07 Fakultät Konstruktions-, Produktions- und Fahrzeugtechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/8
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Item Open Access Application of data-driven surrogate models for active human model response prediction and restraint system optimization(2023) Hay, Julian; Schories, Lars; Bayerschen, Eric; Wimmer, Peter; Zehbe, Oliver; Kirschbichler, Stefan; Fehr, JörgSurrogate models are a must-have in a scenario-based safety simulation framework to design optimally integrated safety systems for new mobility solutions. The objective of this study is the development of surrogate models for active human model responses under consideration of multiple sampling strategies. A Gaussian process regression is chosen for predicting injury values based on the collision scenario, the occupant's seating position after a pre-crash movement and selected restraint system parameters. The trained models are validated and assessed for each sampling method and the best-performing surrogate model is selected for restraint system parameter optimization.Item Open Access Towards learning human-seat interactions for optimally controlled multibody models to generate realistic occupant motion(2023) Fahse, Niklas; Harant, Monika; Roller, Michael; Kempter, Fabian; Obentheuer, Marius; Linn, Joachim; Fehr, JörgItem Open Access An improved development process of production plants using digital twins with extended dynamic behaviour in virtual commissioning and control : Simulation@Operations(2023) Pfeifer, Denis; Scheid, Jonas; Kneifl, Jonas; Fehr, JörgThe challenges in automation system development are driven by short development cycles and individualization along with resource‐constraints. State of the art solutions do not provide the necessary digital tools to apply model‐based methods in automation engineering to achieve higher performing systems. To overcome these issues this paper presents a novel approach to address some of the current challenges in automation systems development using digital twins with extended dynamic behaviour. The study underscores how dynamic models can be imported through standardised interfaces into virtual commissioning (VC) tools, improving the development process by effectively utilising domain‐specific expertise. The paper highlights how these digital twins enhance not only the VC process but can also be applied to model‐based control methods. Initial experiments showcase the utility of digital twins in calculating dynamic acceleration limits during trajectory planning of CNC control and enhancing feed‐forward control. Further, the importance of parameter identification in achieving accurate system models is stressed. The initial results are promising, and future work aims to combine these methods in an industrial application involving a newly developed, individual lightweight robot, demonstrating the potential for enhanced design, accelerated development, and resource efficiency in automation systems.Item Open Access Port-Hamiltonian fluid-structure interaction modelling and structure-preserving model order reduction of a classical guitar(2023) Rettberg, Johannes; Wittwar, Dominik; Buchfink, Patrick; Brauchler, Alexander; Ziegler, Pascal; Fehr, Jörg; Haasdonk, BernardItem Open Access Accident simulations of a novel restraint safety concept for motorcyclists(2023) Maier, Steffen; Fehr, JörgItem Open Access Combining knowledge and information - graph-based description of driving scenarios to enable holistic vehicle safety(2023) Bechler, Florian; Fehr, Jörg; Neininger, Fabian; Knöß, Stefan; Grotz, BernhardCurrently, vehicle safety is based on knowledge from injury values, crash pulses, and driving kinematics which leads to intervention strategies separated into isolated domains of active and passive safety. In this contribution, it is shown how vehicle safety can be approached holistically, allowing for human-centered and scenario-based safety decision-making. For this purpose, information from interior and exterior vehicle sensors can be linked by a mathematical framework, combining the knowledge that is already available in the individual domains. A universal graph representation for driving scenarios is developed to master the complexity of driving scenarios and allow for an optimized and scenario-based intervention strategy to minimize occupant injury values. This novel approach allows for the inclusion of sub-models, expert knowledge, results from previous simulations, and annotated databases. The resulting graph can be expanded dynamically for other objects or occupants to reflect all available information to be considered in case of urgency. As input, interior and exterior vehicle sensor data is used. Further information about the driving situation is subsequently derived from this input and the interaction between those states is described by the graph dynamically. For example, occupant attentiveness is derived from measurable eye gaze and eyelid position. From this quantity, reaction time can be estimated in turn. Combined with exterior information, it is possible to decide on the intervention strategy like e.g. alerting the driver. Physical or data-based functional dependencies can be used to represent such interactions. The uncertainties of the inputs and from the surrogate models are included in the graph to ensure a reliable decision-making process. An example of the decision-making process, by modeling the states and actuators as partially observable Markov decision process (POMDP), shows how to optimize the airbag efficiency by influencing the head position prior to an impact. This approach can be extended by additional parameters like driving environment, occupant occupancy, and seating positions in further iterations to optimize the intervention strategy for occupants. The proposed framework integrates scenario-based driving dynamics and existing knowledge from so far separated safety systems with individual activation logic and trigger points to enable holistic vehicle safety intervention strategies for the first time. It lays the foundation to consider new safety hardware, sensor information, and safety functions through a modular, and holistic approach.Item Open Access Effiziente Modellierung flexibler Robotersysteme zur Echtzeitsimulation am Beispiel eines Leichtbauroboters(2025) Hoschek, Sebastian; Rodegast, Philipp; Gesell, Jakob; Scheid, Jonas; Fehr, JörgDie Echtzeitsimulation mechanischer Systeme und deren digitale Zwillinge gewinnen in der Industrie zunehmend an Bedeutung. Sie ermöglichen unter anderem die Optimierung von Steuerungsalgorithmen, die Vorhersage des Systemverhaltens und die Implementierung von Regelstrategien in der Automatisierungstechnik. Ein Industriepartner entwickelt derzeit einen mobilen Leichtbauroboter für den Einsatz im Logistikbereich, bei dem die hohe Flexibilität der Struktur zu elastischen Durchbiegungen führt. Um die Genauigkeit und Leistungsfähigkeit des Roboters zu verbessern, ist eine präzise Modellierung dieser elastischen Effekte erforderlich. In dieser Arbeit werden zwei verschiedene Modellierungsansätze für die Echtzeitsimulation untersucht. Der erste basiert auf einer physikalischen White-Box-Modellierung als flexibles Mehrkörpersystem, wobei ein klassisches Finite-Elemente-Modell (FEM) durch Modellordnungsreduktion vereinfacht wird, um eine effiziente Berechnung zu ermöglichen. Der zweite Ansatz verwendet ein Finite-Segmente-Modell, das eine Parameteridentifikation erfordert, um eine realitätsgetreue Abbildung des Systemverhaltens zu gewährleisten. Beide Methoden werden auf den Leichtbauroboter angewendet und hinsichtlich ihrer Vor- und Nachteile verglichen. Wesentliche Kriterien sind dabei der Modellierungsaufwand, die Berechnungsgeschwindigkeit und die Genauigkeit der Simulationsergebnisse. Die Ergebnisse liefern eine Entscheidungsgrundlage zur Auswahl geeigneter Modellierungsmethoden in Echtzeitanwendungen.Item Open Access Improved a posteriori error bounds for reduced port-Hamiltonian systems(2024) Rettberg, Johannes; Wittwar, Dominik; Buchfink, Patrick; Herkert, Robin; Fehr, Jörg; Haasdonk, BernardProjection-based model order reduction of dynamical systems usually introduces an error between the high-fidelity model and its counterpart of lower dimension. This unknown error can be bounded by residual-based methods, which are typically known to be highly pessimistic in the sense of largely overestimating the true error. This work applies two improved error bounding techniques, namely (a) a hierarchical error bound and (b) an error bound based on an auxiliary linear problem , to the case of port-Hamiltonian systems. The approaches rely on a secondary approximation of (a) the dynamical system and (b) the error system. In this paper, these methods are adapted to port-Hamiltonian systems. The mathematical relationship between the two methods is discussed both theoretically and numerically. The effectiveness of the described methods is demonstrated using a challenging three-dimensional port-Hamiltonian model of a classical guitar with fluid–structure interaction.Item Open Access Low-dimensional data-based surrogate model of a continuum-mechanical musculoskeletal system based on non-intrusive model order reduction(2023) Kneifl, Jonas; Rosin, David; Avci, Okan; Röhrle, Oliver; Fehr, JörgOver the last decades, computer modeling has evolved from a supporting tool for engineering prototype design to an ubiquitous instrument in non-traditional fields such as medical rehabilitation. This area comes with unique challenges, e.g. the complex modeling of soft tissue or the analysis of musculoskeletal systems. Conventional modeling approaches like the finite element (FE) method are computationally costly when dealing with such models, limiting their usability for real-time simulation or deployment on low-end hardware, if the model at hand cannot be simplified without losing its expressiveness. Non-traditional approaches such as surrogate modeling using data-driven model order reduction are used to make complex high-fidelity models more widely available regardless. They often involve a dimensionality reduction step, in which the high-dimensional system state is transformed onto a low-dimensional subspace or manifold, and a regression approach to capture the reduced system behavior. While most publications focus on one dimensionality reduction, such as principal component analysis (PCA) (linear) or autoencoder (nonlinear), we consider and compare PCA, kernel PCA, autoencoders, as well as variational autoencoders for the approximation of a continuum-mechanical system. In detail, we demonstrate the benefits of the surrogate modeling approach on a complex musculoskeletal system of a human upper-arm with severe nonlinearities and physiological geometry. We consider both, the model’s deformation and the internal stress as the two main quantities of interest in a FE context. By doing so we are able to create computationally low-cost surrogate models which capture the system behavior with high approximation quality and fast evaluations.Item Open Access Efficient simulation strategy to design a safer motorcycle(2023) Maier, Steffen; Fehr, JörgThis work presents models and simulations of a numerical strategy for a time and cost-efficient virtual product development of a novel passive safety restraint concept for motorcycles. It combines multiple individual development tasks in an aggregated procedure. The strategy consists of three successive virtual development stages with a continuously increasing level of detail and expected fidelity in multibody and finite element simulation environments. The results show what is possible with an entirely virtual concept study - based on the clever combination of multibody dynamics and nonlinear finite elements - that investigates the structural behavior and impact dynamics of the powered two-wheeler with the safety systems and the rider’s response. The simulations show a guided and controlled trajectory and deceleration of the motorcycle rider, resulting in fewer critical biomechanical loads on the rider compared to an impact with a conventional motorcycle. The numerical research strategy outlines a novel procedure in virtual motorcycle accident research with different levels of computational effort and model complexity aimed at a step-by-step validation of individual components in the future.