A combination of multiple gravity-assist and impulsive ∆V maneuvers are often utilized for interplanetary missions to outer planets, such as NASA's Galileo and Cassini missions. Such complex interplanetary missions often require the optimization of more than 20 variables, making brute force and traditional NLP methods difficult, if not impossible. In this paper a genetic algorithm, which utilizes the computational power of modern GPUs, is developed to solve the problem of designing advanced mission. By using modern GPUs the computational burden for advanced mission designs can be drastically reduced over traditional CPU optimization programs, allowing the mission designer to explore multiple mission scenarios or other mission options.
This paper describes the orbital dispersion modeling, analysis, and simulation of a near-Earth object (NEO) fragmented and dispersed by nuclear subsurface explosions. It is shown that various fundamental approaches of Keplerian orbital dynamics can be effectively employed for the orbital dispersion analysis of fragmented NEOs. The nuclear subsurface explosion is the most powerful method for mitigating the impact threat of hazardous NEOs although a standoff explosion is often considered as the preferred approach among the nuclear options. In addition to non-technical concerns for using nuclear explosives in space, a common concern for such a powerful nuclear option is the risk that the deflection mission could result in fragmentation of the NEO, which could substantially increase the damage upon its Earth impact. However, this paper shows that under certain conditions, proper disruption (i.e., fragmentation and large dispersion) using a nuclear subsurface explosion even with shallow burial (< 5 m) is a feasible strategy providing considerable impact damage reduction if all other approaches failed.
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