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A monitoring and surveillance system is a mandatory element to ensure that a radioactive waste disposal facility provides and meets the required level of safety during both its operational and closure periods. In this study an innovative approach for the identification and monitoring of the structural integrity of stored radioactive drums is presented. The activities were carried out in the context of the European Project PREDIS, whose goal is the development of a robust and reliable sensing technology for monitoring and long-term storage of radioactive waste packaging systems.
Specifically, the focus was set on the development of innovative and smart sensor technologies, non-destructive examination tools, and/or devices capable to improve the safety of the storage by minimizing the number of interventions of cemented waste packages. Both conventional and innovative techniques, such as muon-tomography, SiLiF neutron and SciFi gamma detection, or contactless and smart radio transceiver technology provided with radiation monitoring sensors, have been considered and adapted for the use under repository conditions. Indeed, gamma and neutron detection techniques exploit the radiological information coming from inside the drums, while imaging techniques proved to be effective in spotting high density objects embedded inside the concrete: together, they allow to improve safety and security and can provide an early alert on behavioral changes hinting at anomalies in the drums.
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.
A semi-automatic thermographic procedure for the assessment of the welded area of resistance projection welded joints has been developed. Currently, to assess the quality of RPW joints destructive tests are used and the more commonly used non-destructive technique is the ultrasonic one. The possibility for a quantitative evaluation of the welded area by thermographic technique has been proved by means of an innovative procedure applied on steel RPW joints with ‘as it’ surface conditions. Measurements obtained by thermography and ultrasound have been compared, to verify the developed procedure.
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.
Liquefied Hydrogen (LH2) or Liquefied Natural Gas (LNG) establish themselves as important energy carriers in the transport sector. Its storage requires tanks with Thermal Super Insulations (TSI) to keep the transported fluid cold. TSI has proven itself in various applications over a long time, but not in the land transport sector, where accidents involving collisions, fires, and their combination are to be expected. The focus of this study is to investigate the behavior of different types of TSI when exposed to a heat source that represents a fire. Therefore, a High-Temperature Thermal Vacuum Chamber (HTTVC) was used that allows the thermal loading of thermal insulation material in a vacuum and measuring the heat flow through the TSI in parallel. Within this study, 5 samples were tested regarding 3 different types of MLI, rock wool, and perlites. The thermal exposure caused different effects on the samples. In practice, this can be connected to the rapid release of flammable gases as well as a Boiling Liquid Expanding Vapour Explosion (BLEVE). These results are relevant for the evaluation of accident scenarios, the improvement of TSI, and the development of emergency measures.
Polymers that help tailoring rheological properties during the casting process have become inevitable constituents for all kinds of high-performance concrete technologies. Due to lacking industries, these typically crude-oil based admixtures are not readily available in many parts of the world, which limits the implementation of more sustainable high-performance construction technologies in these regions. Alternative polymers, which often demand for less processing, can be derived from local plant-based resources. The paper provides experimental data of flow tests of cement pastes with polysaccharides from Triumfetta pendrata A. Rich, acacia gum and cassava without and in the presence of polycarboxylate ether superplasticizer. The flow tests are amended by observations of the zeta potentials and the hydrodynamic diameters in the presence of and without calcium ions in the dispersion medium. The results show that in the presence of and without calcium ions all polysaccharides provide negative zeta potentials, yet, they affect flowability and thixotropy in different ways. Cassava starch, acacia gum, and the gum of Triumfetta pendrata A. Rich qualified well for robustness improvement, strong stiffening, and additive manufacturing, respectively. The reason for the different effects can be found in their average sizes and size distribution. Due to the promising results, a flow chart for local value chains is derived on the example of yet unused cassava wastes, which can be converted in parallel.
The importance of tanks for storing cryogenic fluids in the energy industry is increasing because of ongoing political conflicts and the implementation of more environmentally friendly energy sources. Key representatives for the application of cryogenic tanks are natural gas in the form of Liquefied Natural Gas (LNG) and hydrogen, e. g. in the form of Liquefied Hydrogen (LH2), for which significantly higher transport capacities can be achieved with the same transport volume using cryogenic storages than with storages based solely on overpressure.
What applies to all cryogenic transported fluids in common is their condition that must be maintained. Hence heat flows into the preserved fluid must be minimized. Thermal super Insulations (TSI) based on e. g. multilayer insulations (MLI), perlites, and vacuum are utilized for this purpose and have already proven themselves in similar applications. However, for the use of tanks for cryogenic fluids in the transport sector, there is insufficient knowledge regarding the course and consequences of incidents, which is due to the still short period of use and the few documented incidents, hence few investigations carried out in this field. Typical scenarios in the transport sector represent car accidents with collisions, fires, and their combination, which are associated with extraordinary loads on the tank.
The focus of this study is to investigate the behavior of TSI as a result of their thermal exposure to a heat source representing a fire. It is worth mentioning that this could lead to an increase of the heat flux into a tank by several orders of magnitude, and to permanent damage of the TSI, ultimately rapid release of flammable gas as well as a Boiling Liquide Expanding Vapour Explosion (BLEVE). For this purpose, a high temperature thermal vacuum chamber (HTTVC) has been developed that allows thermal loading of MLI or perlites in a vacuum and simultaneous measurement of heat flow through this TSI. The HTTVC is designed to represent realistic insulation conditions and to withstand thermal loads from typical design fires.
The HTTVC was applied to investigate TSI based on MLI and vacuum. It is shown that the thermal stress caused permanent damage to the TSI, with shrinkage, phase change, pyrolysis, and condensation being significant damage mechanisms. The results are relevant for the evaluation of accident scenarios and can thus contribute to the improvement of TSI and the development of emergency measures for the protection of persons and infrastructures.
Proof load testing may be performed to confirm the reliability of the bridge for an existing classification or to prove the reliability for a higher classification. In this paper, a probabilistic decision analysis approach is applied to the scenario for the evaluation of target proof load in the situation where information on the bridge resistance model is lacking. In this case, the resistance model is established by proof loading and taking very basic prior knowledge into account. The decision scenario is modelled in the context of the proof load test planner who shall choose the required load level for assessment of a bridge. The choice of the load level depends on the risks due to the testing and the expected benefit gain from the test. Information acquired about the loading response from monitoring during the proof load testing is modelled by taking basis in the model uncertainty formulation. The optimal proof load level for classification of a single lane, simply supported bridge of 8m span subjected to live load from very heavy (gross weight > 80 tons) transport vehicles was calculated. The optimal proof load level was identified as leading to a positive expected benefit gain to the decision maker while also satisfying target reliability criteria for remaining service life. The analysis was performed for the evaluation of bridge performance with respect to five classifications of very heavy transport vehicles with different vehicle weights and configurations.
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.
Etalon effects in THz transmittance spectra, either taken in time- domain or frequency-domain, often hamper or even hinder the interpretation of film properties. In this paper we discuss the transferability and applicability of spectroscopic methods typically employed in the near and mid infrared spectral range to eliminate the fringes in the transmittance spectra in the THz spectral range.
Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics.
This work focuses on the design and fabrication of low-cost and fast-response of an electronic nose (E-nose) based on semiconductor gas sensors, for discriminating some synthetic gases such as ammonia (NH3), ethanol (C2H5OH), and hydrogen sulfide (H2S). Additionally, the capability of separating different concentration levels of each considered gases was checked. Dataset treatment of E-nose by using Principal Component Analysis (PCA) showed a good discrimination of the different synthetic gases. Furthermore, perfect classification was reached of different concentration levels of the analysed gases by using Discriminant Function Analysis (DFA). In the light of these results, it could be stated that the developed E-nose system constitutes an inexpensive, rapid, simple to use, and efficient tool for synthetic gases detection.
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.
Microplastic detection in water samples becomes important for tracing microplastic sources. Microplastic may harm desalination facilities by blocking filters and disturbing the marine food chain. Thermo analytical methods such as pyrolysis gas chromatography mass spectroscopy, and spectroscopic methods like (micro) Raman spectroscopy or (micro) Fouriertransform infrared spectroscopy in combination with appropriate filters and sample preparation are suitable for analyzing microplastics on a scale from 1 µm to 1000 µm fast and unambiguous. While the thermo analytical methods are suitable for larger sample volumes, Raman spectroscopy and Fouriertransform infrared spectroscopy are able to detect and analyze single microplastic particles for instance in bottled water. Machine learning algorithms ensure a reliable classification of different plastic materials.
The majority of ultrasonic devices used for non-destructive testing in civil engineering require contact with the surface of the concrete (specimen), which significantly increases the time required for the measurement. This makes it impractical for extensive investigation of large-scale structures such as bridge decks, foundations, or tunnels.
In a pioneering approach, fluidic oscillators are used as contact free ultrasonic sources to overcome the aforementioned limitations. These robust and cost-effective actuators require only pressurised air and are ideally suited for harsh environments. At a constant supply pressure, they generate a continuous mono-frequent actuation signal. Further, varying the supply pressure via a fast pressure regulator was found to generate a frequency modulated signal which enabled time-of-flight measurement with an added advantage of increased signal to noise ratio. To demonstrate the feasibility of this novel idea of non-contact ultrasound, the results of the initial tests are presented.
Investigation of temperature effects on ultrasonic velocity in a prestressed concrete bridge model
(2022)
Ultrasonic measurements have recently been applied in the context of structural health monitoring in civil engineering. The ultrasonic method is very sensitive to environmental conditions and material property changes, which might be caused by prestress losses and loading effects on the bridge. However, it remains a challenge to identify the damage mechanisms causing structural property changes from the above mentioned factors influencing ultrasonics. The focus of this contribution is to investigate the correlation between ultrasonic velocities and temperature by the coda wave interferometry (CWI) analysis. Long-term monitoring of a prestressed concrete bridge model under field conditions was carried out to observe the effect of temperature. The homogeneous change in concrete caused by temperature was revealed by applying the CWI method. The threshold value of the best correlation coefficients for the linear regression of temperature and ultrasonic velocity changes was also studied. Experimental assessment will provide a ratio for temperature correction to evaluate the pre-stressing force decrease and its consequences on crack opening or extension.
Reliability assessment of Structural Health Monitoring (SHM) systems
poses new challenges pushing the research community to address many questions which are still open. For guided wave-based SHM it is not possible to evaluate the system performance without taking into account the target structure and applied system parameters. This range of variables would result in countless measurements.
Factors like environmental conditions, structural dependencies and wave characteristics demand novel solutions for performance analysis of SHM systems compared to those relying on classical non-destructive evaluation. Such novel approaches typically require model-assisted investigations which may not only help to explain and understand performance assessment results but also enable complete studies without costly experiments. Within this contribution, a multi input multi output approach using a sparse transducer array permanently installed on a composite structure to excite and sense guided waves is considered. Firstly, the method and the analysis of path-based performance assessment are presented considering an open-access dataset from the Open Guided Wave platform. Then, a performance analysis of a guided wave-based SHM system using Probability of Detection is presented. To explain some unexpected results, the model-assisted investigations are used to understand the physical phenomena of wave propagation in the test specimen including the interaction with damage. Finally, issues and future steps in SHM systems’ performance assessment and their development are discussed.
Performance assessment for GuidedWave (GW)-based Structural Health Monitoring (SHM) systems is of major importance for industrial deployment.
With conventional feature extraction methods like damage indices, pathbased probability of detection (POD) analysis can be realized. To achieve reliability quantification enough data needs to be available, which is rarely the case.
Alternatives like methods for performance assessment on system level are still in development and in a discussion phase. In this contribution, POD results using an Artificial Intelligence (AI)-based data analysis are compared with those delivered by conventional data analysis. Using an open-access dataset from Open Guided Wave platform, the possibility of performance assessment for GW-based SHM systems using AI-based data analysis is shown in detail. An artificial neural network (ANN) classifier is trained to detect artificial damage in a stiffened CFRP plate. As input for the ANN, classical damage indicators are used. The ANN is tested to detect damage at another position, whose inspection data were not previously used in training. The findings show very high detection capabilities without sorting any specific path but only having a global view of current damage metrics. The systematic evaluation of the ANN predictions with respect to specific damage sizes allows to compute a probability of correct identification versus flaw dimension, somehow equivalent to and compared with the results achieved through classic path-based POD analysis. Also, sensitive paths are detected by ANN predictions allowing for evaluation of maximal distances between path and damage position. Finally, it is shown that the prediction performance of the ANN can be improved significantly by combining different damage indicators as inputs.
Temperature Compensation Strategies for Lamb Wave Inspection using Distributed Sensor Networks
(2022)
The application of temperature compensation strategies is crucial in structural health monitoring approaches based on guided waves. Actually, the varying temperature influences the performance of the inspection system inducing false alarms or missed detection, with a consequent reduction of reliability. This paper quantitatively describes a method to compensate the temperature effect, namely the optimal baseline selection (OBS), extending its application to the case of distributed sensor networks (DSN). The effect of temperature separation between baseline time-traces in OBS are investigated considering multiple couples of sensors employed in the DSN. A combined strategy that uses both OBS and frequent value warning is considered. Theoretical results are compared, using data from two several experiments, which use different frequency analysis with either predominantly A0 mode or S0 mode data or both. The focus is given on the fact that different paths are available in a sensor network and several possible combination of results are available. Nonetheless, introducing a frequent value warning it is possible to increase the efficiency of the OBS approach making use of fewer signal processing algorithms. These confirm that the performance of OBS quantitatively agrees with predictions and also demonstrate that the use of compensation strategies improve detectability of damage.
Gas distribution mapping is important to have an accurate understanding of gas concentration levels in hazardous environments. A major problem is that in-situ gas sensors are only able to measure concentrations at their specific location. The gas distribution in-between the sampling locations must therefore be modeled. In this research, we interpret the task of spatial interpolation between sparsely distributed sensors as a task of enhancing an image's resolution, namely super-resolution. Because autoencoders are proven to perform well for this super-resolution task, we trained a convolutional encoder-decoder neural network to map the gas distribution over a spatially sparse sensor network. Due to the difficulty to collect real-world gas distribution data and missing ground truth, we used synthetic data generated with a gas distribution simulator for training and evaluation of the model. Our results show that the neural network was able to learn the behavior of gas plumes and outperforms simpler interpolation techniques.
Up to now, the inspection of wind turbines with industrial climbers has been considered ”state of the art”. However, ever-larger wind turbines and advancing digitization make modern and automated inspection methods indispensable. Passive thermography can serve as such a digital and atomized method while it is well known for its applications in the inspection of buildings or electrical circuits. However, its application relies on thermal gradients in the inspected object such that a temperature contrast exists between damaged and sound areas. This also holds for unheated structures like rotor blades of wind turbines which show no intrinsic temperature gradient and can hardly be heated. Under certain weather conditions with sufficient solar loading and diurnal temperature variations, passive thermography is suitable for the in-service inspection of rotor blades. However, for a reliable use of passive thermography on ”thermal passive” components, the incorporation of these environmental conditions in the planning and evaluation of thermal inspections is crucial. Additionally, the complex inner structure of wind turbine blades in comparison to other objects and buildings require a specific method referencing the individual rotor blades to each other. This allows the distinction between the thermal response of design-specific structural features and damages or irregularities between the three blades. We show thermal signatures of damage in rotor blades and contrast them with structural characteristics by comparing the three blades. In addition to measurements in industrial environments, laboratory measurements are shown and compared to simulations. The long-term goal is to simulate the influence of different weather parameters and thus gain a better understanding of measurements in the field. The results shown here can be seen as one step towards industrial application.
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.
Due to the diffusive nature of heat propagation in solids, the detection and resolution of internal defects with active thermography based non-destructive testing is commonly limited to a defect-depth-to-defect-size ratio greater than or equal to one. In the more recent past, we have already demonstrated that this limitation can be overcome by using a spatially modulated illumination source and photothermal super resolution-based reconstruction. Furthermore, by relying on compressed sensing and computational imaging methods we were able to significantly reduce the experimental complexity to make the method viable for investigating larger regions of interest. In this work we share our progress on improving the defect/inhomogeneity characterization using fully 2D spatially structured illumination patterns instead of scanning with a single laser spot. The experimental approach is based on the repeated blind pseudo-random illumination using modern projector technology and a high-power laser. In the subsequent post-processing, several measurements are then combined by taking advantage of the joint sparsity of the defects within the sample applying 2D-photothermal super resolution reconstruction. Here, enhanced nonlinear convex optimization techniques are utilized for solving the underlying ill-determined inverse problem for typical simple defect geometries. As a result, a higher resolution defect/inhomogeneity map can be obtained at a fraction of the measurement time previously needed.
In the last few years, the use of machine learning has emerged in the field of distributed fiber optic sensors as a promising approach to enhance their performance and provide new capabilities. In this study, we use machine learning for simultaneous measurements of temperature and humidity in polyimide (PI)-coated optical fibers based on Brillouin Brillouin optical frequency domain analysis (BOFDA). Different non-linear machine learning algorithms are employed, namely polynomial regression, decision trees and artificial neural networks (ANNs), and their discrimination performance is benchmarked against that of the conventional linear regression. The performance is evaluated using leave-one-out cross-validation to ensure that the models are reliable and able to generalize well on new data. We show that nonlinear machine learning algorithms outperform the conventional linear regression and thus could pave the way towards simultaneous cost-effective temperature and humidity distributed sensing, which has the potential to find attractive new applications in the field of civil and geotechnical engineering, from structural health monitoring of dikes and bridges to subsea cables and long pipelines corrosion detection.
Investigation of the internal impact during a 9 m drop test of an accident-safe waste package
(2021)
The safety assessment of packages for the transport of radioactive material follows the IAEA regulations and guidance. The specified regulatory tests cover severe accidents and demonstrate the package containment system integrity.
Special attention must be drawn to the behaviour of the content which could move inside the package due to unpreventable gaps caused by the loading procedure and the structure of the content. A possible internal impact of the content which occurs during the drop tests onto the lid system is investigated. The IAEA regulations SSR-6 and the Guidance SSG-26, revised recently, consider input from Germany and France related to the significance of internal gaps.
In the context of a waste package design assessment, a model was equipped with a representative content to conduct a drop test with an internal impact. The weight and kinetic impact of this content covered all possible real contents. The objective of the test was to maximize the load onto the lid system and to prove the mechanical integrity by complying with the required leak tightness. The test was conducted
conservatively at a package temperature lower than -40 °C at the BAM Test Site Technical Safety.
This paper gives an overview of efforts to address internal gaps and their consequences, and the BAM efforts with the implementation of this topic into IAEA regulations and guidance material. The paper then focuses on the conduction of a drop test and investigation of internal component impact.
The excellent mechanical properties of elastomer seals at a wide range of temperatures as well as their high versatility and recovery potential under several load conditions make these materials well suitable for the application in containers designed for transport and disposal of negligible heat generating radioactive waste. While a seal exchange at defined intervals is typical in many conventional applications, it is impossible or at least hard to perform when principles of minimization of radiation exposure have to be considered which prohibit an avoidable cask handling. An extensive knowledge of the change of the elastomer properties during aging and the availability of reliable end-of-lifetime criteria to guarantee the safe enclosure of the radioactive material for the required time are mandatory. As BAM is involved in most of the national cask licensing procedures and in the evaluation of cask-related long-term safety issues, great efforts have already been made and are still ongoing to scientifically support this task.
Among other representative types of elastomers, specimen made from ethylene propylene diene rubber (EPDM) were tested before, during and after aging to capture the with respect to application most important of their complex mechanical properties.
Exemplary results of these investigations were used to calibrate material models implemented in the commercial finite element software ABAQUS/Standard®. The finite element model already presented in previous works uses a sequential temperature displacement coupling. The calculated compression stress relaxation (CSR) and compression set (CS) values do satisfactorily match the experimental results.
In many investigations performed at BAM both values (CSR and CS) were identified as key indicators of elastomer’s long-term performance.
However, the possibility to correlate these equivalent indicators with performance values such as tightness and leakage rate, measurable in the mounted state, is an important goal of our future work. In the presented study the ABAQUS® feature of “pressure penetration” is introduced in the suggested finite element model for this purpose. It provides the possibility to simulate the penetration of a gas into a possible gap between flange and O-ring causing an opening of a leakage path. Three dimensional and axis-symmetric finite element models were generated to represent flat and grooved flanges of different dimensions.
The sensitivity of the feature to several input parameters is investigated and the observed behavior of the O-ring is correlated with the results of performed leakage tests.
Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics.
In this work we introduce an algorithm based on the well-known Canny approach for effectual crack detection in thermographic films obtained using flying spot thermography (FST) or flying line thermography (FLT). The proposed algorithm performs faster than another algorithm, for crack detection, based on the application of two Sobel filters (one in x and another one in y directions). For FLT it is shown that processing 10-25 % of the thermograms of a thermographic film required to scan a whole sample is enough to obtain good results. In contrast, using the Sobel filter approach requires the processing of twice the thermographic film length. Experimental measurements are performed on a metallic component of complex shape which contains real defects, that is, surface breaking cracks due to industrial use. The specimen is tested using flying line thermography. Three different scanning speeds are tested: 10, 30 and 60 mms-1 with laser powers of 50, 60 and 120 W respectively. The sample and an infrared camera are aligned and fixed on a motorized linear stage. The diode laser LDM500 (500 W max power) is fixed on an optical bench separately from the linear stage. The results obtained
with the proposed algorithm are additionally compared with a previously established algorithm for flying spot thermography based on the Sobel filter. It is shown that the proposed algorithm based on the Canny approach, can be used in automated systems for thermographic non-destructive testing.
Wireless sensor networks provide occupational health experts with valuable information about the distribution of air pollutants in an environment. However, especially low-cost sensors may produce faulty measurements or fail completely. Consequently, not only spatial coverage but also redundancy should be a design criterion for the deployment of a sensor network. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the sensor network can compensate for the outage of sensors. While our results show promising prediction quality of the models, they also indicate the presence of spatially very limited events. We, therefore, conclude that initial measurements with, e.g., mobile units, could help to identify important locations to design redundant sensor networks.
This paper presents first advances in the area of aerial chemical trail following. For that purpose, we equipped a palm-size aerial robot, based on the Crazyflie 2.0 quadrocopter, with a small lightweight metal oxide gas sensor for measuring evaporated ethanol from chemical trails. To detect and localize the chemical trail, a novel detection criterion was developed that uses only relative changes in the transient phase of the sensor response, making it more robust in its application. We tested our setup in first crossing-trail experiments showing that our flying ant robot is able to correlate an odor hit with the chemical trail within 0.14 m. Principally, this could enable aerial chemical trail following in the future.
The duraBASt area at the highway intersection “Köln-Ost” is a large test field for road testing.
It includes the duraBASt test bridge which is approximately 66 m long and 14.25 m broad. This bridge is part of the “road in the 21st Century” project and is equipped with several sensors for structural health monitoring. This feasibility study is carried out to investigate the prerequisites necessary for the constraction of intelligent bridges in the foture. Embedded sensors are able to measure quantities such as temperature, strain, moisture, corrosion, etc. In the presented work, the focus is set on passive RFID-based sensors. The communication and energy supply are realised by a mobile transmitter through the concrete cover. Hence, no cables or batteries are required. The sensors are designed to withstand the alkaline environment in concrete. Thus, moisture and corrosion values are measurable for several decades.
Enhanced photon upconversion using erbium-doped nanoparticles interacting with silicon metasurfaces
(2021)
Photon upconversion (UC) using trivalent erbium (Er+3) doped crystals is a promising concept to harness near infrared photons of the solar spectrum which cannot be directly absorbed by silicon solar cells. However, their UC efficiency at low-intensity 1 sun illumination is not relevant on device level so far. Exploiting giant near-field enhancement effects on metasurfaces is an appealing approach to enable efficient UC at low irradiance conditions. Here, we report on more than 1000-fold enhanced photon UC of NaYF4:Er+3 nanoparticles interacting with the near-fields supported by a silicon metasurface under 1550 nm excitation.
Active thermography as a nondestructive testing modality suffers greatly from the limitations imposed by the diffusive nature of heat conduction in solids. As a rule of thumb, the detection and resolution of internal defects/inhomogeneities is limited to a defect depth to defect size ratio greater than or equal to one. Earlier, we demonstrated that this classical limit can be overcome for 1D and 2D defect geometries by using photothermal laser-scanning super resolution. In this work we report a new experimental approach using 2D spatially structured illumination patterns in conjunction with compressed sensing and computational imaging methods to significantly decrease the experimental complexity and make the method viable for investigating larger regions of interest.
Micro- and nanostructuring with laser-induced periodic surface structures (LIPSS) has been demonstrated to be feasible in a wide variety of materials including metals, semiconductors and dielectrics. Suitable processing regimes for flat, curved and complex surfaces have been identified for many materials, allowing the generation of diverse applications in fields such as optics, tribology and medicine, to name a few. A common side effect when producing such structures in air environment is the formation of a thin surface oxide layer in the laser irradiated areas. Previous studies have shown that oxidation plays an important role in the tribological performance for which the structures where created, and very recently it has been shown that the laser-induced oxide graded layers may contribute to the formation of a new type of embedded low-spatial frequency LIPSS (LSFL) with annomalous orientation parallel to the laser polarization, in addition to the appearance of the well-known high-spatial frequency LIPSS (HSFL) at the surface. In this contribution, we explore this effect experimentally for chromium nitride (CrN) irradiated with femtosecond laser pulses and compare the findings to finite-difference time-domain (FDTD) simulations of the intensity distributions at different depth positions.
Pulse-compression thermography is an emerging technique that has shown versatility by combination of pulsed and lock-in thermography. Accordingly, several aspects of this technique are still unexplored, and some others not fully developed yet. Barker codes were widely used in radar applications due to their simplicity and their optimum autocorrelation function. Nevertheless, applications were limited by the amplitude of the sidelobes present in the autocorrelation function and therefore, several filters have been developed which aim to reduce the sidelobes. However, the filters usually depend on empirical parameters which must be determined for each application. A better alternative would improve the applicability of the Barker codes. In this work, we further develop the pulse-compression thermography technique by introducing a 13-bit modified Barker code (mBC): This allows to drastically reduce the sidelobes characteristic of the 13-bit Barker code (BC). Consequently, the thermographic impulse response, obtained by cross-correlation, is almost free of such sidelobes. Deeper defects become easier to detect in comparison with using a 13-bit Barker code. Numerical simulations using the finite element method are used for comparison and experimental measurements are performed in a sample of steel grade St 37 with machined notches of three different depths: 2 mm, 4 mm and 6 mm.
Novel Technique for On-Line Monitoring of the Curing Process of Fiber Reinforced Polymer Composites
(2021)
A specially developed experimental set up has been integrated into a commercially available FT IR Spectrometer to study the curing of Epoxy Systems. MIR and FIR spectra measured during the curing process at different temperatures of an epoxy system will be presented in dependency of the curing time. The experiments and the selected set up are discussed to demonstrate its potential for future monitoring and ageing control applications during a manufacturing process of polymer composites.
Abstract: The leaks of dangerous goods from actually intact bags detected in the years 2018 to 2020 tend to be at an almost constant high level. These releases of powdery or granular dangerous goods represent violations of the sift-proofness required in the dangerous goods regulations. This article first analyzes the causes. The components of the bags that are affected by leaks are micro-perforations, joins and closures, in particular internal sleeve valves.
A distinction must be made between bags closed in conformity with or contrary to the manufacturer's instructions.
The particle release is determined by a number of influencing factors of the filling substance, the packaging and other boundary conditions. Therefore, a comprehensive test concept is developed in this work, which takes all these factors into account. The application of this test concept facilitates the planning of the test setup and the experiments. On this basis, the complex mechanisms involved in the release of solid substances can be systematically investigated in the test laboratory. To prevent releases of powdery or granular substances from intact bags, it is necessary that the user has access to the closing instructions and the relevant properties of the test substance used for the design type approval. Further experimental investigations are needed to assess whether filling substances change their properties during transport and whether this enables them to escape.
Changes in the measured response of structural systems can be an indication of structural damages. However, such changes can also be caused by the effect of varying environmental conditions. To detect, localize and quantify changes or damages in structural systems subject to varying environmental conditions, physics-based models of the structural systems have to be applied which explicitly account for the influence of ambient conditions on the structural behavior. Data obtained from the structural systems should be used to calibrate the models and update predictions. Bayesian system identification is an effective framework for this task. In this paper, we apply this framework to learn the parameters of two competing structural models of a reinforced concrete beam subject to varying temperatures based on static response data. The models describe the behavior of the beam in the uncracked and cracked condition. The data is collected in a series of load tests in a climate chamber. Bayesian model class selection is then applied to infer the most plausible condition of the beam conditional on the available data.
Laser powder bed fusion is used to create near net shape metal parts with a high degree of freedom in geometry design. When it comes to the production of safety critical components, a strict quality assurance is mandatory. An alternative to cost-intensive non-destructive testing of the produced parts is the utilization of in-situ process monitoring techniques. The formation of defects is linked to deviations of the local thermal history of the part from standard conditions. Therefore, one of the most promising monitoring techniques in additive manufacturing is thermography. In this study, features extracted from thermographic data are utilized to investigate the thermal history of cylindrical metal parts. The influence of process parameters, part geometry and scan strategy on the local heat distribution and on the resulting part porosity are presented. The suitability of the extracted features for in-situ process monitoring is discussed.
Thermographic super-resolution techniques allow the resolution of defects/inhomogeneities beyond the classical limit, which is governed by the diffusion properties of thermal wave propagation. Photothermal super-resolution is based on a combination of an experimental scanning strategy and a numerical optimization which has been proven to be superior to standard thermographic methods in the case of 1D linear defects. In this contribution, we report on the extension of this approach towards a full frame 2D photothermal super-resolution technique. The experimental approach is based on a repeated spatially structured heating using high power lasers. In a second post-processing step, several measurements are coherently combined using mathematical optimization and taking advantage of the (joint) sparsity of the defects in the sample. In our work we extend the possibilities of the method to efficiently detect and resolve defect cross sections with a fully 2D-structured blind illumination.
Due to the rapid thermal cycles involved in additive manufacturing of metals, high internal stresses and peculiar microstructures occur, which influence the parts mechanical properties. To systematically examine their formation, in-process measurements of the temperature are needed. Since the part emissivity is strongly inhomogeneous and rapidly changing in the process, the applicability of thermography for the determination of thermodynamic temperatures is limited. Measuring the thermal radiation in different wavelengths simultaneously, temperature and emissivity can be separated. Here, we present results of a preliminary study using multispectral thermography to obtain real temperatures and emissivities in directed energy deposition (DED) processes.
To increase the competitiveness of jacket substructures compared to monopiles a changeover from an individual towards a serial jacket production based on automated manufactured tubular joints com-bined with standardized pipes has to be achieved. Therefore, this paper addresses fatigue tests of automatical-ly welded tubular X-joints focusing on the location of the technical fatigue crack. For this X-joint, the detect-ed location of the technical crack is then compared to numerical investigations predicting the most fatigue prone notch considering the structural stress approach as well as the notch stress approach. Additionally, the fatigue prone hot spot according to both approaches is compared for a typical offshore jacket double-K-joint to emphasize the significance of the presented outcomes for the existing offshore structures. Besides, the welding process of the automated manufactured tubular X-joints is presented.
Elastomer seals are used in many industrial applications due to their excellent mechanical properties at a wide range of temperatures. Their high versatility and recovery potential under several load conditions make them well suitable for the application in containers designed for transport, storage and/or disposal of radioactive materials. In containers for low and intermediate level radioactive waste, elastomer seals are used as barrier seals, and as auxiliary seals in storage and transportation casks (dual purpose casks) for heat generating radioactive waste, such as spent fuel and high-level waste. While a seal exchange at defined intervals is typical in many conventional applications, it is impossible or at least hard to perform when principles of minimization of radiation exposure have to be considered and prohibit an unnecessary cask handling. An extensive knowledge of the change of the elastomer’s properties during aging and the availability of reliable end-of-lifetime criteria to guarantee the permanent safe enclosure of the radioactive material is mandatory. As BAM is involved in most of the national cask licensing procedures and in the evaluation of cask-related long-term safety issues, great efforts have been already made and are still planned to scientifically support this task.
Compression stress relaxation and compression set were identified as key indicators of elastomer long-term performance and quantitatively investigated in comprehensive test programs. Among other representative types of elastomers, specimens made from ethylene propylene diene rubber (EPDM) were tested before, during and after aging to capture the most important of their complex mechanical properties.
In the presented study, exemplary results were used to simulate the compression stress relaxation and the compression set of elastomer O-rings during aging. Regarding the influence of temperature, the time-temperature superposition principle is applied in the relaxation analysis of elastomer O-rings. The proposed model is implemented in the commercial finite element software ABAQUS/Standard® [1] with a sequential temperature displacement coupling. Numerical results match the experimental compression stress relaxation measurements well. The prediction of compression set values after long-term aging shows a relatively good agreement with the experimental results. Nevertheless, all input parameters derived from the specimen tests, additional assumptions concerning boundary conditions and modeling strategy are discussed with regard to the identified slight discrepancies.
The possibility to extend the finite element model to represent the O-ring seal’s ability to recover after a (fast) partial release is taken into account.
Structural health monitoring (SHM) is a term that groups together techniques adopted to evaluate in a continued fashion the structural integrity and degradation of technical appliances. SHM is particularly attractive for components that are difficult to access or expensive to take off-line. Among many other techniques, SHM can be performed using ultrasonic guided waves (UGW) which have an advantage of traveling over Long distances. Various guided wave modes exist along with many methods for their generation and sensing, e.g by means of interdigital transducers (IDT). This contribution is dedicated to the design and characterization of a flexible piezopolymerbased IDT which allows for the selective excitation of UGW, resulting in more straightforward data analysis. The designed IDT was characterized using a 3D Laser Doppler Vibrometer (3D LDV) in the air to identify and analyze the IDT’s Vibration modes. Then the transducer was mounted on an aluminum plate, and the generated wavefield was measured with the 3D LDV.
According to this investigation, we demonstrate that it is possible to selectively excite desired guided wave mode, namely the A0 mode, suppressing the excitation of the S0 mode. Moreover, the measured wavefield allows for analysis of the directivity of the designed IDT. All in all the results show good correlation between theoretical predictions and measured values, thus allowing to use the current design in terms of selective excitation as it is.