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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).
Solar sails are large and lightweight reflective structures that are propelled by solar radiation pressure. This chapter covers their orbital and attitude dynamics and control. First, the advantages and limitations of solar sails are discussed and their history and development status is outlined. Because the dynamics of solar sails is governed by the (thermo-)optical properties of the sail film, the basic solar radiation pressure force models have to be described and compared before parameters to measure solar sail performance can be defined. The next part covers the orbital dynamics of solar sails for heliocentric motion, planetocentric motion, and motion at Lagrangian equilibrium points. Afterwards, some advanced solar radiation pressure force models are described, which allow to quantify the thrust force on solar sails of arbitrary shape, the effects of temperature, of light incidence angle, of surface roughness, and the effects of optical degradation of the sail film in the space environment. The orbital motion of a solar sail is strongly coupled to its rotational motion, so that the attitude control of these soft and flexible structures is very challenging, especially for planetocentric orbits that require fast attitude maneuvers. Finally, some potential attitude control methods are sketched and selection criteria are given.
Low-thrust space propulsion systems enable flexible high-energy deep space missions, but the design and optimization of the interplanetary transfer trajectory is usually difficult. It involves much experience and expert knowledge because the convergence behavior of traditional local trajectory optimization methods depends strongly on an adequate initial guess. Within this extended abstract, evolutionary neurocontrol, a method that fuses artificial neural networks and evolutionary algorithms, is proposed as a smart global method for low-thrust trajectory optimization. It does not require an initial guess. The implementation of evolutionary neurocontrol is detailed and its performance is shown for an exemplary mission.
Solar sails enable missions to the outer solar system and beyond, although the solar
radiation pressure decreases with the square of solar distance. For such missions, the solar sail may gain a large amount of energy by first making one or more close approaches to the sun. Within this paper, optimal trajectories for solar sail missions to the outer planets and into near interstellar space (200 AU) are presented. Thereby, it is shown that even near/medium-term solar sails with relatively moderate performance allow reasonable transfer times to the boundaries of the solar system.
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
The concept of a laser-enhanced solar sail is introduced and the radiation pressure force model for an ideal laser-enhanced solar sail is derived. A laser-enhanced solar sail is a “traditional” solar sail that is, however, not solely propelled by solar radiation, but additionally by a laser beam that illuminates the sail. The additional laser radiation pressure increases the sail's propulsive force and can give, depending on the location of the laser source, more control authority over the direction of the solar sail’s propulsive force vector. This way, laser-enhanced solar sails may augment already existing solar sail mission concepts and make novel mission concepts feasible.
Conzen, Catharina ; Albanna, Walid ; Weiss, Miriam ; Kürten, David ; Vilser, Walthard ; Kotliar, Konstantin ; Zäske, Charlotte ; Clusmann, Hans ; Schubert, Gerrit Alexander
Impaired cerebral autoregulation and neurovascular coupling (NVC) contribute to delayed cerebral ischemia after subarachnoid hemorrhage (SAH). Retinal vessel analysis (RVA) allows non-invasive assessment of vessel dimension and NVC hereby demonstrating a predictive value in the context of various neurovascular diseases. Using RVA as a translational approach, we aimed to assess the retinal vessels in patients with SAH. RVA was performed prospectively in 24 patients with acute SAH (group A: day 5–14), in 11 patients 3 months after ictus (group B: day 90 ± 35), and in 35 age-matched healthy controls (group C). Data was acquired using a Retinal Vessel Analyzer (Imedos Systems UG, Jena) for examination of retinal vessel dimension and NVC using flicker-light excitation. Diameter of retinal vessels—central retinal arteriolar and venular equivalent—was significantly reduced in the acute phase (p < 0.001) with gradual improvement in group B (p < 0.05). Arterial NVC of group A was significantly impaired with diminished dilatation (p < 0.001) and reduced area under the curve (p < 0.01) when compared to group C. Group B showed persistent prolonged latency of arterial dilation (p < 0.05). Venous NVC was significantly delayed after SAH compared to group C (A p < 0.001; B p < 0.05). To our knowledge, this is the first clinical study to document retinal vasoconstriction and impairment of NVC in patients with SAH. Using non-invasive RVA as a translational approach, characteristic patterns of compromise were detected for the arterial and venous compartment of the neurovascular unit in a time-dependent fashion. Recruitment will continue to facilitate a correlation analysis with clinical course and outcome.
Edge-based and face-based smoothed finite element methods (ES-FEM and FS-FEM, respectively) are modified versions of the finite element method allowing to achieve more accurate results and to reduce sensitivity to mesh distortion, at least for linear elements. These properties make the two methods very attractive. However, their implementation in a standard finite element code is nontrivial because it requires heavy and extensive modifications to the code architecture. In this article, we present an element-based formulation of ES-FEM and FS-FEM methods allowing to implement the two methods in a standard finite element code with no modifications to its architecture. Moreover, the element-based formulation permits to easily manage any type of element, especially in 3D models where, to the best of the authors' knowledge, only tetrahedral elements are used in FS-FEM applications found in the literature. Shape functions for non-simplex 3D elements are proposed in order to apply FS-FEM to any standard finite element.
Ciritsis, Alexander ; Horbach, Andreas ; Staat, Manfred ; Kuhl, Christiane K. ; Kraemer, Nils Andreas
Purpose
In vivo, a loss of mesh porosity triggers scar tissue formation and restricts functionality. The purpose of this study was to evaluate the properties and configuration changes as mesh deformation and mesh shrinkage of a soft mesh implant compared with a conventional stiff mesh implant in vitro and in a porcine model.
Material and Methods
Tensile tests and digital image correlation were used to determine the textile porosity for both mesh types in vitro. A group of three pigs each were treated with magnetic resonance imaging (MRI) visible conventional stiff polyvinylidene fluoride meshes (PVDF) or with soft thermoplastic polyurethane meshes (TPU) (FEG Textiltechnik mbH, Aachen, Germany), respectively. MRI was performed with a pneumoperitoneum at a pressure of 0 and 15 mmHg, which resulted in bulging of the abdomen. The mesh-induced signal voids were semiautomatically segmented and the mesh areas were determined. With the deformations assessed in both mesh types at both pressure conditions, the porosity change of the meshes after 8 weeks of ingrowth was calculated as an indicator of preserved elastic properties. The explanted specimens were examined histologically for the maturity of the scar (collagen I/III ratio).
Results
In TPU, the in vitro porosity increased constantly, in PVDF, a loss of porosity was observed under mild stresses. In vivo, the mean mesh areas of TPU were 206.8 cm2 (± 5.7 cm2) at 0 mmHg pneumoperitoneum and 274.6 cm2 (± 5.2 cm2) at 15 mmHg; for PVDF the mean areas were 205.5 cm2 (± 8.8 cm2) and 221.5 cm2 (± 11.8 cm2), respectively. The pneumoperitoneum-induced pressure increase resulted in a calculated porosity increase of 8.4% for TPU and of 1.2% for PVDF. The mean collagen I/III ratio was 8.7 (± 0.5) for TPU and 4.7 (± 0.7) for PVDF.
Conclusion
The elastic properties of TPU mesh implants result in improved tissue integration compared to conventional PVDF meshes, and they adapt more efficiently to the abdominal wall. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 106B: 827–833, 2018.