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20 years after the successful ground deployment test of a (20 m) 2 solar sail at DLR Cologne, and in the light of the upcoming U.S. NEAscout mission, we provide an overview of the progress made since in our mission and hardware design studies as well as the hardware built in the course of our solar sail technology development. We outline the most likely and most efficient routes to develop solar sails for useful missions in science and applications, based on our developed `now-term' and near-term hardware as well as the many practical and managerial lessons learned from the DLR-ESTEC Gossamer Roadmap. Mission types directly applicable to planetary defense include single and Multiple NEA Rendezvous ((M)NR) for precursor, monitoring and follow-up scenarios as well as sail-propelled head-on retrograde kinetic impactors (RKI) for mitigation. Other mission types such as the Displaced L1 (DL1) space weather advance warning and monitoring or Solar Polar Orbiter (SPO) types demonstrate the capability of near-term solar sails to achieve asteroid rendezvous in any kind of orbit, from Earth-coorbital to extremely inclined and even retrograde orbits. Some of these mission types such as SPO, (M)NR and RKI include separable payloads. For one-way access to the asteroid surface, nanolanders like MASCOT are an ideal match for solar sails in micro-spacecraft format, i.e. in launch configurations compatible with ESPA and ASAP secondary payload platforms. Larger landers similar to the JAXA-DLR study of a Jupiter Trojan asteroid lander for the OKEANOS mission can shuttle from the sail to the asteroids visited and enable multiple NEA sample-return missions. The high impact velocities and re-try capability achieved by the RKI mission type on a final orbit identical to the target asteroid's but retrograde to its motion enables small spacecraft size impactors to carry sufficient kinetic energy for deflection.
Recent Unmanned Aerial Vehicle (UAV) design procedures rely on full aircraft steady-state Reynolds-Averaged-Navier-Stokes (RANS) analyses in early design stages. Small sensor turrets are included in such simulations, even though their aerodynamic properties show highly unsteady behavior. Very little is known about the effects of this approach on the simulation outcomes of small turrets. Therefore, the flow around a model turret at a Reynolds number of 47,400 is simulated with a steady-state RANS approach and compared to experimental data. Lift, drag, and surface pressure show good agreement with the experiment. The RANS model predicts the separation location too far downstream and shows a larger recirculation region aft of the body. Both characteristic arch and horseshoe vortex structures are visualized and qualitatively match the ones found by the experiment. The Reynolds number dependence of the drag coefficient follows the trend of a sphere within a distinct range. The outcomes indicate that a steady-state RANS model of a small sensor turret is able to give results that are useful for UAV engineering purposes but might not be suited for detailed insight into flow properties.
Digital Image Correlation (DIC) is a powerful tool used to evaluate displacements and deformations in a non-intrusive manner. By comparing two images, one of the undeformed reference state of a specimen and another of the deformed target state, the relative displacement between those two states is determined. DIC is well known and often used for post-processing analysis of in-plane displacements and deformation of specimen. Increasing the analysis speed to enable real-time DIC analysis will be beneficial and extend the field of use of this technique.
Here we tested several combinations of the most common DIC methods in combination with different parallelization approaches in MATLAB and evaluated their performance to determine whether real-time analysis is possible with these methods. To reflect improvements in computing technology different hardware settings were also analysed. We found that implementation problems can reduce the efficiency of a theoretically superior algorithm such that it becomes practically slower than a suboptimal algorithm. The Newton-Raphson algorithm in combination with a modified Particle Swarm algorithm in parallel image computation was found to be most effective. This is contrary to theory, suggesting that the inverse-compositional Gauss-Newton algorithm is superior. As expected, the Brute Force Search algorithm is the least effective method. We also found that the correct choice of parallelization tasks is crucial to achieve improvements in computing speed. A poorly chosen parallelisation approach with high parallel overhead leads to inferior performance. Finally, irrespective of the computing mode the correct choice of combinations of integerpixel and sub-pixel search algorithms is decisive for an efficient analysis. Using currently available hardware realtime analysis at high framerates remains an aspiration.
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.