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This dataset was acquired at field tests of the steerable ice-melting probe "EnEx-IceMole" (Dachwald et al., 2014). A field test in summer 2014 was used to test the melting probe's system, before the probe was shipped to Antarctica, where, in international cooperation with the MIDGE project, the objective of a sampling mission in the southern hemisphere summer 2014/2015 was to return a clean englacial sample from the subglacial brine reservoir supplying the Blood Falls at Taylor Glacier (Badgeley et al., 2017, German et al., 2021).
The standardized log-files generated by the IceMole during melting operation include more than 100 operational parameters, housekeeping information, and error states, which are reported to the base station in intervals of 4 s. Occasional packet loss in data transmission resulted in a sparse number of increased sampling intervals, which where compensated for by linear interpolation during post processing. The presented dataset is based on a subset of this data: The penetration distance is calculated based on the ice screw drive encoder signal, providing the rate of rotation, and the screw's thread pitch. The melting speed is calculated from the same data, assuming the rate of rotation to be constant over one sampling interval. The contact force is calculated from the longitudinal screw force, which es measured by strain gauges. The used heating power is calculated from binary states of all heating elements, which can only be either switched on or off. Temperatures are measured at each heating element and averaged for three zones (melting head, side-wall heaters and back-plate heaters).
Optical Instruments require an extremely stable thermal surrounding to prevent loss of data quality by misalignments of the instrument components resulting from material deformation due to temperature f luctuations (e.g. from solar intrusion). Phase Change Material (PCM) can be applied as a thermal damper to achieve a more uniform temperature distribution. The challenge of this method is, among others, the integration of PCM into affected areas. If correctly designed, incoming heat is latently absorbed during phase change of the PCM, i.e. the temperature of a structure remains almost constant. In a cold phase, the heat during phase change is released again latently until the PCM returns to its original state of aggregation. Thus, the structure is thermally stabilized. At FH Aachen– University of Applied Sciences research is conducted to apply PCM directly into the structures of affected components (baffles, optical benches, electronic boxes, etc.). Through the application of Additive Manufacturing, the necessary voids are directly printed into these structures and filled later with PCM. Additive Manufacturing enables complex structures that would not have been possible with conservative manufacturing methods. A corresponding Breadboard was developed and manufactured by Selective Laser Melting (SLM). The current state of research includes the handling and analysis of the Breadboard, tests and a correlation of the thermal model. The results have shown analytically and practically that it is possible to use PCM as an integral part of the structure as a thermal damper. The results serve as a basis for the further development of the technology, which should maximize performance and enable the integration of PCM into much more complex structures.
Searching optimal interplanetary trajectories for low-thrust spacecraft is usually a difficult and time-consuming task that involves much experience and expert knowledge in astrodynamics and optimal control theory. This is because the convergence behavior of traditional local optimizers, which are based on numerical optimal control methods, depends on an adequate initial guess, which is often hard to find, especially for very-low-thrust trajectories that necessitate many revolutions around the sun. The obtained solutions are typically close to the initial guess that is rarely close to the (unknown) global optimum. Within this paper, trajectory optimization problems are attacked from the perspective of artificial intelligence and machine learning. Inspired by natural archetypes, a smart global method for low-thrust trajectory optimization is proposed that fuses artificial neural networks and evolutionary algorithms into so-called evolutionary neurocontrollers. This novel method runs without an initial guess and does not require the attendance of an expert in astrodynamics and optimal control theory. This paper details how evolutionary neurocontrol works and how it could be implemented. The performance of the method is assessed for three different interplanetary missions with a thrust to mass ratio <0.15mN/kg (solar sail and nuclear electric).