07 Fakultät Konstruktions-, Produktions- und Fahrzeugtechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/8

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Author: search.filters.author.Fehr, JörgSubject: search.filters.subject.620

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    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, Jörg
    Surrogate 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.
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    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, Jörg
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    On the validation of human body models with a driver-in-the-loop simulator
    (2018) Kempter, Fabian; Fehr, Jörg; Stutzig, Norman; Siebert, Tobias
    For the development of modern integrated safety systems, standard simulation models of anthropometric test devices, often called crash test dummies, are inappropriate for Pre-Crash investigations due to missing activation possibilities, tuned characteristics for one specific accident scenario and high passive stiffness properties. To validate safety concepts getting active prior to the crash new tools like suitable virtual models of human occupants are required. Human Body Models (HBM) provide a higher biofidelity and can be equipped with active muscle elements enabling different muscle activation strategies. To improve the muscle activation strategy and the stiffness properties of active HBMs, validation processes on the basis of low-acceleration experiments are inevitable. In contrast to Post Mortem Human Surrogates only low-severity tests can be performed with real human subjects. This paper presents the workflow of a validation process based on an academic scale Driver-in-the-Loop (DiL) simulator in combination with a synchronized measurement chain consisting of an Optitrack stereo vision and an electromyography detection system.
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    Finite element simulations of motorcyclist interaction with a novel passive safety concept for motorcycles
    (2021) Maier, Steffen; Doléac, Laurent; Hertneck, Holger; Stahlschmidt, Sebastian; Fehr, Jörg
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    A non-intrusive nonlinear model reduction method for structural dynamical problems based on machine learning
    (2020) Kneifl, Jonas; Grunert, Dennis; Fehr, Jörg
    The paper uses a nonlinear non-intrusive model reduction approach, to derive efficient and accurate surrogate models for structural dynamical problems. Therefore, a combination of proper orthogonal decomposition along with regression algorithms from the field of machine learning is utilized to capture the dynamics in a reduced representation. This allows highly performant approximations of the original system. In this context, we provide a comparison of several regression algorithms based on crash simulations of a structural dynamic frame.
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    Well-scaled, a-posteriori error estimation for model order reduction of large second-order mechanical systems
    (2019) Grunert, Dennis; Fehr, Jörg; Haasdonk, Bernard
    Model Order Reduction is used to vastly speed up simulations but it also introduces an error to the simulation results, which needs to be controlled. The performance of the general to use, a-posteriori error estimator of Ruiner et al. for second-order systems is analyzed and a bottleneck is found in the offline stage making it unusable for larger models. We use the spectral theorem, power series expansions, monotonicity properties, and self-tailored algorithms to speed up the offline stage largely by one polynomial order both in terms of computation time as well as storage complexity. All properties are proven rigorously. This eliminates the aforementioned bottleneck. Hence, the error estimator of Ruiner et al. can finally be used for large, linear, second-order mechanical systems reduced by any model reduction method based on Petrov-Galerkin reduction. The examples show speedups of up to 28.000 and the ability to compute much larger systems with a fixed amount of memory.
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    Evaluation of a novel passive safety concept for motorcycles with combined multi-body and finite element simulations
    (2020) Maier, Steffen; Doléac, Laurent; Hertneck, Holger; Stahlschmidt, Sebastian; Fehr, Jörg
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    Implementation and validation of the extended Hill-type muscle model with robust routing capabilities in LS-DYNA for active human body models
    (2017) Kleinbach, Christian; Martynenko, Oleksandr; Promies, Janik; Häufle, Daniel F. B.; Fehr, Jörg; Schmitt, Syn
    In the state of the art finite element AHBMs for car crash analysis in the LS-DYNA software material named *MAT_MUSCLE (*MAT_156) is used for active muscles modeling. It has three elements in parallel configuration, which has several major drawbacks: restraint approximation of the physical reality, complicated parameterization and absence of the integrated activation dynamics. This study presents implementation of the extended four element Hill-type muscle model with serial damping and eccentric force-velocity relation including Ca2+ dependent activation dynamics and internal method for physiological muscle routing.
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    Modeling, simulation, and vision-/MPC-based control of a PowerCube serial robot
    (2020) Fehr, Jörg; Schmid, Patrick; Schneider, Georg; Eberhard, Peter
    A model predictive control (MPC) scheme for a Schunk PowerCube robot is derived in a structured step-by-step procedure. Neweul-M² provides the necessary nonlinear model in symbolical and numerical form. To handle the heavy online computational burden concerning the derived nonlinear model, a linear time-varying MPC scheme is developed based on linearizing the nonlinear system concerning the desired trajectory and the a priori known corresponding feed-forward controller. Camera-based systems allow sensing of the robot on the one hand and monitoring the environments on the other hand. Therefore, a vision-based MPC is realized to show the effects of vision-based control feedback on control performance. A semi-automatic trajectory planning is used to perform two meaningful experimental studies in which the advantages and restrictions of the proposed (vision-based) linear time-varying MPC scheme are pointed out. Everything is implemented on a slim, low-cost control system with a standard laptop PC.
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    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, Jörg
    The 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.