Recent Submissions
Wirtschaftlichkeit von komplexen Bauprojekten bei Einsatz integrierter Projektabwicklungsmodelle : Entwicklung eines Leitfadens für die Wahl des Projektabwicklungsmodells bei komplexen Bauprojekten
(Stuttgart : Institut für Baubetriebslehre, Universität Stuttgart, 2025) Jünger, Hans Christian; Schmidt, Benedikt; Müller, Elena; Auch, Natalie; Bender, Anna-Lena; Jünger, Hans Christian; Schmalz, Sarina (Mitwirkende); Geppert, Fabian (Mitwirkende); Deichmann, Laura (Mitwirkende); Jain, Ripu (Mitwirkende)
Smart building assessments : optimizing SRI calculation using a BIM-based data exchange framework
(2025) Calandri, Maurizio; Tafelmaier, Louis; Rathje, Torben; Müller, Michael; Henzler, Tobias; Stergiaropoulos, Konstantinos; Kramp, Michael; Glatz, Andreas
Insufficient data availability, limited calculation tools and subjective decisions by experts lead to ambiguous results when determining the Smart Readiness Indicator of a building. Moreover, it is not an integral part of the planning process. Therefore, this study presents a consistent and process-oriented planning and calculation approach. The methodology consists of the combination and exchange of information between building information modeling, labeling system, and data exchange format. It provides a consistent, seamless flow of information throughout integrated planning. This enables an automatic calculation of the planned Smart Readiness Indicator based on a floor-related functionality assessment, as proved using a mock-up. In addition, it provides a clear categorization of the necessary information for the planned components, which can be used for the verification process. Overall, this leads to a significant reduction in the time and effort required for smart building assessments while also increasing their consistency of evaluation.
Extremismusprävention im organisierten Sport : eine qualitative Studie zur strukturellen Verankerung extremismuspräventiver Aktivitäten in ausgewählten Sportvereinen
(2026) Schleifer, Tobias; Seiberth, Klaus; Michelini, Enrico; Kühnle, Felix
Das Erstarken extremistischer Tendenzen stellt die Demokratie in Deutschland vor große Herausforderungen. Sportvereine als zivilgesellschaftliche Freiwilligenorganisationen können eine bedeutende Rolle in der Prävention von Extremismus übernehmen. Der Beitrag untersucht die strukturelle Verankerung extremismuspräventiver Aktivitäten in ausgewählten Sportvereinen auf Grundlage der Organisationstheorie Niklas Luhmanns. In methodischer Hinsicht liegt der Studie ein qualitatives Forschungsdesign zugrunde, bei dem 20 Expert:inneninterviews in neun Vereinen sowie ergänzende Dokumentenanalysen durchgeführt und mittels qualitativer Inhaltsanalyse ausgewertet wurden. Die Ergebnisse zeigen, dass eine nachhaltige strukturelle Verankerung von Extremismusprävention vor allem im Kontext spezifischer Zeitfenster gelingt. Dabei sind extremistische Ereignisse und Bedrohungen offenbar in besonderer Weise geeignet, eine spezifische Sensibilität sowie interne Anschlussfähigkeit für das Thema zu schaffen und damit einen gewissen Handlungsdruck zu erzeugen, aus dem das extremismuspräventive Engagement resultiert.
Untersuchung zur Gegenwarts- und Zukunftsbedeutung der Photovoltaik in der natur- und technisch-wissenschaftlichen Bildung : eine explorative Untersuchung in afrikanischen Ländern
(2025) Dakleu Yewou, Charleine; Zinn, Bernd (Prof. Dr.)
Die Stärkung der MINT-Bildung ist von zentraler Bedeutung für die Entwicklung und Zukunftsfähigkeit afrikanischer Länder. In Anbetracht des hohen Energiebedarfs und der unzureichenden Berücksichtigung von PV in Schulen erforscht dieser Beitrag die Potenziale der PV-Technologie zur Förderung von MINT-Fähigkeiten. Dies betrifft nicht nur Schüler:innen, sondern auch Lehrpersonen und den dringenden Bedarf an nachhaltigen Energielösungen in Afrika. Diesbezüglich besteht das Hauptziel der vorliegenden Dissertationsschrift in der Generierung eines Beschreibungswissens über die Bedeutung der PV-Technologie in Afrika im Kontext der MINT-Bildung. Zur Erreichung dieses Ziels wird der Fokus insbesondere auf die Sekundarschulbildung gelegt und die Untersuchung in sechs Studien gegliedert.
Der empirische Forschungsstand zeigt, dass Inhalte zu PV-Technologie im Bildungsbereich etabliert sind, jedoch nicht explizit im Lehrplan für Sekundarschulen verankert werden. Einige Schulen vermitteln PV-Inhalte, doch aufgrund begrenzter Infrastruktur fehlen praktische Bezüge im Unterricht. Den theoretischen Hintergrund bilden die unterrichtlichen Anforderungen von Klafki, aber auch die Annahmen des Interessenkonstrukts und das Angebots-Nutzungs-Modell werden hinzugezogen. Die Untersuchung setzt ein exploratives Forschungsdesign mit qualitativen und quantitativen Methoden ein, bei dem leitfadengestützte Interviews und Fragebögen verwendet werden, um das PV-Wissen vor, während und nach der Unterrichts-intervention zu erfassen. Die Forschungsergebnisse deuten auf einen Erfolg der Unterrichtsintervention hin. Die Intervention kann in vielfacher Hinsicht dazu beitragen, das Verständnis und die Wahrnehmung der Schüler:innen sowie der Lehrpersonen bezüglich der Gegenwarts- und Zukunftsbedeutung der PV-Technologie nachhaltig und positiv zu beeinflussen. Dies wurde durch praktische Anwendung mit Bezug zu aktuellen Herausforderungen, Interaktion und Diskussion sowie einem Fokus auf zukünftige Beschäftigungsmöglichkeiten, usw. erreicht. Die Bewusstseinsbildung, Interessenweckung und Motivation zur Weiterbildung wurden gestärkt. Die Ergebnisse deuten auf eine Steigerung des Wissensniveaus hin. Die Schüler:innen und Lehrpersonen haben ein gesteigertes Bewusstsein dafür entwickelt, wie bedeutend der Erwerb von MINT-Wissen für den täglichen Einsatz ist. Dies bezieht sich nicht nur auf theoretisches, sondern auch auf praktisches Wissen.
Integrating large language model agents with digital twins for industrial autonomous systems
(2026) Xia, Yuchen; Weyrich, Michael (Prof. Dr.-Ing. Dr. h. c.)
Industrial automation is being reshaped by advances in digitalization and the growing use of cyber-physical systems. Modern production environments demand higher adaptability, faster reconfiguration, and more intuitive system-supported human-machine interaction. These requirements exceed the capabilities of traditional rule-based systems, which are inherently rigid and rely heavily on manually engineered fixed logic. As a result, such systems are unable to autonomously adjust their behavior to accommodate the variability and dynamism of modern production environments.
The central problem addressed in this dissertation is that current industrial automation systems lack a systematic approach for integrating adaptive and generalizable reasoning capabilities. Such reasoning capabilities are required to enable the systems to autonomously interpret, plan, and execute variable user tasks under dynamically changing system conditions and across heterogeneous components.
To address this problem, this dissertation proposes a generalizable three-layer framework that integrates large language models (LLMs), digital twins, and automation systems into an autonomous system. Within this framework, autonomy is conceptualized as a design property that is assigned to system components and enabled by LLM-based reasoning to achieve adaptive and objective-oriented system behavior. Furthermore, the Task–Process–Service–Resource (TPSR) model is introduced as a unified mechanism for transforming user tasks into executable processes. Four functional roles through which LLMs contribute to task automation are identified: process orchestration, service matching, digital resource generation, and agent-as-a-service. Five peer-reviewed studies instantiate and refine these concepts through iterative design cycles based on the design science research methodology.
The developed concepts are demonstrated through case studies and prototype implementations. These demonstrations show that the proposed approach enables adaptive task planning, event-driven control, simulation-based parameterization, and digital model generation. Across these case studies and evaluations, the systems achieve high task executability, command correctness, and content-generation accuracy, automating a substantial portion of the required manual work.
The resulting framework and design knowledge enable the integration of flexible LLM-based reasoning capabilities into industrial automation systems and thereby improve their adaptability and usability, particularly in use cases such as process planning, system control, and information model generation. Limitations arise from the dependency on precise digital representations, the computational demands of LLMs, and the continued need for human intervention in safety-critical conditions. These limitations delineate the boundary conditions of the designed systems and indicate directions for future research.
Design of a freeform uniformity corrector lens for extended sources in elliptical reflectors
(2019) Rausch, Denise; Herkommer, Alois M.
Illumination design usually requires the collection of a large solid angle of radiation from the light source. However, it is known that elliptical reflectors in combination with extended uniform light sources result in a non-uniform irradiance profile at the secondary focus. Within this paper we propose a design method based on phase space transformations, which includes the source extension from the very beginning. We show that an analysis of the local mapping of the source to the target radiance distribution allows a profound understanding of the effects and in consequence a design concept for an additional freeform lens to correct the uniformity at the secondary focus.
Particle swarm optimization for wavefront correction in ophthalmic applications
(2020) Beeck, Andreas; Muckenhirn, Stefan; Herkommer, Alois
Many optical systems require the correction of the cumulative wavefront error of the system for performance optimization. In ophthalmology the wavefront error of the eye corresponds to the visual defect and can be measured up to high-order aberrations today. Lower orders of the wavefront error are usually corrected with spectacles, contact lenses or refractive surgery. In this paper we apply an optimization method called particle swarm optimization to calculate the optimal correction for visual defects based on measured high-order wavefront results. It is shown that an optimized conventional correction and in particular an extended wavefront correction including higher orders yields a better result for the visual Strehl ratio, as compared to a simple conjugate correction scheme.
Electronic features of vacancy, nitrogen, and phosphorus defects in nanodiamonds
(2019) Hertkorn, Jens; Fyta, Maria
Defective nanostructures with a surface termination are the focus of this work. In order to elucidate the influence of the defect on the properties of nanomaterials, we take hydrogen terminated nanodiamonds. Various vacancy defect centers are separately embedded in a nanodiamond at different positions. These include some of the known defects, such as the charged nitrogen-vacancy (NV-), the silicon-vacancy (SiV0), the germanium-vacancy (GeV0), the phosphorous-nitrogen (PN), and the nickel-vacancy (NiV-). For these defective nanodiamonds, we probe the influence of the defect type, its position, as well as the size of the nanodiamond through their structural and electronic features. A detailed and comparative analysis is provided here, based on quantum mechanical simulations. Our results shed light into the inherent differences of these defects in nanodiamonds, allowing for a better understanding of defective nanostructures. In the end, we discuss the potential of tuning their characteristics in view of novel nanotechnological applications.
Exploration of transferable and uniformly accurate neural network interatomic potentials using optimal experimental design
(2021) Zaverkin, Viktor; Kästner, Johannes
Machine learning has been proven to have the potential to bridge the gap between the accuracy of ab initio methods and the efficiency of empirical force fields. Neural networks are one of the most frequently used approaches to construct high-dimensional potential energy surfaces. Unfortunately, they lack an inherent uncertainty estimation which is necessary for efficient and automated sampling through the chemical and conformational space to find extrapolative configurations. The identification of the latter is needed for the construction of transferable and uniformly accurate potential energy surfaces. In this paper, we propose an active learning approach that uses the estimated model’s output variance derived in the framework of the optimal experimental design. This method has several advantages compared to the established active learning approaches, e.g. Query-by-Committee, Monte Carlo dropout, feature and latent distances, in terms of the predictive power and computational efficiency. We have shown that the application of the proposed active learning scheme leads to transferable and uniformly accurate potential energy surfaces constructed using only a small fraction of data points. Additionally, it is possible to define a natural threshold value for the proposed uncertainty metric which offers the possibility to generate highly informative training data on-the-fly.
Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations
(2021) Miksch, April M.; Morawietz, Tobias; Kästner, Johannes; Urban, Alexander; Artrith, Nongnuch
Recent advances in machine-learning interatomic potentials have enabled the efficient modeling of complex atomistic systems with an accuracy that is comparable to that of conventional quantum-mechanics based methods. At the same time, the construction of new machine-learning potentials can seem a daunting task, as it involves data-science techniques that are not yet common in chemistry and materials science. Here, we provide a tutorial-style overview of strategies and best practices for the construction of artificial neural network (ANN) potentials. We illustrate the most important aspects of (a) data collection, (b) model selection, (c) training and validation, and (d) testing and refinement of ANN potentials on the basis of practical examples. Current research in the areas of active learning and delta learning are also discussed in the context of ANN potentials. This tutorial review aims at equipping computational chemists and materials scientists with the required background knowledge for ANN potential construction and application, with the intention to accelerate the adoption of the method, so that it can facilitate exciting research that would otherwise be challenging with conventional strategies.