@misc{MayntzKeimerTegtmeyeretal.2021, author = {Mayntz, Joscha and Keimer, Jona and Tegtmeyer, Philipp and Dahmann, Peter and Hille, Sebastian and Stumpf, Eike and Fisher, Alex and Dorrington, Graham}, title = {Aerodynamic Investigation on Efficient Inflight Transition of a Propeller from Propulsion to Regeneration Mode}, series = {AIAA SCITECH 2022 Forum}, journal = {AIAA SCITECH 2022 Forum}, publisher = {AIAA}, address = {Reston, Va.}, doi = {10.2514/6.2022-0546}, year = {2021}, abstract = {This paper discusses a new way of inflight power regeneration for electric or hybrid-electric driven general aviation aircraft with one powertrain for both configurations. Three different approaches for the shift from propulsion to regeneration mode are analyzed. Numerical cal-culation and wind tunnel results are compared and show the highest regeneration potential for the "Windmill" approach, where the propeller blades are flipped, and rotation is reversed. A combination of all regeneration approaches for a realistic flight mission is discussed.}, language = {en} } @article{SchwagerFleschSchwarzboezletal.2022, author = {Schwager, Christian and Flesch, Robert and Schwarzb{\"o}zl, Peter and Herrmann, Ulf and Teixeira Boura, Cristiano Jos{\´e}}, title = {Advanced two phase flow model for transient molten salt receiver system simulation}, series = {Solar Energy}, volume = {232}, journal = {Solar Energy}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0038-092X (print)}, doi = {10.1016/j.solener.2021.12.065}, pages = {362 -- 375}, year = {2022}, abstract = {In order to realistically predict and optimize the actual performance of a concentrating solar power (CSP) plant sophisticated simulation models and methods are required. This paper presents a detailed dynamic simulation model for a Molten Salt Solar Tower (MST) system, which is capable of simulating transient operation including detailed startup and shutdown procedures including drainage and refill. For appropriate representation of the transient behavior of the receiver as well as replication of local bulk and surface temperatures a discretized receiver model based on a novel homogeneous two-phase (2P) flow modelling approach is implemented in Modelica Dymola®. This allows for reasonable representation of the very different hydraulic and thermal properties of molten salt versus air as well as the transition between both. This dynamic 2P receiver model is embedded in a comprehensive one-dimensional model of a commercial scale MST system and coupled with a transient receiver flux density distribution from raytracing based heliostat field simulation. This enables for detailed process prediction with reasonable computational effort, while providing data such as local salt film and wall temperatures, realistic control behavior as well as net performance of the overall system. Besides a model description, this paper presents some results of a validation as well as the simulation of a complete startup procedure. Finally, a study on numerical simulation performance and grid dependencies is presented and discussed.}, language = {en} } @inproceedings{NowackRoethBuehrigPolaczeketal.2008, author = {Nowack, N. and R{\"o}th, Thilo and B{\"u}hrig-Polaczek, A. and Klaus, G.}, title = {Advanced Sheet Metal Components Reinforced by Light Metal Cast Structures}, series = {Aluminium alloys : their physical and mechanical properties ; [proceedings of the 11th International Conference on Aluminium Alloys, 22 - 26 Sept. 2008, Aachen, Germany ; ICAA 11]}, booktitle = {Aluminium alloys : their physical and mechanical properties ; [proceedings of the 11th International Conference on Aluminium Alloys, 22 - 26 Sept. 2008, Aachen, Germany ; ICAA 11]}, number = {2}, editor = {Hirsch, J{\"u}rgen}, isbn = {978-3-527-32367-8}, pages = {2374 -- 2381}, year = {2008}, language = {en} } @article{SchulzeFeyerlPischinger2023, author = {Schulze, Sven and Feyerl, G{\"u}nter and Pischinger, Stefan}, title = {Advanced ECMS for hybrid electric heavy-duty trucks with predictive battery discharge and adaptive operating strategy under real driving conditions}, series = {Energies}, volume = {16}, journal = {Energies}, number = {13}, publisher = {MDPI}, address = {Basel}, issn = {1996-1073}, doi = {10.3390/en16135171}, pages = {29 Seiten, Art. Nr.: 5171}, year = {2023}, abstract = {To fulfil the CO2 emission reduction targets of the European Union (EU), heavy-duty (HD) trucks need to operate 15\% more efficiently by 2025 and 30\% by 2030. Their electrification is necessary as conventional HD trucks are already optimized for the long-haul application. The resulting hybrid electric vehicle (HEV) truck gains most of the fuel saving potential by the recuperation of potential energy and its consecutive utilization. The key to utilizing the full potential of HEV-HD trucks is to maximize the amount of recuperated energy and ensure its intelligent usage while keeping the operating point of the internal combustion engine as efficient as possible. To achieve this goal, an intelligent energy management strategy (EMS) based on ECMS is developed for a parallel HEV-HD truck which uses predictive discharge of the battery and adaptive operating strategy regarding the height profile and the vehicle mass. The presented EMS can reproduce the global optimal operating strategy over long phases and lead to a fuel saving potential of up to 2\% compared with a heuristic strategy. Furthermore, the fuel saving potential is correlated with the investigated boundary conditions to deepen the understanding of the impact of intelligent EMS for HEV-HD trucks.}, language = {en} } @inproceedings{SchulzeMuehleisenFeyerl2018, author = {Schulze, Sven and M{\"u}hleisen, M. and Feyerl, G{\"u}nter}, title = {Adaptive energy management strategy for a heavy-duty truck with a P2-hybrid topology}, series = {18. Internationales Stuttgarter Symposium. Proceedings}, booktitle = {18. Internationales Stuttgarter Symposium. Proceedings}, publisher = {Springer Vieweg}, address = {Wiesbaden}, doi = {10.1007/978-3-658-21194-3}, pages = {75 -- 89}, year = {2018}, language = {en} } @article{BoehnischBraunMuscarelloetal.2024, author = {B{\"o}hnisch, Nils and Braun, Carsten and Muscarello, Vincenzo and Marzocca, Pier}, title = {About the wing and whirl flutter of a slender wing-propeller system}, series = {Journal of Aircraft}, journal = {Journal of Aircraft}, publisher = {AIAA}, address = {Reston, Va.}, issn = {1533-3868}, doi = {10.2514/1.C037542}, pages = {1 -- 14}, year = {2024}, abstract = {Next-generation aircraft designs often incorporate multiple large propellers attached along the wingspan (distributed electric propulsion), leading to highly flexible dynamic systems that can exhibit aeroelastic instabilities. This paper introduces a validated methodology to investigate the aeroelastic instabilities of wing-propeller systems and to understand the dynamic mechanism leading to wing and whirl flutter and transition from one to the other. Factors such as nacelle positions along the wing span and chord and its propulsion system mounting stiffness are considered. Additionally, preliminary design guidelines are proposed for flutter-free wing-propeller systems applicable to novel aircraft designs. The study demonstrates how the critical speed of the wing-propeller systems is influenced by the mounting stiffness and propeller position. Weak mounting stiffnesses result in whirl flutter, while hard mounting stiffnesses lead to wing flutter. For the latter, the position of the propeller along the wing span may change the wing mode shapes and thus the flutter mechanism. Propeller positions closer to the wing tip enhance stability, but pusher configurations are more critical due to the mass distribution behind the elastic axis.}, language = {en} } @article{PfaffEnningSutter2022, author = {Pfaff, Raphael and Enning, Manfred and Sutter, Stefan}, title = {A risk‑based approach to automatic brake tests for rail freight service: incident analysis and realisation concept}, series = {SN Applied Sciences}, volume = {4}, journal = {SN Applied Sciences}, number = {4}, publisher = {Springer}, address = {Cham}, issn = {2523-3971}, doi = {10.1007/s42452-022-05007-x}, pages = {1 -- 14}, year = {2022}, abstract = {This study reviews the practice of brake tests in freight railways, which is time consuming and not suitable to detect certain failure types. Public incident reports are analysed to derive a reasonable brake test hardware and communication architecture, which aims to provide automatic brake tests at lower cost than current solutions. The proposed solutions relies exclusively on brake pipe and brake cylinder pressure sensors, a brake release position switch as well as radio communication via standard protocols. The approach is embedded in the Wagon 4.0 concept, which is a holistic approach to a smart freight wagon. The reduction of manual processes yields a strong incentive due to high savings in manual labour and increased productivity.}, language = {en} } @inproceedings{Elsen1998, author = {Elsen, Ingo}, title = {A pixel based approach to view based object recognition with self-organizing neural networks}, series = {IECON'98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society}, booktitle = {IECON'98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society}, publisher = {IEEE}, address = {New York}, isbn = {0-7803-4503-7}, doi = {10.1109/IECON.1998.724032}, pages = {2040 -- 2044}, year = {1998}, abstract = {This paper addresses the pixel based classification of three dimensional objects from arbitrary views. To perform this task a coding strategy, inspired by the biological model of human vision, for pixel data is described. The coding strategy ensures that the input data is invariant against shift, scale and rotation of the object in the input domain. The image data is used as input to a class of self organizing neural networks, the Kohonen-maps or self-organizing feature maps (SOFM). To verify this approach two test sets have been generated: the first set, consisting of artificially generated images, is used to examine the classification properties of the SOFMs; the second test set examines the clustering capabilities of the SOFM when real world image data is applied to the network after it has been preprocessed to be invariant against shift, scale and rotation. It is shown that the clustering capability of the SOFM is strongly dependant on the invariance coding of the images.}, language = {en} } @article{KhayyamJamaliBabHadiasharetal.2020, author = {Khayyam, Hamid and Jamali, Ali and Bab-Hadiashar, Alireza and Esch, Thomas and Ramakrishna, Seeram and Jalil, Mahdi and Naebe, Minoo}, title = {A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modelling with Application in Industry 4.0}, series = {IEEE Access}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York, NY}, isbn = {2169-3536}, doi = {10.1109/ACCESS.2020.2999898}, pages = {1 -- 12}, year = {2020}, abstract = {To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements.}, language = {en} } @article{KhayyamJamaliBabHadiasharetal.2020, author = {Khayyam, Hamid and Jamali, Ali and Bab-Hadiashar, Alireza and Esch, Thomas and Ramakrishna, Seeram and Jalili, Mahdi and Naebe, Minoo}, title = {A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modeling with Application in Industry 4.0}, series = {IEEE Access}, volume = {8}, journal = {IEEE Access}, number = {Art. 9108222}, publisher = {IEEE}, address = {New York, NY}, issn = {2169-3536}, doi = {10.1109/ACCESS.2020.2999898}, pages = {111381 -- 111393}, year = {2020}, abstract = {To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements.}, language = {en} }