© the aerospace corporation 2011 determining risk from fragmentation events roger c. thompson the...

© The Aerospace Corporation 2011

Determining Risk from Fragmentation Events

Roger C. ThompsonThe Aerospace corporation

Systems Engineering DivisionThe Aerospace Corporation5 April 2011

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[email protected] Analysis and Simulation

Outline

•Background – space debris and risk modeling

•Debris environment

•The Aerospace Corporation’s experience in space debris and risk modeling– Launch collision avoidance– Debris Analysis Response Team (DART)

•The Aerospace Corporation’s research and development– Methodologies– Software

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mailto:[email protected]


3

Space Debris and Collision Risk Modeling

•Space debris has been growing operational concern for years– ISS Conjunctions, Mar 09– Iridium 33 / Cosmos 2251 collision, Feb 09– USA 193, Feb 08– Chinese ASAT test, Jan 07– On-orbit collisions (e.g. US rocket body / Chinese launch debris, Jan 05)– Multiple recent breakup events (e.g. SL-12, Mar 09, Briz-M, Feb 07)– Launch vehicle debris shedding (e.g. Delta IV / DMSP-17, Nov 06)

Aerospace models risk FROM– Cataloged objects– Background environment– Debris-producing events

Aerospace models risk TO– New launches (LCOLA)– Resident, active spacecraft (DART)– Specific close approach scenarios

as requested

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[email protected] Analysis and Simulation4

Space Debris Analysis

Debris Producing Event:• Collisions• ASATs• On-orbit breakups• Launch shedding

Characterize Debris Event:• Identify objects• Generate modeled debris• Determine breakup time

and location• Background models

Resident Space Object Catalog

Tracking Data:Aerospace Fusion CenterAFRL, NROC, JSPOC,ESA, NASA, AFSPC, SSN

Other space environment Other intelligence

Determine Risk, Create Products:• LCOLA• DART products• COLA• Anomaly resolution• Ops support

Figure is UNCLASSIFIED

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http://upload.wikimedia.org/wikipedia/commons/f/f3/US-MissileDefenseAgency-Seal.svg

http://upload.wikimedia.org/wikipedia/commons/d/d3/US-NationalReconnaissanceOffice-Seal.svg

http://upload.wikimedia.org/wikipedia/en/0/09/JSpOC_final.jpg

http://en.wikipedia.org/wiki/File:Launch_and_Range_Systems_Wing.png

http://en.wikipedia.org/wiki/File:Launch_and_Range_Systems_Wing.png

http://upload.wikimedia.org/wikipedia/commons/e/e5/NASA_logo.svg


Debris Environment

• Space debris comes from multiple sources

– Background debris (naturally occurring or manmade – too small to track)

– Cataloged debris (launch and deployment related – trackable)

– Debris producing events (explosions, collisions)

• Debris producing events generate a moderate number of large debris particles which will get cataloged and a huge number of smaller debris particles which will never be tracked or cataloged

– Smaller particles will eventually dissipate and become part of a slightly enhanced background

– Prior to dissipation, they pose an unseen, elevated risk to resident spacecraft

Size Class Quantity Impact

10 cm or larger

Hundreds •Tracked and cataloged by space surveillance network

•Catastrophic damage to spacecraft

1 cm to10 cm

Tens of thousands

•Most can’t be tracked

•Catastrophic damage to spacecraft

3 mm to1 cm

Millions •Can’t be tracked

• Localized damage only

Smaller than 3 mm

Millions •Can’t be tracked

•Minimal if any damage to spacecraft

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Background Models

• All active spacecraft implicitly accept risk from “background” of small, untrackable objects: micrometeoroids, man-made debris

• Two major background models

– NASA ORDEM 2000– ESA MASTER 05

• Neither model includes recent major breakup events

– ORDEM update being evaluated

• Aerospace applies both models to provide risk points of reference

Average risk/day in LEO = 3x10-6


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LCOLA Support Overview

•Aerospace provides Launch Collision Avoidance (LCOLA) analyses for NRO, SMC, and NASA (Goddard Spaceflight Center) launches– Support specifically required by Mission Directors for all NRO &

SMC missions– NASA support coordinated through OSL– Support to both rehearsals and launch

•Software development began in 1996– Probability of collision would open more launch opportunities– Protection would be consistent with distance-based blackouts

•Launch-on-Minute (LOM) or Launch-on-Second (LOS)

•Range Safety, Space Safety, and Mission Assurance COLAintegrated into a single simple report

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DART – Debris Event Quick Response CONOPS

Trajectory reconstruction

(Aerospace Fusion Center)

Government customers

Aerospace Process

Generate reportsAerospace

customer interface

Asset list

Asset list

Iterate as new data becomes available

Model database

NASA

Target determination

Debris generation

Collision risk assessment

Mission Ground

Sites


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External information

taskingevents



Debris Generation

•Typical ASAT event will produce over 4 million particles (discrete element sets)– Mass distribution

•Cumulative number of fragments of a given mass and larger

– Spread velocity distribution

•Fragment velocities relative to center of mass of debris cloud

•Determines extent of and density variations within debris cloud

– Area/mass distribution

•Function of constituent material densities

Mass Distribution

1

10

100

1000

10000

100000

1000000

10000000

1.00E-07 1.00E-06 1.00E-05 1.00E-04 1.00E-03 1.00E-02 1.00E-01 1.00E+00

1.00E+01

1.00E+02

Mass (kg)

Cu

mu

lati

ve

Nu

mb

er

of

Fra

gm

en

ts

Spread Velocity Distribution

0

500

1000

1500

2000

2500

3000

0 200 400 600 800 1000 1200 1400 1600 1800 2000

Spread Velocity (m/s)

Nu

mb

er o

f F

rag

men

ts >

1 c

m

Fragment Average Cross-sectional AreaSingle Material

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

1.E-04 1.E-03 1.E-02 1.E-01 1.E+00 1.E+01 1.E+02

Mass (kg)

Ave

rag

e C

ross

-Sec

tio

nal

Are

a (m

2)Figures are UNCLASSIFIED

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Collision Risk Assessment

A c

Satellite withcross-section

Expandingdebris cloud

N enc Fi ( )di

0

t

p col 1 e Nenc

Cumulative fluence(average no. impacts,summed over layers)

Collision probability

Path traversed bysatellite throughdebris cloud

Local debrisdensity

enc

Local debris relativeencounter velocity

Probabilistic Continuum Model of Debris Cloud


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Sample DART ReportIridium collision – Worst case 100% Fragmentation

Dec

reas

ing

Ris

k

1.E-12

1.E-11

1.E-10

1.E-09

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1-day cumulativerisk for ≥ 1 cm

Average Risk = 4.7e-7 Maximum Risk = 3.1e-6 Avg. Background (ORDEM2000) Avg. Background (Master2005)

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IRIDIUM 33 collision withCOSMOS 2251

10 Feb 2009 16:55:59.8 UTC

SSC Name Risk

25159 ORBCOMM-4 3.0E-0628893 SINAH-1 2.9E-0624793 IRIDIUM-07 2.8E-0624883 ORBVIEW-2 1.8E-0624841 IRIDIUM-16 1.6E-0624840 IRIDIUM-13 1.5E-0625171 IRIDIUM-54 1.2E-0625531 IRIDIUM-83 1.2E-0625077 IRIDIUM-42 1.2E-0625041 IRIDIUM-40 1.1E-06

Top 10 Worst

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Each scatter dot represents a space asset

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Space Risk for 10-11 Feb



Risk Analyses

•Multiple reports are generated at various stages in timeline

•Many reports aim at providing an understanding of risk for individual or groups of spacecraft

• Reports vary with circumstances of breakup, issue being explored

1.E-08

1.E-07

1.E-06

1.E-05

21-F

eb

24-F

eb

27-F

eb

1-M

ar

4-M

ar

7-M

ar

10-M

ar

13-M

ar

16-M

ar

19-M

ar

Average 3-day Cumulative Satellite Risk

Maximum 3-day Cumulative Satellite Risk

Background (ORDEM2000)

Background (ESA Master2005)

Risk values assume a 10 m2

satellite cross-sectional area.

Average risk over 565 satellites examined

Maximum risk for a single satellite out of the 565 satellites examined

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1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

Mid-Incl LEO Communications Sun-Synchronous Crit-Incl LEO

Ris

k M

ult

ipli

er

.

> 10 cm

1 cm - 10 cm

1 mm - 1 cm

0.0E+00

5.0E-11

1.0E-10

1.5E-10

2.0E-10

2.5E-10

3.0E-10

0 1 2 3 4 5 6 7

Time since breakup (days)

Imp

act

pro

bab

ility

rat

e (p

er s

ec)

Debris cloudBackground debris

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Debris Field Evolution

•Many reports, plots, animations address the evolution of the debris field

• SOAP displays shown real-time at NROC, JSpOC

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

0 10 20 30 40 50 60 70 80 90

Days from Intercept

Par

ticle

s in

Orb

it

10cm or larger1cm or larger3mm of larger

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Debris not to scale

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Chinese vs. US Impact Events

January 2007 Chinese ASAT Event

Intercept Altitude

Only 10% of the particles have decayed in 60 days, and only 18% in one year. In 5 years, only 31% have decayed, and 69% are still in orbit.

The colors represent the density of debris within an altitude band. Higher density means higher probability of encounter for satellites in that band. The density drops as debris is cleaned out by the atmosphere. Time is measured from the impact.

77% of particles decay in 1 day, 90% in 17 days, and over 99% in 97 days. Less than 0.01% remain in orbit after a year.

February 2008 US Intercept

Intercept Altitude Par

ticle

s pe

r 50

km

alti

tude

she

ll

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Comparison to Tracked Debris

Collision+ 60 days

Debris notto scale

•Tracking, cataloging of debris still underway– 1949 objects cataloged (as

of 14 March 2011)– COSMOS debris count is

almost 3 times the Iridium count

•Models matched reasonably well 2 months after collision– Less than half of the current

object count had been cataloged 60 days after event

•~95% of debris is still in orbit

Iridium

Cosmos

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Ballistic Missile Intercept Simulations

• Characterize risk prior to actual events– Debris is short-lived, event will be over before DART can respond

• Analyses focus on the risk from intercept-generated debris to – Resident space objects – People and vehicles on the ground from the reentry of the debris into

the atmosphere

• Debris risk will be dependent on altitude, latitude, and the geometry of the intercept(s)

• Four orbit classes defined to assess risk to RSOs– Sun-synchronous, ~98 inclination– Mid-inclination (45)– Critically-inclined (63)– Communications, ~85 inclination

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Methodology

• For each orbit class– Vary altitudes from 422 - 1122 km (6800 – 7500 km radius)– Eight different altitudes, 100 km increments

• Location of satellite in orbit will be important– Debris is short-lived, but density is relatively high– Satellites will be in the wrong place/wrong time or miss the event

entirely

• Create a Walker constellation for each altitude– 36 planes (every 10 in RAAN)– 36 satellites in each plane (every 10 in Mean Anomaly)

• Total of 10,368 satellites in each orbit class– Provides an estimate of wrong place/wrong time risk in addition to

collision risk from debris encounters

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Methodology (cont’d)

• Perform Collision Risk Assessment (slide 10)– For each debris particle

•Propagate all 10,368 satellites over the life of the debris objects

•Use an exaggerated cross-sectional area to obtain a statistically significant number of “hits”

•Particle flux is a function of the number of “hits” and the volume swept out by the sphere

•Probability is calculated from particle flux

• Fraction of satellites encountering any debris divided by total satellites represents risk (%) of being in the wrong place at the wrong time

• Probability of collision is the calculated risk if the satellite does encounter debris– Maximum and minimum probabilities reported to characterize the

distribution/spread of the debris– Compare to background risk from untracked objects to determine

elevated risk

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Sample Results – Probability of Collision

• Late Boost Intercept

•Mid-Course Intercept

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Sample Results – Relative Risk for 12 Cases Sun-Synchronous Orbits

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0

250

500

750

1000

1250

1500

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2000

2250

2500

12

45

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60

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80

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00

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80

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00

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17

35

Mission Elapsed Time (sec)

Orb

ital

Lif

eti

me

(d

ays

)

SES-2 SECO-2

Other Related Activities• Delta IV debris shedding analyses

– Model development from on-board video– Risk assessment for DSP-23, L-49, L-26

• Upper stage and satellite disposal analyses– Minimize collision risk for disposed GPS,

HEO, and GEO objects for 100+ yrs

• Established the Center for Orbital Reentry and Debris Studies (CORDS) in 1997

1.E-06

1.E-05

1.E-04

1.E-03

0 30 60 90 120

150

180

210

240

270

300

330

360

Satellite orbit RAAN (deg)

Impa

ct p

roba

bilit

y

Debris cloud risk

Background risk

16000

18000

20000

22000

24000

26000

28000

30000

2000 2025 2050 2075 2100 2125 2150 2175 2200

Year

Ap

og

ee/P

erig

ee A

ltit

ud

e (k

m)

MEANPROP Apogee

MEANPROP Perigee

TRACE Apogee

TRACE Perigee

GPS current operational range upper bound

GPS current operational range lower bound

GPS current operational range

NAVSTAR 29 disposal orbit evolution

Debris cloud risk vs. satellite RAAN

L26 debris lifetime vs. shedding time

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Aerospace Debris and Risk Models Compared with MDA, NASA

•MDA, NASA, Aerospace conducted joint study in summer 2007 to compare modeling approaches and results– Motivated by FY-1C event– Each breakup model is based on empirical data from ground- and space-

based tests, but not the same tests– Each model has been in use for a number of years for applications

specific to the developing organization• Each model uses a different set of input parameters

•Approach was to compare individual model results with data measured/collected from two real world events– Highlight areas for potential model improvements

•Study yielded good agreement, joint report briefing issued

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Debris Cloud Risk Model ComparisonAerospace and STEPAL Risk Models

• Plot shows impact risk posed to ISS vs. intercept time for a hypothetical intercept scenario, ISS RAAN = 0, initial mean anomaly = 289.014°

• Results are based on KIDD breakup model (fragments with mass >= 3 mm Al sphere)

Impact Risk Posed to ISS vs. Intercept Time (3mm debris data)

1.0E-13

1.0E-12

1.0E-11

1.0E-10

1.0E-09

1.0E-08

1.0E-07

1.0E-06

1.0E-05

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

0 4 8 12 16 20 24

Intercept time relative to nominal (hrs)

Impact

pro

babili

ty

STEPAL Model

Aerospace Model

Vulnerability Area = 30m2 Cross Sectional Area Of ISS

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Model Comparison Conclusions

•For a missile intercept case, MDA predictions were the most consistent of the three models with measurement data

•For a satellite intercept case, the NASA and Aerospace predictions were more consistent with measurement data (RCS)– NASA/Aerospace showed the best agreement in debris RCS for larger

debris– MDA showed the best agreement in debris RCS for smaller debris– Aerospace showed the best agreement with debris tracks

•Risk analysis will be scenario dependent

Overall, the best agreement between model-to-measured data is found when the intercept event matches the events comprising the

empirical data upon which the model is based

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DART Experience

•2 satellite intercepts (FY-1C and USA-193) and 1 satellite collision (Iridium 33)

•4 launch debris cloud risk assessments

•14 real world close approaches

•29 exercises

•Special analyses where processes applied to answer specific questions

•Model comparison with MDA and NASA– NASA: NASA Standard breakup model/SBRAM risk assessment tool– MDA: KIDD breakup model/REBLE risk assessment tool– Bottom line: satellite risk assessments agree within an order of

magnitude

•Current usage has all been below 1000 km– Includes ballistic missile intercept simulations

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Summary of Aerospace Debris Analysis Activities

•High profile of recent debris event have raised significant concerns about space debris– Govt support to Commercial and Foreign Enterprises (CFE) is of

particular concern

•Aerospace has active research programs addressing multiple aspects of space debris and space situational awareness

•DART and LCOLA processes undergoing continuing evolution– Goal is to evolve initial laboratory capabilities into more operational,

sustainable capability– Use prototype capabilities as guide to Govt acquisition, operations

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© the aerospace corporation 2011 determining risk from fragmentation events roger c. thompson the...

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