An adaptive strategy for active debris removal

作者：White Adam E^*; Lewis Hugh G

来源：Advances in Space Research, 2014, 53(8): 1195-1206.

DOI：10.1016/j.asr.2014.01.021

• Space debris
• Orbital debris
• Remediation
• Active debris removal
• Adaptive
• Strategy

Many parameters influence the evolution of the near-Earth debris population, including launch, solar, explosion and mitigation activities, as well as other future uncertainties such as advances in space technology or changes in social and economic drivers that effect the utilisation of space activities. These factors lead to uncertainty in the long-term debris population. This uncertainty makes it difficult to identify potential remediation strategies, involving active debris removal (ADR), that will perform effectively in all possible future cases. Strategies that cannot perform effectively, because of this uncertainty, risk either not achieving their intended purpose, or becoming a hindrance to the efforts of spacecraft manufactures and operators to address the challenges posed by space debris.
One method to tackle this uncertainty is to create a strategy that can adapt and respond to the space debris population. This work explores the concept of an adaptive strategy, in terms of the number of objects required to be removed by ADR, to prevent the low Earth orbit (LEO) debris population from growing in size. This was demonstrated by utilising the University of Southampton's Debris Analysis and Monitoring Architecture to the Geosynchronous Environment (DAMAGE) tool to investigate ADR rates (number of removals per year) that change over time in response to the current space environment, with the requirement of achieving zero growth of the LEO population.
DAMAGE was used to generate multiple Monte Carlo projections of the future LEO debris environment. Within each future projection, the debris removal rate was derived at five-year intervals, by a new statistical debris evolutionary model called the Computational Adaptive Strategy to Control Accurately the Debris Environment (CASCADE) model. CASCADE predicted the long-term evolution of the current DAMAGE population with a variety of different ADR rates in order to identify a removal rate that produced a zero net growth for that particular projection after 200 years.
The results show that using an adaptive ADR rate generated by CASCADE, alongside good compliance with existing mitigation measures, increases the probability of achieving a constant LEO population of objects greater than 10 cm. This was shown to be 12% greater compared with removing five objects per year, with the additional advantage of requiring only 3.1 removals per year, on average.

• 出版日期2014-4-15