TY  - CHAP
A1  - Finger, Felix
A1  - Götten, Falk
A1  - Braun, Carsten
A1  - Bil, Cees
T1  - Cost Estimation Methods for Hybrid-Electric General Aviation Aircraft
T2  - Asia Pacific International Symposium on Aerospace Technology. APISAT 2019
Y1  - 2019
SP  - 1
EP  - 13
ER  - 
TY  - CHAP
A1  - Finger, Felix
A1  - Braun, Carsten
A1  - Bil, Cees
T1  - Comparative assessment of parallel-hybrid-electric propulsion systems for four different aircraft
T2  - AIAA SciTech Forum 2020, 06.01.2020 - 10.01.2020, Orlando
Y1  - 2020
U6  - http://dx.doi.org/10.2514/6.2020-1502
ER  - 
TY  - CHAP
A1  - Götten, Falk
A1  - Finger, Felix
A1  - Marino, Matthew
A1  - Bil, Cees
A1  - Havermann, Marc
A1  - Braun, Carsten
T1  - A review of guidelines and best practices for subsonic aerodynamic simulations using RANS CFD
T2  - Asia-Pacific International Symposium on Aerospace Technology (APISAT), At Gold Coast, Australia, 04. - 06. Dezember 2019
Y1  - 2019
SN  - 978-1-925627-40-4
ER  - 
TY  - CHAP
A1  - Finger, Felix
A1  - Khalsa, R.
A1  - Kreyer, Jörg
A1  - Mayntz, Joscha
A1  - Braun, Carsten
A1  - Dahmann, Peter
A1  - Esch, Thomas
A1  - Kemper, Hans
A1  - Schmitz, O.
A1  - Bragard, Michael
T1  - An approach to propulsion system modelling for the conceptual design of hybrid-electric general aviation aircraft
T2  - Deutscher Luft- und Raumfahrtkongress 2019, 30.9.-2.10.2019, Darmstadt
N2  - In this paper, an approach to propulsion system modelling for hybrid-electric general aviation aircraft is presented. Because the focus is on general aviation aircraft, only combinations of electric motors and reciprocating combustion engines are explored. Gas turbine hybrids will not be considered. The level of the component's models is appropriate for the conceptual design stage. They are simple and adaptable, so that a wide range of designs with morphologically different propulsive system architectures can be quickly compared. Modelling strategies for both mass and efficiency of each part of the propulsion system (engine, motor, battery and propeller) will be presented.
Y1  - 2019
ER  - 
TY  - CHAP
A1  - Thoma, Andreas
A1  - Fisher, Alex
A1  - Bertrand, Olivier
A1  - Braun, Carsten
ED  - Vouloutsi, Vasiliki
ED  - Mura, Anna
ED  - Tauber, Falk
ED  - Speck, Thomas
ED  - Prescott, Tony J.
ED  - Verschure, Paul F. M. J.
T1  - Evaluation of possible flight strategies for close object evasion from bumblebee experiments
T2  - Living Machines 2020: Biomimetic and Biohybrid Systems
KW  - Obstacle avoidance
KW  - Bumblebees
KW  - Flight control
KW  - UAV
KW  - MAV
Y1  - 2020
SN  - 978-3-030-64312-6
U6  - http://dx.doi.org/10.1007/978-3-030-64313-3_34
N1  - 9th International Conference, Living Machines 2020, Freiburg, Germany, July 28–30, 2020, Proceedings
SP  - 354
EP  - 365
PB  - Springer
CY  - Cham
ER  - 
TY  - CHAP
A1  - Thoma, Andreas
A1  - Stiemer, Luc
A1  - Braun, Carsten
A1  - Fisher, Alex
A1  - Gardi, Alessandro G.
T1  - Potential of hybrid neural network local path planner for small UAV in urban environments
T2  - AIAA SCITECH 2023 Forum
N2  - This work proposes a hybrid algorithm combining an Artificial Neural Network (ANN) with a conventional local path planner to navigate UAVs efficiently in various unknown urban environments. The proposed method of a Hybrid Artificial Neural Network Avoidance System is called HANNAS. The ANN analyses a video stream and classifies the current environment. This information about the current Environment is used to set several control parameters of a conventional local path planner, the 3DVFH*. The local path planner then plans the path toward a specific goal point based on distance data from a depth camera. We trained and tested a state-of-the-art image segmentation algorithm, PP-LiteSeg. The proposed HANNAS method reaches a failure probability of 17%, which is less than half the failure probability of the baseline and around half the failure probability of an improved, bio-inspired version of the 3DVFH*. The proposed HANNAS method does not show any disadvantages regarding flight time or flight distance.
Y1  - 2023
U6  - http://dx.doi.org/10.2514/6.2023-2359
N1  - AIAA SCITECH 2023 Forum, 23-27 January 2023, National Harbor, MD & Online
PB  - AIAA
ER  -