TY - GEN A1 - Bergold, Markus A1 - Brandes, S. A1 - Seufert, B. A1 - Bestle, Dieter T1 - Simulation von Kundenmanövern zur Ermittlung repräsentativer Fahrwerksbelastungen T2 - Lastannahmen und Anforderungsmanagement in der Betriebsfestigkeit : neue Trends : 46. Tagung des Arbeitskreises Betriebsfestigkeit, 09. und 10. Oktober 2019 in Wolfsburg Y1 - 2019 UR - https://dvm-berlin.de/sites/default/files/files/downloads/dvmakbf2019prgneu20190626.pdf SP - 99 EP - 114 PB - DVM, Deutscher Verband für Materialforschung und -prüfung e.V. CY - Berlin ER - TY - GEN A1 - Chao, Chen-Xiang A1 - Bestle, Dieter A1 - Krüger, David T1 - Isight Process for Optimizing Tooth Roots in a High-Performance Gearbox T2 - 3DExperience Conference Germany 2019At: Darmstadt, Germany Y1 - 2019 UR - https://events.3ds.com/sites/default/Files/3dexperience-conference-chao-tu-cottbus-senftenberg.pdf UR - https://www.researchgate.net/publication/341152054_Isight_Process_for_Optimizing_Tooth_Roots_in_a_High-Performance_Gearbox CY - Darmstadt ER - TY - GEN A1 - Martin, Ivo A1 - Hartwig, Lennard A1 - Bestle, Dieter T1 - A multi-objective optimization framework for robust axial compressor airfoil design T2 - Structural and Multidisciplinary Optimization Y1 - 2019 U6 - https://doi.org/10.1007/s00158-018-2164-3 SN - 1615-1488 SN - 1615-147X VL - 59 IS - 6 SP - 1935 EP - 1947 ER - TY - GEN A1 - Kuslits, Márton A1 - Bestle, Dieter T1 - Modeling and Control of a New Differential Steering Concep T2 - Vehicle System Dynamics Y1 - 2019 U6 - https://doi.org/10.1080/00423114.2018.1473616 SN - 1744-5159 SN - 0042-3114 VL - 57 IS - 4 SP - 520 EP - 542 ER - TY - GEN A1 - Chao, Chen-Xiang A1 - Bestle, Dieter A1 - Krüger, David A1 - Meissner, Bernd T1 - Zahnfußtragfähigkeitsoptimierung von Stirnrädern für den Einsatz in Hochleistungsgetrieben mithilfe von Isight T2 - Tagungsband Simulia Anwenderkonferenz, DASSAULT SYSTÈMES User Conference, Hanau, 4.-6. Dezember 2018 Y1 - 2018 UR - https://www.3ds.com/events/dassault-systemes-user-conferences/dassault-systemes-user-conference-germany-2018/download-archive/ UR - https://www.3ds.com/fileadmin/EVENTS/3DS-Events-img/3DS_USER_CONFERENCE_GER_2019/DownloadArchive/2018-DSUC-Chao-UNI-Senftenberg.pdf CY - Hanau ER - TY - GEN A1 - Kuslits, Márton A1 - Bestle, Dieter T1 - Control Design and Performance Evaluation of a New Steering Concept Based on a Custom Multibody Vehicle Model T2 - 5th Internationa Conference on Dynamic Simulation in Vehicle Engineering Y1 - 2018 UR - file:///C:/Users/merker/AppData/Local/Temp/3_Kuslits.pdf SP - 19 EP - 34 CY - Steyr ER - TY - GEN A1 - Xue, Rui A1 - Bestle, Dieter A1 - Wang, Guoping A1 - Zhang, Jiangshu A1 - Rui, Xiaoting A1 - He, Bin T1 - A New Version of the Riccati Transfer matrix Method for Multibody systems Consisting of Chain and Branch Bodies T2 - Multibody System Dynamics Y1 - 2020 U6 - https://doi.org/10.1007/s11044-019-09711-2 SN - 1573-272X SN - 1384-5640 VL - 49 IS - 3 SP - 337 EP - 354 ER - TY - GEN A1 - Kuslits, Márton A1 - Bestle, Dieter T1 - Multiobjective Performance Optimisation of a New Differential Steering Concept T2 - Vehicle System Daynamics Y1 - 2020 U6 - https://doi.org/10.1080/00423114.2020.1804598 SN - 0042-3114 SN - 1744-5159 ER - TY - RPRT A1 - Bestle, Dieter T1 - Verbesserter Systementwurf durch KI-Methoden T2 - 3. VDI-Fachtagung Schwingungen 2021 : Würzburg, 16. und 17. November 2021 Y1 - 2021 SN - 978-3-18-092391-8 SP - 14 PB - VDI-Verlag CY - Würzburg ER - TY - GEN A1 - Bestle, Dieter T1 - Eigenvalue Sensitivity Analysis based on the Transfer Matrix Method T2 - International journal of mechanical system dynamics : IJMSD Y1 - 2021 U6 - https://doi.org/10.1002/msd2.12016 SN - 2767-1402 VL - 1 IS - 1 SP - 96 EP - 107 ER - TY - GEN A1 - Rui, Xue A1 - Bestle, Dieter T1 - Reduced Multibody System Transfer Matrix Method Using Decoupled Hinge Equations T2 - International journal of mechanical system dynamics : IJMSD Y1 - 2021 U6 - https://doi.org/10.1002/msd2.12026 SN - 2767-1402 VL - 1 IS - 2 SP - 182 EP - 193 ER - TY - GEN A1 - Zhou, Qinbo A1 - Fehr, Jörg A1 - Bestle, Dieter A1 - Rui, Xiaoting T1 - Simulation of Elastic Body Dynamics with Large Motion Using Transfer Matrix Method T2 - Multibody System Dynamics Y1 - 2022 U6 - https://doi.org/10.1007/s11044-022-09869-2 SN - 1573-272X SN - 1384-5640 VL - 59 (2023) IS - 3 SP - 269 EP - 292 ER - TY - GEN A1 - de Payrebrune, Kristin A1 - Flaßkamp, Kathrin A1 - Ströhla, Tom A1 - Sattel, Thomas A1 - Bestle, Dieter A1 - al, et T1 - The impact of Al on engineering design procedures for dynamical systems T2 - Electrical Engineering and Systems Science. Systems and Control Y1 - 2024 U6 - https://doi.org/10.48550/arXiv.2412.12230 PB - Cornell University ER - TY - GEN A1 - Zhai, Tianyu A1 - Bestle, Dieter T1 - Design of two coupled fuzzy controllers for a planar direct internat reforming solid oxide fuel cell T2 - Proceedings in Applied Mathematics and Mechanics Y1 - 2024 U6 - https://doi.org/10.1002/pamm.202400077 VL - 24 IS - 4 PB - Wiley-VCH GmbH ER - TY - CHAP A1 - Bestle, Dieter ED - Nachbagauer, Karin ED - Held, Alexander T1 - Optimization processes for automated design of industrial systems T2 - Optimal design and control of multibody systems : proceedings of the IUTAM symposium Y1 - 2024 SN - 978-3-031-49999-9 U6 - https://doi.org/10.1007/978-3-031-50000-8_1 SN - 1875-3507 SP - 3 EP - 15 PB - Springer CY - Hamburg ER - TY - GEN A1 - Kayatas, Zafer A1 - Bestle, Dieter T1 - Scenario identification and classification to support the assessment of advanced driver assistance systems T2 - Applied Mechanics N2 - In recent years, driver assistance systems in cars, buses, and trucks have become more common and powerful. In particular, the introduction of AI methods to sensors, signal fusion, and traffic recognition allows us to step forward from actual level-2 assistance to level-3 A dvanced D river A ssistance S ystems (ADAS), where driving becomes autonomous and responsibility shifts from the driver to the automobile manufacturers. This, however, requires a high-precision risk assessment of failure, which can only be achieved by extensive data acquisition and statistical analysis of real traffic scenarios (which is impossible to perform by humans). Therefore, critical driving situations have to be identified and classified automatically. This paper develops and compares two different strategies—a traditional rule-based approach derived from deterministic causal considerations, and an AI-based approach trained with idealized cut-in, cut-out, and cut-through maneuvers. Application to a 10-h measurement sequence on a German highway demonstrates that the latter has the higher performance, whereas the former misses some of the safety-relevant events to be identified. KW - advanced driver assistance system KW - real traffic situation KW - decision tree KW - machine learning KW - scenario identification KW - scenario classification Y1 - 2024 U6 - https://doi.org/10.3390/applmech5030032 SN - 2673-3161 VL - 5 IS - 3 SP - 563 EP - 578 PB - MDPI ER - TY - GEN A1 - Bielitz, Timo A1 - Bestle, Dieter T1 - Artificial recurrent model for parameter identification of dynamic systems T2 - Proceedings in Applied Mathematics & Mechanics Y1 - 2024 U6 - https://doi.org/10.1002/pamm.202400015 SN - 1617-7061 PB - Wiley-VCH GmbH ER - TY - GEN A1 - Bestle, Dieter A1 - Bielitz, Timo T1 - Real‐time models for systems with costly or unknown dynamics T2 - Special Issue: 94th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM) N2 - Many applications require real‐time simulation where the computer model can be executed at least as fast as the underlying physical system. However, as the complexity of models grows, for example, in case of flexible multibody dynamics accounting for body elasticity, this becomes harder even with high‐end computers and parallel computing. Further the modeling itself could be too cumbersome to come up with causal models, especially in industrial applications, or modeling is inhibited by unknown physical effects. In such cases, data‐based modeling with AI‐strategies may be an alternative to come up with real‐time capable simulation models. Based on measured or costly simulated trajectories, state function values of the dynamic system are first identified from divided differences of subsequent states, and then used for training a feedforward neural network. This can then be used as an approximate surrogate for further simulations or as part of a real‐time application. Applications to the Duffing equation and a closed‐loop mechanism demonstrate high conformity of predicted and directly simulated trajectories reproducing even the bifurcation behavior of the Duffing equation. Y1 - 2024 U6 - https://doi.org/10.1002/pamm.202400008 SN - 1617-7061 VL - 24 IS - 2 ER - TY - GEN A1 - Bielitz, Timo A1 - Bestle, Dieter T1 - Identification of dynamic systems assisted by an autoregressive recurrent model T2 - Proceedings in Applied Mathematics & Mechanics : PAMM N2 - The identification of parameters in dynamic systems usually first requires modeling the system as nonlinear differential equations based on physical principles, which then can be evaluated to search for a set of optimal parameters that enable the mathematical model to reproduce some desired behavior of the real system. To overcome the necessity of complex modeling and to possibly reduce the number of extensive experimental evaluations of the real system or numerical evaluations of the differential equations, strategies from machine learning may be applied. The proposed method uses recurrent architectures such as Long Short-Term Memory (LSTM) networks as core to predict the system development from initial conditions and parameter values. The trainable weights of the model are optimized based on a set of training data containing parameter values and corresponding solution trajectories generated by evaluation of the system to be investigated. The trained model may then be used to identify unknown system parameter values related to a specific solution trajectory by solving an optimization problem for the inputs of the machine learning model. Y1 - 2023 U6 - https://doi.org/10.1002/pamm.202300086 SN - 1617-7061 VL - 23 IS - 2 ER - TY - GEN A1 - Niehoff, Malte A1 - Bestle, Dieter A1 - Kupijai, Philipp T1 - Model-based design optimization taking into account design viability via classification T2 - Engineering Modelling, Analysis and Simulation : the NAFEMS Journal Y1 - 2024 U6 - https://doi.org/10.59972/c7b5hzx7 SN - 3005-9763 VL - 1 IS - 1 SP - 1 EP - 12 PB - NAFEMS Ltd. CY - Knutsford ER - TY - GEN A1 - Mammen, Manasa Mariam A1 - Kayatas, Zafer A1 - Bestle, Dieter T1 - Evaluation of different generative models to support the validation of advanced driver assistance systems T2 - Applied mechanics N2 - Validating the safety and reliability of automated driving systems is a critical challenge in the development of autonomous driving technology. Such systems must reliably replicate human driving behavior across scenarios of varying complexity and criticality. Ensuring this level of accuracy necessitates robust testing methodologies that can systematically assess performance under various driving conditions. Scenario-based testing addresses this challenge by recreating safety-critical situations at varying levels of abstraction, from simulations to real-world field tests. However, conventional parameterized models for scenario generation are often resource intensive, prone to bias from simplifications, and limited in capturing realistic vehicle trajectories. To overcome these limitations, the paper explores AI-based methods for scenario generation, with a focus on the cut-in maneuver. Four different approaches are trained and compared: Variational Autoencoder enhanced with a convolutional neural network (VAE), a basic Generative Adversarial Network (GAN), Wasserstein GAN (WGAN), and Time-Series GAN (TimeGAN). Their performance is assessed with respect to their ability to generate realistic and diverse trajectories for the cut-in scenario using qualitative analysis, quantitative metrics, and statistical analysis. Among the investigated approaches, VAE demonstrates superior performance, effectively generating realistic and diverse scenarios while maintaining computational efficiency. KW - Advanced driver assistance system KW - Real cut-in maneuver KW - Synthetic data KW - Variational autoencoder KW - Generative adversarial networks KW - Machine learning Y1 - 2025 U6 - https://doi.org/10.3390/applmech6020039 SN - 2673-3161 VL - 6 IS - 2 SP - 1 EP - 26 PB - MDPI CY - Basel ER - TY - GEN A1 - Röder, Benedict A1 - Hajipour, Sanam A1 - Ebel, Henrik A1 - Eberhard, Peter A1 - Bestle, Dieter T1 - Automated design of a four-bar mechanism starting from hand drawings of desired coupler trajectories and velocity profiles T2 - Mechanics based design of structures and machines N2 - When a specific, relatively simple motion must be executed with high precision and high speed, simple mechanisms like the four-bar linkage are often the first choice. Combined with control of the crank speed, these systems are able to move the end-effector with any changing speed. Designing a certain mechanism for a pre-defined trajectory, however, usually requires an expert, which is why automatic design by a design assistant system is desirable. Modern AI methods may support such an automatic design, which shall function only based on hand drawings of the desired path and a corresponding velocity profile. For a planar four-bar mechanism, which serves only as an example, this article proposes a general data-driven procedure where first the geometry of a mechanism is synthesized by machine learning such that the track point follows approximately the desired trajectory. Then, the kinematic relation between the track point’s velocity profile and the crank’s angular velocity is learned. This learned inverse kinematics model may be applied to an arbitrary user-defined velocity profile of the track point. Finally, a controller governing the crank torque is designed purely based on data to track the resulting angular crank velocity. Not every drawn motion can necessarily be realized with a mechanism; however, the goal is to identify a mechanism that approximates the drawn curve as accurately as possible. While various design toolboxes already exist, especially for the design of four-bar linkages, this article presents a design pipeline using data-driven approaches that solely learn from forward simulation data. This nurtures the hope that presented approach is also applicable to more complex systems where analytic formulations cannot be easily derived. KW - Design assistant KW - Mechanism design KW - Four-bar linkage KW - Inverse kinematics KW - Machine learning KW - Tracking control Y1 - 2025 U6 - https://doi.org/10.1080/15397734.2025.2543559 SN - 1539-7742 VL - 2025 SP - 1 EP - 25 PB - Taylor & Francis CY - Philadelphia, Pa. ER - TY - GEN A1 - Henkel, Clara A1 - Bestle, Dieter A1 - Flassig, Peter A1 - Janke, Christian A1 - Slaby, Michael T1 - Consideration of balancing within the fan-blisk design process T2 - GPPS Shanghai25 Technical Paper Proceedings N2 - The design of fan-blisks is a multi-criterion optimisation challenge primarily involving the aerodynamic shape optimisation of blade profiles and subsequent blade balancing, i.e., shifting profiles in axial and circumferential directions to avoid stress hotspots. Although it is already known that blade balancing affects aerodynamic properties, this correlation is often not taken into account, which is why it is usually performed as an independent step after solving the aerodynamic design problem. However, this assumption is questionable because such shifts should be used to control both secondary flow effects and resulting stress. Therefore, the paper proposes problem formulations combining both aspects. Since optimisation requires costly numerical evaluations, machine learning methods are investigated to predict aerodynamic performance and stress constraints more efficiently. This enables a global optimisation process by reducing computational costs. Various different surrogate types are investigated, where stress constraints are formulated either as regression task predicting stress maxima, or as classification problem directly assessing design feasibility. Y1 - 2025 UR - https://gpps.global/gpps-shanghai25-proceedings/ U6 - https://doi.org/10.33737/gpps25-tc-017 SP - 1 EP - 9 PB - Global Power and Propulsion Society (GPPS) CY - Zug, Schweiz ER - TY - GEN A1 - Bestle, Dieter A1 - Hajipour, Sanam T1 - Design of a tracking controller based on machine learning T2 - International journal of mechanical system dynamics N2 - Tracking control of multibody systems is a challenging task requiring detailed modeling and control expertise. Especially in the case of closed-loop mechanisms, inverse kinematics as part of the controller may become a game stopper due to the extensive calculations required for solving nonlinear equations and inverting complicated functions. The procedure introduced in this paper substitutes such advanced human expertise by artificial intelligence through the utilization of surrogates, which may be trained from data obtained by classical simulation. The necessary steps are demonstrated along a parallel mechanism called λ-robot. Based on its mechanical model, the workspace is investigated, which is required to set proper initial conditions for generating data covering the used operation space of the robot. Based on these data, artificial neural networks are trained as surrogates for inverse kinematics and inverse dynamics. They provide forward control information such that the remaining error behavior is governed by a linear ordinary differential equation, which allows applying a linear quadratic regulator (LQR) from linear control theory. An additional feedback loop of the tracking error accounts for model uncertainties. Simulation results validate the applicability of the proposed concept. KW - Artificial neural network KW - Inverse dynamics KW - Inverse kinematics KW - Machine learning KW - Tracking control Y1 - 2025 U6 - https://doi.org/10.1002/msd2.70006 SN - 2767-1402 VL - 5 / 2025 IS - 2 SP - 201 EP - 211 PB - Wiley CY - Hoboken, NJ ER - TY - GEN A1 - Mammen, Manasa Mariam A1 - Kayatas, Zafer A1 - Bestle, Dieter T1 - Generation of multiple types of driving scenarios with variational autoencoders for autonomous driving T2 - Future transportation N2 - Generating realistic and diverse driving scenarios is essential for effective scenario-based testing and validation in autonomous driving and the development of driver assistance systems. Traditionally, parametric models are used as standard approaches for scenario generation, but they require detailed domain expertise, suffer from scalability issues, and often introduce biases due to idealizations. Recent research has demonstrated that AI models can generate more realistic driving scenarios with reduced manual effort. However, these models typically focused on single scenario types, such as cut-in maneuvers, which limits their applicability to diverse real-world driving situations. This paper, therefore, proposes a unified generative framework that can simultaneously generate multiple types of driving scenarios, including cut-in, cut-out, and cut-through maneuvers from both directions, thus covering six distinct driving behaviors. The model not only learns to generate realistic trajectories but also reflects the same statistical properties as observed in real-world data, which is essential for risk assessment. Comprehensive evaluations, including quantitative metrics and visualizations from detailed latent and physical space analyses, demonstrate that the unified model achieves comparable performance to individually trained models. The shown approach reduces modeling complexity and offers a scalable solution for generating diverse, safety-relevant driving scenarios, supporting robust testing and validation. KW - Automated driving KW - Variational autoencoder KW - Unified generative model KW - Cut-in KW - Cut-out KW - Cut-through Y1 - 2025 U6 - https://doi.org/10.3390/futuretransp5040159 SN - 673-7590 VL - 5 IS - 4 SP - 1 EP - 20 PB - MDPI CY - Basel ER - TY - GEN A1 - Zhai, Tianyu A1 - Bestle, Dieter T1 - Design of nonlinear model predictive control for planar direct internal reforming solid oxide fuel cell stacks T2 - 2025 44th Chinese Control Conference (CCC) N2 - The paper proposes an optimization-based nonlinear model predictive control (NMPC) strategy for the operation of a planar direct internal reforming solid oxide fuel cell (DIR-SOFC). The strategy focuses on regulating the inlet fuel and air flowrates to meet power demands, maintain the design temperature, and adhere to other operational constraints. A state estimator and optimizer are employed. Control and prediction horizons, as well as the time step lengths are carefully selected to balance estimation accuracy, stability, and computational cost. The obtained results demonstrate the effectiveness of NMPC in managing large-scale energy supply systems. Comparative analyses with varying tuning weights demonstrate the importance of appropriate weight selection regarding control performance and the robustness of NMPC. Both the dynamic modeling of the planar DIR-SOFC and the NMPC framework are implemented in the MATLAB/Simulink environment. KW - Model Predictive Control KW - Solid Oxide Fuel Cell KW - Hybrid Energy Supply Y1 - 2025 SN - 978-988-75816-1-1 U6 - https://doi.org/10.23919/CCC64809.2025.11179672 SP - 3093 EP - 3098 PB - IEEE CY - Piscataway, NJ ER - TY - GEN A1 - Hajipour, Sanam A1 - Zhai, Tianyu A1 - Bestle, Dieter T1 - Data-driven sliding mode control of a rarallel 2D-robot T2 - 2025 8th International Conference on Intelligent Robotics and Control Engineering (IRCE) N2 - The paper introduces an easy-to-implement control strategy for a parallel 2 D robot. To avoid any analytic computations of multibody system dynamics and kinematics of the closed-loop mechanism, two feedforward networks are utilized as surrogates for inverse models. The necessary data are generated using classical simulation starting from random initial conditions and applying random force to cover the full state space used during control. Closed-loop stability of the lambda robot is guaranteed by extending the forward control based on inverse dynamics by a sliding mode controller with super-twisting structure. This approach results in an error decay to very low magnitude orders as an investigation of the tracking performance demonstrating high efficiency and control accuracy even when the plant parameters are disturbed. KW - Sliding mode control KW - Inverse model KW - Machine learning KW - Neural network KW - Super-twisting algorithm Y1 - 2025 SN - 979-8-3503-5710-3 U6 - https://doi.org/10.1109/irce66030.2025.11203089 SP - 75 EP - 80 PB - IEEE CY - Piscataway, NJ ER - TY - GEN A1 - Hajipour, Sanam A1 - Bestle, Dieter T1 - Surrogate-based robust tracking controller for a Lambda robot T2 - Applied mathematics and mechanics : special issue : 95th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM) N2 - Tracking control of multibody systems typically requires precise inversion of complex kinematic and dynamic equations, which can be particularly challenging for closed-loop mechanisms. This study presents a surrogate-based control approach that leverages artificial neural networks to approximate inverse kinematics and dynamics using data generated from classical simulations of a nominal model. These data-driven surrogates enable feedforward prediction of joint trajectories and control inputs without requiring analytical model inversion. To ensure robustness under modeling errors and parametric uncertainties, additional inner- and outer-loop sliding mode control (SMC) loops are integrated. Simulation results from a lambda robot demonstrate high tracking accuracy with asymptotic steady-state errors even under perturbations of system parameters. The proposed framework establishes a structured approach for trajectory tracking by combining data-driven feedforward and robust feedback control suitable for complex nonlinear robotic systems. Y1 - 2025 U6 - https://doi.org/10.1002/pamm.70017 VL - 25 IS - 3 SP - 1 EP - 8 PB - Wiley-VCH CY - Weinheim ER -