TY  - CHAP
A1  - Finger, Felix
A1  - de Vries, Reynard
A1  - Vos, Roelof
A1  - Braun, Carsten
A1  - Bil, Cees
T1  - A comparison of hybrid-electric aircraft sizing methods
T2  - AIAA Scitech 2020 Forum
N2  - 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.
Y1  - 2020
U6  - https://doi.org/10.2514/6.2020-1006
N1  - AIAA Scitech 2020 Forum, Driving aerospace solutions for global challenges, Orlando, 06. - 10. January 2020
ER  - 
TY  - CHAP
A1  - Walter, Peter
A1  - Elsen, Ingo
A1  - Müller, Holger
A1  - Kraiss, Karl-Friedrich
T1  - 3D object recognition with a specialized mixtures of experts architecture
T2  - IJCNN'99. International Joint Conference on Neural Networks. Proceedings
N2  - Aim of the AXON2 project (Adaptive Expert System for Object Recogniton using Neuml Networks) is the development of an object recognition system (ORS) capable of recognizing isolated 3d objects from arbitrary views. Commonly, classification is based on a single feature extracted from the original image. Here we present an architecture adapted from the Mixtures of Eaqerts algorithm which uses multiple neuml networks to integmte different features. During tmining each neural network specializes in a subset of objects or object views appropriate to the properties of the corresponding feature space. In recognition mode the system dynamically chooses the most relevant features and combines them with maximum eficiency. The remaining less relevant features arz not computed and do therefore not decelerate the-recognition process. Thus, the algorithm is well suited for ml-time applications.
Y1  - 1999
SN  - 0-7803-5529-6
U6  - https://doi.org/10.1109/IJCNN.1999.836243
SN  - 1098-7576
N1  - Washington, DC 10-16.07.1999
SP  - 3563
EP  - 3568
PB  - IEEE
CY  - New York
ER  -