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Solar Sail Trajectory Optimization for Intercepting, Impacting, and Deflecting Near-Earth Asteroids
(2005)
Solar Sails for Near- and Medium-Term Scientific Deep Space Missions / W. Sebolt ; B. Dachwald
(2005)
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).
The optical properties of the thin metalized polymer films that are projected for solar sails are assumed to be affected by the erosive effects of the space environment. Their degradation behavior in the real space environment, however, is to a considerable degree indefinite, because initial ground test results are controversial and relevant inspace tests have not been made so far. The standard optical solar sail models that are currently used for trajectory design do not take optical degradation into account, hence its potential effects on trajectory design have not been investigated so far. Nevertheless, optical degradation is important for high-fidelity solar sail mission design, because it decreases both the magnitude of the solar radiation pressure force acting on the sail and also the sail control authority. Therefore, we propose a simple parametric optical solar sail degradation model that describes the variation of the sail film’s optical coefficients with time, depending on the sail film’s environmental history, i.e., the radiation dose. The primary intention of our model is not to describe the exact behavior of specific film-coating combinations in the real space environment, but to provide a more general parametric framework for describing the general optical degradation behavior of solar sails. Using our model, the effects of different optical degradation behaviors on trajectory design are investigated for various exemplary missions.
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.
Improved collapse loads of thick-walled, crack containing pipes and vessels are suggested. Very deep cracks have a residual strength which is better modelled by a global limit load. In all burst tests, the ductility of pressure vessel steels was sufficiently high whereby the burst pressure could be predicted by limit analysis with no need to apply fracture mechanics. The relative prognosis error increases however, for long and deep defects due to uncertainties of geometry and strength data.
This work is an attempt to answer the question: How to use convex programming in shakedown analysis of structures made of materials with temperature-dependent properties. Based on recently established shakedown theorems and formulations, a dual relationship between upper and lower bounds of the shakedown limit load is found, an algorithmfor shakedown analysis is proposed. While the original problem is neither convex nor concave, the algorithm presented here has the advantage of employing convex programming tools.
An optimization method is developed to describe the mechanical behaviour of the human cancellous bone. The method is based on a mixture theory. A careful observation of the behaviour of the bone material leads to the hypothesis that the bone density is controlled by the principal stress trajectories (Wolff’s law). The basic idea of the developed method is the coupling of a scalar value via an eigenvalue problem to the principal stress trajectories. On the one hand this theory will permit a prediction of the reaction of the biological bone structure after the implantation of a prosthesis, on the other hand it may be useful in engineering optimization problems. An analytical example shows its efficiency.
Mechanical stimulation of the cells resulted in evident changes in the cell morphology, protein composition and gene expression. Microscopically, additional formation of stress fibers accompanied by cell re-arrangements in a monolayer was observed. Also, significant activation of p53 gene was revealed as compared to control. Interestingly, the use of CellTech membrane coating induced cell death after mechanical stress had been applied. Such an effect was not detected when fibronectin had been used as an adhesion substrate.