TY - CHAP A1 - Lockan, Michael A1 - Bestle, Dieter T1 - Robustheitsaspekte in verteilten Entwicklungsumgebungen T2 - 4. Dresdener Probabilistik Workshop, 06.10. - 07.10.2011, Dresden Y1 - 2011 UR - http://www.probabilistik.de/vortrag/v2011_lockan.pdf 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 - 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 - 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 - CHAP A1 - Schulz, Anatol A1 - Bestle, Dieter T1 - Structural Design Process Acceleration by Direct Use of Aerodynamic Q3D-Blading Results and Shell-Elements Y1 - 2009 ER - TY - CHAP A1 - Schulz, Anatol A1 - Bestle, Dieter T1 - Acceleration of Compressor Blade Structural Design Process by Direct Use of Efficient 3D Aerofoil Parameterisation and Shell Elements Y1 - 2010 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 - CHAP A1 - Keskin, Akin A1 - Swoboda, Marius A1 - Flassig, Peter Michael A1 - Dutta, Amit Kumar A1 - Bestle, Dieter T1 - New Optimization Strategy for Rapid industrial Blade Design Y1 - 2009 ER - TY - GEN A1 - Chao, Chen-Xiang A1 - Bestle, Dieter A1 - Krüger, David T1 - Tooth Root Shape Optimization of Thin-Rimmed Planet Gears T2 - Proceedings of ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Volume 10: 2019 International Power Transmission and Gearing Conference N2 - Planetary gearboxes in highly sophisticated applications such as turbofan engines are required to have a high power-to-weight ratio and excellent reliability. Hence, thin-rimmed gear units need to be designed as compact as possible which, however, is usually limited by the tooth root load capacity. In order to come up with the best design, a tooth root shape optimization process is developed for thin-rimmed planet gears. Y1 - 2019 SN - 978-0-7918-5930-8 U6 - https://doi.org/10.1115/DETC2019-97345 ER - TY - GEN A1 - Urbano, Josè A1 - Bestle, Dieter A1 - Gerstberger, Ulf A1 - Flassig, Peter Michael T1 - Low-Order Representation of Manufacturing Variations Based on B-Spline Morphing T2 - ASME 2019 International Mechanical Engineering Congress and Exposition, November 11–14, 2019 Salt Lake City, Utah, USA Y1 - 2020 UR - https://asmedigitalcollection.asme.org/IMECE/proceedings-abstract/IMECE2019/59384/V02BT02A053/1072900 SN - 978-0-7918-5938-4 U6 - https://doi.org/10.1115/IMECE2019-10294 PB - American Society of Mechanical Engineers ER - TY - CHAP A1 - Keskin, Akin A1 - Dutta, Amit Kumar A1 - Bestle, Dieter T1 - Alternative Approach for Solving a Multi-Objective Optimization Problem in Aerodynamic Compressor Blade Design Y1 - 2006 ER - TY - CHAP A1 - Keskin, Akin A1 - Bestle, Dieter T1 - Application of Multi-objective Optimization to Axial Compressor Preliminary Design KW - axial compressor KW - aerodynamic design KW - meanline prediction KW - multi-objective optimization Y1 - 2005 ER - TY - GEN A1 - Amtsfeld, Philipp A1 - Bestle, Dieter A1 - Meyer, Marcus ED - Greiner, David ED - Galván, Blas ED - Periaux, Jacques ED - Gauger, Nicolas R. ED - Giannakoglou, Kyriakos C. ED - Winter, G. T1 - Direct 3D Aerodynamic Optimization of Turbine Blades with GPU-accelerated CFD T2 - Extended Abstracts Book, Evolutionary and Deterministic Methods for Design, Optimization and Control with Application to Industrial and Societal Problems, Las Palmas de Gran Canaria, 7.-9.10.2013 Y1 - 2013 SN - 978-84-616-6249-4 PB - Universidad de Las Palmas de Gran Canaria CY - Las Palmas de Gran Canaria ER - TY - GEN A1 - Amtsfeld, Philipp A1 - Bestle, Dieter T1 - Automatic Aerodynamic Optimisation Process for Turbine Blade Section Design N2 - 9th Automatic Design and Optimisation Seminar (ADOS 2011), 28.09.-29.09.2011, Derby KW - Optimisation Y1 - 2011 ER - TY - JOUR A1 - Bestle, Dieter T1 - Optimization of a Platform with respect to Force Contact Conditions JF - Journal of computational and nonlinear dynamics Y1 - 2008 SN - 1555-1423 VL - 3 IS - 4 ER - TY - GEN A1 - Swoboda, Marius A1 - Huppertz, André A1 - Bestle, Dieter T1 - Multidisziplinary Compressor Blading Design Process using Automation and Multi-Objective Optimization Y1 - 2006 ER - TY - JOUR A1 - Nguyen, Tuan-Anh A1 - Bestle, Dieter T1 - Application of Optimization Methods to Controller Design doe Active Suspensions Y1 - 2007 ER - TY - JOUR A1 - Keskin, Akin A1 - Bestle, Dieter T1 - Application of Multi-objective Optimization to Axial Compressor Preliminary Design JF - Aerospace science and technology Y1 - 2006 SN - 0034-1223 SN - 1270-9638 VL - 10 IS - 7 SP - 581 EP - 589 ER - TY - JOUR A1 - Bestle, Dieter A1 - Eberhard, Peter T1 - Optimization of a Contact Surface N2 - Zhe paper introduces ideas from shape optimization to multibody system dynamics. A disk rolling down a given slope is taken as a simple example, for which it is the goal of the optimization to shape the rolling contour of the disk such that it takes a minimum time to cover a certain distance. The shape of the sontour is described by its radius of curvature. The governing equations of motion result from the kinematics of relative motion and the Nowton-Euler formalism. Three different kinds of spirals are defined and optimized. KW - multibody system KW - contact surface KW - optimization KW - archimedes spiral Y1 - 2003 ER - TY - GEN A1 - Swoboda, Marius A1 - Huppertz, André A1 - Keskin, Akin A1 - Otto, Dierk A1 - Bestle, Dieter T1 - Multidisciplinary Compressor Blading Design Process using Automation and Multi-Objective optimization Y1 - 2006 ER - TY - JOUR A1 - Bestle, Dieter A1 - Wachal, Andrej T1 - Multi-criterion Optimization of a Pneumatic Vibroinsulation System KW - Optimization Y1 - 2005 ER - TY - JOUR A1 - Bestle, Dieter A1 - Zahariev, E. T1 - Virtual Nonlinear Multibody Systems Y1 - 2003 ER - TY - JOUR A1 - Eberhard, Peter A1 - Hu, B. A1 - Bestle, Dieter T1 - Longitudinal Wave Propagation in Multiple Impacted Rods Y1 - 2001 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 -