Assessing the use of network theory as a method for developing a targeted approach to Active Debris Removal

Abstract

This thesis reports on the application of network theory to data representing space debris in Low Earth Orbit. The research was designed with a view to developing a targeted approach to Active Debris Removal (ADR). The need for remediation, via ADR, of the space debris environment is regarded as the only means by which we can control the growth of the future debris population to maintain use of Earth orbit. A targeted approach to ADR is required to remove the objects that pose the greatest risk in terms of the creation of further debris by explosions or collisions in the future. Methods of determining target criteria are debated in the literature. Network theory is introduced here as an alternative method that, unlike other methods, does not treat debris-producing events in isolation and examines the role of objects in series of conjunctions. The research involved using networks to represent various aspects of the space debris environment. Network theory analysis was carried out on the datasets to determine specific characteristics such as the presence of clustering and the extent of disassortative mixing. Once general characteristics of the 'space debris networks' were determined, two case studies were used as preliminary investigations to assess the use of network theory for targeting objects for removal. The research shows that network theory can be used to determine that `space debris networks' are robust and disassortative. Although there are limitations due to the uncertainties in the data used to create the networks, the findings suggest that careful development and application of target criteria would result in successful ADR.

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Identifiers

ORCID for H.G. Lewis: orcid.org/0000-0002-3946-8757

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