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Prolonged operations close to small solar system bodies require a sophisticated control logic to minimize propellant mass and maximize operational efficiency. A control logic based on Discrete Mechanics and Optimal Control (DMOC) is proposed and applied to both conventionally propelled and solar sail spacecraft operating at an arbitrarily shaped asteroid in the class of Itokawa. As an example, stand-off inertial hovering is considered, recently identified as a challenging part of the Marco Polo mission. The approach is easily extended to stand-off orbits. We show that DMOC is applicable to spacecraft control at small objects, in particular with regard to the fact that the changes in gravity are exploited by the algorithm to optimally control the spacecraft position. Furthermore, we provide some remarks on promising developments.
This study presents the concept of AstroBioLab, an autonomous astrobiological field laboratory tailored for the exploration of (sub)glacial habitats. AstroBioLab is an integral component of the TRIPLE (Technologies for Rapid Ice Penetration and subglacial Lake Exploration) DLR-funded project, aimed at advancing astrobiology research through the development and deployment of innovative technologies. AstroBioLab integrates diverse measurement techniques such as fluorescence microscopy, DNA sequencing and fluorescence spectrometry, while leveraging microfluidics for efficient sample delivery and preparation.
Humic substances possess distinctive chemical features enabling their use in many advanced applications, including biomedical fields. No chemicals in nature have the same combination of specific chemical and biological properties as humic substances. Traditional medicine and modern research have demonstrated that humic substances from different sources possess immunomodulatory and anti-inflammatory properties, which makes them suitable for the prevention and treatment of chronic dermatoses, allergic rhinitis, atopic dermatitis, and other conditions characterized by inflammatory and allergic responses [1-4]. The use of humic compounds as agentswith antifungal and antiviral properties shows great potential [5-7].
Pulmonary arterial cannulation is a common and effective method for percutaneous mechanical circulatory support for concurrent right heart and respiratory failure [1]. However, limited data exists to what effect the positioning of the cannula has on the oxygen perfusion throughout the pulmonary artery (PA). This study aims to evaluate, using computational fluid dynamics (CFD), the effect of different cannula positions in the PA with respect to the oxygenation of the different branching vessels in order for an optimal cannula position to be determined. The four chosen different positions (see Fig. 1) of the cannulas are, in the lower part of the main pulmonary artery (MPA), in the MPA at the junction between the right pulmonary artery (RPA) and the left pulmonary artery (LPA), in the RPA at the first branch of the RPA and in the LPA at the first branch of the LPA.
Effective training requires high muscle forces potentially leading to training-induced injuries. Thus, continuous monitoring and controlling of the loadings applied to the musculoskeletal system along the motion trajectory is required. In this paper, a norm-optimal iterative learning control algorithm for the robot-assisted training is developed. The algorithm aims at minimizing the external knee joint moment, which is commonly used to quantify the loading of the medial compartment. To estimate the external knee joint moment, a musculoskeletal lower extremity model is implemented in OpenSim and coupled with a model of an industrial robot and a force plate mounted at its end-effector. The algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee. The results show that the algorithm is able to minimize the external knee joint moment in all three cases and converges after less than seven iterations.
Direct methods comprising limit and shakedown analysis is a branch of computational mechanics. It plays a significant role in mechanical and civil engineering design. The concept of direct method aims to determinate the ultimate load bearing capacity of structures beyond the elastic range. For practical problems, the direct methods lead to nonlinear convex optimization problems with a large number of variables and onstraints. If strength and loading are random quantities, the problem of shakedown analysis is considered as stochastic programming. This paper presents a method so called chance constrained programming, an effective method of stochastic programming, to solve shakedown analysis problem under random condition of strength. In this our investigation, the loading is deterministic, the strength is distributed as normal or lognormal variables.
When confining pressure is low or absent, extensional fractures are typical, with fractures occurring on unloaded planes in rock. These “paradox” fractures can be explained by a phenomenological extension strain failure criterion. In the past, a simple empirical criterion for fracture initiation in brittle rock has been developed. But this criterion makes unrealistic strength predictions in biaxial compression and tension. A new extension strain criterion overcomes this limitation by adding a weighted principal shear component. The weight is chosen, such that the enriched extension strain criterion represents the same failure surface as the Mohr–Coulomb (MC) criterion. Thus, the MC criterion has been derived as an extension strain criterion predicting failure modes, which are unexpected in the understanding of the failure of cohesive-frictional materials. In progressive damage of rock, the most likely fracture direction is orthogonal to the maximum extension strain. The enriched extension strain criterion is proposed as a threshold surface for crack initiation CI and crack damage CD and as a failure surface at peak P. Examples show that the enriched extension strain criterion predicts much lower volumes of damaged rock mass compared to the simple extension strain criterion.
Following the recent successful landings and occasional re-awakenings of PHILAE, the lander carried aboard ROSETTA to comet 67P/Churyumov-Gerasimenko, and the launch of the Mobile Asteroid Surface Scout, MASCOT, aboard the HAYABUSA2 space probe to asteroid (162173) Ryugu we present an overview of the characteristics and peculiarities of small spacecraft missions to small solar system bodies (SSSB). Their main purpose is planetary science which is transitioning from a ‘pure’ science of observation of the distant to one also supporting in-situ applications relevant for life on Earth. Here we focus on missions at the interface of SSSB science and planetary defence applications. We provide a brief overview of small spacecraft SSSB missions and on this background present recent missions, projects and related studies at the German Aerospace Center, DLR, that contribute to the worldwide planetary defence community. These range from Earth orbit technology demonstrators to active science missions in interplanetary space. We provide a summary of experience from recently flown missions with DLR participation as well as a number of studies. These include PHILAE, the lander of ESA’s ROSETTA comet rendezvous mission now on the surface of comet 67P/Churyumov-Gerasimenko, and the Mobile Asteroid Surface Scout, MASCOT, now in cruise to the ~1 km diameter C-type near-Earth asteroid (162173) Ryugu aboard the Japanese sample-return probe HAYABUSA2. We introduce the differences between the conventional methods employed in the design, integration and testing of large spacecraft and the new approaches developed by small spacecraft projects. We expect that the practical experience that can be gained from projects on extremely compressed timelines or with high-intensity operation phases on a newly explored small solar system body can contribute significantly to the study, preparation and realization of future planetary defence related missions. One is AIDA (Asteroid Impact & Deflection Assessment), a joint effort of ESA, JHU/APL, NASA, OCA and DLR, combining JHU/APL’s DART (Double Asteroid Redirection Test) and ESA’s AIM (Asteroid Impact Monitor) spacecraft in a mission towards near-Earth binary asteroid system (65803) Didymos. DLR is currently applying MASCOT heritage and lessons learned to the design of MASCOT2, a lander for the AIM mission to support a bistatic low frequency radar experiment with PHILAE/ROSETTA CONSERT heritage to explore the inner structure of Didymoon which is the designated impact target for DART.
We present the novel concept of a combined drilling and melting probe for subsurface ice research. This probe, named “IceMole”, is currently developed, built, and tested at the FH Aachen University of Applied Sciences’ Astronautical Laboratory. Here, we describe its first prototype design and report the results of its field tests on the Swiss Morteratsch glacier. Although the IceMole design is currently adapted to terrestrial glaciers and ice shields, it may later be modified for the subsurface in-situ investigation of extraterrestrial ice, e.g., on Mars, Europa, and Enceladus. If life exists on those bodies, it may be present in the ice (as life can also be found in the deep ice of Earth).
Interplanetary trajectories for low-thrust spacecraft are often characterized by multiple revolutions around the sun. Unfortunately, the convergence of traditional trajectory optimizers that are based on numerical optimal control methods depends strongly on an adequate initial guess for the control function (if a direct method is used) or for the starting values of the adjoint vector (if an indirect method is used). Especially when many revolutions around the sun are re-
quired, trajectory optimization becomes a very difficult and time-consuming task that involves a lot of experience and expert knowledge in astrodynamics and optimal control theory, because an adequate initial guess is extremely hard to find. Evolutionary neurocontrol (ENC) was proposed as a smart method for low-thrust trajectory optimization that fuses artificial neural networks and evolutionary algorithms to so-called evolutionary neurocontrollers (ENCs) [1]. Inspired by natural archetypes, ENC attacks the trajectoryoptimization problem from the perspective of artificial intelligence and machine learning, a perspective that is quite different from that of optimal control theory. Within the context of ENC, a trajectory is regarded as the result of a spacecraft steering strategy that maps permanently the actual spacecraft state and the actual target state onto the actual spacecraft control vector. This way, the problem of searching the optimal spacecraft trajectory is equivalent to the problem of searching (or "learning") the optimal spacecraft steering strategy. An artificial neural network is used to implement such a spacecraft steering strategy. It can be regarded as a parameterized function (the network function) that is defined by the internal network parameters. Therefore, each distinct set of network parameters defines a different network function and thus a different steering strategy. The problem of searching the optimal steering strategy is now equivalent to the problem of searching the optimal set of network parameters. Evolutionary algorithms that work on a population of (artificial) chromosomes are used to find the optimal network parameters, because the parameters can be easily mapped onto a chromosome. The trajectory optimization problem is solved when the optimal chromosome is found. A comparison of solar sail trajectories that have been published by others [2, 3, 4, 5] with ENC-trajectories has shown that ENCs can be successfully applied for near-globally optimal spacecraft control [1, 6] and that they are able to find trajectories that are closer to the (unknown) global optimum, because they explore the trajectory search space more exhaustively than a human expert can do. The obtained trajectories are fairly accurate with respect to the terminal constraint. If a more accurate trajectory is required, the ENC-solution can be used as an initial guess for a local trajectory optimization method. Using ENC, low-thrust trajectories can be optimized without an initial guess and without expert attendance.
Here, new results for nuclear electric spacecraft and for solar sail spacecraft are presented and it will be shown that ENCs find very good trajectories even for very difficult problems. Trajectory optimization results are presented for 1. NASA's Solar Polar Imager Mission, a mission to attain a highly inclined close solar orbit with a solar sail [7] 2. a mission to de ect asteroid Apophis with a solar sail from a retrograde orbit with a very-high velocity impact [8, 9] 3. JPL's \2nd Global Trajectory Optimization Competition", a grand tour to visit four asteroids from different classes with a NEP spacecraft