Type of Document Master's Thesis Author Berryman, John Fagan Author's Email Address firstname.lastname@example.org URN etd-12072005-221725 Title Analytical and Numerical Analysis of Static Coulomb Formations Degree Master of Science Department Aerospace and Ocean Engineering Advisory Committee
Advisor Name Title Schaub, Hanspeter Committee Chair Hall, Christopher D. Committee Member Lovell, Thomas Alan Committee Member Keywords
- Spacecraft Formation
- Coulomb Spacecraft
Date of Defense 2005-11-16 Availability unrestricted AbstractFor close proximity flying on the order of 10-100 meters, Coulomb thrusting presents a
promising alternative to other methods of propulsion. This clean and fuel-efficient propulsion
method is being investigated for use in formation flying and virtual structures. In the latter
application, the individual spacecraft assume fixed positions relative to each other through
the use of Coulomb forces. In the work presented here, an analytical and numerical analysis
is performed on such virtual structures. In the analytical portion, the constant, open-loop
charges necessary to maintain a Hill-frame-static formation are determined for the cases
of linear two- and three-spacecraft formations and for the case of equilateral triangular
formations with spacecraft of equal mass. In addition, analysis is provided for the N-craft
case so that the inter-craft charge products can be determined for any static formation.
In the numerical portion, a genetic algorithm is employed to support the analytical results
by determining formation geometries and charging schemes such that the formation craft
remain static in the Hill frame in the absence of perturbation. The results of the numerical
analysis include examples of static two-craft through nine-craft formations, including several
formations that display a broader range of configurations than considered in previous works.
Issues encountered during the numerical analysis are discussed, as well as the course of
action taken to overcome these issues. Finally, a method is presented by which the genetic
algorithm could be extended to take advantage of cluster computing.
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