The Effectiveness of Space Debris Mitigation Measures ISU’s 16 th Annual International Symposium 21 st February 2012 Adam E. White, Hugh G. Lewis, Hedley Stokes

Introduction Considerable effort was made to identify mitigation measures to constrain the growth of the debris population in LEO Resulting measures were published in – the Inter-Agency Space Debris Coordination Committee (IADC) Space Debris Mitigation Guidelines in 2002 –UN Space Debris Mitigation Guidelines in 2007 The capability to apply these measures is limited and some of these measures have a greater capacity to reduce debris creation than others The challenge of identifying, quantifying and addressing the constraints is the objective of the European Union Framework 7 project - Alignment of Capability and Capacity for the Objective of Reducing Debris (ACCORD) 2

ACCORD project Three stage approach: –Survey the capability of industry to implement debris mitigation measures and identifying existing and future challenges –Review the capacity of mitigation measures to reduce debris creation –Combine capability and capacity indicators within an environmental impact ratings system, which can easily communicate the effectiveness of mitigation practices and identify opportunities for strengthening capability and capacity 3

Quantifying mitigation The University of Southampton’s evolutionary model, the Debris Analysis and Monitoring Architecture for the Geosynchronous Environment (DAMAGE) was used to quantify mitigation measures in LEO Mitigation measures were adopted from the IADC Space Debris Mitigation Guidelines and fully implemented. These are: –Limit the release of mission-related objects –Passivation of spacecraft and rocket bodies at end of mission life –Collision avoidance manoeuvres on operational spacecraft –Post-mission disposal of spacecraft and rocket bodies conforming to the 25-year rule 4

DAMAGE model DAMAGE is a three-dimensional evolutionary computational model Uses a semi-analytical orbital propagator NASA standard breakup model Pair-wise collision prediction algorithm based on the ‘Cube’ approach adopted in NASA’s LEGEND Uses a Monte Carlo (MC) approach to account for stochastic elements The initial debris population used was derived from the Meteoroid and Space Debris Terrestrial Environment Reference (MASTER) 2009 population of objects  10 cm 5

DAMAGE study parameters 6 Objects:  10 cm, orbits intersecting LEO Initial population: MASTER 2009 (1 st May 2009 epoch) Launch traffic: repeating cycle ( ) from MASTER 2009 Conservative solar flux projection Nominal time-step: 5 days NASA Standard Break-up model utilised ‘Cube’-like collision algorithm: 10 km cube-size Projection period: 2009 through Monte Carlo runs per scenario

Mitigation implementation 7 Collision avoidance (CA) –Operational spacecraft manoeuvred to avoid all collisions within their 8 year operational lifetime Mission-related objects (MRO) –No mission-related objects were included Passivation (PASS) –No explosions occurred Post-mission disposal (PMD) –All spacecraft and rocket bodies launched after 1 May 2009 were manoeuvred to a 25-year decay orbit or to a graveyard orbit

Study scenarios 8 Scenario numberMitigation measures implemented MROPASSPMDCA 1 2  3  4  5  6  7   8   9  10   11  12  13   14  15   16  Performance metrics: –NERF (Normalised Effective Reduction Factor) –Synergy Metric

NERF The NERF (Δ) is a ranked number with compares a scenarios capacity to reduce debris The NERF compares the value of a simulation output quantity, Q with the corresponding output quantity from the worst cast (non-mitigation scenario), Q 1, and the ‘best-case’ scenario Q 2 : 9 A ranking of 1 indicates the greatest effect and a ranking of 0 indicates no effect

Synergy metric (S) Synergy occurs when two or more mitigation scenarios implemented together are greater than the sum of the individual mitigation measures implemented separately   is the NERF calculated for a scenario with multiple mitigation measures and ∑ Δ i is the sum of the NERF values calculated for the same mitigation measures when implemented separately: 10 A positive value identifies scenarios where an enhancement of the effectiveness of multiple mitigation measures occurs A negative value represents scenarios where multiple mitigation measures hinder one another

Results Without mitigation the LEO debris population will likely triple in size after 200 years In contrast full adoption of the four key mitigation measures, can limit the growth of the LEO debris population substantially 11

12 Top 8 NERF scenarios

13 Bottom 8 NERF scenarios

Positive Synergy scenarios 14

Negative Synergy scenarios 15

Conclusions Implementing fully all four mitigation measures significantly reduces the creation of new space debris in LEO Post-mission disposal is the most effective individual mitigation measure in LEO but it can lead to an increase in the number of collisions below 500 km Passivation and limiting the release of mission related objects have a lesser although still beneficial impact on the LEO debris population Collision avoidance manoeuvres appear only to have a limited effect on the LEO debris population however, they remain pivotal to the protection of individual space assets and work well with other mitigation measures to reduce the number of collisions 16

Thank you Adam E. White: Financial support for this work was provided by the EU Framework 7 Programme. The authors would like to thank Holger Krag and Heiner Klinkrad (ESA ESOC) for permission to use the MASTER population data.