Refine
Year of publication
Institute
- Fachbereich Luft- und Raumfahrttechnik (798) (remove)
Document Type
- Article (380)
- Conference Proceeding (208)
- Book (107)
- Part of a Book (43)
- Patent (19)
- Report (14)
- Doctoral Thesis (10)
- Conference: Meeting Abstract (6)
- Other (3)
- Conference Poster (2)
Keywords
- Karosseriebau (6)
- Strömungsmaschine (6)
- Turbine (6)
- avalanche (6)
- solar sail (5)
- car body construction (4)
- hydrogen (4)
- snow (4)
- Eisschicht (3)
- GOSSAMER-1 (3)
The optical properties of the thin metalized polymer films that are projected for solar sails are assumed to be affected by the erosive effects of the space environment. Their degradation behavior in the real space environment, however, is to a considerable degree indefinite, because initial ground test results are controversial and relevant inspace tests have not been made so far. The standard optical solar sail models that are currently used for trajectory design do not take optical degradation into account, hence its potential effects on trajectory design have not been investigated so far. Nevertheless, optical degradation is important for high-fidelity solar sail mission design, because it decreases both the magnitude of the solar radiation pressure force acting on the sail and also the sail control authority. Therefore, we propose a simple parametric optical solar sail degradation model that describes the variation of the sail film’s optical coefficients with time, depending on the sail film’s environmental history, i.e., the radiation dose. The primary intention of our model is not to describe the exact behavior of specific film-coating combinations in the real space environment, but to provide a more general parametric framework for describing the general optical degradation behavior of solar sails. Using our model, the effects of different optical degradation behaviors on trajectory design are investigated for various exemplary missions.
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.
In general aviation, too, it is desirable to be able to operate existing internal combustion engines with fuels that produce less CO₂ than Avgas 100LL being widely used today It can be assumed that, in comparison, the fuels CNG, LPG or LNG, which are gaseous under normal conditions, produce significantly lower emissions. Necessary propulsion system adaptations were investigated as part of a research project at Aachen University of Applied Sciences.