@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"},
  doi       = {10.25967/530183},
  pages     = {10 Seiten},
  year      = {2020},
  language  = {en}
}
@inproceedings{ThomaFisherBertrandetal.2020,
  author    = {Thoma, Andreas and Fisher, Alex and Bertrand, Olivier and Braun, Carsten},
  title     = {Evaluation of possible flight strategies for close object evasion from bumblebee experiments},
  series = {Living Machines 2020: Biomimetic and Biohybrid Systems},
  booktitle = {Living Machines 2020: Biomimetic and Biohybrid Systems},
  editor    = {Vouloutsi, Vasiliki and Mura, Anna and Tauber, Falk and Speck, Thomas and Prescott, Tony J. and Verschure, Paul F. M. J.},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-64312-6},
  doi       = {10.1007/978-3-030-64313-3_34},
  pages     = {354 -- 365},
  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)},
  publisher = {IEEE},
  address   = {New York, NY},
  doi       = {10.1109/ICNC47757.2020.9049650},
  pages     = {373 -- 377},
  year      = {2020},
  abstract  = {With the many achievements of Machine Learning in the past years, it is likely that the sub-area of Deep Learning will continue to deliver major technological breakthroughs [1]. In order to achieve best results, it is important to know the various different Deep Learning frameworks and their respective properties. This paper provides a comparative overview of some of the most popular frameworks. First, the comparison methods and criteria are introduced and described with a focus on computer vision applications: Features and Uses are examined by evaluating papers and articles, Adoption and Popularity is determined by analyzing a data science study. Then, the frameworks TensorFlow, Keras, PyTorch and Caffe are compared based on the previously described criteria to highlight properties and differences. Advantages and disadvantages are compared, enabling researchers and developers to choose a framework according to their specific needs.},
  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},
  abstract  = {The number of case studies focusing on hybrid-electric aircraft is steadily increasing, since these configurations are thought to lead to lower operating costs and environmental impact than traditional aircraft. However, due to the lack of reference data of actual hybrid-electric aircraft, in most cases, the design tools and results are difficult to validate. In this paper, two independently developed approaches for hybrid-electric conceptual aircraft design are compared. An existing 19-seat commuter aircraft is selected as the conventional baseline, and both design tools are used to size that aircraft. The aircraft is then re-sized under consideration of hybrid-electric propulsion technology. This is performed for parallel, serial, and fully-electric powertrain architectures. Finally, sensitivity studies are conducted to assess the validity of the basic assumptions and approaches regarding the design of hybrid-electric aircraft. Both methods are found to predict the maximum take-off mass (MTOM) of the reference aircraft with less than 4\% error. The MTOM and payload-range energy efficiency of various (hybrid-) electric configurations are predicted with a maximum difference of approximately 2\% and 5\%, respectively. The results of this study confirm a correct formulation and implementation of the two design methods, and the data obtained can be used by researchers to benchmark and validate their design tools.},
  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},
  publisher = {DGLR},
  address   = {Bonn},
  doi       = {10.25967/490162},
  pages     = {13 Seiten},
  year      = {2020},
  abstract  = {In addition to very high safety and reliability requirements, the design of internal combustion engines (ICE) in aviation focuses on economic efficiency. The objective must be to design the aircraft powertrain optimized for a specific flight mission with respect to fuel consumption and specific engine power. Against this background, expert tools provide valuable decision-making assistance for the customer. In this paper, a mathematical calculation model for the fuel consumption of aircraft ICE is presented. This model enables the derivation of fuel consumption maps for different engine configurations. Depending on the flight conditions and based on these maps, the current and the integrated fuel consumption for freely definable flight emissions is calculated. For that purpose, an interpolation method is used, that has been optimized for accuracy and calculation time. The mission boundary conditions flight altitude and power requirement of the ICE form the basis for this calculation. The mathematical fuel consumption model is embedded in a parent program. This parent program presents the simulated fuel consumption by means of an example flight mission for a representative airplane. The focus of the work is therefore on reproducing exact consumption data for flight operations. By use of the empirical approaches according to Gagg-Farrar [1] the power and fuel consumption as a function of the flight altitude are determined. To substantiate this approaches, a 1-D ICE model based on the multi-physical simulation tool GT-Suite® has been created. This 1-D engine model offers the possibility to analyze the filling and gas change processes, the internal combustion as well as heat and friction losses for an ICE under altitude environmental conditions. Performance measurements on a dynamometer at sea level for a naturally aspirated ICE with a displacement of 1211 ccm used in an aviation aircraft has been done to validate the 1-D ICE model. To check the plausibility of the empirical approaches with respect to the fuel consumption and performance adjustment for the flight altitude an analysis of the ICE efficiency chain of the 1-D engine model is done. In addition, a comparison of literature and manufacturer data with the simulation results is presented.},
  language  = {en}
}