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Forschendes Lernen ist dazu geeignet, epistemische Neugier – definiert als Freude an neuen Erkenntnissen - anzuregen und zu befriedigen. Neben der Selbstwirksamkeit zeigt sich die Neugier als relevant für den Studienerfolg. Allerdings ist bisher nicht geklärt, in welcher Beziehung diese beiden Konstrukte zueinanderstehen.
New European Union (EU) regulations for UAS operations require an operational risk analysis, which includes an estimation of the potential danger of the UAS crashing. A key parameter for the potential ground risk is the kinetic impact energy of the UAS. The kinetic energy depends on the impact velocity of the UAS and, therefore, on the aerodynamic drag and the weight during free fall. Hence, estimating the impact energy of a UAS requires an accurate drag estimation of the UAS in that state. The paper at hand presents the aerodynamic drag estimation of small-scale multirotor UAS. Multirotor UAS of various sizes and configurations were analysed with a fully unsteady Reynolds-averaged Navier–Stokes approach. These simulations included different velocities and various fuselage pitch angles of the UAS. The results were compared against force measurements performed in a subsonic wind tunnel and provided good consistency. Furthermore, the influence of the UAS`s fuselage pitch angle as well as the influence of fixed and free spinning propellers on the aerodynamic drag was analysed. Free spinning propellers may increase the drag by up to 110%, depending on the fuselage pitch angle. Increasing the fuselage pitch angle of the UAS lowers the drag by 40% up to 85%, depending on the UAS. The data presented in this paper allow for increased accuracy of ground risk assessments.
In the last decades, several hundred exoplanets could be detected thanks to space-based observatories, namely CNES’ COROT and NASA’s Kepler. To expand this quest ESA plans to launch CHEOPS as the f irst small class mission in the cosmic visions program (S1) and PLATO as the 3rd medium class mission, so called M3 . PLATO’s primary objective is the detection of Earth like Exoplanets orbiting solar type stars in the habitable zone and characterisation of their bulk properties. This is possible by precise lightcurve measurement via 34 cameras. That said it becomes obvious that accurate pointing is key to achieve the required signal to noise ratio for positive transit detection. The paper will start with a comprehensive overview of PLATO’s mission objectives and mission architecture. Hereafter, special focus will be devoted to PLATO’s pointing requirements. Understanding the very nature of PLATO’s pointing requirements is essential to derive a design baseline to achieve the required performance. The PLATO frequency domain is of particular interest, ranging from 40 mHz to 3 Hz. Due to the very different time-scales involved, the spectral pointing requirement is decomposed into a high frequency part dominated by the attitude control system and the low frequency part dominated by the thermo-elastic properties of the spacecraft’s configuration. Both pose stringent constraints on the overall design as well as technology properties to comply with the derived requirements and thus assure a successful mission.
The paper presents the derivation of a new equivalent skin friction coefficient for estimating the parasitic drag of short-to-medium range fixed-wing unmanned aircraft. The new coefficient is derived from an aerodynamic analysis of ten different unmanned aircraft used for surveillance, reconnaissance, and search and rescue missions. The aircraft is simulated using a validated unsteady Reynolds-averaged Navier Stokes approach. The UAV’s parasitic drag is significantly influenced by the presence of miscellaneous components like fixed landing gears or electro-optical sensor turrets. These components are responsible for almost half of an unmanned aircraft’s total parasitic drag. The new equivalent skin friction coefficient accounts for these effects and is significantly higher compared to other aircraft categories. It is used to initially size an unmanned aircraft for a typical reconnaissance mission. The improved parasitic drag estimation yields a much heavier unmanned aircraft when compared to the sizing results using available drag data of manned aircraft.
The paper presents the derivation of a new equivalent skin friction coefficient for estimating the parasitic drag of short-to-medium range fixed-wing unmanned aircraft. The new coefficient is derived from an aerodynamic analysis of ten different unmanned aircraft used on surveillance, reconnaissance, and search and rescue missions. The aircraft are simulated using a validated unsteady Reynolds-averaged Navier Stokes approach. The UAV's parasitic drag is significantly influenced by the presence of miscellaneous components like fixed landing gears or electro-optical sensor turrets. These components are responsible for almost half of an unmanned aircraft's total parasitic drag. The new equivalent skin friction coefficient accounts for these effects and is significantly higher compared to other aircraft categories. It is used to initially size an unmanned aircraft for a typical reconnaissance mission. The improved parasitic drag estimation yields a much heavier unmanned aircraft when compared to the sizing results using available drag data of manned aircraft.
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.