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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
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.
Packages for the transport of radioactive material are designed to en-dure severe accidents. Packages for the transportation of radioactive material must demonstrate that the package can withstand certain prescribed tests from the IAEA Regulations [1]. In addition to mechanical tests, a thermal test in form of a fire test must be carried out. As packages to be tested at BAM are signifi-cantly larger than previous package designs, BAM has expanded an existing fire test stand. A modular concept is chosen, which means that the arrangement of the burner nozzles can be adapted to the test specimen. The dimensions of the burner rings, the type, the orientation and the number of burner nozzles can be varied depending on the test specimen. In addition, various pumps can be used to set the corresponding mass flow. With the help of a calorimeter test, the fire test stand can be qualified for a specific size of packages regarding the boundary conditions of the IAEA Regulations [1]. Due to the typically wood filled impact limiters in German package designs, a fire test is necessary, as experiments have shown that possible openings that occurred during a mechanical test contributed to the igni-tion of the wood filled impact limiters within the prescribed 30 minutes of the IAEA Regulations [1]. From a series of experiments, two experiments are pre-sented to show the possibilities to obtain different temperatures and temperature rates in the test specimen. In addition to sensor data, the heat flux into the package is calculated to verify that the IAEA boundary conditions are satisfied. In addition to the temperature data, other data such as wind speed and wind direction are also recorded to explain subsequent effects in the measurement data in a comprehen-sible manner.
Germany had been transporting spent fuel to the reprocessing plant in Sellafield and La Hague for decades until around 2005. Resulting from the obligation to take back the vitrified high-level waste from reprocessing six CASTOR® HAW28M filled with 168 canisters with radioactive waste in vitrified form were transported from Sellafield to the interim storage facility Biblis in 2020. Rail wagons were used for the transport to the port in Barrow-in-Furness, where they were loaded into a dedicated seagoing vessel, certified as INF Class 3 according to the INF Code. This was the first time that vitrified high level waste with considerable heat load was transported under a German design approval certificate.
BAM was involved in the authority assessment of the conditions for the sea transport. For the first transport BAM required among others, assessment of temperature distribution during transport, logging of temperatures of cargo bays and graphical imaging of temperatures of the bay with the cask in order to ensure compliance with temperature specifications, e.g. maximal neutron absorber and gasket temperatures. Special interest was taken in the identification of possible events exceeding the specified temperatures considering the different philosophies of IMDG code and its supplement INF code regarding temperature control of hatches. Results show compliance with assumed conditions.
Based on the state-of-the-art research and regulations, the application of the fracture mechanics master curve (MC) concept to ferritic ductile cast iron (DCI) is being investigated in a joint research project between MPA Stuttgart and BAM Berlin. This paper outlines the research approach, the planned investigations and first results of the experimental program.
This research deals with the development of the Vickers hardness knowledge graph, mapping the example dataset in them, and exporting the data-mapped knowledge graph as a machine-readable Resource Description Framework (RDF). Modeling the knowledge graph according to the standardized test procedure and using the appropriate upper-level ontologies were taken into consideration to develop the highly standardized, incorporable, and industrial applicable models. Furthermore, the Ontopanel approach was utilized for mapping the real experimental data in the developed knowledge graphs and the resulting RDF files were successfully evaluated through the SPARQL queries.
In order to reduce the human footprint of CO2 emissions and limit global warming effects hydrogen combustion is becoming increasingly important. To enable fuel cells and gas turbines to operates this carbon free fuel, unprecedently large amounts of hydrogen need to be produced and safely transported and stored. The investigation of the effects of accidents involving hydrogen is therefore becoming of outmost importance. Since hydrogen is usually stored and transported under pressure, one scenario to be considered is the release of hydrogen from a leakage with subsequent ignition. The resulting jet flame must be characterized with respect to the thermal radiation emitted into the environment to define safety regulations. Various models that characterize the resulting flame shape and radiation already exist in the literature, but these are mainly based on empirical data from hydrocarbon jet flames.[1-4] To verify these models, a H2 Jet Flame project conducted at BAM, is investigating the safety of momentum driven hydrogen jet flames. For this purpose, large-scale tests are carried out at the Test Site Technical Safety (BAM-TTS). The object of the investigations is to assess the effects of real scale release scenarios regarding flame geometry and the thermal radiation emitted. Parameters such as release angle, leakage diameter (currently 1 mm to 10 mm), pressure (currently up to max. 250 bar) and mass flow (up to max. 0.5 kg/s) are varied. In addition, influences such as the type of ignition, ignition location as well as delayed ignition can also be investigated. The gained knowledge will be compared with existing jet flame models, to validate these and identify a possible need for further development. In particular, the focus will be laid on the thermal radiation of hydrogen flames. The challenge here is the visualization and characterization of the flame geometry in an open environment. Visualization is performed using infrared (IR) camera systems from at least two viewing angles. Measurements of the heat radiation of jet flames, which can be found in the literature, are mostly based on unsteady outflow conditions. The experimental setup used here allows for the generation of a steady-state outflow for several minutes and thus a direct comparability with existing (steady-state) models. Furthermore, the tests can be carried out for comparative measurements with hydrocarbons (methane, etc.) as well as mixtures of hydrogen and hydrocarbons.
Infrared thermography using a focused (spot or line) beam has proved to be effective for detection of surface breaking cracks on planar samples. In this work, we use the same principle, but applied to complex shaped components, like a rail section, a gear, and a gas turbine blade. We use a six-axis robot arm to move the sample in front of our thermographic setup. Several scanning paths and thermographic parameters are explored: scanning speed, density of points in each scanning slice, laser power and camera frame-rate. Additionally, we explore semi-automatic evaluation algorithms for crack detection, as well as 2D-to-3D registration of the found indications.
For structural health monitoring systems or non-destructive testing, it is crucial to study the interactions of ultrasonic-guided waves with inhomogeneities or damage in structural components. Simulation of these interactions poses a challenge.
After the wave-damage interaction, the numerical model must reproduce features like mode conversion or wave scattering. Mathematically, damages are discontinuities in a computational domain, and each wave interacts differently because of the geometrical features. We propose a quadtree-based meshing in the current contribution to capture these geometrical characteristics.
This kind of discretisation approach requires special techniques to couple cells of different sizes since, after a quadtree decomposition, the problem of so-called hanging nodes may arise. Transition elements based on high-order transfinite shape functions are used to solve this issue. The advantage is that these elements allow the correct coupling of the cells while retaining a high-order interpolation. Moreover, the reuse of the dynamic stiffness matrices can be exploited based on the similarity of the cells. This procedure makes the approach very efficient.
Examples show the scattering characteristics of different guided wave modes after interacting with inhomogeneities and discontinuities in a plate.