1. DEBRIS REMOVAL DESIGN DRIVERS BASED ON TARGET SELECTION 2 nd European Workshop on Active Debris Removal CNES HQ, Paris, 18 th - 19 th July 2012 Adam White: adam.white@southampton.ac.uk Hugh Lewis: H.G.Lewis@soton.ac.uk University of Southampton Hedley Stokes: hedley_stokes@msn.com PHS Space Ltd.

2. It is probable that the space debris population will continue to grow even with a good compliance of commonly adopted mitigation measures This growth will be driven predominately by catastrophic collisions in Low Earth Orbit (LEO) Studies shave shown that Active Debris Removal (ADR) can potentially be an effective measure in reducing the population of space debris in the long-term An important challenge associated with ADR is the choice of targets to be removed The aim of this study is to investigate the effectiveness of ADR when targets are constrained to orbital regimes and object types The work presented is part of the Alignment of Capability and Capacity for the Objective of Reducing Debris (ACCORD) project Introduction

3. ACCORD Project Survey the capability of industry to implement debris mitigation measures Review the capacity of mitigation measures to reduce debris creation  Investigate measures to reduce space debris including ADR scenarios Combine capability and capacity indicators within an environmental impact ratings system Alignment of Capability and Capacity for the Objective of Reducing Debris http://www.fp7-accord.eu/ Aim: To communicate the efficiency of current debris mitigation practices and to identify opportunities for strengthening European capability European Commission FP7

4. ADR Target Selection An effective target selection criterion,, to reduce the risk of large fragmentation collisions occurring is to remove intact targets with the highest mass-collision probability product: (1)  = mass of intact target i  = probability of collision on target i at time t Does not take into account constraints of ADR vehicle design and concept of operations Over a 200 year projection using Eqn. (1), clusters of objects with similar inclination and altitudes emerge Removing targets from only one cluster at a time can focus design drivers for ADR vehicles

5. The top 567 ADR targets orbit parameters for one Monte Carlo (MC) simulation using the Debris Analysis and Monitoring Architecture to the Geosynchronous Environment (DAMAGE) model. Based on Equation (1) Top ADR Targets (1)

6. The top 567 ADR targets orbit parameters for one Monte Carlo (MC) simulation using the Debris Analysis and Monitoring Architecture to the Geosynchronous Environment (DAMAGE) model. Based on Equation (1) Top ADR Targets (1) 1 2 3 4 5

7. 4 1 2 3 5 Top ADR Targets (2)

8. DAMAGE was used to quantify the effect of removing target objects on a yearly basis from these clusters Spacecraft (S/C) and rocket bodies (R/B) debris were assigned to a cluster (1-5), c, based on their Euclidean distance from a user-defined location (in the inclination-altitude parameter space) A cluster selection value, Q c, is assigned to each cluster: (2) –where n is the number of objects in the cluster DAMAGE simulated removals from the cluster having the highest Q c value Methodology

9. Study Scenarios ScenarioTarget selection criterionObject type/s removed 1No remediation- 2 Removal from cluster based on Eqn. (2) R/B 3 Removal from cluster based on Eqn. (2) S/C 4 Removal from cluster based on Eqn. (2) R/B + S/C 5 Removal based on Eqn. (1) R/B + S/C

10. Study Parameters Projection period: 2009 - 2209 Initial population: Meteoroid and Space Debris Terrestrial Environment Reference (MASTER) 2009 (1 st May 2009 epoch) Objects:  10 cm, orbits intersecting LEO Launch traffic: 8-year cycle (2001-2009) from MASTER 2009 Mitigation: passivation (100%), post-mission disposal (90%) following IADC mitigation guidelines Remediation: three removals a year (2020-2209), perigee altitude < 1400 km and eccentricity < 0.5 Time-step: 5 days Number of Monte Carlos (MC) per scenario: 100

11. Average LEO population

12. Summary of Results Scenario12345 Number of objects ≥10 cm 20579 (3383) 16563 (3118) 17293 (2634) 16480 (2477) 16499 (2152) % MC below initial population 1561486362 Number of damaging collisions 24.8 (6.8) 15.2 (4.5) 16.4 (4.7) 14.3 (4.2) 14.2 (4.4) Number of catastrophic collisions 36.8 (7.7) 27.3 (6.9) 29.5 (6.5) 28.2 (5.7) 27.9 (5.8)

13. ERF Values Scenario12345 Effective Reduction Factor (ERF) -7.085.797.237.20 ERF (Damaging collisions) - 0.0170.0150.0180.019 ERF (catastrophic collisions) - 0.0170.0130.0150.016 ratio---1.211.07

14. Cluster Selection 57 , 538 km 64 , 804 km 72.5 , 580 km 83 , 720 km 99 , 692 km

15. Conclusions & ADR Impacts Constraining removals to particular orbital regimes does not appear to compromise the effectiveness of ADR in LEO –ADR vehicles designed to remove multiple objects from a particular orbital regime will have reduced transfer energy requirements Constraining removals to particular orbital regimes leads to the majority of removals from ~83  (mostly R/Bs) and ~99  (mostly S/C) inclination orbits –ADR vehicle designs can be tailored to specific target types and orbital regimes Removing only R/Bs appears to be as effective as removals targeting both R/Bs and S/C –ADR vehicles can be targeted at R/Bs, which have common geometrical properties, grappling points etc., resulting in simpler, repeatable designs

16. Thank you Adam E. White adam.white@southampton.ac.uk Financial support for this work was provided the EU Framework 7 Programme (ACCORD Project, No. 262824). The authors would like to thank Holger Krag and Heiner Klinkrad (ESA ESOC) for permission to use the MASTER population data.