Title page for ETD etd-12072005-221725

Type of Document Master's Thesis
Author Berryman, John Fagan
Author's Email Address berryman@vt.edu
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
  • Spacecraft Formation
  • Coulomb Spacecraft
Date of Defense 2005-11-16
Availability unrestricted
For 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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