with a state-of-the-art gas tomography algorithm. In real-world experiments, we sent the aerial robot along gate-shaped flight patterns over a semi-controlled environment with a static-like gas plume, providing a welldefined ground truth for system evaluation. The reconstructed cross-sectional 2D images closely matched the known ground truth concentration, confirming the system’s high accuracy and reliability. The demonstrated system’s capabilities open doors for potential applications in environmental monitoring and industrial safety, though further testing is planned to ascertain the system’s operational boundaries fully.
Gas Distribution Mapping (GDM) is essential in monitoring hazardous environments, where uneven sampling and spatial sparsity of data present significant challenges. Traditional methods for GDM often fall short in accuracy and expressiveness. Modern learning-based approaches employing Convolutional Neural Networks (CNNs) require regular-sized input data, limiting their adaptability to irregular and sparse datasets typically encountered in GDM. This study addresses these shortcomings by showcasing Graph Neural Networks (GNNs) for learningbased GDM on irregular and spatially sparse sensor data. Our Radius-Based, Bi-Directionally connected GNN (RABI-GNN) was trained on a synthetic gas distribution dataset on which it outperforms our previous CNN-based model while overcoming its constraints. We demonstrate the flexibility of RABI-GNN by applying it to real-world data obtained in an industrial steel factory, highlighting promising opportunities for more accurate GDM models.
This paper explores combining a gimbal-mounted tunable diode laser absorption spectroscopy (TDLAS) sensor and a video camera to create a virtual gas camera for methane leak detection. This provides a low-to-zero-cost extension of typical TDLAS gas tomography systems. A prototype setup mounted on a ground robot is evaluated. Results acquired using a simulated methane leak show the feasibility of the virtual gas camera, accurately detecting methane leaks by overlaying concentrations onto a visual image. While the acquisition time is significantly longer than for traditional gas cameras, potential enhancements are discussed. The study concludes that the virtual gas camera is feasible and useful, despite its longer acquisition time. It serves as a valuable software-only addition to typical TDLAS gas tomography systems, offering quickly-available on-site data augmentation for visual leak assessment at low-to-zero cost.
This paper outlines significant advancements in our previously developed aerial gas tomography system, now optimized to reconstruct 2D tomographic slices of gas plumes with enhanced precision in outdoor environments. The core of our system is an aerial robot equipped with a custom-built 3-axis aerial gimbal, a Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor for CH4 measurements, a laser rangefinder, and a wide-angle camera, combined with a state-of-the-art gas tomography algorithm. In real-world experiments, we sent the aerial robot along gate-shaped flight patterns over a semi-controlled environment with a static-like gas plume, providing a welldefined ground truth for system evaluation. The reconstructed cross-sectional 2D images closely matched the known ground truth concentration, confirming the system’s high accuracy and reliability. The demonstrated system’s capabilities open doors for potential applications in environmental monitoring and industrial safety, though further testing is planned to ascertain the system’s operational boundaries fully.
Gas Distribution Mapping (GDM) is essential in monitoring hazardous environments, where uneven sampling and spatial sparsity of data present significant challenges. Traditional methods for GDM often fall short in accuracy and expressiveness. Modern learning-based approaches employing Convolutional
Neural Networks (CNNs) require regular-sized input data, limiting their adaptability to irregular and sparse datasets typically encountered in GDM. This study addresses these shortcomings by showcasing Graph Neural Networks (GNNs) for learningbased GDM on irregular and spatially sparse sensor data. Our Radius-Based, Bi-Directionally connected GNN (RABI-GNN) was trained on a synthetic gas distribution dataset on which it outperforms our previous CNN-based model while overcoming its constraints. We demonstrate the flexibility of RABI-GNN by applying it to real-world data obtained in an industrial steel factory, highlighting promising opportunities for more accurate GDM models.
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.
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.
The success of this energy transition and the efficient utilization of green hydrogen as a pristine energy carrier hinge upon the cultivation of hydrogen readiness, swift market expansion, and the establishment of crucial value chains within national and European contexts. Paramount to these objectives are reliable quality and safety standards, essential for ensuring supply safety, environmental compatibility, and instilling confidence in these technologies.
In the realm of safety management systems, gas sensors emerge as vital components for detecting hydrogen. This presentation delves into the imperative role of hydrogen systems, emphasizing their capability to monitor and control operations, issue warnings, signal hazardous conditions, and sound alarms. It explores the intricate landscape of functional safety considerations and addresses the challenges associated with measurement uncertainty in sensor testing.
A diverse array of sensors catering to hydrogen, pressure, temperature, and flow are available, each contributing significantly to the safety and efficiency of hydrogen technologies. Applications are envisaged to adhere meticulously to quality infrastructural regulations, codes, and standards, ensuring seamless integration and operation within the broader hydrogen ecosystem.
This discussion underscores the ubiquitous presence of sensor systems across various fields within hydrogen technologies, illustrating their multifaceted contributions to safety, reliability, and overall success in realizing the transformative potential of green hydrogen.
The success of this energy transition and the efficient utilization of green hydrogen as a pristine energy carrier hinge upon the cultivation of hydrogen readiness, swift market expansion, and the establishment of crucial value chains within national and European contexts. Paramount to these objectives are reliable quality and safety standards, essential for ensuring supply safety, environmental compatibility, and instilling confidence in these technologies.
In the realm of safety management systems, gas sensors emerge as vital components for detecting hydrogen. This presentation delves into the imperative role of hydrogen systems, emphasizing their capability to monitor and control operations, issue warnings, signal hazardous conditions, and sound alarms. It explores the intricate landscape of functional safety considerations and addresses the challenges associated with measurement uncertainty in sensor testing.
A diverse array of sensors catering to hydrogen, pressure, temperature, and flow are available, each contributing significantly to the safety and efficiency of hydrogen technologies. Applications are envisaged to adhere meticulously to quality infrastructural regulations, codes, and standards, ensuring seamless integration and operation within the broader hydrogen ecosystem.
This discussion underscores the ubiquitous presence of sensor systems across various fields within hydrogen technologies, illustrating their multifaceted contributions to safety, reliability, and overall success in realizing the transformative potential of green hydrogen.
This seminar series comprises three lectures on drones and ground robots capable of sensing chemical substances and two accompanying hands-on workshops on nano drones equipped with chemical sensors. In the first lecture, Dr. Neumann will talk on a summary of aerial robot olfaction and his research. In the second and third lectures, more details of the nano drones equipped with chemical sensors will be provided, and the participants can get hands on experience of the nano
drones in the accompanying workshops. The lectures will be given in English. This seminar series is supported by FY2023 Researcher Exchanges Program in UEC.
This seminar series comprises three lectures on drones and ground robots capable of sensing chemical substances and two accompanying hands-on workshops on nano drones equipped with chemical sensors. In the first lecture, Dr. Neumann will talk on a summary of aerial robot olfaction and his research. In the second and third lectures, more details of the nano drones equipped with chemical sensors will be provided, and the participants can get hands on experience of the nano
drones in the accompanying workshops. The lectures will be given in English. This seminar series is supported by FY2023 Researcher Exchanges Program in UEC.
This seminar series comprises three lectures on drones and ground robots capable of sensing chemical substances and two accompanying hands-on workshops on nano drones equipped with chemical sensors. In the first lecture, Dr. Neumann will talk on a summary of aerial robot olfaction and his research. In the second and third lectures, more details of the nano drones equipped with chemical sensors will be provided, and the participants can get hands on experience of the nano drones in the accompanying workshops. The lectures will be given in English. This seminar series is supported by FY2023 Researcher Exchanges Program in UEC.
Digital twin for hydrogen refueling stations: An analytical study for safe and efficient operations
(2023)
This analytical overview is the initial study in developing the digital sensors twin for the hydrogen refueling station. The research includes a comprehensive analysis of the current literature, refueling protocol, standards, mathematical model, simulation model, and trends in digital twins and H2 technologies. Furthermore, this study has explored the various HRS attributes, such as leakage, temperature, pressure, and hydrogen flow, and identified different use cases of the hydrogen fuel station to build a digital sensor twin. Various real gas equations, such as the Van der Waals and Able-Noble equation of state (EOS), are applied to analyze the key factors and parameters affecting hydrogen and the MATLAB simulation model to validate the key elements and parameters. This study also focuses on identifying and addressing research gaps in hydrogen technology, contributing to advancing hydrogen infrastructure, and supporting the global transition to cleaner energy solutions.
The transfer of data for the metrological traceability of measurement results is currently taking place in the form of paper-based calibration certificates, mostly. Especially accredited metrology services providers are often bound to paper certificates by regulatory constrictions and sometimes lack of IT-infrastructure. This analog form of metrology data transfer, however, implies hands-on work, in particular for the certificate receiver, as its key values need to be translated into the device- and quality management (QM) system, manually. With thousands of certificates transferred from accredited calibration labs until the industrial regime, this binds a significant amount of human resources and is a well-known source of errors, thus constricting cost-effective and secure workflows.
This contribution highlights the status of the advancing digital transformation in metrology, on the example of force calibration. Although this appealing initiative is to a large extend driven by German stakeholders, it currently evolves to extend across all Europe and far beyond.
A safety or security related assessment of explosions, accidental and intentional scenarios alike, often necessitate performance of replication-tests. Such test results are necessary to clarify the causes within the scope of forensic investigations. To gain important insights into the behavior of structures and materials under such loading, field tests may also be performed in accordance with different test standards. To determine the resistance of building-structures after explosions, estimation of the residual load-bearing capacity in addition to the assessment of dynamic structural response and damage to the building components is important. In most cases an evaluation of structural integrity is based only on the visual damage, resulting in an overestimation of the residual capacity.
The Bundesanstalt für Materialforschung und -prüfung (BAM) operates the Test site for Technical Safety (TTS) on an area measuring about 12 km2 in the Federal State of Brandenburg for execution of true-to-scale explosion tests. At the TTS, building component testing was performed to assess the suitability of different non-destructive testing methods to characterize the dynamic structural response and damage resulting from the detonation of high explosives.
Different blast-loading scenarios were realized by varying the net explosive mass and the standoff distance with all scenarios representing a near-field detonation. The test object was a reinforced concrete wall 2 m high, 2.5 m wide and 20 cm thick, fixed at both vertical edges. The dynamic loading of the wall was characterized with 8 piezoelectric pressure sensors flush-mounted on the front surface, thus measuring the reflected pressures from the shock wave. The tests were conducted with the aim of characterizing the global behavior of the wall under dynamic shock loading and the resulting local damage pattern, respectively. High speed digital image correlation was implemented in combination with multiple acceleration sensors to observe the rear surface of the wall to chart the dynamic deflection during the loading and to determine the residual deformation after the loading had ceased. In addition, one test specimen was instrumented with fiber optic sensor cables, both fixed to the rebars and embedded in the concrete-matrix, respectively. Firstly, these sensors were interrogated during the blast test by a distributed acoustic sensing (DAS) device using a particularly high sampling rate to measure the shock-induced vibrations in the structure with high temporal resolution. This delivers information on dynamics of compression and tension cycles from within the structure. Secondly, the local damage-pattern emerging during the series of blasts was determined via distributed fiber optic strain sensing (DSS) by interrogating the embedded fiber optic sensors with a high spatial resolution DSS device after each blast. This enabled the characterization of non-visual damage to the structure, in particular with regard to the formation of localized cracks in the concrete matrix. The DSS was further complimented by a structure-scanner based on ultrasonic measurements.
Our contribution describes this new test approach in detail. Results of the three datasets, namely dynamic shock loading, global behavior of the test object and the local damage pattern will be presented. The suitability of the implemented measurement methods will be discussed in combination with the challenges in their application for technical safety evaluation of building components under explosive loading.
Robotic Measurement System Concept for Greenhouse Gas Tomography in Wastewater Treatment Plants
(2023)
This paper introduces a conceptual framework for a greenhouse gas (GHG) measurement system designed for wastewater treatment plants (WWTPs). The emphasis is on accurate gas tomography measurements of emissions, crucial for national emission assessments. The proposed system is movable, semi-autonomous, and employs a combination of ground and air robots equipped with integral remote gas sensors and in-situ sensors. The proposed two/two configuration ensures comprehensive coverage of WWTPs, overcoming obstacles. The system concept lays the foundation for future implementation and evaluation, addressing the need for enhanced understanding of GHG emissions from WWTPs.
Robotic Measurement System Concept for Greenhouse Gas Tomography in Wastewater Treatment Plants
(2023)
This paper introduces a conceptual framework for a robotic greenhouse gas (GHG) measurement system designed for wastewater treatment plants (WWTPs). The emphasis is on accurate gas tomography measurement of emissions, crucial for national emission assessments. The proposed system is movable, semi-autonomous, and employs a combination of ground and air robots equipped with integral remote gas sensors and in-situ sensors. Employing a two/two configuration ensures comprehensive coverage of WWTPs, overcoming obstacles. The system concept lays the foundation for future implementation and evaluation, addressing the need for enhanced understanding of GHG emissions from WWTPs.