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An adaptive strategy to control the space debris population

An adaptive strategy to control the space debris population
An adaptive strategy to control the space debris population
As a result of the last 60 years of satellite launches, a significant amount of space debris has been generated in Earth orbit. Growing consensus amongst experts over the last decade, has suggested that removing existing debris, alongside mitigation efforts, can assist in controlling the size of the low Earth orbit (LEO) population. However, no objective or long-term strategy exist to ensure the most effective use of active debris removal (ADR).

The way we utilise near-Earth space, and the way the space environment will behave in the future will directly affect the number of debris objects required to be removed. This then, makes it difficult to identify any potential future ADR strategy that will perform effectively in all possible future cases. This thesis explores a novel adaptive strategy that determines how many debris objects should be removed to control the size of the LEO debris population. The strategy adapts and adjusts the number of removals performed by ADR in response to the evolution of the debris population.

The framework for the strategy was inspired by the methods incorporated in adaptive management and control engineering. The University of Southampton’s Debris Analysis and Monitoring Architecture to the Geosynchronous Environment (DAMAGE) model was used to represent the space environment, whilst a new debris model entitled the Computational Adaptive Strategy to Control Accurately the Debris Environment (CASCADE) was used to predict the evolution of DAMAGE, and required removal rate. Predictions using DAMAGE were run under a variety of launch, explosion, mitigation and solar activity for both the ?10 cm and ?5 cm LEO populations. Two key parameters of the adaptive strategy were also investigated: modifying the frequency of implementation and exploring different high-level objeives for the strategy.

Using the adaptive strategy increased the probability of achieving its objective and required fewer removals, as each prediction had a bespoke number of removals. On average, 3.1 removals (standard deviation: 1.2) were required to provide an 88% probability in preventing the growth of the ?10 cm LEO population. Whereas, implementing realistic variations in launch, explosion, mitigation and solar activity, on average, 6.3 removals (standard deviation: 6.8) were required to prevent the growth of the ?5 cm LEO population with 76% confidence. This compared with a “traditional” strategy of removing five objects per year that only provided 49% confidence. This approach then, represents a rational method to calculate the number of removals required to ensure the future sustainability of outer space activities.
White, Adam Edward
d7f1797e-d6be-47f9-b482-49570bf18b8f
White, Adam Edward
d7f1797e-d6be-47f9-b482-49570bf18b8f
Lewis, Hugh
e9048cd8-c188-49cb-8e2a-45f6b316336a

White, Adam Edward (2014) An adaptive strategy to control the space debris population. University of Southampton, Engineering and the Environment, Doctoral Thesis, 179pp.

Record type: Thesis (Doctoral)

Abstract

As a result of the last 60 years of satellite launches, a significant amount of space debris has been generated in Earth orbit. Growing consensus amongst experts over the last decade, has suggested that removing existing debris, alongside mitigation efforts, can assist in controlling the size of the low Earth orbit (LEO) population. However, no objective or long-term strategy exist to ensure the most effective use of active debris removal (ADR).

The way we utilise near-Earth space, and the way the space environment will behave in the future will directly affect the number of debris objects required to be removed. This then, makes it difficult to identify any potential future ADR strategy that will perform effectively in all possible future cases. This thesis explores a novel adaptive strategy that determines how many debris objects should be removed to control the size of the LEO debris population. The strategy adapts and adjusts the number of removals performed by ADR in response to the evolution of the debris population.

The framework for the strategy was inspired by the methods incorporated in adaptive management and control engineering. The University of Southampton’s Debris Analysis and Monitoring Architecture to the Geosynchronous Environment (DAMAGE) model was used to represent the space environment, whilst a new debris model entitled the Computational Adaptive Strategy to Control Accurately the Debris Environment (CASCADE) was used to predict the evolution of DAMAGE, and required removal rate. Predictions using DAMAGE were run under a variety of launch, explosion, mitigation and solar activity for both the ?10 cm and ?5 cm LEO populations. Two key parameters of the adaptive strategy were also investigated: modifying the frequency of implementation and exploring different high-level objeives for the strategy.

Using the adaptive strategy increased the probability of achieving its objective and required fewer removals, as each prediction had a bespoke number of removals. On average, 3.1 removals (standard deviation: 1.2) were required to provide an 88% probability in preventing the growth of the ?10 cm LEO population. Whereas, implementing realistic variations in launch, explosion, mitigation and solar activity, on average, 6.3 removals (standard deviation: 6.8) were required to prevent the growth of the ?5 cm LEO population with 76% confidence. This compared with a “traditional” strategy of removing five objects per year that only provided 49% confidence. This approach then, represents a rational method to calculate the number of removals required to ensure the future sustainability of outer space activities.

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More information

Published date: November 2014
Organisations: University of Southampton, Astronautics Group

Identifiers

Local EPrints ID: 377012
URI: http://eprints.soton.ac.uk/id/eprint/377012
PURE UUID: 1dfa2481-1e49-4a0f-9ae2-045a756a102b

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Date deposited: 07 Jul 2015 12:47
Last modified: 17 Jul 2017 21:04

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Contributors

Author: Adam Edward White
Thesis advisor: Hugh Lewis

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