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Aktueller Stand der Großladeinfrastruktur – Reallabor am Kronprinzenkai
No full textDer Klimawandel und seine Folgen wirken sich sowohl auf die Umwelt als auch auf die Strukturen der Energielogistik aus. Die Transformation der Mobilität ist dabei ein Treiber, um die Herausforderungen zu bewältigen. Hierzu zählt auch die Elektrifizierung des Güterverkehrs. Dazu müssen in den kommenden Jahren umfangreiche Ausbauten bei Megawatt-Charging-Systemen (MCS) bzw. auch dem Wasserstofftankstellen-Netz erfolgen. Dieses Papier beschäftigt sich mit ersten Ansätzen zur Einordnung der geplanten MCS in die bestehende Ladeinfrastruktur sowie mit Ausführungen zur Umsetzung dieser MCS in Form von Großladeinfrastrukturen und versucht einen Einblick in die aktuellen Ansätze zu geben.Vo
Combination of two FSI methods and their validation based on artificial wind gusts impacting a flexible T-structure
No full textThis is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).The study focuses on the combination of two numerical approaches that are typically not used together in this manner. The first is a well-established partitioned fluid-structure interaction (FSI) simulation methodology relying on a finite-volume fluid solver for curvilinear, block-structured, body-fitted grids written in the Arbitrary Lagrangian–Eulerian (ALE) formulation, and a finite-element solver for the structural analysis. The second approach is an immersed boundary (IB) method employing a continuous and direct forcing strategy. The IB method, often applied to Cartesian grids, is also referred to as an approach to simulate fluid-structure interactions. In this study, both methods are combined to exploit their respective advantages in simulating a complex flow problem. The coupled FSI problem involves the interaction of a thin, flexible structure deforming under the dynamic load of a wind gust (task 1). The gust itself is generated by an artificial wind gust generator, which includes a paddle that partially obstructs the wind tunnel's outlet, thereby defining an FSI problem of its own (task 2). For task 1, the classical partitioned ALE approach is employed, while the IB method is more appropriate for task 2. Using available experimental measurement data for both the fluid flow and the structural deformation, the combined simulation framework is first validated for the case without gust. In a second step, the more challenging FSI problem of discrete gusts impacting the T-structure is thoroughly analyzed and the predicted data are compared with the available measurement data. For both cases without and with gusts, a very good agreement between simulation and experiment is achieved, which justifies the chosen approach.Vo
Individual and situational characteristics of the occurrence of cyber sickness in the context of virtually supported military training
No full textCyber sickness (CS) is considered a major challenge in the use of virtual reality (VR). This impacts the planned implementation of VR in the training of operational forces. The present work aimed to investigate the prevalence and onset of CS during virtually supported military training using head-mounted displays (HMDs) and explore related predictor variables. For this purpose, a quantitative cross-sectional study was conducted in which German soldiers (N = 100) were exposed to an immersive fifteen-minute VR scenario. We measured CS severity, age, heart rate (HR), and skin conductance (SC). Using newly developed categories to classify CS severity, the results showed a small prevalence (4%) of CS in the studied sample. Susceptibility to CS was the only predictor of the occurrence of CS symptoms. Accordingly, the present work provides evidence that CS may play a minor role in affecting virtually supported operational training. At the same time, the easily detectable susceptibility to CS promises rapid detection of vulnerable users. Implications and further research are discussed to detect, control, and mitigate CS.Vo
Deep learning-assisted real-time defect detection and process control for electrode manufacturing of lithium-ion battery cells
No full textDetecting and preventing defects on electrode surfaces during the manufacturing of lithium-ion battery cells remains a crucial challenge to avoid further cascading effects in subsequent stages of the manufacturing chain. Variations in surface quality or individual contamination can adversely affect battery performance and lifespan, potentially posing safety risks. This paper presents a deep-learning assisted system for detection and classification of coating defects in battery electrodes and subsequent process optimization strategies. Following improvement of product quality and a reduction of the reject rate in the coating and drying process of electrodes, the research contributes to the enhancement of overall efficiency in lithium-ion battery cell manufacturing. To validate the practical application of the system, a case study is conducted in the coating and drying processes of the battery cell pilot line production CELLFAB of the RWTH Aachen University. The results indicate great potential for enhancement of real-time defect detection and further optimization of process parameters.Vo
An illumination based backdoor attack against crack detection systems in laser beam welding
No full textDeep neural networks (DNNs) have been wildly used in engineering and have achieved state-of-the-art performance in prediction and measurement tasks. A solidification crack is a serious fault during laser beam welding and it has been proven to be successfully detected using DNNs. Recently, research on the security of DNNs is receiving increasing attention because it is necessary to explore the reliability of DNNs to avoid potential security risks. The backdoor attack is a serious threat, where attackers aim to inject an inconspicuous pattern referred to as trigger into a small portion of training data, resulting in incorrect predictions in the reference phase whenever the input contains the trigger. In this work, we first generate experimental data containing actual cracks in the welding laboratory for training a crack detection model. Then, targeting this scenario, we design a new type of backdoor attack to induce the model to predict the crack as a normal state. Considering the stealthiness of the attack, a common phenomenon during the welding process, illumination, is used as the backdoor trigger. Experimental results demonstrate that the proposed method can successfully attack the crack detection system and achieve over 90% attack success rate on the test set.Vo
Digitalisierung als Herausforderung und Chance für die Migrations- und Integrationsverwaltung
No full textVo
White Paper zur Beurteilung der Resonanzstabilität mittels impedanzbasiertem Stabilitätskriterium
No full textTo ensure a future-proof electrical energy supply, the expansion of renewable energy sources and grid infrastructure is essential. This challenging task largely falls to distribution grid operators, who must also ensure that inverter-connected systems in Germany are tested for resonance stability in the future. This white paper provides a practical introduction to the topic, guided by the impedance-based stability criterion and findings from grid and inverter impedance measurements. Results from measurement campaigns to determine grid and inverter impedances are interpreted within the context of the stability criterion, identifying how resonance stability should be ensured in the future grid.Vo
A data-driven approach for automating the design process of deep drawing tools
No full textThe deep drawing tool development process, from method planning and design of tools to tool try-out and final commissioning, is very time-consuming and requires extensive iterative manual effort, particularly during the try-out stage. To accelerate the entire process, integrating obtained knowledge from the tool try-out stage into the early design stage offers significant potential. Towards automating tool design, this paper proposes a data-driven approach using a generative neural network to predict active surfaces of deep drawing tools based on given deep drawn parts, laying the foundation for incorporating try-out knowledge. The model is trained on active tool surfaces and their corresponding deep drawn parts, including variation of geometrical parameters and process parameters in deep drawing simulation. The approach is evaluated using simulated data from deep drawing processes. The proposed solution demonstrates an advancement in automatically generating the active tool surfaces for both the punch and the die directly from the desired deep drawn parts.Vo