8.1 Sensorik, mess- und prüftechnische Verfahren
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- Accoustic emission (1)
- Bouts (1)
- Convolutional LSTM (1)
- Corrugated fibreboard box (1)
- Crack (1)
- Damage detection (1)
- Dangerous goods packagings (1)
- Distributed robotics (1)
- Drop test (1)
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- 8.1 Sensorik, mess- und prüftechnische Verfahren (7)
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- 3 Gefahrgutumschließungen; Energiespeicher (1)
- 3.1 Sicherheit von Gefahrgutverpackungen und Batterien (1)
- 7 Bauwerkssicherheit (1)
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Paper des Monats
- ja (3)
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.
Contamination of natural bodies of water or soil with oils and lubricants (or generally, hydrocarbon derivatives such as petrol, fuels, and others) is a commonly found phenomenon around the world due to the extensive production, transfer, and use of fossil fuels. In this work, we develop a simple system for the on-field detection of total petroleum hydrocarbons (TPHs) in water and soil. The test is based on the measurement of the fluorescence signal emitted by the molecular rotor 2-[ethyl[4-[2-(4-nitrophenyl)ethenyl]phenyl]amino]ethanol (4-DNS-OH). This dye is embedded in a hydrophobic polymeric matrix (polyvinylidene fluoride), avoiding interactions with water and providing a robust support for use in a test strip fashion. Together with the strips, an embedded optical system was designed for fluorescence signal read-out, featuring a Bluetooth low-energy connection to a commercial tablet device for data processing and analysis. This system works for the detection and quantification of TPHs in water and soil through a simple extraction protocol using a cycloalkane solvent with a limit of detection of 6 ppm. Assays in surface and sea waters were conclusive, proving the feasibility of the method for in-the-field operation.
For dangerous goods packagings, drop testing onto an essential unyielding target can be used to assess the mechanical resistance to impact loads. Adopted regulations like ADR/RID require that the impact surface provided shall be integral with a mass at least 50 times than that of the heaviest package to be tested. The problem is that many manufacturers do not possess impact targets that satisfy the required 50 times mass ratio for regulative drop tests during series production. The objective of this work is to verify existing and define improved criteria for impact target structures based on systematic investigations. Previous evidence highlights the relevance of other parameters in addition to the mass ratio. Therefore, in this research, a variation of drop test parameters was carried out experimentally. Furthermore, numerical vibration analysis was applied to investigate the deformability of the impact surface. The results conclude that the mass ratio of 1:50 cannot be defined as a decisive criterion. In order to determine the influence of further drop test parameters, the research findings were used to validate a parametric model which assesses impact target deflection. An approximation quality of over 90 % was achieved. As a result, new evaluation criteria are proposed. Firstly, a method for identifying critical impact target designs is provided. Secondly, a new comprehensive formula compares the approximated maximum deflection of a real impact target to the respective theoretical threshold derived from a worst-case assumption. In practice, this leads to great advantages in the evaluation of already installed impact targets for dangerous goods packagings.
Using digital twins for decision making is a very promising concept which combines simulation models with corresponding experimental sensor data in order to support maintenance decisions or to investigate the reliability. The quality of the prognosis strongly depends on both the data quality and the quality of the digital twin. The latter comprises both the modeling assumptions as well as the correct parameters of these models. This article discusses the challenges when applying this concept to realmeasurement data for a demonstrator bridge in the lab, including the data management, the iterative development of the simulation model as well as the identification/updating procedure using Bayesian inference with a potentially large number of parameters. The investigated scenarios include both the iterative identification of the structural model parameters as well as scenarios related to a damage identification. In addition, the article aims at providing all models and data in a reproducibleway such that other researcher can use this setup to validate their methodologies.
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.
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.