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The digitalization of quality control processes and the underlying data infrastructures for safety relevant components, such as hydrogen pressure vessels, plays a significant role in the transition towards Industry 4.0. In the current safety regulations for hydrogen pressure vessels, there is no established concept for structural health monitoring. The development of a reliable structural health monitoring methodology for monitoring the structural integrity of pressure vessels enables a fast-forward transition from personnel- and costintensive recurring inspections, a.k.a. periodic maintenance, to predictive maintenance. In the work presented; we investigated the application of ultrasonic guided wave propagation to monitor and assess the condition of Type IV composite overwrapped pressure vessel (COPV). A sensor network of fifteen piezo-electric wafers is placed on the carbon fibre reinforced composite cylinder. Five different artificial damage configurations are created by gluing two different weight blocks on three different locations. The database containing measured guided wave data sets is enriched by two different boundary conditions. We utilized an open-source software, openBIS labnotebook, to store and analyse experimental datasets. The guided wave ultrasonic signals were investigated and analysed by using commonly used ultrasonic features (e.g., amplitude, frequency, time of flight) as well as non-traditional time-series features (kurtosis, skewness, variance). The features were used to calculate damage index and the detection performance for the results has been evaluated. The results suggest that both traditional and non-traditional features assume significant importance in artificial damage detection. The future works will additionally involve the impacts of operational conditions, such as periodic pressure variations temperature loadings as well as material degradations.

The applications of composite overwrapped pressure vessels (COPVs) in extreme conditions, such as storing hydrogen gases at very high pressure, impose new requirements related to the system's integrity and safety. The development of a structural health monitoring (SHM) system that allows for continuous monitoring of the COPVs provides rich information about the structural integrity of the component. Furthermore, the collected data can be used for different purposes such as increasing the periodic inspection intervals, providing a remaining lifetime prognosis, and also ensuring optimal operating conditions. Ultimately this information can be complementary to the development of the envisioned digital twin of the monitored COPVs. Guided waves (GWs) are preferred to be used in continuous SHM given their ability to travel in complex structures for long distances. However, obtained GW signals are complex and require advanced processing techniques. Machine learning (ML) is increasingly utilized as the main part of the processing pipeline to automatically detect anomalies in the system's integrity. Hence, in this study, we are scrutinizing the potential of using ML to provide continuous monitoring of COPVs based on ultrasonic GW data. Data is collected from a network of sensors consisting of fifteen Piezoelectric (PZT) wafers that were surface mounted on the COPV. Two ML algorithms are used in the automated evaluation procedure (i) a long short-term memory (LSTM) autoencoder for anomaly detection (defects/impact), and (ii) a convolutional neural network (CNN) model for feature extraction and classification of the artificial damage sizes and locations. Additional data augmentation steps are introduced such as modification and addition of random noise to original signals to enhance the model's robustness to uncertainties. Overall, it was shown that the ML algorithms used were able to detect and classify the simulated damage with high accuracy.

The application of composite overwrapped pressure vessels (COPV) to store hydrogen and other compressed gases, especially when operating at high pressures, imposed the need for an innovative and reliable approach to ensure the safe operation of the system. Continuous structural health monitoring (SHM) based on ultrasonic guided waves (GWs) is a promising approach due to the ability of the wave to propagate for long distances and go around complex structures, moreover the high sensitivity to various failure modes such as delamination, matrix cracking and debonding. In this study, we scrutinize the behavior of (GWs) within the COPV using a network of PZT elements that are used for excitation and sensing. A laser doppler vibrometer (LDV) was also used to scan the surface of the vessel in various directions. L(0,1) and L(0,2) were observed in the captured signal. The L(0, 2) appeared to be a dominant mode in the COPV and was capable of propagating along the entire length and maintaining a good signal-to-noise ratio. The L(0,2) mode maintained the same phase velocity when it is captured at various excitation angles (0, 45, and 90 degrees). The reduced effect of the orthotropy of the materials on the propagating waves is an important result as it will reduce the complexity in data processing when performing damage identification.

Supervision an der Berufsschule mit angehenden Erzieher*innen ist ein Thema, das sich in den Publikationen zur Supervision selten findet. Der folgende Beitrag ergänzt das Feld um die Darstellung von Erkenntnissen aus einem Projekt zur Berufsrollenreflexion an einer Fachschule für Sozialwesen und erarbeitet eine Argumentationslinie zur Implemen- tierung von Ausbildungssupervision. Supervision unterstützt dabei nicht allein die Kom- petenzentwicklung der Schüler*innen, sondern auch die Rahmen- und Organisationsbe- dingungen der Ausbildung werden zum Reflexionsthema für die Schule und Praxisinsti- tutionen. Vor allem mit Blick auf Ausbildungsabbrüche und den Fachkräftemangel im sozialen Bereich eine notwendige Betrachtungsebene, bei der ein supervisorischer Blick wichtige Impulse setzt.

Analog computers can be revived as a feasible technology platform for low precision, energy efficient and fast computing. We justify this statement by measuring the performance of a modern analog computer and comparing it with that of traditional digital processors. General statements are made about the solution of ordinary and partial differential equations. Computational fluid dynamics are discussed as an example of large scale scientific computing applications. Several models are proposed which demonstrate the benefits of analog and digital-analog hybrid computing.

The contribution focuses on the fate of the First Republic of Georgia (1917-1921), from Karl Kautsky’s point of view, and on the German Social Democrats’ changing view of the October Revolution. Starting out from the dictum: “Who addresses Stalin, must also address Lenin, who speaks of Stalinist crimes, must also discuss the October Revolution”, Kautsky’s view of the “Menshevist” Social Democratic Workers Party, which ruled Georgia between 1917 and 1921 and was the German Social Democrats’ “brother party”, and of the “Social Democratic Farmers Republic of Georgia” and its sad end is presented. For Kautsky, the fate of Georgia was exemplary evidence for the correctness of his scathing criticism of the Bolshevik “October putsch” which, as he said, had annihilated the achievements of the February Revolution.

An der Technischen Universität Berlin wurden Studienreformprojekte im Modul "Lineare Algebra für Ingenieure" über mehrere Jahre durchgeführt, um u.?a. mehr Zeit für die persönliche Betreuung von Studierenden trotz großer Teilnehmerzahlen zu gewährleisten. Die eingeführten Maßnahmen wurden auf unterschiedliche Weise evaluiert und zum Teil auf das Modul "Analysis I für Ingenieure" transferiert. In diesem Beitrag wird über die damit verbundenen Prozesse berichtet. 10.11.2014 | Katherine Roegner & Michael Heimann (Berlin)