Open Access

Diese Formelsammlung umfasst die Fachgebiete Technische Thermodynamik, Wärme- und Stoffübertragung sowie Technische Strömungsmechanik. Sie basiert auf den gleichnamigen Lehrveranstaltungen der Autoren an der Hochschule für angewandte Wissenschaften Kempten. Die Formelsammlung stellt grundlegende Berechnungsgleichungen, umfangreiche Stoffdaten sowie Lösungsdiagramme für zentrale ingenieurtechnische Berechnungsaufgaben systematisch gegliedert bereit. Da diese Fachgebiete durch eine hohe Komplexität gekennzeichnet sind, und dadurch insbesondere Einsteigerinnen und Einsteigern den Zugang erschweren können, wurde bei der Erstellung besonderer Wert auf eine möglichst kompakte und übersichtliche Darstellung gelegt. Anschauliche Skizzen und Diagramme unterstützen dabei das Verständnis der Zusammenhänge. Bei der Stoffdatensammlung liegt der Fokus auf der Bereitstellung von Stoffdaten, die für ingenieurtechnische Anwendungen besonders relevant sind. Bei den Formelzeichen wurde, soweit möglich, auf die im deutschsprachigen Raum in etablierten Lehrbüchern überwiegend gebräuchliche Notation zurückgegriffen, um den Zugang zu erleichtern. Damit eignet sich die Formelsammlung insbesondere als vorlesungs- und prüfungsbegleitendes Hilfsmittel für Studierende ingenieurwissenschaftlicher Studiengänge an Hochschulen und Universitäten. Darüber hinaus richtet sie sich an Ingenieurinnen und Ingenieure mit entsprechenden Vorkenntnissen, die im beruflichen Alltag schnell auf wesentliche Gleichungen, Stoffdaten und Diagramme zugreifen möchten.

This study investigates the influence of data distribution and image quality on the performance of fully supervised deep neural networks (DNNs) from the YOLO family for individual plant detection. Several sugar beet datasets (Sugar Beets 2016, Lincoln beet, CropAndWeed, and a newly collected dataset) were used for this analysis. A series of experiments was conducted to examine the effects of model size, random training seeds, dataset size and composition, image quality, and varying agricultural conditions on detection accuracy. The objective of this work is to identify the factors with the greatest impact on model performance in order to enable targeted dataset design and reduce annotation effort. The results show that missing or imprecise annotations have a significantly smaller impact on detection performance than noisy or blurred images. Furthermore, the findings indicate that using approximately 30–40% of the total 26,449 images is sufficient to achieve an optimal balance between detection accuracy and annotation effort.

In industrial applications, AI-based methods are gaining increasing significance in optical systems for tasks such as identification, inspection, and classification. The appeal of these methods lies in their operator-friendly implementation and often superior performance, particularly in complex classification tasks. However, despite the substantial potential of AI-based methods, their application is frequently constrained by the significant resources needed to acquire an appropriate training dataset. For instance, in the sand casting industry, the complexity of optical inspection for cast parts is compounded by considerable variation in local surface topography and global object geometry. This challenge pertains not only to the volume of images required but also to the extensive process of accurate labelling. In this work, we investigate the effectiveness and limitations of synthetic training data for an AI-based optical code reader, designed for the identification and tracking of cast parts. This scanner is capable of detecting and classifying a unique code, called Cast Code, developed specifically for the casting industry, enabling the identification of individual cast part numbers. For synthetic image generation, we utilize physically based rendering, which allows comprehensive control over all rendering parameters. This approach facilitates both a systematic investigation of parameter relevance and an automated labelling process for the training datasets. Our results indicate that detailed geometric modelling of both, the local surface topography and the global object geometry of the pins has a notable positive impact on the neural network’s recognition accuracy, achieving accuracy rates of up to 84 %, on real pin images, using synthetic training datasets, only.

Never change a running system“ – und wenn man es doch macht? Genau dieser Frage widmet sich das EU-Interreg-Projekt „QualiCheese“ (BA0100106). Das Konsortium aus MCI Internationale Hochschule GmbH, KLEVERTEC (HS Kempten), muva kempten GmbH und Bioalpin eGen untersucht, welche Herausforderungen bei der Umstellung von Verpackungsfolien für Schnittkäse auf nachhaltigere Materialien auftreten.

Grid-connected multi-string battery energy storage systems (BESS) operating under sustained high-power conditions face heat accumulation, power derating, and cooling-related efficiency losses, leading to trade-offs among thermal management, efficiency, and system availability. To manage these competing objectives, this work proposes a mixed-integer nonlinear programming (MINLP)-based multi-objective optimization (MOO) framework for optimal power allocation across parallel strings under air cooling. The optimization incorporates an equivalent circuit model (ECM) with SOC-, temperature-, and C-rate-dependent internal resistance, temperature-driven derating, and an experimentally derived inverter loss model. High-fidelity control is achieved by co-simulating the optimizer with an electro-thermal BESS simulation that captures spatial thermal dynamics of the battery pack. The simulation provides online state of charge (SOC) and temperature feedback and is validated against industry-measured battery pack temperatures, revealing core temperatures up to 4.5 °C higher than those predicted by conventional 0D average thermal models calibrated to pack surface measurements. Simulation results show that the baseline MINLP controller with evenly weighted objectives achieves approximately 3% higher round-trip efficiency (RTE) and system availability compared to the industrial benchmark droop control while minimizing thermal derating. Linearized optimal control without thermal optimization results in higher maximum string temperatures up to 40 °C and 4% higher derating losses. A Pareto-based objective prioritization improves energy performance by up to 5% while keeping temperatures below the 35 °C derating threshold. System scalability analysis shows that operating an optimal subset of strings outperforms conventional full-string operation, achieving 5.5% higher RTE with comparable availability and thermal performance. The framework is released open-source for adaptation to custom BESS applications.