@inproceedings{FingerGoettenBraunetal.2019, author = {Finger, Felix and G{\"o}tten, Falk and Braun, Carsten and Bil, C.}, title = {On Aircraft Design Under the Consideration of Hybrid-Electric Propulsion Systems}, series = {APISAT 2018: The Proceedings of the 2018 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2018)}, booktitle = {APISAT 2018: The Proceedings of the 2018 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2018)}, publisher = {Springer}, address = {Singapore}, isbn = {978-981-13-3305-7}, doi = {10.1007/978-981-13-3305-7_99}, pages = {1261 -- 1272}, year = {2019}, abstract = {A hybrid-electric propulsion system combines the advantages of fuel-based systems and battery powered systems and offers new design freedom. To take full advantage of this technology, aircraft designers must be aware of its key differences, compared to conventional, carbon-fuel based, propulsion systems. This paper gives an overview of the challenges and potential benefits associated with the design of aircraft that use hybrid-electric propulsion systems. It offers an introduction of the most popular hybrid-electric propulsion architectures and critically assess them against the conventional and fully electric propulsion configurations. The effects on operational aspects and design aspects are covered. Special consideration is given to the application of hybrid-electric propulsion technology to both unmanned and vertical take-off and landing aircraft. The authors conclude that electric propulsion technology has the potential to revolutionize aircraft design. However, new and innovative methods must be researched, to realize the full benefit of the technology.}, language = {en} } @inproceedings{GoettenFingerBraunetal.2019, author = {G{\"o}tten, Falk and Finger, Felix and Braun, Carsten and Havermann, Marc and Bil, C. and Gomez, F.}, title = {Empirical Correlations for Geometry Build-Up of Fixed Wing Unmanned Air Vehicles}, series = {APISAT 2018: The Proceedings of the 2018 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2018)}, booktitle = {APISAT 2018: The Proceedings of the 2018 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2018)}, publisher = {Springer}, address = {Singapore}, isbn = {978-981-13-3305-7}, doi = {10.1007/978-981-13-3305-7_109}, pages = {1365 -- 1381}, year = {2019}, abstract = {The results of a statistical investigation of 42 fixed-wing, small to medium sized (20 kg-1000 kg) reconnaissance unmanned air vehicles (UAVs) are presented. Regression analyses are used to identify correlations of the most relevant geometry dimensions with the UAV's maximum take-off mass. The findings allow an empirical based geometry-build up for a complete unmanned aircraft by referring to its take-off mass only. This provides a bridge between very early design stages (initial sizing) and the later determination of shapes and dimensions. The correlations might be integrated into a UAV sizing environment and allow designers to implement more sophisticated drag and weight estimation methods in this process. Additional information on correlation factors for a rough drag estimation methodology indicate how this technique can significantly enhance the accuracy of early design iterations.}, language = {en} } @inproceedings{MerkensHebel2021, author = {Merkens, Torsten and Hebel, Christoph}, title = {Sharing mobility concepts - flexible, sustainable, smart}, series = {Proceedings of the 1st UNITED - Southeast Asia Automotive Interest Group (SAIG)}, booktitle = {Proceedings of the 1st UNITED - Southeast Asia Automotive Interest Group (SAIG)}, isbn = {978-3-902103-94-9}, pages = {43 -- 44}, year = {2021}, language = {en} } @article{GoettenFingerHavermannetal.2021, author = {G{\"o}tten, Falk and Finger, Felix and Havermann, Marc and Braun, Carsten and Marino, M. and Bil, C.}, title = {Full configuration drag estimation of short-to-medium range fixed-wing UAVs and its impact on initial sizing optimization}, series = {CEAS Aeronautical Journal}, volume = {12}, journal = {CEAS Aeronautical Journal}, publisher = {Springer}, address = {Berlin}, issn = {1869-5590 (Online)}, doi = {10.1007/s13272-021-00522-w}, pages = {589 -- 603}, year = {2021}, abstract = {The paper presents the derivation of a new equivalent skin friction coefficient for estimating the parasitic drag of short-to-medium range fixed-wing unmanned aircraft. The new coefficient is derived from an aerodynamic analysis of ten different unmanned aircraft used for surveillance, reconnaissance, and search and rescue missions. The aircraft is simulated using a validated unsteady Reynolds-averaged Navier Stokes approach. The UAV's parasitic drag is significantly influenced by the presence of miscellaneous components like fixed landing gears or electro-optical sensor turrets. These components are responsible for almost half of an unmanned aircraft's total parasitic drag. The new equivalent skin friction coefficient accounts for these effects and is significantly higher compared to other aircraft categories. It is used to initially size an unmanned aircraft for a typical reconnaissance mission. The improved parasitic drag estimation yields a much heavier unmanned aircraft when compared to the sizing results using available drag data of manned aircraft.}, language = {en} } @inproceedings{ThomaFisherBraun2020, author = {Thoma, Andreas and Fisher, Alex and Braun, Carsten}, title = {Improving the px4 avoid algorithm by bio-inspired flight strategies}, series = {DLRK2020 - „Luft- und Raumfahrt - Verantwortung in allen Dimensionen"}, booktitle = {DLRK2020 - „Luft- und Raumfahrt - Verantwortung in allen Dimensionen"}, pages = {10 Seiten}, year = {2020}, language = {en} } @inproceedings{HauggKreyerKemperetal.2020, author = {Haugg, Albert Thomas and Kreyer, J{\"o}rg and Kemper, Hans and Hatesuer, Katerina and Esch, Thomas}, title = {Heat exchanger for ORC. adaptability and optimisation potentials}, series = {IIR International Rankine 2020 Conference}, booktitle = {IIR International Rankine 2020 Conference}, doi = {10.18462/iir.rankine.2020.1224}, pages = {10 Seiten}, year = {2020}, abstract = {The recovery of waste heat requires heat exchangers to extract it from a liquid or gaseous medium into another working medium, a refrigerant. In Organic Rankine Cycles (ORC) on Combustion Engines there are two major heat sources, the exhaust gas and the water/glycol fluid from the engine's cooling circuit. A heat exchanger design must be adapted to the different requirements and conditions resulting from the heat sources, fluids, system configurations, geometric restrictions, and etcetera. The Stacked Shell Cooler (SSC) is a new and very specific design of a plate heat exchanger, created by AKG, which allows with a maximum degree of freedom the optimization of heat exchange rate and the reduction of the related pressure drop. This optimization in heat exchanger design for ORC systems is even more important, because it reduces the energy consumption of the system and therefore maximizes the increase in overall efficiency of the engine.}, language = {en} } @inproceedings{NikolovskiRekeElsenetal.2021, author = {Nikolovski, Gjorgji and Reke, Michael and Elsen, Ingo and Schiffer, Stefan}, title = {Machine learning based 3D object detection for navigation in unstructured environments}, series = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, booktitle = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, publisher = {IEEE}, isbn = {978-1-6654-7921-9}, doi = {10.1109/IVWorkshops54471.2021.9669218}, pages = {236 -- 242}, year = {2021}, abstract = {In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine.}, language = {en} } @inproceedings{FingerdeVriesVosetal.2020, author = {Finger, Felix and de Vries, Reynard and Vos, Roelof and Braun, Carsten and Bil, Cees}, title = {A comparison of hybrid-electric aircraft sizing methods}, series = {AIAA Scitech 2020 Forum}, booktitle = {AIAA Scitech 2020 Forum}, doi = {10.2514/6.2020-1006}, pages = {31 Seiten}, year = {2020}, language = {en} } @inproceedings{KreyerMuellerEsch2020, author = {Kreyer, J{\"o}rg and M{\"u}ller, Marvin and Esch, Thomas}, title = {A Map-Based Model for the Determination of Fuel Consumption for Internal Combustion Engines as a Function of Flight Altitude}, series = {Deutscher Luft- und Raumfahrtkongress 2019, „Luft- und Raumfahrt - technologische Br{\"u}cke in die Zukunft", Darmstadt, 30. September bis 2. Oktober 2019}, booktitle = {Deutscher Luft- und Raumfahrtkongress 2019, „Luft- und Raumfahrt - technologische Br{\"u}cke in die Zukunft", Darmstadt, 30. September bis 2. Oktober 2019}, publisher = {Deutsche Gesellschaft f{\"u}r Luft- und Raumfahrt - Lilienthal-Oberth e.V}, address = {Bonn}, doi = {10.25967/490162}, pages = {13 Seiten}, year = {2020}, language = {en} } @inproceedings{DinghoferHartung2020, author = {Dinghofer, Kai and Hartung, Frank}, title = {Analysis of Criteria for the Selection of Machine Learning Frameworks}, series = {2020 International Conference on Computing, Networking and Communications (ICNC)}, booktitle = {2020 International Conference on Computing, Networking and Communications (ICNC)}, doi = {10.1109/ICNC47757.2020.9049650}, pages = {373 -- 377}, year = {2020}, language = {en} }