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- 2023 (9) (entfernen)
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- 2D/3D thermographic registration (1)
- ATR (1)
- Alkali-activated materials (1)
- Artificial Intelligence (1)
- Autonomous underwater vehicles (1)
- Bouts (1)
- Carbonation (1)
- Complex shaped component testing (1)
- Computer Vision (1)
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- 8 Zerstörungsfreie Prüfung (5)
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- 8.1 Sensorik, mess- und prüftechnische Verfahren (3)
- 7.2 Ingenieurbau (2)
- 1 Analytische Chemie; Referenzmaterialien (1)
- 1.9 Chemische und optische Sensorik (1)
- 7.4 Baustofftechnologie (1)
- 8.3 Thermografische Verfahren (1)
- 8.5 Röntgenbildgebung (1)
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.
The development of a Lab-on-a-Chip (LoC) is presented, which can detect reactive phosphorous compounds in the gas phase in combination with an optochemical hand-held sensor. The LoC prototype contains three pairs of sensing materials containing fluorescent indicator dyes in various carrier materials. By measuring the fluorescence response to phosphoryl chloride, a surrogate compound, the detection of chemical warfare agents (CWAs) in gas phase becomes possible within seconds, introducing a novel approach to CWA detection.
This paper scopes the specialties of a hazard identification study for large and extra-large unmanned undersea vehicles (UUVs). A generic node/function structure is derived from different large and extra-large UUV designs, partially own vehicle designs from research projects, and partially from commercial vehicles. For each node, a short overview of its components and the proper function or operations is defined. A set of guide words is used to prompt a hazard discussion for each node, which identifies unwanted functions, resulting in potential hazards and unmitigated consequences. Related to the SafeMASS-Report from DNV, this work concentrates on selected topics of the bridge-related function on voyage, control & monitoring, and abnormal situations. For unmanned vehicles without any person on board, these three topics become more important for underwater vehicles with very limited connectivity or temporary disruptions of communication whereby an operator in a remote-control center could only be provided with rough vehicle or mission states. Therefore, vehicle control, whether in hard- or software, must handle most tasks belonging to the considered functions automatically or autonomously. Hence, the most appropriate reactions of the system to unwanted system
behavior must be implemented. This HAZID study could, on the one hand, be used as background for such an implementation, and on the other hand, it is used to verify the implemented actions on the risks and hazards.
Gas source localization (GSL) helps mitigate the impact of industrial accidents and natural disasters. While GSL may be dangerous and time-consuming when performed by humans, swarms of agile and inexpensive nano aerial robots may increase the safety and efficiency of gas source localizations. Since the small payloads of nano aerial robots limit the sensing and computing resources, strategies adapted from biological swarms, such as colonies of social insects, are used to coordinate robot swarms. Most swarm GSL strategies are based on the assumption that the maxima of gas concentrations are sufficiently close to the gas sources. However, prior studies have indicated that the occurrence of “bouts”, a metric for the intermittency of gas distributions, may advantageously be used as a more accurate gas source proximity indicator. This paper presents a swarm GSL strategy employing bouts as source proximity indicators and a bio-inspired pheromone system for communication. Nano aerial robots, deployed in this study, act as agents and emit pheromone markers in an artificial environment upon detecting bouts. Leveraging the concept of artificial potential fields, the agents switch between exploiting the knowledge of the swarm by following pheromone gradients and exploring the search space by targeting a random point. The agents are repelled by each other and by walls to avoid collisions. The swarm GSL strategy is implemented into three nano aerial robots and validated in a real-world experiment in an indoor environment with a single gas source. The results indicate that the the swarm GSL strategy presented in this paper is capable of GSL in indoor environments and that the intermittency of gas distributions is a better source proximity indicator than the mean concentration.
The new crack luminescence method offers the possibility of making fatigue surface cracks in metallic materials more visible during inspections through a special coating system. This coating system consists of two layers, whereby the first layer has fluorescent properties and emits visible light as soon as it is irradiated by UV light. The top layer is black and is designed to prevent the fluorescent layer from emitting if no crack develops in the underlying material. The technique proved particularly useful in a wide variety of fatigue tests of steel components under laboratory conditions. Moreover, it has the potential to be used in various industrial applications. To enable industrial deployment and integration into maintenance strategies, a concept study is developed in this contribution, resulting in a qualification framework that can serve as a foundation for determining the reliability of the crack luminescence system in terms of a probability of detection curve. Within this study, factors causing measurement variability and uncertainty are being determined and their influences assessed. Due to the extension of the system by a moving computer vision system for automated crack detection using artificial intelligence, additional long-term effects associated with structural health monitoring systems need to be incorporated into an extended probability of detection study as part of the technical justification. Finally, important aspects and findings related to design of experiments are discussed, and a framework for reliability assessment of a new optical crack monitoring method is presented, emphasizing the influence of various uncertainty parameters, including long-term effects such as system ageing.
The current understanding of the carbonation of alkali-activated concretes is ham-pered inter alia by the wide range of binder chemistries used. To overcome some of the limitations of individual studies and to identify general correlations between their mix design parameters and carbonation resistance, the RILEM TC 281-CCC working group 6 compiled carbonation data for alkali-activated concretes and mortars from the literature. For comparison purposes, data for blended Portland cement-based concretes with a high percentage of SCMs (≥ 66 % of the binder) were also included in the database. A preliminary analysis of the database indicates that w/CaO ratio and w/b ratio exert an influence on the carbonation resistance of alkali-activated concretes but, contrary to what has been reported for concretes based on (blended) Portland cements, these are not good indicators of their carbonation resistance when considered individually. A better indicator of the carbonation resistance of alkali-activated concretes under conditions approxi-mating natural carbonation appears to be their w/(CaO + Na2O + K2O) ratio. Furthermore, the analysis points to significant shortcomings of tests at elevated CO2 concentrations for low-Ca alkali-activated concretes, indicating that even at a concentration of 1 % CO2, the outcomes may lead to inaccurate predictions of the carbonation coefficient under natural exposure conditions.
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.
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.