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Institute
- Fachbereich Luft- und Raumfahrttechnik (144) (remove)
Recent analysis of scientific data from Cassini and earth-based observations gave evidence for a global ocean under a surrounding solid ice shell on Saturn's moon Enceladus. Images of Enceladus' South Pole showed several fissures in the ice shell with plumes constantly exhausting frozen water particles, building up the E-Ring, one of the outer rings of Saturn. In this southern region of Enceladus, the ice shell is considered to be as thin as 2 km, about an order of magnitude thinner than on the rest of the moon. Under the ice shell, there is a global ocean consisting of liquid water. Scientists are discussing different approaches the possibilities of taking samples of water, i.e. by melting through the ice using a melting probe. FH Aachen UAS developed a prototype of maneuverable melting probe which can navigate through the ice that has already been tested successfully in a terrestrial environment. This means no atmosphere and or ambient pressure, low ice temperatures of around 100 to 150K (near the South Pole) and a very low gravity of 0,114 m/s^2 or 1100 μg. Two of these influencing measures are about to be investigated at FH Aachen UAS in 2017, low ice temperature and low ambient pressure below the triple point of water. Low gravity cannot be easily simulated inside a large experiment chamber, though. Numerical simulations of the melting process at RWTH Aachen however are showing a gravity dependence of melting behavior. Considering this aspect, VIPER provides a link between large-scale experimental simulations at FH Aachen UAS and numerical simulations at RWTH Aachen. To analyze the melting process, about 90 seconds of experiment time in reduced gravity and low ambient pressure is provided by the REXUS rocket. In this time frame, the melting speed and contact force between ice and probes are measured, as well as heating power and a two-dimensional array of ice temperatures. Additionally, visual and infrared cameras are used to observe the melting process.
Searching optimal continuous-thrust trajectories is usually a difficult and time-consuming task. The solution quality of traditional optimal-control methods depends strongly on an adequate initial guess because the solution is typically close to the initial guess, which may be far from the (unknown) global optimum. Evolutionary neurocontrol attacks continuous-thrust optimization problems from the perspective of artificial intelligence and machine learning, combining artificial neural networks and evolutionary algorithms. This chapter describes the method and shows some example results for single- and multi-phase continuous-thrust trajectory optimization problems to assess its performance. Evolutionary neurocontrol can explore the trajectory search space more exhaustively than a human expert can do with traditional optimal-control methods. Especially for difficult problems, it usually finds solutions that are closer to the global optimum. Another fundamental advantage is that continuous-thrust trajectories can be optimized without an initial guess and without expert supervision.