102,138 research outputs found
Subcutaneous and Intra-Arterial Nitroglycerin Administration to Facilitate Femoral Artery Access for Neuroendovascular Procedures on Infants and Toddlers
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MR Imaging of Inner Ear Endo-perylinfatic Space at 3 Tesla after intratympanic Contrast Agent Administration in Definite Meniere's Disease
No full textOn the use of A* search for active debris removal mission planning
No full textThis paper focuses on the optimal design of an active debris removal mission. A cluster of debris orbiting in sun-synchronous orbit is considered. A numerical score is associated with each debris on the basis of its level of threat. The mission goal is to maximize the cumulative score of the removed debris, while meeting operational constraints on both the total mission ΔV and time. The optimization problem, that is equivalent to a Time-Dependent Orienteering Problem, is formulated in the paper as a search problem on a graph and solved by A*, an optimal tree search algorithm. Three admissible heuristics for enhancing A* performance on the ADR mission design problem are derived in the paper as the exact solutions of relaxed versions of the original combinatorial problem. Their effectiveness is assessed on missions of increasing dimension and complexity, and compared with that of a commercial branch-and-bound solver on an original 0–1 integer linear programming formulation of the problem. A fast-computation near-optimal transfer strategy, which cleverly exploits the J2 perturbation to achieve the correct alignment between the orbital planes, is used to pre-calculate the ΔV spent by the spacecraft to move between any pair of debris on a discrete grid of departure/arrival epochs. Numerical results are presented for a 21-debris cluster, by analyzing the effect of the debris score distribution, of the total mission time, and of the maximum transfer duration on the computational time required by the different algorithms to optimally solve the problem
Evolutionary optimization of multirendezvous impulsive trajectories
No full textThis paper investigates the use of evolutionary algorithms for the optimization of time-constrained impulsive multirendezvous missions. The aim is to find the minimum-ΔV trajectory that allows a chaser spacecraft to perform, in a prescribed mission time, a complete tour of a set of targets, such as space debris or artificial satellites, which move on the same orbital plane at slightly different altitudes. For this purpose, a two-level design approach is pursued. First, an outer-level combinatorial problem is defined, dealing with the simultaneous optimization of the sequence of targets and the rendezvous epochs. The suggested approach is first tested by assuming that all transfer legs last exactly the same amount of time; then, the time domain is discretized over a finer grid, allowing a more appropriate sizing of the time window allocated for each leg. The outer-level problem is solved by an in-house genetic algorithm, which features an effective permutation-preserving solution encoding. A simple, but fairly accurate, heuristic, based on a suboptimal four-impulse analytic solution of the single-target rendezvous problem, is used when solving the combinatorial problem for a fast guess at the transfer cost, given the departure and arrival epochs. The outer-level problem solution is used to define an inner-level NLP problem, concerning the optimization of each body-to-body transfer leg. In this phase, the encounter times are further refined. The inner-level problem is tackled through an in-house multipopulation self-adaptive differential evolution algorithm. Numerical results for case studies including up to 20 targets with different time grids are presented
Numerical Analysis of Reacting Nonequilibrium Two-dimensional Nozzle Flows Including Boundary Layer and Shock Waves
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Prime esperienze di controllo degli adulti di Capnodis tenebrionis (L.) (Coleoptera Buprestidae) in Molise
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