INDEMN. A long-term collision risk prediction tool for constellation design. Romain Lucken, Damien Giolito,

Transcription

1 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

2 Share My Space: Who are we? Private company created in partners, 3 collaborators, 16 students Based in Paris, globally committed Space situational Awareness Active Debris Removal Services Soon Technology demonstration Research & Education 2

3 INDEMN: Motivation The context 466 satellites launched in 2018 New record! Large constellations to come > 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

4 Future constellations Operator / Manufacturer Number Weight [kg] Unveil. Launch Orbit [km] Bandwidth Band Inter-satellite link Present O3b/TAS+boeing GB/s Ka Aucune 20 en Iridium Next/TAS -Orbital ATK Mbit/s L-Ka K 30 (Nov. 2017) OneWeb/Airbus TB/s total Ku-Ka Aucune st launch Telesat LEO/ Airbus SSTL / Loral 117 NA Several TB/s total Ka Optique 2 launches 2018 LeoSat/TAS Haut débit Ka Optique st launch Starlink / SpaceX 4600 NA Haut débit Ku-Ka Optical Prototype on-orbit Boeing/Boeing Sat NA 2016 > Haut débit V Aucune Samsung 4600 NA TB/s V GHz + Airbus EarthNow, and others 4

5 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 [2] G. L. Somma, C. Colombo, and H. G. Lewis. A Statistical LEO Model to Investigate Adaptable Debris Control Strategies. 7th ECSD, Darmstadt [3] G. L. Somma, H. G. Lewis, and C. Colombo. Sensitivity Analysis for a Space Debris Environment Model. 7th EUCASS, Milan, [4] N. L. Johnson et al. NASA's new breakup model of evolve 4.0. Advances in Space Research 28-9,

6 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

7 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

8 Atmospheric density Atmosphere density data from NRL-MISE-00 averaged over orbit angles Proposed fit Radial speed due to the drag Loss term 8

9 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

10 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 year deorbitation: 90% compliance Single size populations No explosion 10

11 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)

12 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

13 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

14 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

15 Conclusions A handy statistical tool Fast computations Flexible inputs Populations - models - previsions Benchmarked and validated An online demonstrator available (free) 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

16 Share My Space Network We are particularly grateful to 16

17 Feel free to visit our website or contact to learn more. Let s meet at IAC 2018 in Bremen! Thank you for your attention 17