INDEMN A long-term collision risk prediction tool for constellation design Romain Lucken, Damien Giolito, romain.lucken@sharemyspace.global 5th European Workshop on Space Debris Modeling and Remediation CNES, Paris, June 26th 2018
Share My Space: Who are we? Private company created in 2016 2 partners, 3 collaborators, 16 students Based in Paris, globally committed Space situational Awareness Active Debris Removal Services Soon Technology demonstration Research & Education 2
INDEMN: Motivation The context 466 satellites launched in 2018 New record! Large constellations to come > 15 000 satellites in operation permanently by 2030 Many more launches Increasing risk and uncertainty Cebreros Test-Bed telescope New prediction tool React to policy and business changes Select the best strategies Frequently updated resource of observational data 3
Future constellations Operator / Manufacturer Number Weight [kg] Unveil. Launch Orbit [km] Bandwidth Band Inter-satellite link Present O3b/TAS+boeing 27 700 2008 2014 8000 1 GB/s Ka Aucune 20 en 2018-2019 Iridium Next/TAS -Orbital ATK 66 + 9 860 2009 2018 780 1.4 Mbit/s L-Ka K 30 (Nov. 2017) OneWeb/Airbus 648+252 150 2015 2019 1200 10 TB/s total Ku-Ka Aucune 2019 1st launch Telesat LEO/ Airbus SSTL / Loral 117 NA 2016 2021 1000-1248 Several TB/s total Ka Optique 2 launches 2018 LeoSat/TAS 78-108 1250 2015 2022 1400 Haut débit Ka Optique 2019 1st launch Starlink / SpaceX 4600 NA 2015 2024 1110-1325 Haut débit Ku-Ka Optical Prototype on-orbit Boeing/Boeing Sat 1396-2956 NA 2016 > 2024 1200 Haut débit V Aucune Samsung 4600 NA 2015 2028 1500 300 TB/s V 22.55-190 GHz + Airbus EarthNow, and others 4
INDEMN: Model overview 1D statistical model of object density N classes of objects Customer satellites Intact objects Collision debris Explosion debris Initialization with TLE data Projection on a 1D axis Source terms Collision, explosion, future launches Sink terms Drag, EoL de-orbitation [1] G. L. Somma, H. G. Lewis, and C. Colombo. Adaptive remediation of the space debris environment using feedback control. A6-IP.3 IAC Guadalajara 2016. [2] G. L. Somma, C. Colombo, and H. G. Lewis. A Statistical LEO Model to Investigate Adaptable Debris Control Strategies. 7th ECSD, Darmstadt 2017. [3] G. L. Somma, H. G. Lewis, and C. Colombo. Sensitivity Analysis for a Space Debris Environment Model. 7th EUCASS, Milan, 2017. [4] N. L. Johnson et al. NASA's new breakup model of evolve 4.0. Advances in Space Research 28-9, 2001 5
INDEMN: Collision & explosion modules Explosions Debris generated according to the NASA breakup model Collision probability integrated over size distribution functions Allows for (basic) description of smaller debris (< 10 cm) Catastrophic & damaging collisions Collisions Object density Number of objects generated by 1 collision between (1) and (2) 6
Deorbitation strategies Compliance to the 25-year deorbitation rule Observed 20% Target (IADC 2012) 90% Background population Constellations (1) (2) Failure at beginning of the deorbitation Uniform failure probability during maneuver Deorbitation loss term Launch source term Assumption (1) more conservative Level of compliance Mission/deorbitation times 7
Atmospheric density Atmosphere density data from NRL-MISE-00 averaged over orbit angles Proposed fit Radial speed due to the drag Loss term 8
Numerical stability Model similar to a 1D fluid model CFL condition needs to be met Deorbitation velocity as a function of the altitude Two levels of solar activity 2 m diameter spherical object, 1 t/m3 9
IADC 2012 benchmark [5] J.-C. Liou et al., Stability of the Future LEO Environment. IADC-12-08, Rev. 1, 2013 Environment prediction until 2213 (Analogous to Somma et al.) 2 populations Intact Collision debris Assumptions Same launch profile as 2016 25-year deorbitation: 90% compliance Single size populations No explosion 10
Bastida et al. benchmark (Acta Astronaut. 2016) 5 populations Intact Collision debris Operational satellites Deorbiting satellites Failed satellites Background Constellation Assumptions Background: same as IADC 2012 [6] B. Bastida Virgili et al., Risk to space sustainability from large constellations of satellites, Acta Astronaut. 126 (2016) 154-162 11
Collision avoidance maneuvers Mean number of satellites lost due to collisions Collision avoidance with larger debris only Loss rate decreases linearly with the collision avoidance success rate Collision avoidance success rate Note: Depends only on the local density (no influence of the drag) 12
Constellation altitude Model inputs Altitude spread of ± 5 km Success rate of collision avoidance maneuvers 60% Observations Very low risk at low altitude (lifetime limited) Maximum at 800 km Almost constant from 1100 to 1500 km 13
2-constellation scenario Constellation (1) Same as previously 1200 km altitude 30 km of altitude spread 60% collision avoidance success Constellation (2) Same as (1) with 4 times more satellites 1400 km altitude Launches shifted by 6 years 14
Conclusions A handy statistical tool Fast computations Flexible inputs Populations - models - previsions Benchmarked and validated An online demonstrator available (free) https://indemn.herokuapp.com/ Multiple outputs - Future improvements Number of objects Population densities Mean losses Probability of losing 1 or more satellites Collision frequency Improve the model for smaller debris (validation case?) Uncertainty quantification 15
Share My Space Network We are particularly grateful to 16
Feel free to visit our website http://sharemyspace.global or contact romain.lucken@sharemyspace.global to learn more. Let s meet at IAC 2018 in Bremen! Thank you for your attention 17