Publikationsserver der Ostbayerischen Technischen Hochschule Regensburg
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Eigenvalue perturbation in drivetrain analysis and redesign
No full textThe optimisation of the dynamic behavior of drive systems often involves targeted modifications of the system characteristics. Structural and parametric modifications are used to satisfy the constraints of the dynamic requirements. However, many optimisations are still achieved by intuition or parameter variations, even though more streamlined and easy-to-implement tools such as the eigenvalue perturbation method are available. In this article, the eigenvalue perturbation method in the form of an eigenvalue sensitivity analysis is used to efficiently optimise the dynamic behavior for two different use cases using different optimisation measures. This paper demonstrates how eigenvalue perturbation theory can efficiently optimise drivetrain dynamics by systematically modifying system parameters. Two case studies show how eigenvalue sensitivity analysis achieves targeted frequency shifts to avoid resonances: (1) adapting shaft stiffness and control parameters in a torsional drivetrain, and (2) adjusting structural modifications in a wind turbine bedplate. The study introduces the eigenvector tensor product as a weighting matrix, identifying key parameters for effective redesign. Compared to conventional parameter studies, this method enables precise control over system dynamics with minimal computational effort, making it highly applicable for vibration mitigation and drivetrain optimisation
It’s Quick to be Square: Fast Quadratisation for Quantum Toolchains
No full textMany of the envisioned use-cases for quantum computers involve optimisation processes. While there are many algorithmic primitives to perform the required calculations, all eventually lead to quantum gates operating on quantum bits, with an order as determined by the structure of the objective function and the properties of target hardware. When the structure of the problem representation is not aligned with structure and boundary conditions of the executing hardware, various overheads degrading the computation may arise, possibly negating any possible quantum advantage.
Therefore, automatic transformations of problem representations play an important role in quantum computing when descriptions (semi-)targeted at humans must be cast into forms that can be “executed” on quantum computers. Mathematically equivalent formulations are known to result in substantially different non-functional properties depending on hardware, algorithm and detail properties of the problem. Given the current state of noisy intermediate-scale quantum (NISQ) hardware, these effects are considerably more pronounced than in classical computing. Likewise, efficiency of the transformation itself is relevant because possible quantum advantage may easily be eradicated by the overhead of transforming between representations. In this paper, we consider a specific class of higher-level representations, that is, PUBOs, and devise novel automatic transformation mechanisms into widely used QUBOs that substantially improve efficiency and versatility over the state of the art. In addition, we conduct a comprehensive investigation of industry-relevant problem formulations and their conversion into a quantum-specific representation, identifying significant obstacles in scaling behaviour and demonstrating how these can be circumvented
New Results in Numerical and Experimental Fluid Mechanics XV : Contributions to the 24th STAB/DGLR Symposium, Regensburg, Germany, 2024
No full textThis book offers timely insights into research on numerical and experimental fluid mechanics and aerodynamics. It reports on findings by members of the Deutsche Strömungsmechanische Arbeitsgemeinschaft, STAB (German Aerodynamics/Fluid Mechanics Association) and the Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal Oberth e.V., DGLR (German Society for Aeronautics and Astronautics) and covers both nationally and EC-funded projects. Continuing on the tradition of the previous volumes, the book highlights innovative solutions, promoting translation from fundamental research to industrial applications. It addresses academics and professionals in the field of aeronautics, astronautics, ground transportation, and energy alike
High-resolution flow field investigations in membrane lungs, considering the complex blood rheology
No full textDespite major improvements over the last years, coagulative disorders and clotting
phenomena in membrane lungs (MLs) are still considerable complications in extracorporeal
membrane oxygenation (ECMO). ECMO is an increasingly used treatment for patients with
severe respiratory failure or cardiac arrest [1]. For both, evaluation of therapeutic decisions
and fundamental research on patient specific intra-device clotting phenomena, the direct
visualization and analysis of clot formation in combination with a detailed flow field
correlation is highly desirable and therefore an intensively followed research topic.
Modelling blood flow and shear induced coagulation in MLs is challenging. The relevant
geometry of oxygenator fibers and chaining threads is complex and spans several length scales.
In relevant scales and regimes, blood shows several significant non-Newtonian effects.
Viscosity impacts shear rate, which is important in several coagulation mechanisms.
Additionally, coagulation processes are influencing fluid properties and geometry
significantly. Existing approaches of previous research work are only able to consider some,
but not all relevant effects and geometrical details. Due to the enormous size of the discretized
geometries, highly detailed viscosity and coagulations models are not applicable. Our goal is
to develop a model for combined viscosity and coagulation properties of blood flow in MLs.
In our work, we compare the influence of different levels of detail of the ML geometry as well
as the influence of considering realistic blood flow behavior (viscosity change by considering
the local hematocrit distribution within the Fåhraeus-Lindqvist-Effect) on the resulting flow
field in relevant subsections of a ML.
High-resolution micro-CT geometry reconstructions [1] are compared to idealized generic
fiber representations. For realistic blood flow modelling, Newtonian representation is
compared to the established Carreau-Yasuda and a multiphase Euler-Euler approach.
Results are presented for relevant subsections as well as for the complete ML
Optimierung Offenporiger Asphalte für Geh- und Radwegbereiche unter dem Aspekt der Schwammstadt
No full textAnsätze zur planerische und bautechnische Optimierungen von innerörtlichen Geh- und Radwegen mit Einsatz von Offenporigen Asphalten unter dem Aspekt der Schwammstad
From Agents to Copilots: a Systematic Review of Digital Assistant Technology Adoption in Proprietary Productivity Software
No full textThis study provides a systematic review of how the impact and adaptation of digital assistant technologies (DATs) are defined, operationalized, and studied, synthesizing key domains where DATs generate or are expected to generate value. Based on an analysis of 61 articles published since 2013, it identifies five main areas of impact: productivity and efficiency, business development, resource optimization, quality enhancement, and the promotion of learning and creativity. The review highlights DAT adoption across various disciplines and industries, while revealing limited longitudinal research on benefits and adaptation. Key gaps remain in understanding strategic use and sustained impact. Future research should explore longitudinal comparisons of recently introduced generative AI-driven DATs and their organizational implications. This review contributes to information systems research by structuring current knowledge on DAT adoption and outcomes, and by proposing a research agenda to support deeper exploration of their value and long-term integration
LEVERAGING FIVE QUESTIONNAIRES TO ANALYZE STUDENT LEARNING STRATEGIES AND GENERATE AI-POWERED INDIVIDUALIZED LEARNING PATHS
No full textBackground:
The COVID-19 pandemic has significantly accelerated the shift toward online and blended learning in higher education, placing renewed emphasis on the individualization of learning content to meet diverse student needs. Even high-quality learning materials may fail to engage learners if they do not align with students’ personal preferences and learning styles. Identifying these learner preferences, therefore, emerges as a critical challenge.
Objectives:
This paper presents ongoing work within a larger research project aimed at employing artificial intelligence to recommend optimal learning path for students in specific courses. Beyond mere optimization, the goal is to ensure the best possible fit between learning materials and individual learners.
Sample & Methods:
A total of 27 students from technical degree programs took part in this survey. All participation was voluntary, and data were handled in full compliance with GDPR regulations. Although our broader project integrates fine-grained learning analytics from Moodle, the present abstract focuses exclusively on the self-report questionnaire results. Participants completed five instruments:
1. Index of Learning Styles (ILS)
2. LIST-K (Learning and Study Strategies Inventory – Short version)
3. BFI-10 (Big Five Inventory – 10 items)
4. Custom Preferences Instrument, capturing preferences for specific learning elements (e.g. instructional videos, lecture notes, summaries) and basic demographic data
5. Motivational Value Systems Questionnaire (MVSQ), piloted last semester to assess value orientations and motivational drivers
Results:
Preliminary analyses of the questionnaire data reveal:
- Learning Styles (ILS): The majority lean toward the visual learning type (M = 5.740, SD = 3.430).
- Learning Strategies (LIST-K): High scores on metacognitive strategies (M = 3.000; SD = 0.520) and collaboration with peers (M = 3.190; SD = 0.540).
- Preferred Learning Elements: Summaries, overviews, and self-checks are most favored.
- Value Orientations (MVSQ): Students are primarily driven by the pursuit of personal achievement (M = 4.400; SD = 11.140).
Conclusion & Significance:
By integrating these five standardized questionnaires, we gain valuable insights into student learning preferences—insights that complement our Moodle analytics in the broader project. Observed trends suggest that learning materials should be concise and designed to facilitate peer interaction and knowledge deepening. These findings will guide the refinement of our AI-driven recommendation engine, enhancing its ability to deliver personalized learning paths that boost both engagement and effectiveness
Vertical Sound Localization: Precision and Robustness to Reverberation in a Large-Scale Study
No full textVertical sound localization, the ability to perceive the elevation of a sound source, is a fundamental aspect of human auditory perception, yet it remains far less understood than horizontal localization. Existing studies rely on small sample sizes, limiting the generalizability of findings. The presented study bridges that gap by examining vertical sound localization and precision in a cohort exceeding 150 participants, making it the largest investigation of its kind to date.Participants were exposed to broadband noise stimuli from various elevations under controlled anechoic conditions. The elevation localization error was measured by comparing perceived sound source elevations to actual positions of speakers of a curved array. Azimuth angles of arrival were altered between 0°, 45° and 90°. For a small group the experiment was repeated in echoic conditions, to gain insight in reverberation and reflection robustness of vertical sound localization.This large-scale study establishes benchmarks for vertical sound localization precision and robustness, advancing our understanding of human auditory spatial perception. These findings have implications for audio technology development, such as spatial audio rendering and hearing aid design, and lay the groundwork for further exploration into the neural and anatomical underpinnings of vertical localization
Aggregate Production Planning under Risk of Disruption
No full textIn recent years, large scale disruptions to global supply chains, like the Covid pandemic, a ship blocking the Suez canal or sanctions against Russia, have caused production to slow down or even to stand still, causing shortages and massive losses for affected businesses. Even if only specific companies were originally affected, shortages and delays rippled along the supply network.
The established approach to deal with disruptions is to utilize safety stock and capacity to compensate for fluctuations in uncertain quantities like customer demand. This approach is tried and tested for small fluctuations. To address larger disruptions, like the above given, very high safety stock and capacity would be needed, which would lead to unnecessarily high costs.
Resilience has often been viewed as an expensive capability that drives costs. Recent studies however advocate for the development of lean resilience concepts, creating new capabilities, which enable resilience and can deal with large fluctuations, reimagining resilience from the perspectives of efficiency and value creation.
This contribution identifies gaps in current research and establishes structural deficits of approaches discussed in the literature regarding supply chains. Firstly, the need for rigorous, quantitative definitions of resilience and relevant disruptions is justified.
Then, a stochastic model for aggregate production planning that includes capabilities to compensate for such large fluctuations along several dimensions is proposed. Lastly, this model is then applied to a case study pertaining to a realistic supply chain under the risk of large scale disruptions and the results of this approach are evaluated
Laser welding of polymer foils with spatially adapted intensity distributions
No full textAbsorber-free laser transmission welding is characterized by its contactless energy input and geometricflexibility and enables the precise and clean joining of polymer films without absorbing additives or adhesives. It is therefore well suited for applications with high demands regarding process reliability and cleanliness such as packaging, fluid containersor as sealing film in medicaland food industry. A homogeneous weld seam temperature is necessary for a large processwindow. In this work, the naturally Gaussian-shaped intensity distributionof the laser beam is there foreconverted into a donut-shaped and a flat-top-shaped distribution. When using the donut-shape, the processwindow for welding polypropylene or polyethylene films is increased by up to a factor of 3. At the same time, the weld seam strength almost corresponds to the strength of the base material