July 2015 

NASA/TM2015-218780

NESC-RP-14-00948

Joint Polar Satellite System (JPSS) 

Micrometeoroid and Orbital Debris (MMOD) 

Assessment 

Michael D. Squire/NESC 

Langley Research Center, Hampton, Virginia 

William J. Cooke 

Marshall Space Flight Center, Huntsville, Alabama 

Joel Williamsen 

Institute for Defense Analyses, Alexandria, Virginia 

Donald Kessler 

Independent Consultant, Asheville, North Carolina 

William E. Vesely 

NASA Headquarters, Washington DC 

Scott H. Hull 

Goddard Space Flight Center, Beltsville, Maryland 

William Schonberg 

Missouri University of Science and Technology, Rolla, Missouri 

Glenn E. Peterson, and Alan B. Jenkin 

The Aerospace Corporation, El Segundo, California 

Steven L. Cornford 

Jet Propulsion Laboratory, Pasadena, California 
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July 2015 

NASA/TM2015-218780

NESC-RP-14-00948 

Joint Polar Satellite System (JPSS) 

Micrometeoroid and Orbital Debris (MMOD) 

Assessment 

Michael D. Squire/NESC 

Langley Research Center, Hampton, Virginia 

William J. Cooke 

Marshall Space Flight Center, Huntsville, Alabama 

Joel Williamsen 

Institute for Defense Analyses, Alexandria, Virginia 

Donald Kessler 

Independent Consultant, Asheville, North Carolina 

William E. Vesely 

NASA Headquarters, Washington DC 

Scott H. Hull 

Goddard Space Flight Center, Beltsville, Maryland 

William Schonberg 

Missouri University of Science and Technology, Rolla, Missouri 

Glenn E. Peterson, and Alan B. Jenkin 

The Aerospace Corporation, El Segundo, California 

Steven L. Cornford 

Jet Propulsion Laboratory, Pasadena, California 
Available from: 

NASA STI Program / Mail Stop 148 

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Hampton, VA 23681-2199

Fax: 757-864-6500

Acknowledgments 

The NESC team would like to recognize the contributions of the following people 

in support of this assessment: 

Ivonne Rodriguez, GSFC – Debris Assessment Software (DAS) and Bumper 

Support and JPSS-1 Assessment Support 

Genevieve Devaud, Ball Aerospace and Technologies Corporation – JPSS-1 

Construction Details 

Steven Evans, Marshall Space Flight Center (MSFC) – Hydrocode 

Simulations 

James Hyde, Johnson Space Center (JSC) – Bumper and Shielding Design 

Support 

Dana Lear, JSC – Bumper and Shielding Design Support 

Iain Miller, Stinger Ghaffarian Technologies/GSFC – JPSS-1 Wiring Harness 

Design Details 

The use of trademarks or names of manufacturers in the report is for accurate reporting and does not 

constitute an official endorsement, either expressed or implied, of such products or manufacturers by the 

National Aeronautics and Space Administration.
NASA Engineering and Safety Center

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Joint Polar Satellite System (JPSS) Micrometeoroid and Orbital 

Debris (MMOD) Assessment 

April 23, 2015 
NASA Engineering and Safety Center 

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Report Approval and Revision History 

NOTE: This document was approved at the April 23, 2015, NRB. This document was 

submitted to the NESC Director on May 18, 2015, for configuration control. 

Approved:

Original Signature on File 5/18/15

NESC Director Date

Version Description of Revision Office of Primary 

Responsibility Effective Date

1.0 Initial Release Michael Squire, NESC 

Principal Engineer, 

LaRC

4/23/15

1.1 Added an additional name to the 

signature sheet. 

(No signature required) 

Michael Squire, NESC 

Principal Engineer, 

LaRC

4/23/15
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Table of Contents 

Technical Assessment Report 

1.0 Notification and Authorization ..................................................................................................... 9

2.0 Signature Page .............................................................................................................................. 10

3.0 Team List ...................................................................................................................................... 11

3.1 Acknowledgements ........................................................................................................... 11

4.0 Executive Summary ..................................................................................................................... 12

5.0 Background and Problem Description ....................................................................................... 15

5.1 Stakeholder Request and Assessment Plan ....................................................................... 15

5.2 JPSS-1 Description ........................................................................................................... 15

5.3 JPSS Preliminary MMOD Risk Assessments ................................................................... 16

5.4 NASA’s MMOD Risk Assessment Process ..................................................................... 18

6.0 MMOD Environment Models ..................................................................................................... 23

6.1 Orbital Debris Models ...................................................................................................... 23

6.1.1 ORDEM 3.0 Structure, Assumptions and Supporting Data .............................................. 24

6.1.2 Orbital Debris Model Flux Comparisons .......................................................................... 34

6.1.3 Orbital Debris Damage Fluence Comparisons ................................................................. 46

6.1.4 Conclusions Regarding ORDEM 3.0 ................................................................................ 67

6.1.5 NASA-STD-7009 Evaluation Credibility Assessment ..................................................... 69

6.2 Meteoroid Models ............................................................................................................. 75

6.2.1 Description ........................................................................................................................ 75

6.2.2 Model Comparison ........................................................................................................... 76

6.2.3 Previous NESC MEM Assessment ................................................................................... 77

7.0 Elements of JPSS MMOD Risk Assessment .............................................................................. 78

7.1 JPSS BLEs ........................................................................................................................ 78

7.1.1 BLE Description ............................................................................................................... 78

7.1.2 Applications and Modifications ........................................................................................ 79

7.1.3 BLE Comparisons ............................................................................................................. 87

7.2 Avionics Box Failure Criteria Assessment ..................................................................... 110

7.3 Wire Bundle Risk Assessment ........................................................................................ 114

7.4 Debris Shape Assumptions ............................................................................................. 122

7.5 Pressure Vessel Failure Criteria ...................................................................................... 125

7.6 Updated JPSS Risk Assessment...................................................................................... 129

7.7 Correlation of MMOD Environment Models to Failure History .................................... 131

8.0 Findings, Observations, and NESC Recommendations.......................................................... 132

8.1 Findings .......................................................................................................................... 132

8.2 Observations ................................................................................................................... 136

8.3 NESC Recommendations ............................................................................................... 138

9.0 Alternate Viewpoints ................................................................................................................. 141

10.0 Other Deliverables ..................................................................................................................... 150

11.0 Lessons Learned ......................................................................................................................... 150

12.0 Recommendations for NASA Standards and Specifications .................................................. 150
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13.0 Definition of Terms .................................................................................................................... 150

14.0 Acronyms List ............................................................................................................................ 151

15.0 References ................................................................................................................................... 153

16.0 Appendices .................................................................................................................................. 155

Appendix A. Evidence Network Evaluation ........................................................................................ 156

Appendix B. Examination of Possible Sources of High-Density 1-mm Particles................................ 162

Appendix C. Delta and Atlas Centaur Tanks as Possible Sources of Stainless Steel Orbital Debris ... 181

Appendix D. CTU Risk Assessment .................................................................................................... 197

Appendix E. JPSS-1 Zenith Deck Orbital Debris Wire Harness Failure Assessment ......................... 209

List of Figures 

Figure 5.2-1. The JPSS-1 Spacecraft ...................................................................................................... 16

Figure 5.3-1. Location of Propulsion Tank on JPSS-1 ........................................................................... 17

Figure 5.4-1. MMOD Risk Assessment Process .................................................................................... 20

Figure 5.4-2. DAS 2.0 Data Flow ........................................................................................................... 21

Figure 5.4-3. Bumper III Data Flow ....................................................................................................... 22

Figure 6.1-1. Flux of 1 cm Debris Measured through the Haystack Radar Beam at Altitudes up to 

2000 km in 2003, Compared to an ORDEM 3.0 Prediction of the Flux........................... 25

Figure 6.1-2. Flux measured between 800 km and 850 km in 2003 by Haystack and HAX as a 

function of debris size compared to ORDEM 3.0 predicted flux and the flux predicted 

by the SSN. ....................................................................................................................... 26

Figure 6.1-3. Flux measured between 800 km and 850 km in 2003 by Goldstone as a function of 

debris size compared to ORDEM 3.0 predicted flux and the flux predicted by the 

SSN. The Goldstone radar fills in the 0.03 cm to 0.06 cm flux measurements. .............. 27

Figure 6.1-4. Example of Crater Found on Windows Caused by Paint Flake, 7.8 x 6.1mm in 

Diameter, estimated Particle Size is 0.26mm ................................................................... 28

Figure 6.1-5. Example of Impact Hole found on Shuttle Radiator Facesheet caused by Stainless 

Steel: Facesheet 0.72 x 0.57mm hole Diameter, Estimated Impactor Size 0.15mm ........ 29

Figure 6.1-6. Example of ORDEM 3.0 Model Fit for Radiator Holes caused by High and Medium 

Density Debris .................................................................................................................. 30

Figure 6.1-7. ORDEM 3.0 Data Sources; No Data Exist between 1 mm and 3 mm and must be 

Modeled ............................................................................................................................ 31

Figure 6.1-8. Combining Shuttle Data with Radar Data at 400 km. The shuttle data are assumed to 

originate from what is called a surface degradation model, where debris sizes less than 

about 1 mm follow a different size distribution law. This model is then added to the 

debris size distribution resulting from satellite breakups. ................................................. 32

Figure 6.1-9. ORDEM 3.0 Model Prediction for the ISS at 400 km Altitude ........................................ 32

Figure 6.1-10. ORDEM 3.0 Model Prediction for JPSS-1 at 824 km Altitude ........................................ 34

Figure 6.1-11. ORDEM 3.0 and ORDEM2000 Orbital Debris Flux for JPSS Orbit at Beginning, 

Middle, and End of Expected Mission .............................................................................. 35

Figure 6.1-12. ADEPT and MASTER-2009 Orbital Debris Flux for JPSS Orbit .................................... 36

Figure 6.1-13. MASTER-2009 Orbital Debris Flux in 2017 Broken Down by Source ........................... 37

Figure 6.1-14. ORDEM 3.0 Orbital Debris Flux in 2017 for the JPSS-1 Orbit, Broken Down by 

Source ............................................................................................................................... 38

Figure 6.1-15. Comparison of Flux at JPSS Altitude for Year 2017 including all Models ...................... 39
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Figure 6.1-16. Variation of Orbital Debris Flux in 2017 as a Function of Altitude (Linear Scale) ......... 40

Figure 6.1-17. Variation of 1 mm Orbital Debris Flux with Altitude for 2017 (log scale) ...................... 41

Figure 6.1-18. ORDEM 3.0 1 mm Orbital Debris Flux as a Function of Altitude by Particle Type ........ 42

Figure 6.1-19. Variation of Orbital Debris Flux as a Function of Time during JPSS-1 Mission 

(2017-2024) ...................................................................................................................... 43

Figure 6.1-20. Variation of Orbital Debris Flux with Inclination at JPSS Altitude (824 km) .................. 44

Figure 6.1-21. ORDEM 3.0 Environmental Flux versus Impactor Size ................................................... 48

Figure 6.1-22. Distribution of Damage Fluence Over Impactor Size ....................................................... 50

Figure 6.1-23. Distribution of Damage Fluence over Impact Velocity .................................................... 51

Figure 6.1-24. Distribution of Damage Fluence over Impact Angle ........................................................ 52

Figure 6.1-25. ORDEM2000 and ORDEM 3.0 Environmental Flux versus Impactor Size .................... 53

Figure 6.1-26. Distribution of Damage Fluence over Impactor Size; Results from All Cube Faces 

using ORDEM2000 with the Baseline NNO BLE............................................................ 54

Figure 6.1-27. Distribution of Damage Fluence over Impact Velocity. Results from all Cube Faces 

using ORDEM2000 with the Baseline NNO BLE............................................................ 55

Figure 6.1-28. Distribution of Damage Fluence over Impact Angle; Results from all Cube Faces 

using ORDEM2000 with the Baseline NNO BLE............................................................ 56

Figure 6.1-29. MASTER-2009 Environmental Flux versus Impactor Size ............................................. 57

Figure 6.1-30. Distribution of Damage Fluence over Impactor Size; Results from all Cube Faces 

using MASTER-2009 with the Baseline NNO BLE ........................................................ 58

Figure 6.1-31. Distribution of Damage Fluence over Impact Velocity; Results from all Cube Faces 

using MASTER-2009 with the Baseline NNO BLE ........................................................ 59

Figure 6.1-32. Distribution of Damage Fluence over Impact Angle; results from all cube faces using 

MASTER-2009 with the Baseline NNO BLE .................................................................. 60

Figure 6.1-33. Distribution of Damage Fluence over Impactor Size; Results from all Cube Faces 

using ADEPT with the MASTER-2009 Velocity/Direction Distribution and the 

Baseline NNO BLE .......................................................................................................... 62

Figure 6.1-34. Distribution of Damage Fluence over Impactor Size; Results from All Cube Faces 

using ADEPT with the ORDEM 3.0 Velocity/Direction Distribution and the Baseline 

NNO BLE ......................................................................................................................... 63

Figure 6.1-35. Distribution of Damage Fluence over Impact Velocity; Results from All Cube Faces 

using ADEPT with the MASTER-2009 Velocity/Direction Distribution and the 

Baseline NNO BLE .......................................................................................................... 64

Figure 6.1-36. Distribution of Damage Fluence over Impact Velocity; Results from all Cube Faces 

using ADEPT with the ORDEM 3.0 Velocity/Direction Distribution and the Baseline 

NNO BLE ......................................................................................................................... 64

Figure 6.1-37. Distribution of Damage Fluence over Impact Angle; Results from all Cube Faces 

using ADEPT with the MASTER-2009 Velocity/Direction Distribution and the 

Baseline NNO BLE .......................................................................................................... 65

Figure 6.1-38. Distribution of Damage Fluence over Impact Angle; Results from all Cube Faces 

using ADEPT with the ORDEM 3.0 Velocity/Direction Distribution and the Baseline 

NNO BLE ......................................................................................................................... 66

Figure 6.1-39. NASA-STD-7009 Credibility Assessment for ORDEM 3.0 >3 mm Particle Size 

Regime .............................................................................................................................. 70

Figure 6.1-40. NASA-STD-7009 Credibility Assessment for ORDEM 3.0 <3 mm Particle Size 

Regime .............................................................................................................................. 73
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Figure 6.2-1. Meteoroid Flux Comparison for JPSS Orbit (D/S = Divine-Staubach) ............................ 76

Figure 7.1-1. Typical BLC for a Dual-wall System under Normal Projectile Impact ............................ 79

Figure 7.1-2. Comparison of Steel-on-Al BLEs without and with the High-End Transition Velocity 

Modification for 0° Impacts .............................................................................................. 82

Figure 7.1-3. Comparison of Steel-on-Al BLEs without and with the High-End Transition Velocity 

Modification for 45° Impacts ............................................................................................ 83

Figure 7.1-4. Comparison of New Soyuz OM BLE and NNO BLE with the High-End Transition 

Velocity Modification for 30° Impacts ............................................................................. 85

Figure 7.1-5. Comparison of New Soyuz OM BLE and NNO BLE with the High-End Transition 

Velocity Modification for 45° Impacts ............................................................................. 85

Figure 7.1-6. Critical Diameter versus Impact Velocity for a Stainless Steel Particle with Impact 

Angle of 0° ........................................................................................................................ 88

Figure 7.1-7. Critical Diameter versus Impact Velocity for a Stainless Steel Particle with Impact 

Angle of 45° ...................................................................................................................... 89

Figure 7.1-8. Distribution of Damage Fluence over Impactor Size; Results from all Cube Faces 

using the Modified Wilkinson BLE .................................................................................. 90

Figure 7.1-9. Distribution of Damage Fluence over Impact Velocity; Results from all Cube Faces 

using the Modified Wilkinson BLE .................................................................................. 91

Figure 7.1-10. Distribution of Damage Fluence over Impact Velocity; Results from only the South 

and North Faces using the Modified Wilkinson BLE ....................................................... 92

Figure 7.1-11. Distribution of Damage Fluence over Impact Angle; Results from all Cube Faces 

using the Modified Wilkinson BLE .................................................................................. 93

Figure 7.1-12. Distribution of Damage Fluence over Impact Angle; Results from only the South and 

North Faces using the Modified Wilkinson BLE.............................................................. 94

Figure 7.1-13. Effect of Changing Bumper Thickness on NNO BLE Predictions [Ref. 22] ................... 95

Figure 7.1-14. Effect of Changing Bumper Thickness on Reimerdes BLE Predictions [Ref. 22] ........... 95

Figure 7.1-15. Critical Wall Thickness versus Impactor Diameter for the Case of an Aluminum 

Particle Impacting normally at 7 km/s ............................................................................ 100

Figure 7.1-16. Critical Wall Thickness versus Impactor Diameter for the Case of a Stainless Steel 

Particle Impacting Normally at 14 km/s ......................................................................... 101

Figure 7.1-17. Critical Wall Thickness versus Impact Velocity for the Case of a 1 mm-Diameter 

Aluminum Particle Impacting Normally ........................................................................ 102

Figure 7.1-18. Critical Wall Thickness versus Impact Velocity for the Case of a 1 mm-diameter 

Stainless Steel Particle Impacting Normally ................................................................... 103

Figure 7.1-19. Distribution of Damage Fluence over Impactor Size; Results from all Cube Faces 

using the Modified Reimerdes Formulation ................................................................... 106

Figure 7.1-20. Distribution of Damage Fluence over Impact Velocity; Results from all Cube Faces 

using the Modified Reimerdes Formulation ................................................................... 107

Figure 7.1-21. Distribution of Damage Fluence over Impact Angle; Results from all Cube Faces 

using the Modified Reimerdes Formulation ................................................................... 108

Figure 7.2-1. JPSS-1 CTU Location and Orientation ........................................................................... 111

Figure 7.2-2. JPSS-1 Internal Critical Element Locations ................................................................... 112

Figure 7.2-3. Sample NESC-Sponsored Hydrocode Assessments ....................................................... 112

Figure 7.3-1. JPSS-1 Showing Wire Bundles ....................................................................................... 115

Figure 7.3-2. Typical SPH Hydrocode Predictions for Steel and Aluminum Orbital Debris Impacts . 116
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Figure 7.3-3. Number of Wires Cut in 36-wire Bundle for Given Combinations of Orbital Debris 

Densities, Diameters, and Velocities .............................................................................. 116

Figure 7.3-4. Probability of Critical Failure versus Wire Harness Size Given Randomly Placed 

Redundant Wire Failure .................................................................................................. 117

Figure 7.3-5. Cumulative Number of Cable Failures for Three Cable Sizes (1-Foot Length, 

Zentih/Nadir Orientation, 1 Year Exposure) .................................................................. 118

Figure 7.3-6. Enhanced Shield Configuration for Defeat of 3mm Aluminum Orbital Debris 

Particles ........................................................................................................................... 119

Figure7.3-7. Hydrocode Evaluation of Enhanced MMOD Shield for Steel and Aluminum Orbital 

Debris .............................................................................................................................. 119

Figure 7.3-8. Calculation of Exposed Areas for Wiring Harnesses by Approach Angle ..................... 120

Figure 7.4-1. Fragment Shapes from Impact Experiments [ref. 30] ..................................................... 123

Figure 7.4-2. SBM “Flake” Compared to Sphere Debris Shape Assumption (Current Assumption) .. 124

Figure 7.5-1. Application of the “25 Percent Thickness Rule” for the Exposed Portion of the JPSS 

Propellant Tank ............................................................................................................... 126

List of Tables 

Table 5.3-1. Risk Assessments for JPSS-1 Critical Components ......................................................... 18

Table 6.1-1. Cube Damage Fluence Resulting from use of ORDEM 3.0 with the NNO BLE ............. 49

Table 6.1-2. CTU Damage Fluence Resulting from use of ORDEM 3.0 with the NNO BLE ............. 49

Table 6.1-3. Cube Damage Fluence Resulting from use of ORDEM 3.0 with the NNO BLE; 

Impact Angle Limit Changed to 85° ................................................................................. 52

Table 6.1-4. Cube Damage Fluence Resulting from Use of ORDEM2000 with the Baseline NNO 

BLE ................................................................................................................................... 54

Table 6.1-5. Cube Damage Fluence Resulting from use of MASTER-2009 with the Baseline NNO 

BLE ................................................................................................................................... 58

Table 6.1-6. Cube Damage Fluence Resulting from use of ADEPT with the MASTER-2009 

Velocity/Direction Distribution and the Baseline NNO BLE ........................................... 61

Table 6.1-7. Cube Damage Fluence Resulting from use of ADEPT with the ORDEM 3.0 

Velocity/Direction Distribution and the Baseline NNO BLE ........................................... 62

Table 6.2-1. JPSS-1 Orbital Debris Risk versus Meteoroid Risk.......................................................... 77

Table 7.1-1. Fractions of Solid, Molten, and Vaporous Material .......................................................... 81

Table 7.1-2. Cube Damage Fluence Resulting from use of ORDEM 3.0 with the Modified 

Wilkinson BLE ................................................................................................................. 90

Table 7.1-3. Cube Damage Fluence Resulting from use of ORDEM 3.0 with the NNO BLE using 

the “Critical Wall Thickness Method” ............................................................................ 104

Table 7.1-4. Cube Damage Fluence Resulting from use of ORDEM 3.0 with the Original 

Reimerdes Formulation ................................................................................................... 104

Table 7.1-5. Cube Damage Fluence Resulting from use of ORDEM 3.0 with the Modified 

Reimerdes Formulation ................................................................................................... 105

Table 7.1-6. Cube Damage Fluence Resulting from use of ORDEM 3.0 with the Bumper 

Reimerdes Implementation ............................................................................................. 105

Table 7.2-1. Results of NESC Risk Assessments for Two Internal CTU Configurations .................. 113

Table 7.3-1. Effect of Shadowing and Reduced Criticality on a Typical 8-Foot Cable ...................... 118

Table 7.3-2. Orbital Debris Penetration Risk for Enhanced Wiring Shield (Ppen = Probability of 

Penetration) ..................................................................................................................... 121
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Table 7.5-1. MMOD Risk Assessment Results Calculated by DAS for a Generic Pressure Vessel... 128

Table 7.6-1. Risk Assessments for JPSS-1 Critical Components for Designs as of May 2014 

compared to October 2014 .............................................................................................. 130
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1.0 Notification and Authorization

Andre Dress, Joint Polar Satellite System (JPSS) Deputy Project Manager at Goddard Space 

Flight Center (GSFC), requested an independent evaluation of the Micrometeoroid and Orbital 

Debris (MMOD) models used in the latest JPSS MMOD risk assessment. The principal focus of 

the assessment was to compare Orbital Debris Engineering Model version 3 (ORDEM 3.0) with 

the Meteoroid and Space Debris Terrestrial Environment Reference version 2009 (MASTER2009) and Aerospace Debris Environment Projection Tool (ADEPT) and provide 

recommendations to the JPSS Project regarding MMOD protection. 

Michael Squire, NASA Engineering and Safety Center (NESC) Principal Engineer at Langley 

Research Center, was assigned to lead this assessment. 

The key stakeholders for this assessment were Andre Dress, JPSS Deputy Project Manager; 

Kenneth Yienger, JPSS Chief Engineer; and Glenn Iona, JPSS Flight Project Mission System 

Engineer. 

Other interested organizations included the Office of Safety and Mission Assurance (OSMA), 

the Office of the Chief Engineer, and other satellite missions with orbital locations that result in 

high MMOD risk. 
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2.0 Signature Page

Submitted by: 

Team Signature Page on File – 5/28/15 

Mr. Michael D. Squire Date 

Significant Contributors: 

Dr. William J. Cooke Date Dr. Joel Williamsen Date 

Mr. Donald Kessler Date Dr. William E. Vesely Date 

Mr. Scott M. Hull Date Dr. William Schonberg Date 

Dr. Glenn E. Peterson Date Mr. Alan B. Jenkin Date 

Dr. Steven L. Cornford Date 

Signatories declare the findings, observations, and NESC recommendations compiled in the 

report are factually based from data extracted from program/project documents, contractor 

reports, and open literature, and/or generated from independently conducted tests, analyses, and 

inspections. 
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3.0 Team List

Name Discipline Organization

Core Team

Michael Squire NESC Lead NESC/LaRC

Joe Pellicciotti Former NESC Chief Engineer NESC/GSFC

Steven Cornford Systems Analysis JPL

Scott Hull Orbital Debris GSFC

Alan Jenkin MMOD Simulation The Aerospace Corporation

Don Kessler Orbital Debris Independent Consultant

Glenn Peterson MMOD Simulation The Aerospace Corporation

William Schonberg MMOD Missouri University of Science 

and Technology

Roger Thompson MMOD Simulation Chantilly Aerospace

William Vesely OSMA NASA HQ

Joel Williamsen MMOD Institute for Defense Analyses

Roy Savage MTSO Program Analyst NESC/LaRC

Consultants

Eric Christiansen Hypervelocity Impact Technology JSC

William Cooke Micrometeoroid Environment MSFC

Mark Matney Orbital Debris Environment JSC

Administrative Support

Melinda Meredith Project Coordinator AMA/LaRC

Linda Burgess Planning and Control Analyst AMA/LaRC

Christina Williams Technical Writer AMA/LaRC

3.1 Acknowledgements 

The NESC team would like to recognize the contributions of the following people in support of 

this assessment: 

Ivonne Rodriguez, GSFC – Debris Assessment Software (DAS) and Bumper 

Support and JPSS-1 Assessment Support 

Genevieve Devaud, Ball Aerospace and Technologies Corporation – JPSS-1 

Construction Details 

Steven Evans, Marshall Space Flight Center (MSFC) – Hydrocode Simulations 

James Hyde, Johnson Space Center (JSC) – Bumper and Shielding Design Support 

Dana Lear, JSC – Bumper and Shielding Design Support 

Iain Miller, Stinger Ghaffarian Technologies/GSFC – JPSS-1 Wiring Harness 

Design Details 
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4.0 Executive Summary

Micrometeoroid and orbital debris (MMOD) risk assessments that use the newest orbital debris 

environment model, the Orbital Debris Engineering Model version 3.0 (ORDEM 3.0), will likely 

result in higher risk predictions than assessments performed with the previous model, 

ORDEM2000, for spacecraft operating between 600 and 1000 km and not shielded against debris 

up to 3 mm in size. This was the case for the Joint Polar Satellite System (JPSS), where assessed 

MMOD risk was up to 26 times higher using ORDEM 3.0 on JPSS-1 (the second satellite in the 

JPSS constellation) components considered critical to supporting post-mission disposal. The 

JPSS Project requested that the NESC assess ORDEM 3.0 relative to other orbital debris 

environment models and assist in mitigating the JPSS-1 MMOD risk.

The NESC team compared the orbital debris flux for ORDEM 3.0 to that of three other models: 

ORDEM2000, the European Space Agency’s (ESA) Meteoroid and Space Debris Terrestrial 

Environment Reference version 2009 (MASTER-2009), and The Aerospace Corporation’s 

Aerospace Debris Environment Projection Tool (ADEPT). Using JPSS-1 orbital parameters, the 

four models agree to within a factor of two for most particle sizes larger than 3 mm, but diverge 

significantly for smaller particles. In the 1 mm size range, ORDEM 3.0 has a flux at the JPSS-1 

altitude (824 km) ~13 times greater than the ORDEM2000 flux and ~400 times the ADEPT and 

MASTER-2009 fluxes. Predictions of damage (penetration) fluence on a generic 1-meter/side 

cube in the JPSS-1 orbit were also compared for the four models. Damage fluence for ORDEM 

3.0 was ~30 times the ORDEM2000 damage fluence, ~85 times the ADEPT damage fluence, 

and ~160 times the MASTER-2009 damage fluence. The combination of higher flux and the 

presence of high density (i.e., density of steel) particles were the primary contributors to the 

higher fluence for ORDEM 3.0. 

These comparisons verified that ORDEM 3.0 has a higher flux in general and that using it results 

in higher assessed risk for JPSS-1. The NESC team also sought to understand the underlying 

reasons for the differences between the models and if the assumptions and methodology used in 

the development of ORDEM 3.0 were valid. ORDEM 3.0 uses radar measurements for particles 

as small as ~3 mm and observations of impact damage on the Space Shuttle orbiters’ radiator 

panels and windows to ground the model in direct measurements for particles less than ~3 mm. 

The Space Shuttle impact data represent debris in only 400 to 600 km altitudes, which is the 

orbital range for the Space Shuttle. The NESC team has a higher confidence in risk predictions 

for these regimes because of the direct measurements that were made on returned Space Shuttle 

surfaces. The LEO-to-GEO Environment Debris (LEGEND) model is used in ORDEM 3.0 to 

predict the environment mostly in the size range above 3 mm in orbital regimes with no direct 

measurements and is used to extrapolate the environment into the future. Both ORDEM 3.0 and 

ORDEM2000 use surface degradation models to represent the source of the majority of debris 

particles smaller than 3 mm. These models are tuned to reproduce observed surface impacts. In 

ORDEM 3.0, most of these particles come from satellites and rocket bodies in near-circular, high 

inclination orbits. In ORDEM2000, a larger fraction of these particles come from satellites and 

rocket bodies in highly eccentric orbits. One key resulting difference between ORDEM 3.0 and 
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ORDEM2000 is a much higher concentration of 1 mm diameter particles for ORDEM 3.0 at 

orbits higher than ~600 km. This difference is a result of the assumptions made concerning the 

source of orbital debris particles and how large a population would be necessary at higher 

altitudes to produce the observed Space Shuttle impact damage. The assumptions in ORDEM 

3.0 are different, but not judged to be either more or less valid, than those used in the 

development of ORDEM2000, and are the basis for the higher fluxes and damage fluence for 

ORDEM 3.0. The physical source of this higher population of 1 mm particles has not yet been 

explained by either the Orbital Debris Program Office or the NESC team. Another important 

difference in the models is many of the particles are high density in ORDEM 3.0 while all are 

assumed aluminum (i.e., medium density in ORDEM3.0) in ORDEM2000. The high density 

component increases risk because the higher density will cause greater damage than aluminum of 

the same size. 

When compared to the other models, ORDEM 3.0 has several advantages: (1) by using the Space 

Shuttle impact data, ORDEM 3.0 incorporates the most extensive use of direct measurement 

data, (2) of the three other models evaluated, only ORDEM2000 reasonably predicts the 

observed shuttle impact data, (3) ORDEM 3.0 contains high density (i.e., material density of 

steel) particles not included in the other models, and (4) ORDEM 3.0 contains contributions from 

recent debris-producing events, only also included completely in ADEPT. There are still 

substantial uncertainties with ORDEM 3.0 because of the lack of available direct measurement 

data for < 3 mm particles at altitudes above 600 km, but these are challenges faced by all of the 

orbital debris environment models. The NESC team recommended that the JPSS-1 Project use 

ORDEM 3.0 for MMOD risk assessments, while acknowledging the uncertainties inherent to 

orbital debris environment models (there is an alternate opinion on this topic in Section 9 of this 

report). Because the uncertainties are large and not characterized, the NESC team recommended 

that the uncertainties need to be characterized and efforts made to reduce the uncertainties. Also, 

the NESC team did not make any recommendation as to the general use of ORDEM 3.0 beyond 

JPSS-1 since this was beyond the defined scope of this activity. 

The JPSS Project also asked that the NESC team consider how using the newest meteoroid 

model, the Meteoroid Engineering Model (MEM) Release 2 (MEMR2), affected the MMOD risk 

assessments. MEMR2 was evaluated against three versions of the Divine-Staubach model (used 

in MASTER-2009). Fluxes produced by the four meteoroid models were shown to be in close 

agreement over the range of the altitudes considered. It is important to note that the contribution 

from MEMR2 to the overall assessed risk is an order of magnitude less than that of ORDEM 3.0. 

The NESC team evaluated other elements of the MMOD risk assessment process that contribute 

to the overall uncertainty in assessed risk values. Refinement in these areas as described below 

will reduce uncertainty and, in some cases, possibly reduce assessed MMOD risk. 

 The ballistic limit equations (BLEs) used for JPSS-1 risk assessments were not developed

for the specific shield configurations used on JPSS-1 or for high density impactors.

Existing BLEs were modified for use on JPSS-1. As a result, conservatisms might be

associated with the BLEs used by JPSS and how they are applied. The variation of
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damage fluence on the generic 1 meter/side cube using some candidate alternative BLEs 

was quantitatively evaluated. 

 The NESC team evaluated failure criteria used for avionics boxes. The results showed 

that it may be possible for assessed risk to be reduced by addressing the failure of 

individual cards within an avionics box rather than assuming any penetration of the box 

results in a failure. 

 The wire harnesses were not represented in the initial JPSS-1 risk assessments and may 

be a factor that would increase the assessed risk. A methodology was developed using 

hydrocode simulation software to assess risk to wire bundles based on individual wire 

failures. With the aid of the NESC team, JPSS implemented design modifications to 

increase protection for the wire bundles and other components. Additional protection 

included a heavier insulating blanket incorporating layers of Kevlar®, and spacers to 

increase the gap between the blanket and vulnerable surfaces. 

 The failure criteria used for pressure vessels are outdated and not applied consistently 

within the MMOD risk assessment community. There may be conservatisms associated 

with how these failure criteria are used by JPSS. 

 Orbital debris particle shape is not included in the orbital debris environment nor in the 

overall risk assessment. The assumption that all particles are spherical adds to 

uncertainty in the risk assessment process and resulting values of assessed risk. 

In summary, given the known uncertainties in the orbital debris environment models, the NESC 

recommends the JPSS Project use ORDEM 3.0 for MMOD risk assessments related to JPSS-1. 

However, the use of ORDEM 3.0 beyond JPSS-1 was not assessed as this was beyond the 

defined scope of this activity. Finally, uncertainties associated with the MMOD risk assessment 

process, including those associated with the orbital debris environment models, damage 

prediction, and failure criteria should be understood and investigated to refine overall assessed 

MMOD risk for all spacecraft. 
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5.0 Background and Problem Description

5.1 Stakeholder Request and Assessment Plan 

The Joint Polar Satellite System (JPSS)1 Project requested an independent evaluation of the 

micrometeoroid and orbital debris (MMOD) models used in JPSS-1 MMOD risk assessments. 

Initial risk assessments performed using the newest MMOD environment models resulted in a 

substantial increase in assessed risk for several JPSS components compared to assessments 

performed using older models. For example, the probability for catastrophic damage to the 

JPSS-1 propellant tank increased from 1.1 percent to 26.0 percent over the life of the spacecraft 

when switching to the newest models. As a result, the JPSS Project increased MMOD shielding 

to the propellant tank in order to satisfy requirements to preserve end-of-mission controlled 

reentry capability. Several of the science instruments onboard JPSS-1 may also have elevated 

MMOD risk estimates. The science instruments had not been assessed because it was not 

required to do so to satisfy reentry requirements, but this may call into question the viability of 

the JPSS-1 science-gathering mission for the designed 7-year mission lifetime. 

The increased MMOD assessed risk is due primarily to the use of the newest NASA orbital 

debris model, the Orbital Debris Engineering Model, version 3.0 (ORDEM 3.0). For this NESC 

assessment, the NESC was asked to provide an independent evaluation of ORDEM 3.0 and the 

meteoroid environment model, Meteoroid Engineering Model Release 2 (MEMR2), and compare 

them to other models, such as the Meteoroid And Space Debris Terrestrial Environment 

Reference version 2009 (MASTER-2009) used by the European Space Agency (ESA) and the 

ADEPT used by The Aerospace Corporation. The NESC team was also asked to provide 

recommendations concerning current JPSS MMOD protection and the methodology used to 

perform its MMOD risk assessment. 

5.2 JPSS-1 Description 

The JPSS Project is a series of polar-orbiting environmental satellites, formed through a 

partnership between NASA and the National Oceanographic and Atmospheric Administration 

(NOAA). NASA has the responsibility for developing and building the JPSS spacecraft, and 

NOAA will operate them. The mission of JPSS is to gather a variety of global sea, land, and 

atmospheric measurements to forecast weather conditions, assess environmental hazards, and 

provide continuity of critical weather satellite assets through 2025. The first satellite in the JPSS 

constellation, the Suomi National Polar-orbiting Partnership (S-NPP), was launched in October 

2011 with a designed mission life of 5 years. The second satellite is JPSS-1. JPSS-1 is 

essentially the same design as S-NPP and will be launched in 2017 on a 7-year mission. The 

third satellite of the JPSS constellation will be JPSS-2, which is scheduled to be launched to 

provide operational continuity for JPSS after JPSS-1. All three spacecraft will be in a 98.7° 

1

 JPSS is the restructured program formerly known as the National Polar-orbiting Operational Environmental 

Satellite System (NPOESS). JPSS is the project, while JPSS-1 is the second mission in the series. 
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circular orbit at a nominal altitude of 824 km. The focus of this assessment was JPSS-1 (see 

Figure 5.2-1). 

Figure 5.2-1. The JPSS-1 Spacecraft 

5.3 JPSS Preliminary MMOD Risk Assessments 

The risk of MMOD penetration was evaluated for JPSS-1 as part of the Orbital Debris 

Assessment Report (ODAR) required by NPR 8715.6A, NASA Procedural Requirements for 

Limiting Orbital Debris. The assessment methods and technical requirements to support that 

NPR are found in NASA-STD 8719.14A, Process for Limiting Orbital Debris. The MMOD 

penetration risk is addressed in Requirement 4.5-2:

“Requirement 4.5-2. Limiting debris generated by collisions with small objects when operating 

in Earth or lunar orbit: For each spacecraft, the program or project shall demonstrate that, 

during the mission of the spacecraft, the probability of accidental collision with orbital debris 

and meteoroids sufficient to prevent compliance with the applicable post-mission disposal 

requirements is less than 0.01 (Requirement 56507).”

JPSS will comply with the disposal requirement by performing a controlled reentry following the 

completion of the scientific mission. Critical JPSS-1 components required to perform the 

controlled reentry were identified and assessed as described in Section 5.4, using the Debris 

Assessment Software (DAS) version 2.0.2 tool for preliminary evaluation, followed by the 

Bumper II risk assessment tool for refinement of the risk assessments for most vulnerable 
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components. Both DAS and Bumper assessments used the ORDEM2000 orbital debris and 

Space Station Program (SSP) 30425 meteoroid environment models. Most initial DAS 

assessment inputs were imported from the similar S-NPP prelaunch assessment, edited as 

necessary to reflect design changes between the two spacecraft (e.g., relocation of components, 

addition of new components). Throughout the entire assessment, input information details were 

refined, not only as the design changed, but also as the details of the design became better known 

to the analysts. A thorough understanding of the spacecraft construction early in the process is 

crucial to obtaining a meaningful assessment. 

Early DAS assessments of the JPSS-1 penetration risk (performed through mid-2013) indicated 

that the overall risk would exceed the requirement by a factor of ~3, with the propulsion module 

accounting for over 80 percent of the risk. The propulsion tank was believed to be particularly 

exposed (continuously oriented into the velocity direction) whenever the spacecraft entered sun 

safe mode, estimated as one day per year throughout the mission (this was later revised to half 

that time) (see Figure 5.3-1). In light of this, the propulsion module became the focus of a 

detailed evaluation using Bumper II. The assessed risk was revised from 2.7 percent with DAS 

to 1.2 percent using Bumper II, still exceeding the required 1 percent spacecraft risk with this 

single component. The JSC Hypervelocity Impact Technology (HVIT) group was consulted to 

confirm the results and to help determine what supplemental shielding could be added to afford 

sufficient protection over the propulsion module. An updated DAS assessment showed 

significant vulnerability for several additional components, which were added to the HVIT 

Bumper II assessment. 

Figure 5.3-1. Location of Propulsion Tank on JPSS-1 
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During the assessment process, the ORDEM 3.0 debris environment model and Bumper III risk 

assessment tool (updated to incorporate ORDEM 3.0) became available for use. JPSS decided to 

use the updated model (as well as the latest meteoroid model, MEMR2) for further assessments. 

The new models and tools were adopted because the JPSS-1 mission is intended to operate for a 

relatively long time, in a very aggressive debris environment, performing an extremely high 

value function for weather forecasting. It was agreed that this was the most appropriate course 

of action to ensure that this national asset would be adequately protected against penetration 

risks. The Bumper III assessment indicated risks as much as 30 times higher than the previous 

Bumper II/ORDEM2000 assessment, invoking questions regarding the applicability of the 

ORDEM 3.0 environment model and the ways in which it was being used for JPSS-1. 

Table 5.3-1 shows the relative MMOD risk assessments using the ORDEM2000/SSP 30425 

versus ORDEM 3.0/MEMR2 for five of the critical JPSS-1 component types. 

Table 5.3-1. Risk Assessments for JPSS-1 Critical Components 

Component Probability of Penetration

ORDEM2000/

SSP 30425

ORDEM 3.0/

MEMR2

Propellant Tank* 1.1% 26.0%

Propellant Lines 3.2% 49.7%

Battery #1 (Stbd) 1.2% 40.9%

Battery #2 (Port) 1.3% 43.4%

Command and Telemetry Unit (CTU) 0.5% 9.7%

Power Control and Distributor Unit (PCDU) #1 

(Stbd)

0.1% 0.3%

PCDU #2 (Port) 0.1% 0.3%

Spacecraft Control Processor (SCP) #1 (Forward) 0.7% 14.5%

SCP #2 (Aft) 0.2% 2.9%

5.4 NASA’s MMOD Risk Assessment Process 

The first step in the risk assessment process, as defined in NASA-STD-8719.14A, is to identify 

those components critical to the planned disposal. For the JPSS-1 assessment, a reliability block 

diagram was examined to identify all components necessary to perform the planned controlled 

reentry disposal and the role of redundancy in the design. In addition to the entire propulsion 

module, 31 individual components were identified as critical to the disposal maneuvers and 

potentially vulnerable to MMOD damage. As many as a dozen additional component types were 

considered to be “robust by design,” based on their construction, location, or redundancy, so no 

further assessment was deemed necessary for those. 
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The physical location on the spacecraft of each critical component was then examined to 

determine which surfaces are vulnerable to penetration by a hypervelocity projectile. All 

components are shadowed on at least one side by either the spacecraft itself or by other 

substantial components that would prevent a projectile from reaching the critical component 

(probability of penetration is assumed to be zero for that side). In many cases, a critical surface 

is partially shadowed, leaving some percentage of its area vulnerable to penetration. This 

vulnerable area is calculated or estimated by the analyst. For the purposes of the DAS 

assessments, these surfaces were evaluated with respect to six principal directions (velocity, antivelocity, port, starboard, nadir, and zenith); this differs from the Bumper tool, which uses a 

detailed three-dimensional model to consider angled trajectories. The critical surface itself is 

considered to be the inside surface of a component – for the purposes of this evaluation, any 

penetration of a projectile through a component wall is considered to cause failure of the 

component. The exception is for the propellant tank, which as a pressure vessel DAS considers 

the wall thickness to be only one-fifth as thick, to account for the increased stress in the 

component wall. 

When all critical surfaces are identified, the area, orientation, and shielding of each critical 

surface is determined, using engineering drawings and designer-supplied dimensions. Shielding 

is expressed in terms of areal density (material bulk density multiplied by the shield thickness) 

and distance from the critical surface. All shielding layers between the critical surface and space 

are identified (e.g., multi-layer insulation (MLI) blankets, thermal fins, or composite panel face 

sheets), following assessment guidelines defined by the GSFC Orbital Debris Group for details 

beyond those specified in NASA-STD 8719.14A. 

The DAS assessment tool is available online from the Orbital Debris Program Office (ODPO) at 

http://orbitaldebris.jsc.nasa.gov/mitigate/das.html. A block diagram showing the data flow in 

and out of DAS is shown in Figures 5.4-1 and 5.4-2. DAS uses an algorithm based on areal 

density and distance from the critical surface for all shielding layers to determine the minimum 

penetrating particle size for a range of angles and velocities. The algorithm includes two tests to 

determine the effectiveness of each shielding layer, adjusting a correction factor accordingly 

through a range between the fixed minimum and maximum effectiveness values. DAS then uses 

ORDEM2000 and SSP 30425 to determine the cumulative flux of particles equal to or larger 

than the minimum penetrating size in each direction/velocity bin, arriving at the expected 

number of penetrating impacts for that critical surface during the mission. The expected number 

of penetrations is then converted to a probability of penetration for that critical surface. 
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Figure 5.4-1. MMOD Risk Assessment Process 

Identify DisposalCritical Components

Enter Data into DAS 

and Run Assessment

Compile Results, 

Including Redundancy

Collect Data on the 

Shielding

Determine Shielding 

over Each Critical 

Surface

Determine Shadowing 

by Other Components

Identify Critical 

Surfaces for Failure

Determine the 

Location of Each

Report

Comprehensive Risks 

for the Mission

Compile Bumper 

Results, Including 

Redundancy

Collect Data to 

Support Bumper 

Assessment

Identify any “Tall 

Poles” for Further 

Assessment
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Figure 5.4-2. DAS 2.0 Data Flow 

Results for each component must be accumulated and manipulated separately to determine the 

total risk for each component and to account for redundancy. The DAS assessment often 

identifies very low vulnerability for some components, but much higher vulnerability for others. 

It may, therefore, be necessary to refine the assessment of the components at higher assessed risk 

using a higher fidelity assessment tool. For NASA assessments, the higher fidelity MMOD 

penetration risk assessment tool is Bumper, which is available from the HVIT. Note that while 

the DAS algorithm is intended to yield more conservative risk assessments than Bumper, 

conditions have been identified when that is not always the case. 

The higher fidelity Bumper assessment tool functions differently from the DAS in several 

important ways. First, the orbital debris and meteoroid environment models are run externally. 

Bumper also uses a three-dimensional finite element model (FEM) of the spacecraft to determine 

the exposure to each discrete element, allowing shadowing to be incorporated more completely. 

Bumper offers a selection of BLEs to address a variety of possible shielding configurations and 

failure criteria, as opposed to the single algorithm built into DAS. Finally, the output from the 

Bumper tool is more detailed than that from DAS, giving information on the risks from various 

populations of projectiles as well as the total expected number of impacts. The more complex 

process of performing a Bumper assessment is evident in Figure 5.4-3. 
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Figure 5.4-3. Bumper III Data Flow 

The process for employing the Bumper tool begins with constructing a simplified solid model of 

the spacecraft, including FEMs for all critical components and shielding or shadowing features. 

The environment models are then run externally, and a conversion program is used to combine 

multiple years and to generate a Bumper-compatible format; note that either the newest NASA 

environment models or previous versions can be used for this step. The critical surface input 

information collected above is reinterpreted as layer thicknesses (as opposed to areal density) 

and specific material properties can be considered. Finally, an experienced operator interprets 

which BLE is appropriate for a given construction. Bumper is run in three modules, the first 

determining the exposure of each element in the FEM, the second defining the shield 

construction and appropriate BLEs, and the third introducing the environment model to 

determine the expected number of penetrations. Bumper must be run separately for orbital 

debris and meteoroid penetration risks. 
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6.0 MMOD Environment Models

The NASA OSMA supports the development and maintenance of NASA’s MMOD environment 

models. The meteoroid model is maintained at MSFC by the Meteoroid Environment Office 

(MEO), and the orbital debris environment model is maintained at JSC by the ODPO. These two 

models together describe the MMOD environment. The MMOD environment models are used 

as input to tools, such as DAS and Bumper, that predict the orbital debris or micrometeoroid flux 

as a function of size and location in space. 

6.1 Orbital Debris Models 

The development of orbital debris models has tended to follow the same development pattern as 

older meteoroid models. However, unlike meteoroid models, the orbital debris models have 

become more complex due to an increasing amount of debris, which includes newly found 

sources. The ODPO maintains two major orbital debris models, where each serves a different 

purpose: 

1. LEO-to-GEO Environment Debris Model (LEGEND), also known as the Long-term 

Evolutionary Model. The LEGEND model consists of a number of sub-models, which 

include a launch traffic model, an atmospheric drag model, orbit propagation models, 

satellite breakup models, surface degradation models, and mission-related models to 

account for unique sources of debris such as aluminum oxide from solid rocket firings 

and sodium-potassium (NaK) droplets from radiator coolant. These models were 

developed from a combination of ground testing and historical trends. LEGEND uses 

Monte Carlo simulations to predict possible future environments as a result of various 

traffic models, and it can simulate mitigation and remediation options to manage the 

orbital debris environment. It also serves as a data input source for the ORDEM model. 

2. ORDEM is also known as the Engineering Model. The ORDEM model allows 

engineers to determine the expected population of man-made debris objects for a given 

orbit and timeframe and is used by other applications to ensure spacecraft are protected 

against debris damage to some predetermined reliability. The model relies on direct 

measurements of the environment as much as possible, but uses LEGEND as a predictor 

when interpolations or extrapolations of the data are required. LEGEND can be thought 

of as a source of data for ORDEM when it is used to predict future changes in the 

environment and fill in the data gaps in measurements of the current environment. If all 

the debris sources were completely understood, then the ORDEM model would agree 

with the LEGEND model; if the models disagree, then either some measurements have 

been misinterpreted, there is an unidentified source of debris, or the known debris 

sources are not behaving as expected. 
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6.1.1 ORDEM 3.0 Structure, Assumptions and Supporting Data 

ORDEM 3.0 represents the latest ORDEM model, which is an update from ORDEM2000, 

released over 10 years ago. This update was overdue and necessitated by a series of events 

including the 2007 Chinese Fengyun-1C (FY-1C) antisatellite test and the Iridium 33/Cosmos 

2251 collision. More importantly, an accumulation of MMOD impact data on the Space Shuttle 

orbiter up through 2011 was available, which was three times greater than the number of impacts 

used for ORDEM2000. It is the analysis and interpretation of the Space Shuttle impacts that are 

most important to the orbital debris environment affecting the JPSS Project. 

Four sets of direct measurements of the debris environment went into ORDEM 3.0: 

1. The United States Space Surveillance Network (SSN): A catalog of Earth-orbiting 

objects larger than ~10 cm in low Earth orbit (LEO). 

2. The Massachusetts Institute of Technology’s Lincoln Laboratory Long Range Imaging 

Radar (aka Haystack) and Haystack Auxiliary (HAX) Radar: Narrow beam, short 

wavelength mono-static radars in Massachusetts that sample the LEO debris environment 

for sizes greater than 6 mm. 

3. The Goldstone Radar System: Narrow beam, short wavelength bi-static radars in 

Southern California that sample the LEO debris environment for sizes greater than 3 mm. 

To convert radar cross-section (RCS) to debris size, the ODPO calibrates debris 

fragments on a radar range to relate debris size to RCS. These fragments were produced 

in 1992 by a Department of Defense (DoD) hypervelocity ground test, designed to 

catastrophically break up a 35 kg spacecraft [ref. 1]. 

3. Space Shuttle orbiter surfaces: Between 1995 and 2011, a total 1.94 flight-years of 

3.6 m2of window surfaces, and a total of 1.87 flight-years of 117 m2of radiator surface 

were examined in detail for hypervelocity impact damage. Most of that data was 

obtained at flight altitudes between 350 km and 400 km, but a few flights were as high as 

600 km. This source will be referred to as “shuttle data” in this report. 

Hypervelocity damage to the Shuttle surfaces was examined and catalogued by location, 

crater or hole size, and chemistry of residue associated with the damage. Scanning 

electron microscope techniques developed in the 1990's from meteoroid and debris 

research at JSC were used to determine the chemistry of the impacting objects and 

separate orbital debris impacts from meteoroid impacts [ref. 2]. 

 

This accumulation of data represented a significant increase over the amount of data going into 

previous models. 

Constructing ORDEM 3.0, Radar Data 

Constructing an ORDEM model from radar data is relatively straightforward, with the largest 

uncertainty resulting from converting the measured RCS to size or mass. The process essentially 
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consists of adjusting the amount of debris in various orbits in the model to match the measured 

data. These measurements have been nearly continuous since the mid 1990's and are made by 

pointing the radar in a fixed direction for many hours. In Figures 6.1-1 through 6.1-3, the 

Haystack was pointed east, 75° above the horizon [ref. 3]. Flux is measured relative to the radar 

beam; it is converted to the flux relative to a spacecraft by assigning orbits to the flux from either 

the SSN, the LEGEND model, or the measured inclination from the Haystack measurements, 

then testing the orbits against the measurements.

Figure 6.1-1. Flux of 1 cm Debris Measured through the Haystack Radar Beam at Altitudes up to 

2000 km in 2003, Compared to an ORDEM 3.0 Prediction of the Flux 
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Figure 6.1-2. Flux measured between 800 km and 850 km in 2003 by Haystack and HAX as a 

function of debris size compared to ORDEM 3.0 predicted flux and the flux predicted by the SSN. 

For sizes less than about 0.6 cm, the radar begins to lose sensitivity and those data are not used. 

The ORDEM 3.0 curve was fit to the SSN total +8xxxx data

2

, since the official SSN catalog is 

incomplete and does not include detected objects detected that are waiting to be cataloged. 

 

2

 8xxxx data refers to orbiting objects that have been tracked, but are not yet in the SSN catalog. To be in the 

catalog, an object must be identified with a specific launch and a country of origin. This information may not 

always be available for all tracked objects, especially those recently discovered. 
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Figure 6.1-3. Flux measured between 800 km and 850 km in 2003 by Goldstone as a function of 

debris size compared to ORDEM 3.0 predicted flux and the flux predicted by the SSN. The 

Goldstone radar fills in the 0.03 cm to 0.06 cm flux measurements. 

Constructing ORDEM 3.0, Space Shuttle Surface Impact Data 

Using direct impact damage on any spacecraft surface has less uncertainty than measurements by 

radar since no matter what the actual size, velocity, or mass density, the measurement is 

essentially a "damage flux" on spacecraft surfaces. However, it has one major disadvantage: it 

is only valid for the altitude where the damage occurred. Like the radar measurements, this 

damage flux is converted to a damage flux that other orbits might experience by assigning orbits 

from other data that may be contributing the flux, then testing predicted damage against the 

damage measurements. However, unlike the radar measurements, there are very little data on 

how the flux might vary with altitude since all the shuttle flights were below 600 km, with 90 

percent of the data being below 400 km [ref. 4]. Any effort to apply these data to higher 

altitudes requires an understanding of the source of the debris, with the only clues within the 

shuttle data being the chemistry of residue associated with the damage. 

A total of 640 hypervelocity impact features were found on the Space Shuttle orbiter payload bay 

door radiators, and another 1986 impact craters were found on the Space Shuttle orbiter 

windows. Figure 6.1-4 is an example of a crater caused by a paint impact on a shuttle window, 

and Figure 6.1-5 is an example of a penetration hole through a shuttle radiator facesheet caused 

by a stainless steel impact. Approximately 70 percent of the impact features were classified as 

unknown, either because they were not fully analyzed or no impact residue could be identified 
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relative to the surface that was being examined (e.g., no identifiable residue could be expected 

from an aluminum impact into an aluminum surface). On those surfaces where residue was 

identified, about half were identified as orbital debris and the other half meteoroid; therefore, the 

unknowns were assumed to be likewise equally divided. The orbital debris impacts were divided 

into three groups: those originating from high density (>6g/cm3), medium density (2 to 6g/cm3), 

and low density debris (<2g/cm3) [ref. 5]. 

Figure 6.1-4. Example of Crater Found on Windows Caused by Paint Flake, 7.8 x 6.1mm in 

Diameter, estimated Particle Size is 0.26mm 

JSC 28033 Orbiter Meteoroid/Orbital Debris Impacts: Space Shuttle-50 (6/92) through Space Shuttle-86 (10/97)

SEM 059 Space Shuttle-59 Scanning Electron Microscope Analysis of Hypervelocity Impact on Side Hatch Window
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Figure 6.1-5. Example of Impact Hole found on Shuttle Radiator Facesheet caused by Stainless 

Steel: Facesheet 0.72 x 0.57mm hole Diameter, Estimated Impactor Size 0.15mm 

The sizes of the impacting particles were not directly determined from the damage 

characteristics, rather the ORDEM 3.0 model combined with the Bumper model was used to 

determine the flux model that would cause the damage found on the Space Shuttle given its orbit 

and orientation as a function of time. Figure 6.1-6 shows the ORDEM 3.0 model fit to the 

radiator holes caused by high density and medium density debris. Similar curves were made for 

the craters on other surfaces for all three density groups. 

Radiator_facesheets_SEM.ppt
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Figure 6.1-6. Example of ORDEM 3.0 Model Fit for Radiator Holes caused by High and Medium 

Density Debris 

It is important to note that debris sizes between 0.5 mm and 1 mm were found to be dominated 

by high density debris consisting of stainless steel, copper and other high density metals. The 

higher density debris becomes more important because it also has both greater penetration 

capabilities and longer orbital lifetimes than lower density materials. 

When the shuttle data were combined with the radar data, it produced a plot similar to that shown 

in Figure 6.1-7. While there was a relatively small gap in the available data between 

1 and 3 mm debris, it represented an unexpected large change in the flux (i.e., larger than would 

be expected by any known debris source). This large increase in flux at the 1 mm size, combined 

with an unexpected dominant high density population, led the ODPO to conclude that there must 

exist an un-modeled source of orbital debris. This un-modeled source would have the 

characteristics of producing a large population of 1 mm debris and smaller, with very little 

production of debris larger than 3 mm. While interpolation of the flux between 1 and 3 mm may 

give adequate results for spacecraft operating near 400 km altitude, determination of the source 

of debris smaller than 1 mm is required before applying these results to other altitudes. 
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Figure 6.1-7. ORDEM 3.0 Data Sources; No Data Exist between 1 mm and 3 mm and must be 

Modeled 

Constructing ORDEM 3.0, LEGEND and the Surface Degradation Model

As previously described, the LEGEND model depends on identifying a debris source along with 

the amount of debris it produces, then propagates that debris to predict the resulting long-term 

flux. The data supporting the collision breakup models in LEGEND have been tested by both 

ground- and space-based experiments, though these tests have been limited to catastrophic 

breakup tests of payloads. LEGEND accurately predicts the measured environment for sizes 

down to about 10 cm, but fails to accurately predict measurements for smaller sizes, possibly due 

to a lack of data to support upper stage rocket explosions and non-catastrophic collision models. 

Some debris sources require special models to address them; examples are NaK coolant released 

from Russian RORSATs3, Al2O4 from solid rocket firings in orbit, and paint from spacecraft 

surfaces. The source required to produce the 1 mm and smaller flux measured by the shuttle data 

is illustrated in Figure 6.1-8. The shape of the curve matches the surface degradation model used 

in LEGEND to model paint from spacecraft surfaces, except the composition of the material is 

high density metals and the largest debris size is near 1 mm. The surface degradation model was 

 

3

 The Radar Ocean Reconnaissance Satellite (RORSAT) program was a series of Russian satellites powered by 

nuclear reactors that were cooled using a liquid NaK coolant. As RORSATs broke up in orbit, the NaK coolant was 

released forming spherical droplets. 
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then modified to fit the shuttle data, resulting in the ORDEM 3.0 model prediction at 400 km, 

illustrated for the ISS altitude in Figure 6.1-9. 

Figure 6.1-8. Combining Shuttle Data with Radar Data at 400 km. The shuttle data are assumed to 

originate from what is called a surface degradation model, where debris sizes less than about 1 mm 

follow a different size distribution law. This model is then added to the debris size distribution 

resulting from satellite breakups. 

Figure 6.1-9. ORDEM 3.0 Model Prediction for the ISS at 400 km Altitude 
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The ODPO did not identify a particular source for this debris, but noted that many spacecraft 

surfaces are about 1 mm thick, and those surfaces are subject to erosion by numerous impacts by 

both small debris and meteoroids, possibly leading to a slow loss in spacecraft surfaces and a 

growth in the 1 mm debris population. Having not identified any particular class of objects 

generating this debris, the ODPO made the neutral assumption that all spacecraft surfaces were 

continually generating this distribution of small debris at a rate that would produce the flux 

measured by the shuttle surfaces. To do this, the LEGEND model was used to predict the flux at 

various altitudes and inclinations as the orbits of the newly generated debris decayed due to 

atmospheric drag. The results of this assumption are shown in Figure 6.1-10 for the JPSS-1 

mission. 

As a result of the assumption that all satellite and rocket body surfaces were generating the 1 mm 

and smaller debris, the increase in flux with altitude follows the general orbit element 

distribution of existing intact objects. However, this assumption has implications for the 

resulting debris environment. For example, if the source of debris were in highly elliptical orbits 

rather than following the near-circular orbits of the general object population, then the resulting 

altitude distribution of the 1 mm flux could be much different. Depending on the perigee 

distribution of the elliptical orbits, the flux at JPSS-1 altitude could be either higher or lower than 

predicted by ORDEM 3.0 by an order of magnitude or more. 

The following findings and observations are related to the construction of ORDEM 3.0. 

F-1. The degree of uncertainty present in the prediction of the 3 mm orbital debris flux 

by any orbital debris model at altitudes higher than 600 km has not been 

characterized, but is expected to be large because of the lack of direct 

measurements of orbital debris particles smaller than 3 mm at altitudes higher 

than where the Space Shuttle orbiter operated. 

F-2. For the flux for particles < 3 mm, orbital debris model validation for altitudes 

above 600 km is most effective using in situ data (e.g., Space Shuttle impact 

data).

O-1. The NASA standard break-up model used by LEGEND does not account for the 1 

mm population observed in Space Shuttle impact data. 

O-2. The surface degradation model in ORDEM 3.0 dominates the population of 

particles smaller than 3 mm. 

 

 This is based on a default assumption that intact object surface 

degradation generates particles that caused the Space Shuttle impacts. 

 Because of the orbital distribution of the source intact objects, most 

debris-producing surfaces are located in near-circular orbits between 700 

and 1000 km altitude. 
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O-3. The generation of 1 mm stainless steel debris via surface degradation in 

ORDEM 3.0 is assumed to continue into the future beyond the dates of Space 

Shuttle impact data. 

Figure 6.1-10. ORDEM 3.0 Model Prediction for JPSS-1 at 824 km Altitude 

6.1.2 Orbital Debris Model Flux Comparisons 

Various MMOD models are available for use in spacecraft risk assessment. ODPO has produced 

various versions of ORDEM (e.g., ORDEM96, ORDEM2000, and ORDEM 3.0). ESA has also 

produced an environment model called Meteoroid and Space Debris Terrestrial Environment 

Reference (MASTER), with the most recent version being MASTER-2009 (earlier models were 

MASTER-2001 and MASTER-2005). The Aerospace Corporation has also developed an orbital 

debris model, ADEPT, with the latest version originating in 2012. The NESC completed an 

analysis to gauge the differences in flux between ORDEM 3.0 and several of these other models 

in order to determine how the results for ORDEM 3.0 should be viewed. 

Figure 6.1-11 shows the orbital debris flux for ORDEM 3.0 and ORDEM2000. Three years are 

depicted: 2017, 2021, and 2024 for the JPSS-1 nominal orbit altitude of 824 km. Values 

between years for both ORDEM 3.0 and ORDEM2000 are similar (<35 percent difference at 

0.1 mm particle size) indicating that there is little difference between the intra-model flux values 

over the expected JPSS mission. However, there is a noticeable difference between the models 

in absolute value at particle sizes below ~2 to 3 mm. ORDEM 3.0 utilizes the Space Shuttle 

impact data to estimate the flux at the small particle sizes. ORDEM2000, while also using some 

early in situ shuttle data, is largely based upon examination of the Long Duration Exposure 

Facility (LDEF), so there is some difference between the basic data sets. But how they 
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extrapolate their respective data sets to higher altitudes causes the large difference for JPSS 

(discussed in greater detail later). An additional source of difference between the models is that 

ORDEM 3.0 also includes the effects of the FY-1C anti-satellite test, the Iridium-33/Cosmos2251 collision, and other debris generating events since 2000. 

Figure 6.1-11. ORDEM 3.0 and ORDEM2000 Orbital Debris Flux for JPSS Orbit at Beginning, 

Middle, and End of Expected Mission 

Figure 6.1-12 shows the flux versus size profile for ADEPT and MASTER-2009 for 2017, 2021, 

and 2024. As with the ORDEM models, there is small difference between the ADEPT and 

MASTER profiles for the different years of the JPSS mission. ADEPT is currently a 

fragmentation model and relies on collisions and explosions for its particle generation. ADEPT 

models particle sizes down to 1 cm and then uses data from large collision events (like the 

Iridium-33/Cosmos 2251 collision) along with catalog data to extrapolate downward to 1 mm. 

Improvements to ADEPT to accurately model collision and explosions to sub-mm sizes are 

currently being worked, but were unavailable for this assessment. Additional improvements to 

incorporate other sources of small particle generation are also being planned. Both the FY-1C 

and Iridium/Cosmos events were included in ADEPT. 

MASTER-2009 does not model the Iridium/Cosmos event, but does include the FY-1C test. 

MASTER-2009 is built as a source-dependent model with individual physical models for each 

source. That is, MASTER-2009 models the individual sources of the particles and then checks 

the source parameters for particles sizes 3 mm and larger against the radar and other empirical 

data. For sizes smaller than 3 mm, the sources are checked against retrieved in situ data from 

LDEF, Hubble Space Telescope (HST) solar arrays retrieved during service missions 1 and 3, 

and the European Retrievable Carrier (EuReCa). 
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Figure 6.1-12. ADEPT and MASTER-2009 Orbital Debris Flux for JPSS Orbit 

As MASTER builds its flux profiles based on models of individual sources, it also allows for the 

output of the flux for each of those sources. The MASTER sources are: collisions, explosions, 

intact (launched) objects, NaK droplets, aluminum dust and slag from solid rocket motor (SRM) 

firings, paint flakes, MLI, and ejecta. It also allows for the independent addition of large onorbit collision/explosive events. The FY-1C test is currently being modeled in MASTER-2009 

in this way. Naturally occurring micrometeoroids and annual meteoroid streams are also 

available as options, but were not included in the flux comparison to facilitate an apples-toapples (i.e., orbital debris only) comparison of the models. The flux breakdown is shown in 

Figure 6.1-13 by source for particles less than 10 cm in size. The dominant sources above 

~0.6 mm are fragments from historical explosions and collisions while the dominant sources 
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between 0.2 and 0.6 mm are ejecta (from small particle impacts) and aluminum SRM slag. The 

dominant sources below 0.2 mm are ejecta and paint flakes. 

Figure 6.1-13. MASTER-2009 Orbital Debris Flux in 2017 Broken Down by Source 

ORDEM 3.0 also breaks the flux down by source, but it breaks the sources down in a manner 

different from MASTER-2009. The categories for ORDEM 3.0 are: high density (e.g., stainless 

steel), medium density (e.g., aluminum), low density (plastic), NaK droplets, and intact objects. 

Figure 6.1-14 shows the flux as a function of size for the three largest categories (the intacts and 

the NaK droplets do not appear on this scale). The low density particle contribution is less than 

the medium density particles at all depicted sizes and is below the higher density particles for 

sizes smaller than ~2.5 mm, and so will not be further considered. The higher density flux is 

below the medium density flux at all depicted size ranges except at approximately 1 mm, where 

it slightly exceeds the medium density flux. This is precisely where the risk analysis for most 

space vehicles, including JPSS, indicates the greatest susceptibility to small particle damage. 

These 1 mm high density particles generate the majority of the risk that JPSS faces. 
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Figure 6.1-14. ORDEM 3.0 Orbital Debris Flux in 2017 for the JPSS-1 Orbit, Broken Down by 

Source 

Figure 6.1-15 shows the 2017 fluxes from all the available models for the JPSS orbit altitude. In 

addition to MASTER-2009, the two previous versions of MASTER, MASTER01 and 

MASTER05, are included to give an idea of the variability in the MASTER predictions 

(MASTER05, like ADEPT, does not yield results below 1 mm). Also, the MEMR2 flux is 

depicted to show that, at this size range and orbit, the micrometeoroid flux is less than or equal to 

any of the models. At the size range for which radar measurements are available (>3 mm), the 

models are in fairly good agreement with each other, and at 1 mm, MASTER05, MASTER2009, and ADEPT are still in fairly good agreement. However, at sizes below 1 mm, the 

differences between the models are substantial. ORDEM 3.0 shows the largest flux at 1 mm, 

~13 times the value of ORDEM2000 and ~400 times the values of MASTER-2009 and ADEPT. 
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Figure 6.1-15. Comparison of Flux at JPSS Altitude for Year 2017 including all Models 

The orbital debris environment that a satellite will face depends strongly on the altitude of the 

orbit. Large objects create the small particles and therefore it follows that the initial small 

particle debris environment will reflect the large object distribution. Figure 6.1-16 shows the 

fluxes for the 1 mm, 1 cm, and 10 cm particles sizes as a function of altitude for the various 

models. Given the difference in fluxes at the 1 mm size, ORDEM2000 and ORDEM 3.0 are 

shown in plots separate from ADEPT and MASTER-2009. Also note that ORDEM2000 does 

not model altitudes above 2000 km. Even so, the shape of the various curves is similar. The 

peak values at all sizes and for all four models occur at approximately 750 to 850 km altitude. 

This is where the highest concentration of satellites reside (Sun-synchronous orbits) and so it 

follows that this is where most debris will also be created. There is a secondary peak at 1400 

to1500 km; the Globalstar, Strela-1, and Strela-3 constellations reside at this altitude. (Strela-1 is 

an inoperative Soviet constellation of small military communications satellites; there are 369 of 

them in orbit in this altitude range with another 143 Strela-3 satellites). ORDEM2000 shows an 

unexpected large trough at ~875 km altitude for the 1 mm particles; the reason for this is 

unknown, but is likely due to the assumptions made as to the source of the small particles in the 

ORDEM2000 model. Further examination of the ORDEM2000 results indicates that this trough 

is not present at either the 1 cm (shown) or 0.1 mm (not shown) particle sizes. At ISS altitude, 

the flux is orders of magnitude less than at higher altitudes in all the models. Decay accounts for 

this. As the altitude decreases, the atmospheric drag increases and the particles, regardless of 

size, have a shorter lifetime. As the particles generated at higher altitudes decay, they speed up 

their descent so that the flux at lower altitudes becomes less. 
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Figure 6.1-16. Variation of Orbital Debris Flux in 2017 as a Function of Altitude (Linear Scale) 

Figure 6.1-17 shows the same 1 mm fluxes as depicted in the top row of the plots in 

Figure 6.1-16, but is on a log scale to facilitate comparison between the models. At the lower 

ISS/Space Shuttle altitudes (400 to 600 km), ORDEM2000 and ORDEM 3.0 agree fairly well. 

They are both based on the shuttle data so their agreement is not unexpected (noting that 

ORDEM 3.0 is based on a larger amount of shuttle data and includes particle density information 

from spectrographic analysis of crater damage). It is when those models are extrapolated to 

higher altitudes that they diverge. At JPSS altitude (824 km), ORDEM 3.0 is approximately a 

factor of 13 higher than ORDEM2000. Even though both models are starting from roughly the 

same point, the differences in how the extrapolation occurs results in a substantial difference in 

the fluxes at higher altitudes. 

ORDEM 3.0 uses a surface degradation model for the production of small particles that is based 

on balancing the observed flux corresponding to the shuttle data with the decay of small particles 

created at higher altitudes. The particles are assumed to come off all large intact orbiting objects 

at a uniform rate; the particle production for each individual object is solely dependent on the 

surface area of that object. The particles are then propagated to decay to ISS/Space Shuttle 

altitude where the flux is computed. The rate at which the particles leave the surface of the intact 

objects is adjusted to balance the observed Space Shuttle flux. Since all intact objects are 

producing small particles, most of the small particles are starting off in near-circular (i.e., low 
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eccentricity) orbits. ORDEM2000 also uses a surface degradation model for all intact objects, 

but applies magnification factors to populations of small particles coming off objects 

predominantly in highly eccentric orbits. Given the highly eccentric orbits, many ORDEM2000 

small particles are beginning their life in orbits crossing ISS/Space Shuttle altitude, rather than 

having to decay to get to ISS/Space Shuttle altitude. As a consequence, fewer small particles are 

produced in ORDEM2000 than in ORDEM 3.0. In addition, the highly eccentric small particles 

of ORDEM2000 will impact higher altitude LEO satellites much less than the near-circular small 

particles slowly decaying through those altitudes in ORDEM 3.0. 

Neither ADEPT nor MASTER are based on the shuttle data. Instead, both models are based on 

the empirical data with a lower limit of ~3 mm, and then ADEPT extrapolates downward to 

1 mm while MASTER uses source models. As a consequence, their fluxes at ISS/Space Shuttle 

altitude are much lower than the ORDEM models. At JPSS altitude, ORDEM 3.0 flux is ~400 

times larger than either ADEPT or MASTER. MASTER, being based on source models, and 

ADEPT, being a fragmentation and extrapolated catalog model, do not use a surface degradation 

model to extrapolate upwards. 

Figure 6.1-17. Variation of 1 mm Orbital Debris Flux with Altitude for 2017 (log scale) 

Figure 6.1-18 shows the 1 mm ORDEM 3.0 flux broken down by source (high density and 

medium density) as a function of altitude. While all the models show an expected increase from 

ISS/Space Shuttle altitude up to ~700-800 km, ORDEM 3.0 shows a sharp peak, especially of 

the steel particles, at 950 km, which is not present in the other models. This is caused by the 

assumption in the degradation model that particles are coming off the surface of all large intact 

objects at the same rate into near-circular orbits. The aluminum particles that are also being 

created by the surface degradation model decay more rapidly so the steel particles reside longer 

at 950 km. Most (~90 percent by mass) of the objects at that 950 km altitude are either old 

Soviet satellites utilizing the Kaur-1 bus or the SL-8 upper stage rocket bodies that placed them 
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into orbit. Information on the material composition of the Kaur-1 bus was not available, but it is 

thought that the Karu-1 bus is covered with solar cells. However, it is known that the SL-8 

rocket bodies are made predominantly of aluminum and only have small amounts of steel. 

Therefore, it is unlikely that the SL-8s are a significant source of the steel particles. More 

research is needed to determine if the Kaur-1 bus contains stainless steel that could produce the 

predicted particles, but at this point in time, it is also thought to be unlikely. 

Figure 6.1-18. ORDEM 3.0 1 mm Orbital Debris Flux as a Function of Altitude by Particle Type 

Each of the models allows for the computation of the flux over a range of times. The version of 

ADEPT used for this study has a starting epoch of 2012 and extends for 200 years. The version 

of MASTER-2009 used for this study begins in 1957 and ends in 2055. ORDEM 3.0 is based on 

data from the late 1990s until approximately 2011, but the software code allows for the selection 

of years from 2010 to 2035. By contrast, the previous version, ORDEM2000, covers the years 

1991 to 2030. The expected JPSS-1 mission is from 2017 to 2024, and all the models cover this 

period. Figure 6.1-19 shows the flux by year for 1 mm, 1 cm, and 10 cm particles for ADEPT, 

MASTER-2009, and ORDEM 3.0 for the JPSS-1 orbit. There is little change over the timeframe 

of the JPSS mission for each model (on the order of a few tens of percent, which is small when 

compared to the orders of magnitude difference between the models), and for the larger 1 and 

10 cm particles, the models agree to within a factor of 50 percent. Over the JPSS mission, the 

flux values are not expected to change enough to have a significant effect on the penetration 

results presented in the next section of this report. Therefore, the 2017 values will be used in that 

analysis. The full nominal mission timeframe was used for the assessment ultimately reported to 

OSMA. 
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Figure 6.1-19. Variation of Orbital Debris Flux as a Function of Time during JPSS-1 Mission 

(2017-2024) 

Within each of the models, the flux has a dependency on the inclination of the primary object’s 

(i.e., satellites) orbit. Figure 6.1-20 shows the 1 mm, 1 cm, and 10 cm fluxes as a function of 

inclination for ADEPT, MASTER-2009, and ORDEM 3.0 at JPSS altitude (824 km). The peak 

flux occurs for primary objects that are in near polar (Sun-synchronous) orbits. Most of the 

debris in LEO is in near-polar orbits and as the primary object has the opportunity to counterrotate against the debris, the flux increases since the primary object and the debris are more 

likely to approach each other head-on, resulting in a high average relative velocity at impact. 

Conversely, for near-equatorial orbits with inclinations near 0°, the primary object is 

encountering most of the debris at a substantial wedge angle which lowers the average relative 

velocity. For near-equatorial orbits with inclinations near 180°, the flux is similarly low, but 

slightly higher than the flux for inclinations near 0°. This is due to a smaller amount of debris 

with inclinations near 180°. Therefore, primary objects in these inclinations will see more 
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counter-rotating debris than the near-0° orbits. However, the difference between the peak and 

valley values of the flux indicates that the inclination dependency, regardless of model, is on the 

order of a factor of 2. 

Figure 6.1-20. Variation of Orbital Debris Flux with Inclination at JPSS Altitude (824 km) 

In summary, ORDEM 3.0 is predicting much higher fluxes in the 1 mm size range at JPSS 

altitude than any of the other models (~13x ORDEM2000, ~400x ADEPT and MASTER-2009). 

A large portion of these particles is assumed to be steel based on spectroscopic examination of 

the shuttle data. The physical source of these steel particles is currently unknown, but ORDEM 

3.0 assumes a surface degradation model involving a constant production rate per unit area of all 

intact orbiting objects to produce them that results in the higher flux at JPSS altitude. This effect 

is exacerbated by the slower decay of high density particles relative to medium density particles. 

The effect that the higher flux has on a sample cubic shape at JPSS- altitude will be discussed in 

the following section. 
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The following findings were identified based on the results of this section: 

F-3. The four models that were compared (ORDEM 3.0, ORDEM2000, MASTER2009, and the current version of ADEPT) agree within a factor of ~2 for most 

debris sizes larger than 3 mm, where ground radar has provided data describing 

the environment over a large range of altitudes (i.e., from LEO-to-GEO). 

F-4. The models disagree significantly for particles < 3 mm, which is also the size 

that poses the highest penetration risk to most spacecraft. 

F-5. A higher 1 mm particle flux and assumptions behind the source of those 

particles constitute the primary drivers for the differences in flux between 

ORDEM 3.0 and ORDEM2000. 

F-6. There are four factors contributing to the divergence of flux values between 

ORDEM2000 and ORDEM 3.0 from 400 to 600 km and the JPSS orbital 

altitude: 

1. In ORDEM2000, high-eccentricity intact objects were assumed to have a 

significantly higher particle production rate relative to low-eccentricity 

intact objects. ORDEM 3.0 does not make a distinction between the rate 

of production from high-eccentricity and low-eccentricity intact objects, 

so its 1 mm population has a higher relative contribution from circular 

orbits than does ORDEM2000. 

2. The impact data used to generate ORDEM 3.0 identify a higher proportion 

of particles as high density particles (stainless steel) at the higher altitudes, 

the source of which is assumed to come from intact objects 

(e.g., spacecraft and rocket bodies) in low eccentricity/high inclination 

orbits. 

3. ORDEM 3.0 includes high density particles, which are not included in 

ORDEM2000. The higher density particles, with a lower area to mass 

ratio, will decay less rapidly than medium density particles assumed for 

ORDEM2000, contributing to a greater total flux at higher altitudes. 

4. Surface degradation model particle emanation rates in ORDEM2000 and 

ORDEM 3.0 are significantly different. 

F-7. The greater JPSS MMOD risk using ORDEM 3.0 compared to ORDEM2000 is 

due to two factors: 

1. ORDEM 3.0 has a ~13x higher particle flux than ORDEM2000 at the 

JPSS orbital altitude. 

2. ORDEM 3.0 contains high density particles. Smaller high density 

particles cause the same amount of damage as larger medium density 

particles and are more numerous. ORDEM2000 does not model high 

density particles.
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F-8. Because all the orbital debris models are consistent at all altitudes for debris 

fluxes of particles ~3 mm and larger, designing spacecraft to withstand impacts 

by particles up to 3 mm in diameter will provide the least uncertainty in orbital 

debris risk. 

F-9. The orbital debris risk for JPSS-1 would be reduced if the orbital altitude were 

<800 km or >1000 km. 

 The results of the orbital debris model comparisons showed the highest 

flux between 800 and 1000 km for all of the orbital debris models 

analyzed. 

6.1.3 Orbital Debris Damage Fluence Comparisons 

In this section, predictions of damage fluence on a generic cube using the various orbital debris 

models are presented and compared to better study the combined effects of flux and density on 

the comprehensive penetration risk assessment. Damage was defined as penetration into the 

cube. The cube was selected to remove most JPSS-specific aspects of the problem, thereby 

broadening the applicability of the results. The length of the edges of the cube was one meter, 

thereby establishing an area on each face of 1 m2. The bus was assumed to be fixed in the 

rotating local vertical local horizontal (LVLH) frame. One face had a normal vector always 

pointing in the “north” direction (parallel to orbital angular momentum vector). A second face 

had a normal vector always pointing in the “south” direction (i.e., opposite of north).

The cube did retain some JPSS aspects to maintain relevance. It was assumed to fly in the JPSS1 orbit, and the mission duration was assumed to be 7 years (2017 to 2024). The bus was rotated 

about pitch by 15° from the LVLH frame. This represented the tilt of the surface on which the 

command and telemetry unit (CTU) box is mounted. A dual wall model was assumed for the 

cube surfaces, and the model parameters were the same as those used for the CTU box by the 

Bumper analysis cited by this study [ref. 6]. An aluminum inner wall represented the CTU box 

surface and had a thickness of 0.152 cm. An aluminum outer wall represented the MLI and had 

a thickness of 0.034 cm. It had the same mass per unit area as the MLI. Both the inner and outer 

walls had a density of 2.7 g/cm3

and a yield strength of 40 ksi. The spacing between the inner 

and outer walls was 5.08 cm. The dual wall model parameters (type of aluminum, wall 

thicknesses, and spacing) were supplied by the HVIT [ref. 6]. Note that this model of the CTU 

was later updated for JPSS assessments to reflect additional information and design changes. 

6.1.3.1 Meteoroid and Orbital Debris Risk Assessment (MODRA) 

The Aerospace MODRA code was used to compute damage fluence for each face of the 

representative cube. MODRA performs a computation similar to that of Bumper. MODRA 

sweeps through all surface elements in a finite element model of a spacecraft. Each surface 

element is defined by orientation, area, and parameters needed by a damage model. In this case, 

each bus face is a surface element, and damage is defined as penetration of the inner wall behind 

the MLI. 
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For each surface element, MODRA sweeps through all particle flux components. Flux 

components are defined by bins characterized by impact velocity, azimuth, and elevation in the 

LVLH frame. Flux components are also defined by populations. As discussed in Section 6.0, 

ORDEM 3.0 produces five populations: low density particles (e.g., paint flakes), medium density 

particles (e.g., aluminum), high density particles (e.g., stainless steel), NaK droplets, and intact 

objects. For MASTER-2009 runs, density bins specified in an input file were used as MODRA 

population bins. ADEPT runs were made using the flux components (hence population bins) 

from either ORDEM 3.0 or MASTER-2009.

For each flux component, MODRA uses a damage model to determine critical particle diameter 

or mass that can penetrate the surface element. The code determines penetrating flux at the 

critical particle diameter or mass from the environmental flux versus diameter/mass file provided 

by the debris environment model. The flux at the critical diameter or mass is interpolated 

between size/mass points in the environmental flux file via linear interpolation in log-log space. 

MODRA multiplies the resulting penetrating flux by the projected area of the surface element, 

by the impact probability of the flux component (determined from the environmental model 

velocity and direction distributions), and by the mission duration to obtain the damage fluence 

for the flux component. MODRA then sums the damage fluences from all flux components and 

surface elements to obtain the total damage fluence. 

6.1.3.2 Results Using ORDEM 3.0 with the Baseline New Non-optimum (NNO) BLE 

The damage model was a dual-wall BLE. The baseline BLE used for the analysis was the NNO 

equation (also known as the Whipple bumper equation). This is the same BLE that was used in 

the Bumper analysis cited for this study [ref. 6]. The actual implementation in MODRA is the 

ESA Space Environment Information System (SPENVIS) [ref. 7] formulation with “NASA ISS” 

parameters modified to make it consistent with the NNO BLE implementation in Bumper 

[ref. 8]. One of the modifications was implementation of a 65° impact angle limit (i.e., for 

impact angles greater than 65°), the critical diameter corresponding to an angle of 65° is used. 

This angle limit was motivated by observations that for impact angles greater than 65°, most 

inner wall damage is caused by outer wall fragments (not by impactor fragments) and therefore 

the damage does not drop off as rapidly as for lower impact angles [ref. 9]. A second 

modification is that the high velocity region boundary of the NNO BLE was made to be 

dependent on impactor density (i.e., the velocity boundary was 7 km/s for impactor density less 

than or equal to 7 g/cm3, and 9.1 km/s for impactor density > 7 g/cm3) (to model stainless steel 

impactors). A third modification is that the normal component of impact velocity (not the full 

impact velocity magnitude, as in SPENVIS) is compared to the low velocity region and high 

velocity region boundaries. 

The first set of results to be shown are those resulting from using ORDEM 3.0 with the baseline 

NNO BLE. Figure 6.1-21 shows the environmental flux versus impactor size for each of the 

ORDEM 3.0 populations. The curve of total flux versus size is from the SIZEFLUX_SC.OUT 

file generated by ORDEM 3.0. The data for the individual populations were extracted from the 

IGLOOFLUX_SC.OUT file. Two points per size decade are available from this file. The flux 
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values shown were obtained by summing fluxes from all igloo bins defined by azimuth, 

elevation, and velocity. The plot shows that the high density flux points are lower than the 

medium density flux points for most of the size range, with the exception of the vicinity near 

1 mm, where high density and medium density fluxes are comparable. Therefore, for most sizes 

the medium density population dominates the environmental flux. However, it will be seen that 

the medium density population does not dominate the damage fluence. 

Figure 6.1-21. ORDEM 3.0 Environmental Flux versus Impactor Size 

Table 6.1-1 shows the resulting damage fluence on each cube face from each of the populations. 

The table also shows the percentage of the total damage fluence that is contributed by the high 

density population. Overall, the high density population contributes 91 percent of the damage 

fluence for this shielding scenario. Since the high density environmental flux is approximately 

the same as the medium density environmental flux at a common size near 1 mm, but is less for 

all other sizes, the dominance of the high density population on the damage fluence must be due 

to the more damaging nature of the high density impactors. 
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Table 6.1-1. Cube Damage Fluence Resulting from use of ORDEM 3.0 with the NNO BLE 

As a check on the setup of the MODRA cube run, a CTU run was performed for comparison to 

previous Bumper results. The cube dimensions were scaled down to those of the CTU box, and 

percentage of view blocked for each face was visually estimated from a spacecraft computeraided design (CAD) image. Table 6.1-2 shows the results for each CTU face alongside the 

corresponding Bumper results from reference 6. The nadir face is not included since it is 

mounted to the spacecraft bus. Agreement of the damage fluences is very good for faces with 

10 percent blockage, as well as for the total. 

Table 6.1-2. CTU Damage Fluence Resulting from use of ORDEM 3.0 with the NNO BLE 

Figure 6.1-22 illustrates the distribution of the cube damage fluence over impactor size. The plot 

is a histogram with the x-axis (impactor diameter) in log space. The bin width is 1/100th of a 

decade (determined by the SIZEFLUX_SC.OUT file produced by ORDEM 3.0). Damage 

fluences on all the cube faces are combined. From this plot, it is seen that debris impactors in the 

size range 0.6 to 3 mm dominate the damage fluence. Below an impactor size of 1.2 mm, the 

damage fluence is determined by the high density population because medium density impactors 

smaller than 1.2 mm are not able to penetrate the MLI and inner wall. Above an impactor size of 

1.2 mm, both the medium density and high density populations contribute to the damage fluence. 

Larger debris particles impact too infrequently to dominate risk, and smaller particles cannot 

penetrate the MLI and inner wall of the cube faces. 
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Figure 6.1-22. Distribution of Damage Fluence Over Impactor Size 

Figure 6.1-23 shows the distribution of damage fluence over impact velocity. Damage fluences 

on all the cube faces are combined. The plot is a histogram with an x-axis (impact velocity) bin 

width of 1 km/s. From this plot it is seen that surface penetration velocity varies from 1 to 

18 km/s. The most likely values (peak in the histogram) are in the 14 to 15 km/s bin. However, 

a significant fraction of the total damage fluence occurs at lower velocities. 
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Figure 6.1-23. Distribution of Damage Fluence over Impact Velocity 

Figure 6.1-24 shows the distribution of damage fluence over impact angle. Damage fluences on 

all the cube faces are combined. The plot is a histogram with an x-axis (impact angle) bin width 

of 5°. From this plot it is seen that surface penetration impact angle varies from 0 to 85°. The 

most likely values (peak in the histogram) are between 0 and 35°. However, a significant 

fraction of the total damage fluence occurs at higher impact angles. The resulting distribution is 

determined by both the fixed orientation of the cube in the LVLH frame as well as the orbital 

distributions of the five populations in ORDEM 3.0. 
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Figure 6.1-24. Distribution of Damage Fluence over Impact Angle 

A MODRA run was executed with the variation that the impact angle limit of 65° was replaced 

with a value of 85°. Table 6.1-3 shows the resulting damage fluence on each cube face from 

each of the populations. In this case, the total damage fluence was 4.62. In comparison, the 

damage fluence for the case with the impact angle limit set to 65° was 4.95. The high density 

population still contributes the same percentage (91 percent) of the damage fluence. Using a 

higher impact angle limit reduced the total damage fluence, but the reduction was very small 

since the most common penetrating impacts occur at angles between 0 and 35°. 

Table 6.1-3. Cube Damage Fluence Resulting from use of ORDEM 3.0 with the NNO BLE; Impact 

Angle Limit Changed to 85° 
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6.1.3.3 Results Using ORDEM2000 with the Baseline NNO BLE 

A MODRA run to determine cube damage fluence was executed with the variation that the 

ORDEM2000 debris environment was used in place of the ORDEM 3.0 debris environment. 

The baseline NNO BLE and a 65° angle limit was used for this run. 

Since ORDEM2000 assumes that all debris particles are aluminum, a single population bin with 

impactor density of 2.8 g/cm3 was implemented. 

Figure 6.1-25 shows the environmental flux versus impactor size at the JPSS orbit from 

ORDEM2000 along with the total flux versus size curve from ORDEM 3.0 (summed over the 

different populations). The ORDEM2000 curve is from the flt.dat file. There are 20 points per 

size decade. The plot shows that the ORDEM2000 flux is lower for all sizes with the exception 

of the ranges from approximately 2.8 to 4 mm and above 18 cm. 

Figure 6.1-25. ORDEM2000 and ORDEM 3.0 Environmental Flux versus Impactor Size 

Table 6.1-4 shows the damage fluence on each cube face when the ORDEM2000 debris 

environment is used with the baseline NNO BLE in MODRA. In this case, the total damage 

fluence is 0.169, which is ~29 times lower than the damage fluence of 4.95 using ORDEM 3.0. 

Note that the contribution to the total ORDEM 3.0 damage fluence by the medium density 

population, which has the same density as aluminum, is 0.436 (Table 6.1-1). 
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Table 6.1-4. Cube Damage Fluence Resulting from Use of ORDEM2000 with the Baseline NNO 

BLE 

Figure 6.1-26 illustrates the distribution of the cube damage fluence over impactor size resulting 

from using ORDEM2000 with the baseline NNO BLE. Damage fluences on all the cube faces 

are combined. This plot does not have the bi-modal pattern that is present in the same plot for 

ORDEM 3.0 due to the dominance of both medium density and high density particles 

(Figure. 6.1-22). The lowest size contributing damage fluence is 1 mm, which is higher than the 

lowest size contributing damage fluence from ORDEM 3.0 (0.676 mm). 

Figure 6.1-26. Distribution of Damage Fluence over Impactor Size; Results from All Cube Faces 

using ORDEM2000 with the Baseline NNO BLE 
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Figure 6.1-27 shows the distribution of damage fluence over impact velocity resulting from using 

ORDEM2000 with the baseline NNO BLE. Damage fluences on all the cube faces are 

combined. From this plot it is seen that the velocity range of penetrating particles is slightly 

broader than it was in the same plot for ORDEM 3.0 (Figure. 6.1-23). This may be due to the 

higher weighting factors on debris populations on highly eccentric orbits used by ORDEM2000. 

The peak in the low velocity range is not as high relative to the rest of the distribution as it is in 

the ORDEM 3.0 plot. 

Figure 6.1-27. Distribution of Damage Fluence over Impact Velocity. Results from all Cube Faces 

using ORDEM2000 with the Baseline NNO BLE 

Figure 6.1-28 shows the distribution of damage fluence over impact angle resulting from using 

ORDEM2000 with the baseline NNO BLE. Damage fluences on all the cube faces are 

combined. From this plot it is seen that the distribution is more uniform than in the same plot for 

ORDEM 3.0 (Figure 6.1-24). This may be due to the higher weighting factors on debris 

populations on highly eccentric orbits used by ORDEM2000. 
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Figure 6.1-28. Distribution of Damage Fluence over Impact Angle; Results from all Cube Faces 

using ORDEM2000 with the Baseline NNO BLE 

6.1.3.4 Results Using MASTER-2009 with the Baseline NNO BLE 

A MODRA run to determine cube damage fluence was executed with the variation that the 

MASTER-2009 debris environment was used in place of the ORDEM 3.0 debris environment. 

The baseline NNO BLE with the 65° angle limit was used for this run. 

MASTER-2009 reconstructs the current debris environment by modeling historical events and 

processes. Populations include explosion fragments, collision fragments, solid rocket motor slag 

and dust, ejecta from meteoroid impacts, paint flakes from surface degradation, multi-layer 

insulation pieces, launch-related operational debris, and NaK droplets. A debris population for 

the FY-1C collision is included, but there is no debris population for the Iridium 33/Cosmos 

2251 collision. MASTER-2009 also contains a meteoroid population, but this was not used in 

this analysis to facilitate comparison with ORDEM 3.0 results. MASTER-2009 output files 

include flux versus size files (*c.dia) and standard environment interface (SEI) files (*.sei) for 

each population. The SEI files contain information similar to that of the ORDEM 3.0 igloo flux 

file. Flux is broken down by velocity, direction, size, and density bins. For this analysis, a 

preprocessor was developed to convert the SEI files into the flux component distribution file 

used by MODRA. 

Figure 6.1-29 shows the environmental flux versus impactor size for each of the MASTER-2009 

density bins. The curve of total flux versus size is from the *c.dia file generated by MASTER-
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2009. The data for the individual density bins is extracted from the *.sei files for all populations, 

except meteoroids, and summed together. Two points per size decade are shown. The flux 

values shown were obtained by summing fluxes from all bins defined by azimuth, elevation, and 

velocity. Note that particles with density from 0.5 to 1.5 g/cm3 dominate the total flux at given 

size from 1 mm to 10 cm. The aluminum density bin (2.5 to 3 g/cm3) dominates in the size range 

0.1 to 1 mm. The flux from 4.5 to 5 g/cm3 particles is comparable to the aluminum particle flux 

in the size range from 10 to 100 μm. For this orbit, MASTER-2009 outputs zero flux for 

particles with density > 5 g/cm3. 

Figure 6.1-29. MASTER-2009 Environmental Flux versus Impactor Size 

Table 6.1-5 shows the damage fluence on each cube face from each of the density bins when the 

MASTER-2009 debris environment is used with the baseline NNO BLE in MODRA. In this 

case, the total damage fluence is 0.03, which is a factor of 165 lower than the damage fluence of 

4.95 using ORDEM 3.0. Particles with density from 0.5 to 3 g/cm3 all contribute in similar 

proportions to the total damage fluence. 

Particles with density above 3 g/cm3 (i.e., above aluminum density of 2.8 g/ cm3) do not 

contribute to the damage fluence (i.e., there are no high density particles in the size range that 

could penetrate the sample cube). 
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Table 6.1-5. Cube Damage Fluence Resulting from use of MASTER-2009 with the Baseline NNO 

BLE 

Figure 6.1-30 illustrates the distribution of the cube damage fluence over impactor size resulting 

from using MASTER-2009 with the baseline NNO BLE. Damage fluences on all the cube faces 

are combined. This plot does not have the bi-modal pattern that is present in the same plot for 

ORDEM 3.0 due to the dominance of both medium density and high density particles (Figure 

6.1-22). The lowest size contributing damage fluence is 1 mm, which is higher than the lowest 

size contributing damage fluence from ORDEM 3.0 (0.676 mm). 

Figure 6.1-30. Distribution of Damage Fluence over Impactor Size; Results from all Cube Faces 

using MASTER-2009 with the Baseline NNO BLE 

Figure 6.1-31 shows the distribution of damage fluence over impact velocity resulting from using 

MASTER-2009 with the baseline NNO BLE. Damage fluences on all the cube faces are 

combined. From this plot it is seen that the velocity range of penetrating particles is slightly 
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broader than it was in the same plot for ORDEM 3.0 (Figure 6.1-23). This may be due to a 

greater fraction of total flux attributable to debris populations on highly eccentric orbits. The 

peak in the low velocity range is not as high relative to the rest of the distribution as it is in the 

ORDEM 3.0 plot. 

Figure 6.1-31. Distribution of Damage Fluence over Impact Velocity; Results from all Cube Faces 

using MASTER-2009 with the Baseline NNO BLE 

Figure 6.1-32 shows the distribution of damage fluence over impact angle resulting from using 

MASTER-2009 with the baseline NNO BLE. Damage fluences on all the cube faces are 

combined. From this plot it is seen that the distribution is more uniform than in the same plot for 

ORDEM 3.0 (Figure 6.1-24). This may be due to a greater fraction of total flux attributable to 

debris populations on highly eccentric orbits. 
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Figure 6.1-32. Distribution of Damage Fluence over Impact Angle; results from all cube faces using 

MASTER-2009 with the Baseline NNO BLE 

6.1.3.5 Results Using ADEPT with the Baseline NNO BLE 

A MODRA run to determine cube damage fluence was executed with the variation that the 

ADEPT debris environment was used in place of the ORDEM 3.0 debris environment. The 

baseline NNO BLE and a 65° angle limit was used for this run. 

The current version of ADEPT was designed with emphasis placed on projecting the future 

debris environment down to 1 cm from future launch traffic, explosions and collisions [ref. 10]. 

The initial population consists of the following sub-populations: 

 Unclassified catalog objects. 

 A statistical population to represent the difference between the number of objects 

in the unclassified catalog and the total unclassified number cited by the United 

States Strategic Command’s (USSTRATCOM) Space Control and Space 

Surveillance (“Unknown population”) website.

 A population of untracked debris (10 cm down to 1 cm) determined by power law 

extrapolation (“small population”). The long term plan is to replace this 

population with a reconstruction of the current environment from historical events 

and processes (similar to the approach in MASTER-2009).
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 Debris down to 1 cm from the FY-1C and Iridium 33/Cosmos 2251 collisions as 

modeled by the Aerospace breakup modeling code IMPACT (“FYCIC 

population”).

To generate a flux versus size curve for MODRA analysis, the ADEPT initial population flux 

was further extrapolated down to 0.1 mm. Hence, the base for the extrapolation includes the 

Unknown, Small, and FY-1C populations. 

Impact velocity and direction distributions for MODRA analysis can eventually be extracted 

from the ADEPT populations, but the tools to accomplish this were not yet available for this 

analysis. To circumvent this limitation, two MODRA runs were made. In the first run, the 

ADEPT flux versus size curve and the MASTER-2009 normalized velocity and direction 

distribution were used as inputs for MODRA. In the second run, the ADEPT flux versus size 

curve and the ORDEM 3.0 normalized velocity and direction distribution were used as inputs to 

MODRA. 

Density information is available for the FYCIC population generated by IMPACT, but the tools 

to extract this information for MODRA were not yet available for this analysis. Therefore, it was 

assumed that all impactors have density of aluminum (consistent with ORDEM2000) (i.e., a 

single population bin with impactor density of 2.8 g/cm3 was implemented). 

Table 6.1-6 shows the damage fluence on each cube face when the ADEPT debris environment 

is used with the MASTER-2009 velocity/direction distribution and the baseline NNO BLE in 

MODRA. In this case, the total damage fluence is 0.0566. Table 6.1-7 shows the damage 

fluence on each cube face when the ADEPT debris environment is used with the ORDEM 3.0 

velocity/direction distribution and the baseline NNO BLE in MODRA. In this case, the total 

damage fluence is 0.0575. Swapping the velocity/direction distribution had minimal effect on 

the total damage fluence, which is a factor of ~86 lower than the damage fluence of 4.95 using 

ORDEM 3.0. 

Table 6.1-6. Cube Damage Fluence Resulting from use of ADEPT with the MASTER-2009 

Velocity/Direction Distribution and the Baseline NNO BLE 
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Table 6.1-7. Cube Damage Fluence Resulting from use of ADEPT with the ORDEM 3.0 

Velocity/Direction Distribution and the Baseline NNO BLE 

Figure 6.1-33 illustrates the distribution of the cube damage fluence over impactor size resulting 

from using ADEPT with the MASTER-2009 velocity/direction distribution and the baseline 

NNO BLE. Figure 6.1-34 illustrates the distribution of the cube damage fluence over impactor 

size resulting from using ADEPT with the ORDEM 3.0 velocity/direction distribution and the 

baseline NNO BLE. Damage fluences on all the cube faces are combined. These plots do not 

have the bi-modal pattern that is present in the same plot for ORDEM 3.0 due to the dominance 

of both medium density and high density particles (Figure 6.1-22). The lowest size contributing 

damage fluence is 1 mm, which is higher than the lowest size contributing damage fluence from 

ORDEM 3.0 (0.676 mm). 

Figure 6.1-33. Distribution of Damage Fluence over Impactor Size; Results from all Cube Faces 

using ADEPT with the MASTER-2009 Velocity/Direction Distribution and the Baseline NNO BLE 
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Figure 6.1-34. Distribution of Damage Fluence over Impactor Size; Results from All Cube Faces 

using ADEPT with the ORDEM 3.0 Velocity/Direction Distribution and the Baseline NNO BLE 

Figure 6.1-35 shows the distribution of damage fluence over impact velocity resulting from using 

ADEPT with the MASTER-2009 velocity/direction distribution and the baseline NNO BLE. 

Damage fluences on all the cube faces are combined. The shape of the distribution is similar to 

that from the MASTER-2009 run (Figure 6.1-31). Figure 6.1-36 shows the distribution of 

damage fluence over impact velocity resulting from using ADEPT with the ORDEM 3.0 

velocity/direction distribution and the baseline NNO BLE. In this case, the shape of the 

distribution at high velocities is similar to that from the ORDEM 3.0 run (Figure 6.1-23). The 

peak at lower velocities is not as high relative to the rest of the distribution as in the velocity 

distribution from the ORDEM 3.0 run. 
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Figure 6.1-35. Distribution of Damage Fluence over Impact Velocity; Results from All Cube Faces 

using ADEPT with the MASTER-2009 Velocity/Direction Distribution and the Baseline NNO BLE 

Figure 6.1-36. Distribution of Damage Fluence over Impact Velocity; Results from all Cube Faces 

using ADEPT with the ORDEM 3.0 Velocity/Direction Distribution and the Baseline NNO BLE 

Figure 6.1-37 shows the distribution of damage fluence over impact angle resulting from using 

ADEPT with the MASTER-2009 velocity/direction distribution and the baseline NNO BLE. 
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Damage fluences on all the cube faces are combined. The shape of the distribution is similar to 

that from the MASTER-2009 run (Figure 6.1-32). Figure 6.1-38 shows the distribution of 

damage fluence over impact angle resulting from using ADEPT with the ORDEM 3.0 

velocity/direction distribution and the baseline NNO BLE. The shape of the distribution is 

similar to that from the ORDEM 3.0 run (Figure 6.1-24). 

Figure 6.1-37. Distribution of Damage Fluence over Impact Angle; Results from all Cube Faces 

using ADEPT with the MASTER-2009 Velocity/Direction Distribution and the Baseline NNO BLE 
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Figure 6.1-38. Distribution of Damage Fluence over Impact Angle; Results from all Cube Faces 

using ADEPT with the ORDEM 3.0 Velocity/Direction Distribution and the Baseline NNO BLE 

6.1.3.6 Conclusions

Damage fluence on a generic cube was determined for various orbital debris models and 

variations of damage equations. The Aerospace MODRA code was used to compute damage 

fluence for each bus face. The baseline results were for the combination of ORDEM 3.0 with the 

NNO BLE. A test run on the JPSS CTU box produced results that compared well with 

corresponding Bumper results. Results for the cube showed that the high density particle 

(i.e., stainless steel) population accounts for ~90 percent of the total damage fluence. The total 

damage fluence on all cube faces was 4.95. Debris particles in the size range 0.6 mm to 3 mm 

dominate the damage fluence. Results also show that high velocity impacts dominate total 

damage fluence, although low velocity impacts also contribute a noticeable fraction of the total 

damage fluence. When the impact angle limit in the NNO BLE is changed from 65 to 85°, the 

total damage fluence is reduced slightly to 4.62. 

MODRA runs were performed using the ORDEM2000, MASTER-2009, and ADEPT debris 

models with the NNO BLE. The ORDEM2000 and ADEPT runs assumed that all impactors are 

aluminum. MASTER-2009 only produced fluxes for particles with density of 5 g/cm3or less. In 

all cases, the resulting cube damage fluence was lower than the damage fluence resulting from 

use of ORDEM 3.0. The MASTER-2009 and ADEPT damage fluence values were similar, but 

less than the ORDEM2000 damage fluence value. It was noted that the ORDEM2000 damage 

fluence was much closer to the ORDEM 3.0 damage fluence contributed by just the medium 
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density population (aluminum) than to the total ORDEM 3.0 damage fluence including the high 

density particle population. 

6.1.4 Conclusions Regarding ORDEM 3.0 

6.1.4.1 ORDEM 3.0 versus other Models 

In many ways, ORDEM 3.0 does not predict a significantly different hazard than ORDEM2000. 

For example, a spacecraft at 400 km altitude can expect a lower hazard prediction from ORDEM 

3.0 than by ORDEM2000, depending on its regions of spacecraft vulnerability, as shown in 

Figure 6.1-9, where the ORDEM 3.0 flux is compared to the ORDEM2000 flux. This is the 

result of ORDEM2000 using less in situ data with larger uncertainties from a different (and 

shorter) time span than ORDEM 3.0. As a result, the newer model was fit with a curve that 

required fewer source assumptions. At higher altitudes, the ORDEM 3.0 flux represents a 

significantly higher hazard, but only if the spacecraft is vulnerable to debris sizes less than 3 mm, 

as shown in Figure 6.1-10. 

The shuttle impact data set when ORDEM2000 was developed had about 1/3 the exposure time 

as the shuttle data used in ORDEM 3.0. As a result, impacts as large as 1 mm were not included 

in the ORDEM2000 data, with the largest impacts being from debris of about 0.5 mm. At 

smaller sizes, most of the data revealed paint and aluminum oxide as the dominant debris type. 

However, the major data source used for ORDEM2000's small debris population was LDEF 

[ref. 11]. Because LDEF had a fixed orientation relative to the orbital velocity vector, the 

distribution of craters around LDEF's surfaces were used to conclude that most of the impacts 

were from objects in highly elliptical orbits. For example, especially noticeable were aluminum 

oxide impacts on LDEF's rear surface that could come only from high eccentricity orbits with 

inclinations similar to the orbital inclination of LDEF. This may be still be true of medium 

density and low density debris, and perhaps ORDEM 3.0 should have been modeled using this 

assumption for the LD debris; however, it would have reduced the ORDEM 3.0 hazard from the 

less than 3 mm population by only about 10 percent. 

Another characteristic to note concerning ORDEM2000 is that it does not contain the debris 

populations modeled from the major collision events including the FY-1C test and the 

Iridium/Cosmos collision. 

ORDEM 3.0 is burdened with the same limitations as all existing orbital debris models resulting 

from the absence of any in situ debris penetration data beyond 400-600 km altitude. All debris 

models have a level of uncertainty due to this reality. However, when compared to the other 

models, ORDEM 3.0 makes the greatest use of available in situ data for the development of the 

model. Indeed, MASTER-2009 and ADEPT poorly predict the flux at 400-600 km altitude that 

would be necessary to reproduce the observed Space Shuttle impact data. The NESC team 

understands the assumptions made and processes used to develop the ORDEM 3.0 debris 

population for regimes where no in situ or radar data exist, but it is still a concern that there is (as 

of yet) no source identified to explain the 1 mm steel particle population. Overall, the NESC 

team recommends ORDEM 3.0 as the best option for use in JPSS-1 MMOD risk assessments. 
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The NESC team considered the possibility of using ORDEM 3.0 minus the high density debris 

component, but decided that it was not appropriate to use only portions of the model when not 

designed to do so. For an alternate opinion on this subject, see Section 9.0. 

F-10. In spite of the identified uncertainties, ORDEM 3.0 possesses several advantages 

over ORDEM2000, MASTER-2009 and the current version of ADEPT: 

1. ORDEM 3.0 incorporates the most extensive use of Space Shuttle impact 

data, including residue chemical analysis. As a result of safety concerns about 

the orbiters, radiator and window surfaces represent the most complete, 

thoroughly examined returned spacecraft surfaces. 

2. MASTER-2009 and ADEPT do not predict the 1 mm particle flux reflected by 

the Space Shuttle impact data. 

3. ORDEM 3.0 contains high density particles that are not included in the other 

models, but are present in the Space Shuttle impact data. 

4. ORDEM 3.0 includes an updated large-object catalog, including contributions 

from the FY-1C event, the Iridium-Cosmos collision, and other recent debrisproducing events. ORDEM2000 includes none of these, MASTER-2009 

includes only the FY-1C debris, and ADEPT includes FY-1C and IridiumCosmos debris.

6.1.4.2 Possible Sources of 1 mm Population 

Although the ODPO did not identify a source for the 1 mm high density debris, the NESC team 

did speculate and research possible sources. To determine a source, one must first identify 

potential sources from the metals used in their construction, and then identify a mechanism that 

would generate a debris population consisting of mostly 1 mm debris. 

Two spacecraft structure types were identified as containing stainless steel: propellant tanks used 

in upper stage rockets and liquid rocket engines. Aerospace identified that the propellant tanks 

in Delta I 2nd stages, Delta II 2nd stages, and Centaur upper stages were made of stainless steel, 

and that over 200 of these upper stages are still in orbit [ref. 12]. No attempt has been made to 

identify the materials used in Russian upper stages or spacecraft although anecdotal information 

obtained through the Inter-Agency Debris Coordination Committee (IADC) states that most, if 

not all, Russian/Soviet upper stages used aluminum propellant tanks. Informal contacts with 

individuals involved with testing liquid rocket engines provided mixed reports, with one saying 

that no erosion of the stainless steel surfaces was observed during testing and another saying that 

it is common. 

However, what can be concluded with certainty is that upper stages with stainless steel tanks 

have exploded at high LEO altitudes, producing a possible reservoir of stainless steel debris, 

some exploding in orbits as high as 1500 km. In another analysis by Aerospace [Appendix C, 

ref. 13], it was concluded that these explosions alone are insufficient to be the only source of 

stainless steel debris. At most, these explosions represent a reservoir that is declining now that 
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their operation includes depleting excess fuel to prevent future explosions. But both explosions 

and collisions are capable of generating small fragments, with most of the expected collision 

events unlikely to produce a detectable change in the upper stage. One can confidently predict 

that existing debris fragments between 3 mm and a few cm are both numerous enough and 

massive enough to cause non-catastrophic debris-producing events with upper stage rockets as 

well as payloads. In addition, if the 1 mm debris population is as large as predicted in ORDEM 

3.0, then that population would also contribute to an increasing 1 mm debris population and has 

the potential to become self-generating. What is missing is any experimental data to predict the 

amount of debris generated, as well as a model analysis to determine if the rate of generation is 

consistent with the Space Shuttle impact data. 

Another possible explanation for the abundance of 1 mm particles may be a function of the 

LEGEND evolutionary model. The debris population model in LEGEND does not include ejecta 

from impacts from particles from 1-10 cm, nor a ground-tested non-catastrophic collision model 

that may apply to upper stages. 

The following finding is related to the 1 mm steel particle source. 

F-11. No source for 1 to 3 mm steel debris particles in ORDEM 3.0 has been 

definitively identified. Possible contributors include: 

• Surface degradation (e.g., collisions with MMOD) of stainless steel rocket 

stages with thicknesses near 1 mm. 

• Explosions of non-passivated stages. 

• Ejecta from non-catastrophic collisions. The debris population model in 

LEGEND does not include ejecta from impacts from particles from 1 to 

10 cm, nor a ground-tested non-catastrophic collision model that may 

apply to upper stages. 

6.1.5 NASA-STD-7009 Evaluation Credibility Assessment 

To gain a different perspective of ORDEM 3.0, the NESC team performed an informal 

credibility assessment based on NASA-STD-7009, Standard for Models and Simulations. The 

credibility assessment scale (CAS) was created to use a standardized process and nomenclature 

to inform NASA decision-makers about the credibility of model and simulation results. Since 

credibility is subjective, the CAS does not purport to determine credibility, but merely assesses 

on a graduated scale, key factors that contribute to a decision-maker’s assessment of credibility 

[ref. 14]. The CAS is divided into eight factors, which are described later in this section. There 

are five levels numbered 0 to 4 with which to grade each factor. A higher level indicates greater 

confidence in establishing credibility as it relates to the factor evaluated. The values can be 

rolled into a combined credibility score and compared to target values set by the project. 

For this assessment, the credibility factors and levels were used to give a qualitative assessment 

of ORDEM 3.0 and how NESC recommendations in this report could improve credibility as 

represented in the CAS. Figures 6.1-39 and 6.1-40 show the results of the credibility assessment. 

ORDEM 3.0 was treated as two separate models: one for orbital debris particles 
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>3 mm diameter, and one for particles <3 mm diameter. For the >3 mm regime, there is a higher 

confidence in the model results because there are radar measurements to ground and validate the 

model. Conversely, only for the 400 to 600 km altitude range, for which there is Space Shuttle 

impact data, are there any direct measurements available for particles <3 mm. This assessment 

was also directed to the modeled environment at the JPSS-1 orbital altitude, so any advantage 

gained at the 400 to 600 km altitude does not apply in this case. The disparity in confidence 

between the two model size regimes is reflected in the CAS matrices for the two cases 

(Figures 6.1-39 and 6.1-40). In the figures, the magenta line represents the current evaluation 

level for each factor. Some factors also have a blue line, placed higher than the magenta, which 

represents the level to which the factor might be improved if improvements outlined in the 

recommendations for this assessment are implemented. 

Credibility Assessment for Particles Greater than ~3 mm 

The results from the credibility assessment for ORDEM 3.0 for particles >3 mm can be seen in 

Figure 6.1-39. How the NESC team rated each factor is discussed in Figure 6.1-39. 

Figure 6.1-39. NASA-STD-7009 Credibility Assessment for ORDEM 3.0 >3 mm Particle Size 

Regime 
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M&S Development 

Verification: Level 3/Formal numerical error estimation – Verification is the process of 

determining that the application accurately represents the underlying mathematical model 

and its solution from the perspective of the intended uses of the M&S [ref. 14]. It 

confirms that the model is coded correctly and does not contain errors. The ODPO 

development process for ORDEM 3.0 was judged to be adequate for verifying the model. 

Validation: Level 4/Results agree with real-world data – Validation is the process of 

determining the degree to which a model presents an accurate representation of the real 

world. Validation requires a source of data to which results from the model or simulation 

can be compared. ORDEM 3.0 was validated against the large radar measurement 

dataset available for particles >3 mm in diameter. A limitation was that the data source 

used to develop the model was in some cases the same as that used for validation. 

M&S Operations 

Input Pedigree: Level 4/Input data agree with real-world data – Input Pedigree 

evaluates the degree to which input data compares favorably with measured real-world 

data. Radar data was used as input data for ORDEM 3.0 in this size regime. While it still 

has uncertainties, radar measurements are a mature and well-understood process for 

obtaining direct measurements of the larger orbital debris particles. 

Results Uncertainty: Level 2→3/Deterministic analysis or expert opinion→

Nondeterministic analysis – Results Uncertainty indicates the degree to which 

uncertainties are quantified and are based on nondeterministic analysis. Uncertainty in 

ORDEM 3.0 has not been quantified to any significant extent and is based primarily on 

expert opinions. While the nature of the environment being modeled is such that there 

will be large uncertainties, efforts to perform nondeterministic uncertainty analysis may 

be possible and could result in an improvement to Level 3 for this factor. 

Results Robustness: Level 1→3/Qualitative estimates→Sensitivity known for many 

parameters – Results Robustness is the degree of how well the sensitivities of the model 

are known. Very limited sensitivity studies were performed for ORDEM 3.0, resulting in 

a Level 1 rating, which could be improved to as high as a Level 3 if more extensive 

sensitivity studies were performed. 

Supporting Evidence 

Use History: Level 2/Used before for critical decisions – Use History assesses the 

heritage of the model by evaluating how extensively it has been used for similar 

applications in the past. ORDEM 3.0 is gradually being adopted by spaceflight projects 
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for critical decisions, and its predecessors have been in use for many years. Independent 

mission data, aside from radar data used in the model, are not available to validate 

predictions. 

M&S Management: Level 3/Predictable process – M&S Management determines how 

well the model’s processes are managed. This would include assessing product 

management, process definition, continuous improvement, and configuration 

management. ORDEM 3.0 has a predictable and understandable process, and ODPO is 

working to continuously improve the model. One weakness noted is the lack of 

documentation describing ORDEM 3.0’s validation and verification. There is a user’s 

guide, which is useful, but it does not contain details on the model’s best uses and 

limitations. 

People Qualifications: Level 3.5/Extensive experience and use of recommended 

practices for this particular M&S – People Qualifications assesses the qualifications of 

the personnel developing, using, and interpreting the results from the model. The 

personnel who control ORDEM 3.0 possess a very high level of expertise and experience. 

The only consideration that prevented this factor rating a 4 was the fact that ODPO is 

understaffed and many of the team recommendations have not already been 

accomplished only because of a lack of resources. 

Credibility Assessment for Particles Less than ~3 mm 

The results from the credibility assessment for ORDEM 3.0 for particles <3 mm can be seen in 

Figure 6.1-40. Overall, the results are seen to exhibit a lower level of confidence in the model 

for this size regime because of the absence of any direct measurements in the JPSS orbit altitude. 

How the NESC team rated each factor is discussed in Figure 6.1-40. 
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Figure 6.1-40. NASA-STD-7009 Credibility Assessment for ORDEM 3.0 <3 mm Particle Size 

Regime 

M&S Development 

Verification: Level 3/Formal numerical error estimation – The ODPO development 

process for ORDEM 3.0 was judged to be adequate for verifying the model. Verification 

of the application is independent of the particle size regime so this matches the level for 

the >3 mm regime. 

Validation: Level 1→2/Conceptual and mathematical models agree with simple 

referents/Results agree with experimental data or other M&S on unit properties – Real 

world data are not available to validate the model, highlighting one of the primary 

weaknesses of ORDEM 3.0. Some impact data that are available may be employed to 

help validate the model at 400-600 km, which may in turn have an effect on the results 

for the higher altitudes. 
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M&S Operations 

Input Pedigree: Level 1→4/Input data traceable to informal documentation/Input data 

agree with real-world data – The only input data available used are Space Shuttle impact 

data. In situ impact measurements at altitudes higher than 600 km, for which there 

currently are no plans, would benefit the input pedigree a great deal. 

Results Uncertainty: Level 1→3/Qualitative estimates/Non-deterministic analysis –

Only qualitative estimates on uncertainty have been performed for ORDEM 3.0. The 

NESC team recommended that the uncertainty be quantified, which would raise this 

factor to a level 3. 

Results Robustness: Level 1→3/Qualitative estimates→Sensitivity known for many 

parameters – As is the case for the model in the >3 mm size regime, sensitivity studies 

would increase the level in this factor to 3.

Supporting Evidence 

Use History: Level 1/Passes simple tests – ORDEM 3.0 in the <3 mm size regime passes 

simple tests. ORDEM 3.0 for this smaller size regime does not give the same level of 

confidence as the >3 mm model. 

M&S Management: Level 3/Predictable process and People Qualifications: Level 

3.5/Extensive experience and use of recommended practices for this particular M&S –

These categories are strengths for ORDEM 3.0 for both size regimes. The rationale given 

for the >3 mm model applies for the <3 mm model. 

The following findings and observations are related to the credibility assessment. 

F-12. ORDEM 3.0 has no technical documentation describing the validation and 

verification or user guide information detailing the best uses and limitations. 

• Data for this assessment was provided to the NESC team by ODPO in the 

form of presentations, spreadsheets, and verbal exchanges. 

O-4. The credibility assessment methodology in NASA-STD-7009 (NASA Standard for 

Models and Simulations) was useful in providing a high-level evaluation of 

ORDEM 3.0, highlighting opportunities for overall improvement and building 

confidence in the model. This was especially the case for orbital debris particle 

regimes <3 mm, where focus areas include validation, input pedigree, results 

uncertainty, results robustness, and model and simulation management. OSMA 

has determined that this standard is not mandatory for ORDEM 3.0, and an 

evaluation of this decision was outside the scope of this assessment. 
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6.2 Meteoroid Models 

6.2.1 Description 

NASA’s meteoroid environment model is the MEM maintained by the MEO located at MSFC. 

MEM provides only the meteoroid environment and is independent from ORDEM, which 

supplies only the orbital debris environment. This is unlike MASTER-2009, which combines a 

meteoroid environment model and an orbital debris environment model. MASTER-2009 

integrates the Divine-Staubach meteoroid model with its orbital debris model into one 

application. ESA also has another meteoroid model called the Interplanetary Meteoroid 

Engineering Model (IMEM). The model IMEM is based on modeling and limited in situ

measurements, but is undergoing an extensive rework and is currently not used by ESA for 

predicting the meteoroid environment. Divine-Staubach is also under review by ESA. 

MEM and the next generation, MEMR2, compute the meteoroid flux on a spacecraft along a 

user-provided trajectory. In addition to providing the overall flux of microgram-or-larger 

meteoroids, MEM reports the meteoroid flux directionality and speed distribution. MEM 

improved on prior models by using a physics-based model that yields accurate meteoroid fluxes 

between 0.2 and 2 au4

from Earth. MEM results have been validated against observations (in 

particular, meteor observations from the Canadian Meteor Orbit Radar) and against other 

models, such as the Grün meteoroid flux model. 

MEM generates a customized meteoroid environment, pulling data from four sporadic meteoroid 

source distributions in order to calculate the number, direction, and speed of meteoroids incident 

on a spacecraft traveling along an input trajectory, which can be input as a series of Cartesian 

state vectors or two-line elements. MEM takes the spacecraft's motion into account and reports 

meteoroid directionality and speed relative to the spacecraft. MEM has three sub-models for 

spacecraft in near-Earth, lunar, and interplanetary space. If the user chooses the Earth or lunar 

sub-models, MEM calculates the meteoroid flux taking the planet or Moon's gravitational 

focusing and shielding into account. The flux is calculated for each state vector or two-line 

element provided; MEM provides the user with the average flux and the standard deviation of 

the flux over the spacecraft's trajectory. The software provides supplementary output files that 

break this flux down by direction (equal-angle bins and quasi-equal area threat igloos) and by 

speed interval. At the completion of a run, the software presents the user with the main results 

and with a graph of the meteoroid speed distribution. 

MEM is designed to work in conjunction with other engineering software tools and offers the 

user several options for customization. For instance, the input trajectories can be easily 

generated using Systems Tool Kit (STK), and the output files can be used with the Bumper threat 

assessment software. The user can choose the limiting mass, one of three spacecraft orientations 

(one stationary and two rotating), one of two output coordinate frames, and one of three output 

resolutions. The user has the option to analyze every input state vector or to choose a random 

 

4

 au = astronomical unit = 149698 km, which is the average distance from the Earth to the Sun 
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sub-sample; the user can also choose whether or not to save a detailed flux output file for each 

state vector and whether to calculate the standard deviation of the flux along the trajectory. 

Thus, MEM can be customized to provide both low-resolution estimates of the meteoroid flux 

along the trajectory as a whole along with high-resolution descriptions of the flux at each point 

along the trajectory, depending on the needs of the user. The current version of MEM is 

MEMR2. 

6.2.2 Model Comparison 

MASTER-2009 integrates the Divine-Staubach meteoroid model with its orbital debris model 

into one application. As was done for the orbital debris models in Section 6.1.2, the flux of the 

meteoroid component of MASTER-2009 was compared to MEMR2, along with older versions 

of MASTER: MASTER-2005 and MASTER-2001. The results are displayed in Figure 6.2-1 

plotted as flux as a function of diameter. MEMR2 produces values as a function of mass, so 

MEMR2 results were converted to mm assuming spherical particles with a density of 1 g/cm3. 

Figure 6.2-1. Meteoroid Flux Comparison for JPSS Orbit (D/S = Divine-Staubach) 

As illustrated in Figure 6.2-1, the meteoroid models show close correlations for the size regimes 

analyzed, unlike the comparison of the orbital debris models. 

The flux is also less for meteoroids than for orbital debris at most altitudes (see Section 6.1.2 and 

Figure 6.1-15). When the meteoroid and orbital debris components of the JPSS-1 component 

risk assessments are separated, it is clear that the relative contribution to the overall risk is 

greater for orbital debris than micrometeoroids. This is the case for both the current and the 

older environment models. This can be seen in Table 6.2-1, which displays the number of 

penetrations predicted from Bumper for a 7-year mission due to either micrometeoroids or 

orbital debris using the environment model indicated. The SSP 30425 meteoroid model results 

are shown for comparison to ORDEM2000 since that model is used by DAS and was used for 

initial Bumper assessments. The values are for components prior to any extra shielding or 

modifications to decrease MMOD risk. 
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Table 6.2-1. JPSS-1 Orbital Debris Risk versus Meteoroid Risk 

Component ORDEM3

# of 

penetrations

MEM

# of 

penetrations

Ratio of 

OD:M

ORDEM2000

# of 

penetrations

SSP 30425

# of 

penetrations

Ratio of 

OD:M

Propellant 

Lines

6.66E-01 2.06E-02 32 2.92E-02 3.81E-03 8

Propellant Tank 2.95E-01 5.58E-03 53 1.00E-02 8.94E-04 11

Battery #1 5.24E-01 1.45E-03 361 1.14E-02 8.52E-04 13

Battery #2 5.67E-01 1.48E-03 383 1.20E-02 8.54E-04 14

CTU 1.02E-01 2.52E-04 405 4.61E-03 1.16E-04 40

PCDU #1 2.92E-03 3.64E-05 80 8.09E-04 1.74E-05 46

PCDU #2 3.32E-03 3.51E-05 95 8.56E-04 1.67E-05 51

SCP #1 1.56E-01 1.77E-04 881 6.52E-03 1.19E-04 55

SCP #2 2.93E-02 1.27E-04 231 1.84E-03 5.26E-05 35

Avg 280 30

It is clear from the table that the orbital debris risk is at least an order of magnitude greater than 

the micrometeoroid risk and is driving the total JPSS MMOD risk.

6.2.3 Previous NESC MEM Assessment 

In 2009, the NESC performed an independent review of the Constellation Program’s MMOD 

risk analysis process [ref. 15]. In the course of that earlier assessment, the NESC team evaluated 

MEM, the predecessor to MEMR2. The differences between MEM and MEMR2 are primarily 

in the user interface, so the conclusions drawn from the 2009 activity that applied to MEM apply 

to MEMR2. The 2009 report did not discover any deficiencies with MEM that would 

recommend not using it for MMOD risk assessments. The 2009 NESC team found that 

exclusion of some higher speed meteoroid particles and the global meteoroid density assumption 

of 1 g/cm3 would most likely lead to an under-prediction of risk. In other words, the actual 

micrometeoroid risk to spacecraft may actually be higher than indicated. The 2009 NESC team 

also recommended that uncertainties be derived for MEM, something in which the MEO is in the 

process of implementing. 

The following findings and observations are related to the meteoroid environment models. 

F-13. The assessed risk associated with meteoroids for individual JPSS components 

is roughly two orders of magnitude smaller on average than the assessed risk 

from orbital debris when using ORDEM 3.0 and MEMR2. The difference is 

roughly one order of magnitude when using ORDEM2000 and SSP 30425. 

F-14. The meteoroid flux for 0.1 to 10 mm diameter sizes given by MEMR2 is 

within an order of magnitude of that produced by the Divine-Staubach model, 

which is used by MASTER-2009.
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F-15. MEM was reviewed by the NESC in 2009 [ref. 15] and found to be 

appropriate for use in MMOD risk assessments. MEMR2 differs from MEM 

primarily in the graphical user interface. Neither MEM nor MEMR2 assess 

uncertainties, which was discussed in the 2009 NESC report. 

O-5. MEMR2 is calibrated to ground-based radar observations and the data from 

which the Grün flux was derived. 

O-6. ESA’s iMEM model is based on modeling and limited in situ measurements. 

ESA has recently concluded that iMEM is not representing the meteoroid 

environment adequately and is initiating an extensive rework of the model. 

The Divine-Staubach model (used in MASTER-2009) is also no longer used 

by ESA. 

7.0 Elements of JPSS MMOD Risk Assessment

The fundamental components of any MMOD risk assessment are MMOD environment models, 

damage response predictor equations (BLEs, etc.), and failure criteria. The MMOD environment 

is discussed in detail in Section 6.0. This section will discuss and assess the response predictor 

equations (specifically, BLEs) and the failure criteria used by the JPSS Project. 

7.1 JPSS BLEs 

BLE modifications discussed and reviewed include the adaption of a BLE used most often to 

characterize the response of aluminum structures to high speed impacts by aluminum projectiles 

to: 1) impacts by steel projectiles, and 2) non-aluminum bumper materials. 

The predictions of this BLE in the case of very high velocity impacts are also studied and 

compared against those of alternative equations that might be more applicable to the types of 

structural elements used in the JPSS satellites.

7.1.1 BLE Description 

A response predictor equation, such as a BLE, is developed to characterize the performance of a 

hypervelocity impact shield. Such an equation defines the threshold particle size that causes, for 

example, perforation or detached spall from the inner wall of a multi-wall system as a function of 

velocity, impact angle, particle density, shield and inner wall thicknesses, and particle shape. 

BLEs are typically drawn as lines of demarcation between regions of inner-wall failure and no 

failure in two-dimensional projectile diameter-impact velocity space. When graphically 

represented they are referred to as BLCs. 

The high-speed impact testing that provides data for the development of BLEs and BLCs 

typically use spherical projectiles fired in light gas guns at impact velocities between 3 and 

7 km/s (although some can reach velocities up to 10 km/s now). These data are then fitted with 

scaled single-wall equations below approximately 3 km/s, and with theoretical momentum and/or 

energy-based penetration relationships above approximately 7 km/s to obtain three-part BLCs 

that cover the full range of impact velocity from approximately 0.5 to 16 km/s. The transitional 
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velocity region (from approximately 3 to 7 km/s for normal aluminum-on-aluminum impacts) 

takes the form of a linear interpolation between the low and high velocity regions. 

Figure 7.1-1 shows a typical BLC for a dual-wall system (i.e., Whipple shield) under normal 

projectile impact. In Region I, the projectile is deformed following its impact on and passage 

through the outer (i.e., bumper) plate, but remains mainly intact as it travels towards and 

eventually strikes the inner wall of the dual-wall system. For aluminum projectiles impacting 

aluminum bumpers, Region I is typically impact velocities below 3 km/s. In Region I, the form 

of the BLC is analogous to that of a single-wall curve. In Region II, the projectile is fragmented 

and the energy of the impacting projectile and ejected shield material is dispersed over an 

increasingly larger area of the inner wall. As a result, the ability of the dual-wall system to resist 

inner-wall failure (whether defined as a perforation or detached rear-side spall) increases as 

reflected in the curve. This gives rise to the bucket shape of the BLC for a dual-wall system. In 

Region III (which typically starts at 7 km/s for aluminum-on-aluminum impacts), the projectile is 

completely melted and the impulse delivered to the rear wall is increasingly more difficult to 

resist. 

Figure 7.1-1. Typical BLC for a Dual-wall System under Normal Projectile Impact 

F-16. Over 90 percent of the orbital debris impacting the JPSS-1 spacecraft have a 

velocity above 7 km/s, with an average velocity over 12 km/s. Because of this, 

the BLE Region III curve requires critical understanding. 

7.1.2 Applications and Modifications 

Initial risk assessments for JPSS-1 were performed using ORDEM2000 and a modified version 

of what is frequently referred to as the NNO BLE [ref. 16]. The NNO BLE was developed 

primarily for aluminum-on-aluminum impacts and for configurations with bumpers or shields 

that are sufficiently thick so as to cause significant fragmentation of an incoming projectile. This 

BLE has been applied to the various components and wall materials of JPSS-1 by equivalencing 
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JPSS-1 materials and wall thicknesses to aluminum on a mass density basis. For example, in the 

event of a non-aluminum bumper on JPSS-1 of a specified thickness and having a certain density 

(ρ), the thickness (t) of an equivalent aluminum shield has been calculated as follows: 

ρaluminimt

equiv alum

bumper = ρmatl denisityJPSS bumper tJPSS matlbumper (1) 

so that 

t

equiv alum

bumper = tJPSS matlbumper (ρmatl densityJPSS bumper / ρaluminim) (2) 

Equations (1) and (2) are usually applied to lightweight multilayer thermal blankets and other 

heavier materials to allow wall configurations with those materials to be part of a risk assessment 

exercise performed by Bumper. The exception is the case of a Kevlar® MMOD blanket for 

which NASA has developed a more complicated equivalencing procedure [ref. 17]. However, 

even in this case, the procedure appears to be ad-hoc and is without proper documentation or 

source referencing. Interestingly enough, other than these ad-hoc equivalencing procedures, 

there are no written guidelines in existence for how to apply the NNO BLE (or any other BLE) in 

situations that lie outside of the materials and/or geometries for which it was developed. This 

approach is similar to that used for DAS assessments, where shielding layer inputs are limited to 

areal density (density multiplied by thickness). 

To make the original 1993 version of the NNO BLE applicable to the MMOD environment 

description as given in ORDEM 3.0, it was modified to include a higher high-end transition 

velocity (i.e., transition from Region II to Region III) option that would engage if a risk 

assessment run ever called for a calculation involving the impact of a high density particle, such 

as steel. This so-called high-end transition velocity in the BLE for a dual-wall system is the 

impact velocity beyond which the projectile is believed to be substantially melted. As mentioned 

previously, for normal aluminum-on-aluminum impacts, this transition velocity is approximately 

7 km/s. However, for non-aluminum projectiles impacting aluminum plates, this value can be 

expected to be something other than 7 km/s. Since ORDEM2000 assumed all particles to be 

made of aluminum, this new feature of the NNO BLE was never used in risk assessments 

performed for JPSS-1 using ORDEM2000; only risk assessments performed using ORDEM 3.0 

made use of this new capability. 

Initial attempts at modifying the high-end transition velocity for dual-wall BLEs subjected to 

high density projectile impact were undertaken in the 2003-2004 time period [ref. 18]. Based on 

an analysis of data from tests involving steel projectiles impacting traditional Whipple shields 

and stuffed Whipple shields, a high-end transition velocity of 9.5 km/s was proposed. The 

modification to the NNO BLE to include a higher high-end transition velocity for high density 

particles was incorporated using the results of a previous NESC study [ref. 19]. In this 

assessment, it was determined that a value of 9.1 km/s would be an appropriate high-end 

transition velocity for steel (or particles with a material density close to steel) particles impacting 

aluminum plates [ref. 20]. This value was obtained using the SESAME tabular equation of state 
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(EOS) to determine the impact pressure (and then the corresponding impact velocity) that would 

be required for an iron projectile to be substantially melted on impact on an aluminum plate. 

In this current study, the Mie-Gruneisen EOS was used as part of an analytical 1-D shock 

physics-based calculation to determine the fractions of solid, molten, and vaporous material 

remaining in steel and aluminum projectiles impacting thin aluminum plates (%S, %L, and %V, 

respectively). The results of these calculations are presented in Table 7.1-1.

Table 7.1-1. Fractions of Solid, Molten, and Vaporous Material 

For aluminum-on-aluminum impact, projectile melt begins at impact velocities near 5.5 km/s 

with the projectile being completely melted at approximately 6.9-7.0 km/s. These values agree 

with the information also published in JSC 66619 [ref. 20]. For steel-on-aluminum impacts, 

projectile melt likely begins at impact speeds between 7.0 and 7.5 km/s, with the projectile being 

substantially melted at an impact velocity of approximately 8.5 km/s. The difference between 

this value and the value calculated in reference 20 is likely due to the use of the Mie-Gruneisen 

EOS in scenarios involving impact velocities and pressures that push the limits of its 

applicability. 

In reference 18, the NESC team also found mention of different low-end transition velocities for 

non-aluminum projectiles. The low-end transition velocity is the impact speed at which the 

projectile begins to shatter or fragment (i.e., Region I to Region II transition). For aluminum-onaluminum impacts, this occurs at approximately 3 km/s. Much like there being a different highend transition velocity for non-aluminum projectiles impacting aluminum plates, it is logical to 

conclude that there would be a different low-end transition velocity under similar impact 

conditions. However, the current modifications to aluminum projectile-based BLEs to render 

them applicable to steel projectile impacts do not include any changes to the low-end transition 

velocity. It would appear that having alternate low-end transition velocities for the materials or 

material classes in ORDEM 3.0 would more accurately reflect the response of spacecraft wall 

systems to high speed impacts by projectiles made of those types of materials. 

Velocity

(km/s) %S %L %V %S %L %V

4.0 100.0 0.0 0.0

5.0 100.0 0.0 0.0

5.5 99.5 0.5 0.0

6.5 28.3 71.7 0.0

6.8 5.1 94.9 0.0

7.0 0.0 100.0 0.0 100.0 0.0 0.0

8.0 36.7 63.3 0.0

8.3 8.2 91.8 0.0

9.0 0.0 100.0 0.0

Aluminum on Aluminum Steel on Aluminum

%S-%L-%V For Projectile Material
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The effects of incorporating the 9.1 km/s high-end transition velocity in an aluminum projectilebased BLE to render it applicable to steel projectile impacts are shown in Figures 7.1-2 and 

7.1-3. In these figures, the NNO BLE is plotted for steel projectiles impact aluminum bumper 

plates. Figure 7.1-2 presents a 0° (normal) impact, while Figure 7.1-3 presents a 45° (oblique) 

impact. 

Figure 7.1-2. Comparison of Steel-on-Al BLEs without and with the High-End Transition Velocity 

Modification for 0° Impacts 

It is interesting to note that the use of a higher high-end transition velocity results in a smaller 

critical particle diameter at that new transition velocity value. This reduced critical diameter is 

an outcome of the increased high-end transition velocity value, not an imposed value. 
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Figure 7.1-3. Comparison of Steel-on-Al BLEs without and with the High-End Transition Velocity 

Modification for 45° Impacts 

In both of these figures, the regions between the two curves are those projectile diameter-impact 

velocity combinations that the original NNO predicts would not cause inner wall failure (because 

they lie below the curve). However, the modified NNO BLE (with its new high-end transition 

velocity – for normal impacts – of 9.1 km/s) predicts that those projectile diameter-impact 

velocity combinations would cause failure to the inner wall of a dual-wall system (because they 

now lie above the BLE). Not having been completely melted by the impact on the lower density 

aluminum plate, these higher density steel projectiles still possess enough energy (as well as 

momentum) to inflict serious damage on the inner wall plate. 

F-17. The choice of 9.1 km/s as the Region II to Region III transition velocity for steelon-aluminum uses valid reasoning and assumptions and realistically models the 

phenomenology associated with the HVI of a steel projectile on a thin aluminum 

plate. However, this value may not be an appropriate choice for other bumper 

materials (e.g., MLI). 

When ORDEM 3.0 was used instead of ORDEM2000, the higher high-end transition velocity in 

the modified version of the NNO BLE was used in Bumper III because ORDEM 3.0 had the 

capability to include variable particle densities and provided debris flux information for high 

density particles such as steel. The NESC team believes that it was the use of the new ORDEM 

version together with the use of the higher high-end transition velocity in the modified NNO 

BLE that was a primary contributor to the high increase in assessed risk for JPSS when using 

ORDEM 3.0 as opposed to ORDEM2000. 
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At the time of this assessment, there were no BLEs available that were developed specifically for 

the various structural elements of JPSS-1. As a result, risk assessments were being performed 

using equations that were not developed for the materials or wall configurations on JPSS-1. 

Where necessary and because no other option was available, the modified version of the NNO 

BLE was used with non-aluminum materials equivalenced to aluminum on a mass density basis 

using the process described previously. As such, the NESC team investigated whether using the 

modified version of the NNO BLE is appropriate for the JPSS wall configurations in the case of 

high density projectile impacts. In other words, how close are the predictions of the modified 

NNO BLE equations to the actual response characteristics of those materials/wall configurations 

under high density projectile impacts? 

In 2013, NASA developed a new BLE that would be applicable to the case of high density 

projectiles impacting a very specific and specialized wall configuration, namely, that of the 

Soyuz orbital module (OM). This wall configuration can be, ostensibly, considered as a dualwall configuration. If the array of the Soyuz OM bumper materials is equivalenced to a single 

aluminum wall, then the modified NNO BLE could, in theory, also be used to predict the 

response of the Soyuz OM wall system to high density projectile impact. The Soyuz OM wall 

system can therefore be used to see how well the modified NNO BLE predicts the response of a 

dual-wall system for which it was not developed, but can nonetheless be used following 

appropriate equivalencing of the original system’s configuration parameters.

A study was conducted to compare the predictions of the NNO BLE (modified to be applicable 

to high density projectile impacts) for the Soyuz OM wall configuration against those of the new 

Soyuz OM steel-projectile-based BLE. If the predictions of the two BLEs were found to be 

relatively close, that would give some confidence to the practice of using modified NNO BLEs 

in situations involving high density projectiles impacting wall configurations for which BLEs do 

not currently exist. This confidence could be increased further with some tests using steel 

projectiles and comparing results against the predictions of the revised NNO BLEs for high 

density projectile impact. However, if the predictions were found to be not very close, some 

testing would certainly be called for to assess the validity of this equivalencing approach and/or 

its continued use. 

Figures 7.1-4 and 7.1-5 compare the recently developed steel projectile BLE for the Soyuz OM 

and the NNO BLE with the revised high-end transition velocity for the geometric parameters of 

the Soyuz OM wall configuration. Figure 7.1-4 presents the 30° impacts while Figure 7.1-5 

presents the 45° impacts. 
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Figure 7.1-4. Comparison of New Soyuz OM BLE and NNO BLE with the High-End Transition 

Velocity Modification for 30° Impacts 

Figure 7.1-5. Comparison of New Soyuz OM BLE and NNO BLE with the High-End Transition 

Velocity Modification for 45° Impacts 
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To be able to apply the revised NNO BLE to the Soyuz OM wall system, the multi-material 

Soyuz OM bumper was first equivalenced to a monolithic aluminum bumper on a mass density 

basis. Specifically, 

ρaluminimt

equiv alum

bumper = ρareal density

Soyuz OM bmpr (3) 

so that 

t

equiv alum

bumper = ρareal density

Soyuz OM bmpr / ρaluminim (4) 

All other geometric parameters (inner wall thickness and stand-off distance) were kept the same 

as in the Soyuz OM wall system. 

Figures 7.1-4 and 7.1-5 show the modified NNO BLE and the new Soyuz OM BLE are fairly 

close until the low-end transition velocity (approximately 3 km/s for 30°impacts, and 3.5 km/s 

for 45°impacts). However, above the low-end transition velocity the two curves diverge – the 

new Soyuz BLE has the canonical “bucket” shape while the modified NNO BLE more resembles 

the BLE of a single wall, not a dual-wall, system. 

In fact, above 9 -10 km/s, the critical projectile diameters predicted by the new Soyuz OM BLE 

can exceed that of the modified NNO BLE by as much as 50 percent of the modified NNO BLE 

values. Thus, using the modified NNO BLE in this particular case would contribute to a more 

conservative value of assessed risk. If the same trends hold true for the JPSS wall configurations, 

then it is also possible that using modified NNO BLEs in a JPSS risk assessment might result in 

more conservative (i.e., higher) values of assessed risk. 

It is also important to note that this divergence between the new Soyuz OM BLE and the 

modified NNO BLE is not due to the introduction of the higher high-end transition velocity. 

Very little difference was found to exist between the original and modified NNO BLEs for a 

Soyuz OM wall configuration. It was found that both the original and modified NNO BLEs are 

relatively flat beyond an impact velocity of approximately 3 km/s, and both resemble a single 

wall BLE. Hence, moving the higher-end transition velocity from 7 km/s to 9.1 km/s had very 

little effect on the NNO BLE for this particular wall configuration. 

This is in stark contrast to the plots in Figures 7.1-2 and 7.1-3, which showed quite a bit of 

difference between the original and modified NNO BLEs for a space station module wall 

configuration. In this particular case, the modified NNO BLE retained the ‘bucket’ shape of the 

original NNO BLE following the modification in the high-end transition velocity. As a result, 

the differences between the original and modified NNO BLEs were quite significant in those 

figures. 

F-18. There are no BLEs available that were developed specifically for the JPSS-1 

structural elements. As a result, risk assessments are being performed using 

equations that were not developed for the materials or wall configurations used on 
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JPSS-1. Using the modified NNO, adjusted for materials used by JPSS, may be 

predicting smaller critical diameters (i.e., increased assessed risk) than would BLEs 

purposely developed for actual JPSS-1 configurations.

7.1.3 BLE Comparisons 

This section considers alternative damage models (i.e., BLEs) to the NNO BLE, which was used 

initially by JPSS for their MMOD risk assessments. The two BLEs discussed are the Modified 

Wilkinson BLE and the Remeirdes BLE. Damage fluence comparisons were performed for 

these BLEs using the same generic cube described for the orbital debris model damage fluence 

comparisons in Section 6.1.3. Each MODRA run uses ORDEM 3.0 as the orbital debris model, 

and the results are compared to the MODRA run using the baseline NNO BLE/ORDEM 3.0 

discussed in Section 6.1.3.2. 

7.1.3.1 Modified Wilkinson BLE 

Damage Fluence Results Using ORDEM 3.0 with the Modified Wilkinson BLE 

A MODRA run was executed with the variation that the Modified Wilkinson BLE was used in 

place of the NNO BLE in the high velocity range. The original Wilkinson BLE is described in 

reference 21. The modified version applies a factor of 0.8 (20 percent reduction) to the critical 

diameter computed using the original Wilkinson BLE. A reduction in critical diameter was 

suggested separately by Elfer and Bjorkman [ref. 21]. Elfer proposed it to account for lower 

impactor debris cloud dispersion angles observed in hydrocode runs of impacts at velocities of 

7 and 13 km/s. Wilkinson’s analysis was done at higher, meteoroid impact velocities, which 

resulted in higher vaporization of aluminum impactors, and an associated higher dispersion 

angle. A lower impactor debris cloud dispersion angle results in higher momentum loading on 

the inner wall and hence smaller impactors can penetrate. Bjorkman proposed the 0.8 factor 

based on the potential for elastic rebound, which could increase the momentum loading. 

The modified Wilkinson formulation applies for normal impact velocities higher than the high 

velocity region boundary (Region III, see Section 7.1.1 and Figure 7.1-1). For normal impact 

velocities in Region I, the low velocity NNO formulation for critical diameter was used. For 

normal impact velocities in Region II, the critical diameter was interpolated. The same velocity 

boundaries were used as in the NNO formulation, including the modification for high density 

impactors. The same 65° impact angle limit was also used. 

The original Wilkinson equations contain a failure factor , where is the speed of 

sound in the inner wall. Reference 21 states that a value for the factor km/s was 

used in early Bumper codes. Setting km/s (the value for aluminum), the resulting failure 

factor value used in this analysis was =1.377. 

Figure 7.1-6 shows a plot of critical diameter versus impact velocity for a stainless steel particle 

with an impact angle of 0° (normal incidence). Curves are shown for both the NNO BLE and the 

Modified Wilkinson BLE. The plot shows that for impact velocities greater than the low 

 a

cc  22 2

c

  2  .0 27

a

 c

c2  1.5

 a

cc  22
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velocity region boundary at 3 km/s, the critical diameter computed using the Modified Wilkinson 

BLE is higher than the critical diameter computed from the NNO BLE. The difference increases 

as impact velocity increases. Figure 7.1-7 shows the same plot, but for an impact angle of 45°. 

The boundary velocities are shifted to the right because they are the full impact velocities and not 

the normal components of impact velocity. 

Figure 7.1-6. Critical Diameter versus Impact Velocity for a Stainless Steel Particle with Impact 

Angle of 0° 
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Figure 7.1-7. Critical Diameter versus Impact Velocity for a Stainless Steel Particle with Impact 

Angle of 45° 

Table 7.1-2 shows the damage fluence on each cube face from each of the populations when the 

Modified Wilkinson BLE is used. In this case, the total damage fluence was 1.45, whereas in 

comparison, the damage fluence using the NNO BLE was 4.95. The reduction in damage 

fluence corresponds to the higher critical diameter shown in Figures 7.1-6 and 7.1-7. Overall, 

the high density population contributes 89 percent of the damage fluence (versus 91 percent 

when using the NNO BLE). It is noted that on the ram face, where impacts at low incidence 

angles with high normal velocity components dominate, the damage fluence from the modified 

Wilkinson BLE (0.196) is much lower than from the NNO BLE (2.9). On the South and North 

faces, where impacts at high incidence angles with low normal velocity components dominate, 

the difference in damage fluence is smaller (0.584 from modified Wilkinson versus. 0.901 from 

NNO). 
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Table 7.1-2. Cube Damage Fluence Resulting from use of ORDEM 3.0 with the Modified 

Wilkinson BLE 

Figure 7.1-8 illustrates the distribution of the cube damage fluence over impactor size resulting 

from use of the Modified Wilkinson BLE. Damage fluences on all the cube faces are combined. 

From this plot it is seen that the sharp rise observed in the distribution from the NNO BLE at 

1.2 mm has been significantly reduced. 

Figure 7.1-8. Distribution of Damage Fluence over Impactor Size; Results from all Cube Faces 

using the Modified Wilkinson BLE 

Figure 7.1-9 shows the distribution of damage fluence over impact velocity resulting from use of 

the Modified Wilkinson BLE. Damage fluences on all the cube faces are combined. From this 

plot it is seen that damage fluence in the high velocity region has been reduced significantly 

compared to results from the NNO BLE. This is consistent with the large reduction on the ram 
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face. Damage fluence is only slightly reduced in the low velocity region, where the NNO 

formulation dominates. 

Figure 7.1-9. Distribution of Damage Fluence over Impact Velocity; Results from all Cube Faces 

using the Modified Wilkinson BLE 

Figure 7.1-10 shows the distribution of damage fluence over impact velocity for only the South 

and North faces resulting from use of the Modified Wilkinson BLE. Damage fluences from the 

North and South faces are combined. From this plot it is seen that damage fluence is 

concentrated in the low velocity region, where the NNO formulation dominates. The peak of the 

distribution is near the low velocity region boundary of 3 km/s, where critical diameter is lowest. 
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Figure 7.1-10. Distribution of Damage Fluence over Impact Velocity; Results from only the South 

and North Faces using the Modified Wilkinson BLE 

Figure 7.1-11 shows the distribution of damage fluence over impact angle resulting from use of 

the Modified Wilkinson BLE. Damage fluences on all the cube faces are combined. The 

distribution is similar to that from use of the NNO BLE (Figure 7.1-5). 
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Figure 7.1-11. Distribution of Damage Fluence over Impact Angle; Results from all Cube Faces 

using the Modified Wilkinson BLE 

Figure 7.1-12 shows the distribution of damage fluence over impact angle for only the South and 

North faces resulting from use of the Modified Wilkinson BLE. Damage fluences from the 

North and South faces are combined. While impacts at high incidence angles are more frequent 

on the South and North faces, few of these impactors actually penetrate the inner wall. 
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Figure 7.1-12. Distribution of Damage Fluence over Impact Angle; Results from only the South 

and North Faces using the Modified Wilkinson BLE 

7.1.3.2 Reimerdes BLE 

The most recent risk assessments performed by JPSS now use modifications to the NNO BLE as 

proposed in Reference 22. These modifications address several of the main issues associated 

with the NNO BLE. The first is that bumper thickness is included as a parameter only in the 

Region I and Region II formulations of the NNO BLE – it is completely absent from the Region 

III formulation. Figure 7.1-13 shows a series of plots of the NNO BLEs for a particular dualwall system with changing bumper thickness. 
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Figure 7.1-13. Effect of Changing Bumper Thickness on NNO BLE Predictions [Ref. 22] 

In Figure 7.1-13, the effects of changing the bumper thickness are seen to occur only in Regions 

I and II of the NNO BLE. Figure 7.1-14 shows a series of plots for the same dual-wall system 

and the same bumper thickness variations, but this time using the Reimerdes BLE. 

Figure 7.1-14. Effect of Changing Bumper Thickness on Reimerdes BLE Predictions [Ref. 22] 
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It is important to note that not only does the Reimerdes BLE take into account variations in 

bumper thickness in all three regions of the BLE, but the Reimerdes BLE also exhibits the 

appropriate behavior as the bumper thickness becomes increasingly small (a feature also missing 

from the NNO BLE). Namely, as bumper thickness approaches 0, the dual-wall Reimerdes BLE 

approaches a standard single-wall BLE for the inner wall of the dual-wall system. This is to be 

expected because as the thickness of the bumper becomes exceedingly small, its usefulness in 

breaking up impacting projectiles does likewise, leaving only the inner wall to act as a singlewall in responding to the impact projectiles. Also, as expected, the Reimerdes BLE is identical 

to the NNO BLE for sufficiently thick bumper plates. 

The modifications to the NNO BLE were first proposed in 1993 in an effort to render the NNO 

BLE applicable to dual- and triple-wall systems with very thin or very lightweight bumper plates 

(i.e., ts / dp < ~0.20, the lower limit of applicability for the NNO BLE). In this manner, the 

modified version of the NNO BLE (i.e., the Reimerdes BLE) was applicable to the design and 

development of the ESA’s ISS Columbus module. The Reimerdes BLE was subsequently used 

in an optimization study that included the mass of the spacers that would be needed in a dualwall design configuration [ref. 22]. 

Initial experimental validation of the Reimerdes BLE was presented in reference 22. In that 

study, eight tests were performed using two dual-wall configurations with different bumper 

thicknesses. In both cases, the Reimerdes BLE showed a substantial improvement in the 

predictions of ballistic limits when compared against those of the NNO BLE. However, it is 

important to note that all eight tests were performed at normal impact trajectories – the 

predictions of the Reimerdes BLE have yet to be compared against empirical data obtained from 

oblique impact tests. Reference 22 also cited the need to perform some testing nearer to the 

lower end of impact velocity spectrum to assess the performance of the Reimerdes BLE at those 

impact speeds. 

Most recently, the Reimerdes BLE was presented in NASA’s Handbook for Designing MMOD 

Protection [ref. 23] and the SHIELD computer program’s user’s manual [ref. 24] as an option for 

use in the case of exceedingly thin or lightweight bumpers. The Reimerdes BLE was first 

encoded in Bumper in 2008 with several updates and code corrections through 2012. Bumper 

encodes the Reimerdes BLE as a set of formulas that ultimately require the solution of an 

implicit equation for critical projectile diameter given an impact velocity and the geometry of the 

dual-wall configuration. 

The NNO BLE in Region III assumes that the impactor is completely shattered, regardless of 

shield thickness. Therefore, it is expected to under-predict risk posed by larger impactors. 

Reference 22 presents hypervelocity test results confirming that the NNO failed to predict 

penetrations for sufficiently large impactors (Figures 8 and 9 of reference 22). The Reimerdes 

formulation introduces a factor, , that transitions from the NNO dual wall BLE in Region III 

to a single wall BLE as the ratio of shield thickness to particle size decreases. The 

factor is applied to the inner wall thickness required to stop an impactor (referred to here as the 

*

F2

ps dt

*

F2
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“critical wall thickness”) of a given size as computed from the NNO formulation in the high 

velocity region. The factor is not a regression fit to data, but is rather a quadratic expression in 

terms of that is derived from three-end point conditions. The first condition is that, when 

there is no shielding ( ), the critical wall thickness is equal to that from a single wall 

BLE, i.e., , where is the ratio of critical wall thickness from the single wall BLE 

to that from the NNO formulation. The second condition is that the critical wall thickness is 

equal to that from the NNO when is equal to the critical value required to completely 

shatter the impactor (i.e., . This critical value is specified by the NNO 

formulation). The third condition is that the slope of the quadratic expression is zero when 

, i.e., . The resulting expression for is

when (3) 

when (4) 

As part of this analysis, the mathematical derivation of the expression for was reconstructed 

and verified. This expression for , which is also shown in reference 9, is more generalized 

than the expression in reference 22, and was coded in MODRA. Note: Reference 9 appears to 

incorrectly apply the factor to critical particle diameter. Reference 6 shows that it is applied 

to critical wall thickness and the mathematical derivation is consistent with that approach. 

O-7. NASA TM-2009-214789 [ref. 24] appears to apply the Reimerdes BLE dual wallto-single wall transition factor, , differently than specified by Reimerdes et al. 

[ref. 22] and verified mathematically by the NESC team. Reference 9 applies 

to the critical particle diameter, while the prescribed method in reference 22 is to 

apply to the critical wall thickness. 

The Reimerdes formulation as specifically described in reference 21 also has two other features. 

One is that it replaces the NNO formulation in the low velocity region with the single wall 

equation of Frost [ref. 25]. The second feature is that it computes the low velocity region 

boundary as a function of using a regression fit, whereas in the NNO formulation it was 

fixed to a value of 3 km/s. 

ps dt

dt

ps

 0

DS

rF /

*

2  DS

r /

ps dt

1

*

F2   

c

dt

ps

 

c

 dtdt

psps   0

*

dF2 dtd ps

*

F2

      2 1   / 1

2

/ /

*

2 

















  DS

c

ps

ps

DS

c

ps

ps

DS

r

dt

dt

r

dt

dt

rF  

c

 dtdt

psps

1

*

F2   

c

 dtdt

psps

*

F2

*

F2

*

F2

*

F2

*

F2

*

F2

ps dt
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The factor is applied to the NNO high velocity region formulation for normal impact 

velocities in Region III, and the single wall BLE is used for normal impact velocities in Region I. 

For normal impact velocities between the low and high velocity region boundaries, critical wall 

thickness was interpolated. The same high velocity region boundary was used as in the NNO 

formulation, including the modification for high density impactors. The same 65° impact angle 

limit was also used. 

To accommodate the implementation of the factor, a modification was made to MODRA. 

Previous NNO results from MODRA were based on critical diameter computed directly using 

the BLE. Results presented in the following material from this approach are labeled “critical 

diameter method.” Using this approach for the Reimerdes formulation would require 

implementation of an iteration to solve for the critical impactor diameter since it is not available 

in closed form as is a function of impactor diameter. While the Bumper subroutine contains 

a routine that iterates to solve for critical diameter, this approach would not be efficient within 

the MODRA algorithmic structure. Instead, the NNO BLE was rearranged to compute critical 

wall thickness (wall thickness required to prevent an impactor from penetrating) at diameter 

values in the ORDEM 3.0 flux versus size data, and MODRA was modified to interpolate critical 

diameter at the actual wall thickness. Results presented in the following material from this 

approach are labeled “critical wall thickness method.” Results presented in the following 

material show that the effect of interpolation error on the results is small. The same modification 

was made for the NNO-only implementation to provide for an apples-to-apples comparison 

between NNO results and Reimerdes formulation results. 

A subroutine formulation of the Reimerdes formulation used for Bumper was provided in HVIT 

[ref. 26]. This was used to ensure that the equivalent formulation was coded into MODRA. 

Several observations on the descriptions in references 22 and 9 and the subroutine version 

motivated running several variations of the MODRA implementation. 

In the subroutine, there are two formulations for that apply to two different ranges of the 

shield spacing to impactor diameter ratio . The formulation for the case when 

has the value (where is a constant) substituted into the expression. The 

formulation for the case when has the value substituted into the 

expression. This formulation was implemented in one version of MODRA and the results 

presented in the following material are labeled “Bumper Reimerdes implementation.” 

According to reference 9 (Eq. 43) and reference 22 (Eqs. 4 and 5), , i.e., it 

depends on the ratio of particle density to shield density. This was implemented in another 

version of MODRA and the results presented in the following material are labeled “original 

Reimerdes formulation.”

*

F2

*

F2

*

F2

*

F2

dS p dS p  30   s

 2.0

c

ps

cdt

s

c

dS p  30   s

 .0 25

c

ps

cdt

  sps c

ps

 cdt 
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Additionally, it was observed that reference 22 may have an error in the algorithm for the 

parameter used to calculate . References 22 and 9 state that this parameter is evaluated 

only at the high velocity region boundary. This algorithm is implemented in the Bumper 

subroutine. However, it was determined during this analysis that this approach causes the 

resulting critical wall thickness to exceed the value from the single wall BLE. A MODRA case 

was run with the parameter evaluated at the actual normal impact velocity of the flux component, 

and the results presented in the following material are labeled “Modified Reimerdes 

formulation.”

Figure 7.1-15 shows critical wall thickness versus impactor diameter for the case of an aluminum 

particle impacting normally at 7 km/s for the cube dual wall parameters. Three curves are 

shown: one generated from the NNO BLE, one generated from the single wall Frost BLE, and 

one generated from the original Reimerdes formulation. The plot shows that for small impactor 

sizes, the original Reimerdes curve is the same as the NNO curve. When impactor diameter is 

large enough so that falls below the critical value, the original Reimerdes curve separates 

from the NNO curve and asymptotically approaches the single wall curve as diameter increases. 

Figure 7.1-16 shows a similar plot for the case of a stainless steel particle impacting normally at 

14 km/s. In addition to the three curves mentioned for Figure 7.1-15, this plot also has curves 

generated from the Bumper Reimerdes implementation and the Modified Reimerdes formulation. 

The plot shows that the Bumper Reimerdes curve is lower than the original Reimerdes curve and 

will correspond to a lower damage fluence. In addition, as impactor diameter increases, the 

original Reimerdes curve exceeds the single wall curve, which is not a desired result. This result 

would mean that with larger impactors, the performance of the dual-wall system described by the 

original Reimerdes BLE would actually be worse than a single wall, which is not consistent with 

reality. This occurs because the parameter is evaluated only at the high velocity region 

boundary (9.1 km/s for a stainless steel impactor) in accordance with reference 22. The 

Modified Reimerdes curve, which is the result when is evaluated at the actual normal impact 

velocity of the flux component (in this case, 14 km/s), approaches, but does not exceed the single 

wall curve. Since this is the desired result, it appears that the specification in reference 22 to 

evaluate the parameter only at the high velocity region boundary is incorrect. 

O-8. There appears to be an error in the algorithm that defines the ratio of critical wall 

thickness from the single wall BLE to that from the NNO BLE ( ) in the 

Reimerdes BLE definition set forth by Reimerdes et al. in reference 22. The 

specification in reference 22 to evaluate the parameter only at the high 

velocity region boundary results in a BLE curve (impactor diameter versus critical 

wall thickness) that exceeds the single wall curve as the impactor diameter 

increases, instead of approaching the single wall curve asymptotically as would be 

DS

r /

*

F2

ps dt

DS

r /

DS

r /

DS

r /

DS

r /

DS

r /
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expected. Evaluating the parameter at the actual normal component of impact 

velocity appears to rectify the problem. 

Figure 7.1-15. Critical Wall Thickness versus Impactor Diameter for the Case of an Aluminum 

Particle Impacting normally at 7 km/s 

DS

r /
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Figure 7.1-16. Critical Wall Thickness versus Impactor Diameter for the Case of a Stainless Steel 

Particle Impacting Normally at 14 km/s 

Figure 7.1-17 shows critical wall thickness versus impact velocity for the case of a 

1 mm-diameter aluminum particle impacting normally for the cube dual wall parameters. Three 

curves are shown: one generated from the NNO BLE, one generated from the single wall Frost 

BLE, and one generated from the original Reimerdes formulation. The plot shows that the 

original Reimerdes formulation has a lower boundary for the low velocity region than the NNO 

low velocity region boundary. In the low velocity and interpolation regions, the original 

Reimerdes curve is lower than the NNO curve. In effect, the NNO curve is higher than the 

single wall curve. In the high velocity region, the original Reimerdes curve is the same as the 

NNO curve. This occurs because a 1 mm-diameter aluminum particle is small enough to be 

completely shattered by the shield (MLI). 

Figure 7.1-18 shows a similar plot for the case of a 1-mm diameter stainless steel particle. In 

addition to the three curves mentioned for Figure 7.1-17, this plot also has curves generated from 

the Bumper Reimerdes implementation and the Modified Reimerdes formulation. In the high 

velocity region, the original and Modified Reimerdes curves are higher than the NNO and 

Bumper Reimerdes implementation curves, which are the same. The reason for this result is that 

a 1 mm-diameter stainless steel particle is only partially shattered in the original and Modified 

Reimerdes formulations, which account for the higher density of stainless steel. In the NNO 
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formulation and the Bumper Reimerdes BLE implementation, the particle is completely 

shattered. 

Figure 7.1-17. Critical Wall Thickness versus Impact Velocity for the Case of a 1 mm-Diameter 

Aluminum Particle Impacting Normally 
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Figure 7.1-18. Critical Wall Thickness versus Impact Velocity for the Case of a 1 mm-diameter 

Stainless Steel Particle Impacting Normally 

In Figure 7.1-14, it can be seen that the Reimerdes BLE is below the NNO BLE for dual-wall 

systems with thin or lightweight bumpers. The region between the NNO BLE and the 

corresponding Reimerdes BLE are those projectile diameter-impact velocity combinations that 

the NNO predicts would not cause inner wall failure (because they lie below the curve) in such 

dual-wall configurations. However, because they lie above the Reimerdes BLE, that BLE 

predicts that those projectile diameter-impact velocity combinations would cause failure to the 

inner wall of a dual-wall system because of the presence of a thin or light-weight bumper. Not 

having been completely disrupted by the thin bumper, the Reimerdes BLE predicts those 

projectiles to still possess enough energy (as well as momentum) to inflict serious damage on the 

inner wall plate. As a result, using the Reimerdes BLE in such cases (i.e., for wall configurations 

with exceedingly light or thin bumpers, such as those where a MLI blanket is acting as a bumper) 

would contribute to a more conservative (i.e., higher) value of assessed risk. 

Damage Fluence Using ORDEM 3.0 with the Reimerdes Modification to the NNO BLE 

Another MODRA run was executed with the variation that the Reimerdes modification to the 

NNO BLE was used in place of the baseline NNO BLE. A description of this formulation is 

presented in reference 22. 
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Table 7.1-3 shows the damage fluence on each cube face from each of the populations when the 

NNO BLE is used in the version of MODRA that interpolates critical diameter (“critical wall 

thickness method”). In this case, the total damage fluence was 5.60. The damage fluence 

obtained using the version of MODRA that directly computes critical diameter (“critical 

diameter method”) was 4.95. The difference was due to the interpolation in the “critical wall 

thickness” method is relatively small compared to the effect of other uncertainties. 

Table 7.1-3. Cube Damage Fluence Resulting from use of ORDEM 3.0 with the NNO BLE using 

the “Critical Wall Thickness Method”

Table 7.1-4 shows the damage fluence on each cube face from each of the populations when the 

original Reimerdes formulation is used. In this case, the total damage fluence is 17.4, which is 

higher than the damage fluence of 5.60 using the NNO BLE. The damage fluence has been 

increased because larger high density particles are not shattered by the MLI. 

Table 7.1-4. Cube Damage Fluence Resulting from use of ORDEM 3.0 with the Original Reimerdes 

Formulation 

Table 7.1-5 shows the damage fluence on each cube face from each of the populations when the 

Modified Reimerdes formulation is used. In this case, the total damage fluence is 16.8, which is 

slightly lower than the damage fluence of 17.4 using the original Reimerdes formulation. The 

damage fluence has been decreased to a small amount because critical wall thickness does not 

exceed the value from the single wall BLE at larger sizes. 
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Table 7.1-5. Cube Damage Fluence Resulting from use of ORDEM 3.0 with the Modified 

Reimerdes Formulation 

Table 7.1-6 shows the damage fluence on each cube face from each of the populations when the 

Bumper Reimerdes implementation is used in MODRA. In this case, the total damage fluence is 

8.97, which is lower than the damage fluence of 16.8 using the Modified Reimerdes formulation. 

The damage fluence from the Modified Reimerdes formulation is higher because more stainless 

steel particles can penetrate the shield (MLI) without being completely shattered. 

Table 7.1-6. Cube Damage Fluence Resulting from use of ORDEM 3.0 with the Bumper Reimerdes 

Implementation 

Figure 7.1-19 illustrates the distribution of the cube damage fluence over impactor size resulting 

from use of the Modified Reimerdes formulation. Damage fluences on all the cube faces are 

combined. From this plot it is seen that fewer particles at the low end of the size range are 

penetrating compared to the NNO case (Figure 7.1-2) and more particles in the middle of the size 

range are penetrating. 
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Figure 7.1-19. Distribution of Damage Fluence over Impactor Size; Results from all Cube Faces 

using the Modified Reimerdes Formulation 

Figure 7.1-20 shows the distribution of damage fluence over impact velocity resulting from use 

of the Modified Reimerdes formulation. Damage fluences on all the cube faces are combined. 

From this plot it is seen that fewer low velocity particles are penetrating compared to the NNO 

case (Figure 7.1-3) and more high velocity particles are penetrating. 
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Figure 7.1-20. Distribution of Damage Fluence over Impact Velocity; Results from all Cube Faces 

using the Modified Reimerdes Formulation 

Figure 7.1-21 shows the distribution of damage fluence over impact angle resulting from use of 

the Modified Reimerdes formulation. Damage fluences on all the cube faces are combined. 

From this plot it is seen that more particles are penetrating at most angles compared to the NNO 

case (Figure 7.1-4). 
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Figure 7.1-21. Distribution of Damage Fluence over Impact Angle; Results from all Cube Faces 

using the Modified Reimerdes Formulation 

7.1.3.2 Over-Prediction of Small Particle Penetrations by the NNO BLE 

The use of the Reimerdes formulation addresses the reduced effectiveness of the shield at 

shattering impactors as impactor size increases. At the opposite size extreme, as impactor size 

decreases, the shield should reduce the energy and momentum of the impactor material before it 

reaches the inner wall. In other words, the critical diameter should increase, and the critical 

inner wall thickness should decrease. The NNO equation for critical diameter in the high 

velocity regime, which assumes that the impactor is completely shattered, is [ref. 9]. 

where is the inner wall thickness, S is the spacing between the inner and outer walls, is the 

inner wall yield strength, is the density of the impacting particle, is the density of the 

shield, V is the impact velocity, and is the impact angle. Note that the formula does not 

contain the shield thickness (i.e., there is no dependence of critical diameter on shield 

thickness). The formula assumes that the shield thickness has been set to the critical (minimum) 

value needed to shatter an impactor of a specific size, i.e.: 

.

 

  9131 32

32 31 31

*

2

cos

70 .3 918





V

St

Fd

sp

w

c



w

t 

 p  s



s

t

  spsp c

psps

  cddtdt 
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This is because the NNO equation is originally intended for designing inner wall and shield 

thicknesses to stop penetration by impactors of a specific size. 

In reality, the shield thickness is fixed and does not vary with impactor diameter. Therefore, the 

NNO in the high velocity regime does not model the impeding effect of a shield thickness that is 

higher than the critical value. This means that the NNO BLE may under-estimate critical 

diameter (or equivalently, over-estimate critical wall thickness) for smaller, more numerous 

impacts when shield thickness is higher than the critical value. In other words, use of the NNO 

BLE in a situation with fixed shield thickness and a range of impactor diameters (as specified by 

an environment model like ORDEM 3.0) will result in an over-prediction of damage fluence by 

smaller impactors. Finding a way to modify the NNO BLE to account for the impeding effect of 

shield thickness larger than the critical value may reduce the conservatism in the estimation of 

penetration risk. 

7.1.3.3 Conclusions 

When the Modified Wilkinson BLE is used in place of the NNO BLE, the total damage fluence 

is reduced to 1.45, approximately one-third of the equivalent NNO result. Damage fluence is 

reduced primarily in the high velocity region, where the Modified Wilkinson formulation 

replaces the NNO formulation. Damage fluence is only slightly reduced in the low velocity 

region, where the NNO formulation is not modified. 

A MODRA run was performed using ORDEM 3.0 with the Reimerdes formulation of the NNO 

BLE. The Reimerdes formulation introduces a transition from the NNO BLE to the single wall 

BLE as impactor size increases and is less shattered by shielding (in this case, MLI). It was 

found that the source reference on the Reimerdes formulation [ref. 22] may have an error in the 

algorithm. The reference 22 version with the error is implemented in the Bumper subroutine 

[ref. 26]. A modification was identified that resolves the error. The resulting cube damage 

fluence using the Modified Reimerdes formulation was 16.8. The damage fluence from the 

Modified Reimerdes formulation is higher than the damage fluence from the NNO BLE because 

there are high density particles in the size range that contributes to the damage fluence, which are 

not shattered by the MLI layer. 

It was also observed that the NNO BLE may over-estimate the damage fluence in penetration 

risk analyses that consider a range of particle sizes but involve a fixed shield (MLI) thickness. 

This is because the NNO formulation in the high velocity region does not have any dependence 

on shield thickness. It assumes the shield thickness is at the critical value to shatter a given size 

particle. For smaller particles with higher fluxes, the critical thickness assumed by the NNO 

BLE is smaller than the actual shield thickness, and therefore the shield impedance faced by 

these particles is not modeled. The Reimerdes formulation does not address this issue since it 

models the reduced shattering of larger particles by the shielding. 

The following findings and observations are related to the BLE comparison. 
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F-19. The BLEs used for JPSS were developed for manned spacecraft and take a 

conservative approach to model spacecraft penetrations beyond a 7 km/s normal 

impact velocity. 

– HVI test results suggest that the actual Region III BLE may provide higher 

performance and yield less assessed risk than currently assumed in the 

modified NNO BLE. 

– Using a modified Wilkinson BLE (80 percent of original Wilkinson BLE, as 

recommended by Elfer and Bjorkman) resulted in a factor of ~3.4 lower 

probability of penetration for a 1 meter cube at the JPSS orbit than the NNO 

BLE. 

F-20. Use of the NNO BLE for fixed shield thickness and a range of impactor diameters, 

as specified by an environment model like ORDEM 3.0, will result in an overprediction of damage fluence (i.e., increased conservatism) by smaller impactors. 

7.2 Avionics Box Failure Criteria Assessment 

This section presents the results of an examination of the risk of orbital debris penetration and 

failure of critical CTU internal components. The objective was to determine whether redefining 

failure criteria to include failure of critical internal CTU components -- not just failure of the 

outer shell of the box -- would correspond to a decrease in calculated orbital debris critical 

failure risk. Updated information from the vendor showed that for the CTU, the majority of 

internal components are critical. Therefore, the analysis presented in this section is notional and 

illustrates possibilities for refining avionics failure criteria by considering damage to internal 

components and not solely using the assumption of a box penetration as the defined failure 

criterion. 

Figure 7.2-1 shows the position and construction of the CTU as part of JPSS-1. On the port side 

next to the CTU is the spacecraft’s radiator, on the starboard side are cables, on the zenith side is 

MLI, on the front and aft are avionics boxes, and the bottom is covered by the spacecraft 

structure. Preliminary risk assessments of the CTU in May 2014 indicated that it was one of the 

largest remaining contributors to orbital debris critical failure risk since many of its internal 

components were required to safely reenter at the end of the JPSS-1 mission (JSC calculated a 

9.7 percent risk of MMOD penetrating the CTU box). Unlike most other reentry-critical 

components on JPSS-1, the CTU employs redundancy internally: redundant cards within one 

box, as opposed to separate boxes. 

The NESC team’s objectives in examining the CTU were to:

 Examine the internal/external CTU structure, Bumper risk assessment, and failure 

criteria.

 Identify critical internal functions and components of the CTU. 

 Perform hydrocode assessments of orbital debris penetration through external MLI and 

CTU elements to determine particle sizes and velocities that caused internal critical 
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element failure. 

 Perform independent risk assessments of orbital debris failure risk using ORDEM 3.0 

particle sizes and velocities. 

 Compare results to those derived from Bumper by HVIT. 

 Suggest alternative failure criteria (risk criteria, penetration equations, etc.) for future 

NASA orbital debris risk assessments of CTU and other critical JPSS elements. 

Through a query of the JPSS-1 vendor, Ball Aerospace, the NESC team learned that the HVITderived risk for the CTU was based on an incorrect conception of the CTU design. The actual 

MLI was only about one-third the areal density initially reported to HVIT. 

Figure 7.2-1. JPSS-1 CTU Location and Orientation 

F-21. The JPSS configuration used to perform the MMOD risk assessment resulted in a 

9.7 percent failure risk for the CTU contained configuration inaccuracies. These 

deviations tended to underestimate the assessed risk: 

1. The risk assessment assumed a 5 cm (2-inch) stand-off between the MLI 

blanket and the CTU, while the actual stand-off varies, and is less than 

5 cm in some locations. 

2. The risk assessment used an MLI density of 0.091 g/cm2, while the actual 

blanket has a density of 0.034 g/cm2. 

Figure 7.2-2 shows the internal structure of the CTU box. Originally, the NESC team was told 

that only the gray areas within the box power supply and command cards were critical (Case 1). 

Subsequently, the NESC team was informed that the sun sensor at the front of the box (red area) 

was also critical (Case 2). 
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Figure 7.2-2. JPSS-1 Internal Critical Element Locations 

Based on this information, the NESC team performed assessments at MSFC using the smooth 

particle hydrodynamic (SPH) in C (SPHC) hydrocode that established the size and velocity of 

orbital debris particles that would penetrate the MLI, outer CTU box, and inner card protective 

layers, exposing the critical internal cards to debris cloud spray from the penetration process. 

Figure 7.2-3 shows a representative sample of the types of hydrocode evaluations performed. 

Note that the orbital debris impacts occur at a very high velocity (14.61 km/sec) and obliquity 

(75°) when approaching the front and top surfaces of the CTU (in the red area shown in 

Figure 7.2-2). The hydrocode impact evaluation not only established the size and velocity that 

would penetrate the box, but where it would penetrate and whether structures under the debris 

cloud were impacted. 

Figure 7.2-3. Sample NESC-Sponsored Hydrocode Assessments 

Following the establishment of the size, velocity, and location of aluminum and steel orbital 

debris particles that penetrate the box and reach critical internal elements (for both Cases 1 and 
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2), the NESC team used the ORDEM 3.0 orbital debris environment to evaluate the likelihood of 

penetrating critical internal element cards. Table 7.2-1 shows the results of this assessment. The 

effect of including the MLI layer can be seen by comparing the single wall only risk of 8.791 to 

the MLI+Single Wall risk of 0.06195. Note that when the sun sensor is considered noncritical 

(Case 1), a large reduction from a risk of 0.06195 (assuming MLI and a single wall of the CTU 

shell) to 0.011299 (a factor of 5.5) in orbital debris risk is possible. This is because the number 

of critical internal elements are fewer, and no internal elements are located in the area of the box 

most likely to be penetrated (the red, forward area of the box shown in Figure 7.1-2). However, 

when the sun sensor is considered critical (Case 2), nearly every penetration is critical and the 

probability of critical failure decreases by ~20 percent (i.e., from 0.06195 to 0.04997). The risk 

assessment from HVIT is also shown for reference. 

Table 7.2-1. Results of NESC Risk Assessments for Two Internal CTU Configurations 

Risk Assessment Performed

Surface Single Wall MLI + Single Wall Sun Sensor Critical 

Reduced Area (Case 2)

Sun Sensor not Critical 

Reduced Area (Case 1)

HVIT

Top (Cards) 0.8394 0.00667 0.00400 0.00300

Top (Cables) 0.2798 0.0022 0.00133 0.00100

Top (Power 

Supplies)

0.4029 0.00336 0.00168 0.00160

Top Total 1.522 0.01229 0.00217 0.0056 0.0162

Port (Cards) 0.0002 0.00027 0.000160 0.000123

Port (Cables) 0.00006 0.00008 0.000014 0.0000141

Port Total 0.0002 0.00036 0.00018 0.000137 0.0008

Stbrd (Cables) 2.7819 0.00679 0.00246 0.001845

Stbrd (Power 

Supplies)

1.0015 0.00244 0.00021 0.000205

Stbrd Total 3.7833 0.00923 0.00267 0.00205 0.0167

Front (Cards) 2.0409 0.024995 0.024995 0

Front (Power 

Supplies)

0.9797 0.011600 0.011600 0

Front Total 3.0705 0.0366 0.036597 0 0.0584

Total Risk 8.3791 0.06195 0.04997 0.011299 0.0921

Conclusions 

The NESC team’s objective for this task was to determine whether redefining failure criteria to 

include failure of critical internal CTU components (not just failure of the outer shell of the box) 

would allow a significant decrease in calculated orbital debris critical failure risk. Initial 

indications (with few critical internal CTU components) indicated substantial improvement in 

assessed risk was possible. However, a later assessment showed that the number of critical CTU 

components were numerous and, most importantly, covered internal areas that were most likely 

to be penetrated by orbital debris. In the end, although the NESC team demonstrated that such 

an assessment could readily be performed, there was little reason to pursue this strategy in this 

case. Doing so would also require establishing the capability of internal components to resist 

impact-generated contamination after box penetration—not worth the effort for the expected 

reduction in critical risk for the CTU. 

Additional details for this assessment are included as Appendix D. 
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F-22. Higher fidelity analyses of the internal structure and expected failure modes for 

critical electronics box interior elements aboard JPSS-1 (i.e., examining the 

resistance of interior critical elements to penetration within each box) may result 

in a lower MMOD assessed risk for those boxes (compared to assuming any box 

penetration is a failure). 

 This examination must be balanced with long-term concerns over interior 

box element contamination from penetrations. 

 This is a first-order assessment and thorough risk evaluation would require 

HVI testing to verify hydrocode results. 

7.3 Wire Bundle Risk Assessment 

This section presents the results of two hypervelocity impact failure risk assessments for JPSS-1 

critical (i.e., necessary for controlled reentry) wire bundles exposed to the ORDEM 3.0 orbital 

debris environment at its 824 km, 98.8° inclination orbit. The first “generic” approach predicted 

the number of wires broken by orbital debris ejecta emerging from normal impact with MLI 

covering 36-, 18-, and 6-strand wire bundles at a 5 cm standoff using SPH hydrocode. This 

approach also included a mathematical approach for computing the probability that redundant 

wires were severed within the bundle. Based in part on the high computed risk of a critical wire 

bundle failure from the generic approach, an enhanced orbital debris protection design was 

implemented, consisting of betacloth-reinforced MLI suspended at a 5 cm standoff over a seven 

layer betacloth and Kevlar® blanket, draped over the exposed wire bundles. A second SPHbased risk assessment was conducted that also included the beneficial effects from the high 

obliquity (75°) of orbital debris impact and shadowing by other spacecraft components and 

resulted in a considerably reduced likelihood of critical wire bundle failure compared to the 

original baseline design. This second approach is consistent with earlier wire failure assessments 

for the James Webb Space Telescope [ref. 27] and other spacecraft such as the Advanced X-ray 

Astrophysics Facility [ref. 28], which assumed that any penetration of the shield over the wire 

bundle caused failure of the bundle. 

F-23. Exterior wire bundles, especially those on the most-exposed zenith side, on JPSS1 have not been considered in the risk assessment, and their inclusion in future 

risk assessments can be expected to increase the assessed MMOD risk. 

Task 1: Generic Risk Assessment of Baseline MLI over Wire Bundles

The NESC team performed SPHC hydrocode assessments of orbital debris penetration through a 

typical wire harness with baseline MLI blanket protection, as shown in Figure 7.3-1. The SPHC 

hydrocode is a C language implementation developed by Stellingwerf (1985-95). The present 

incarnation of SPHC, V12.8, runs up to one million particle problems on Windows personal 

computers or work stations having 1.5 Gb of memory. In smooth particle hydrodynamics, the 

“particle” is the analog of the mesh point in a traditional hydrocode. A SPH particle consists of a 

fixed mass of material at a given position in space, together with a smoothing function, or 

“kernel,” that defines the particle’s extent. 
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Figure 7.3-1. JPSS-1 Showing Wire Bundles 

As shown in Figure 7.3-2, a typical cable is assumed to consist of 36 wires (18 redundant wire 

pairs) where every wire pair is considered critical to the function of the cable. The wires were 

placed in a hexagonal pattern in order to scale the damage seen in 36 wires to smaller wire 

bundles (18- and 6- wire bundles). It is noted that the actual hexagonal patterns undergoing 

hydrocode assessment were of 37, 19, and 7 wires, with the damage to the last (deepest) wire 

neglected in the risk results for the 36, 18, and 6 wire strands. The risk assessment considered a 

one-year exposure to the ORDEM 3.0 environment with a zenith/nadir wire orientation. A 5 cm 

standoff of baseline MLI-to-wire harness was included in the hydrocode run. 

Figures 7.3-2 and 7.3-3 show typical results from the SPH analyses for a predicting the number 

of wires cut considering a variety of orbital debris impact materials, velocities, and diameters. 

Note that the expected number of penetrated wires increases with velocity, diameter, and density 

of the projectile. Figure 7.3-4 shows the likelihood of an entire cable failing based on the 

number of redundant wire failures. As shown, once more than half of the wires (i.e., 19 wires) 

are penetrated in a 36-wire bundle, there is a 100 percent chance that two redundant wires have 

been hit, thus disabling the critical instrument that the wire is feeding. The likelihood of critical 

instrument failure increases with the number of wires penetrated until 100 percent is reached 

when penetrating more than half the wires. 
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Figure 7.3-2. Typical SPH Hydrocode Predictions for Steel and Aluminum Orbital Debris Impacts 

Figure 7.3-3. Number of Wires Cut in 36-wire Bundle for Given Combinations of Orbital Debris 

Densities, Diameters, and Velocities 
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Once the number of penetrated wires are predicted through SPH hydrocode assessment and 

associated with a probability of cable failure, the analyst can determine the probability of those 

conditions occurring on orbit using ORDEM 3.0 [ref. 29]. In this case, an Excel spreadsheet was 

developed that interpolates the size and velocity of steel and aluminum particles causing from 

1 to 36 wire failures based on the hydrocode results, then calculates the likelihood of those 

particle combinations impacting the cables for a 1-foot length of cable in a year. 

Figure 7.3-4. Probability of Critical Failure versus Wire Harness Size Given Randomly Placed 

Redundant Wire Failure 

Figure 7.3-5 shows the expected probability of orbital debris-induced cable failure for a 1-year 

exposure of a 1-foot length of 6-, 18-, and 36-strand cables, where every strand within a cable 

carries a critical function and has a redundant strand somewhere in the cable carrying the same 

critical function. However, real spacecraft cables are often bundled together, shadowing one 

another, and located in orientations and locations where other spacecraft components shadow 

them. They also often carry more than just critical functions. As shown in Table 7.3-1, the 

baseline number of fails determined from the analysis is multiplied by factors that represent 

shadowing from different perspectives relative to the cable and a factor that takes into account 

wire redundancy in each cable. The result is an estimated number of fails that is a factor of ~20 

less than the baseline. 
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Figure 7.3-5. Cumulative Number of Cable Failures for Three Cable Sizes (1-Foot Length, 

Zentih/Nadir Orientation, 1 Year Exposure) 

Table 7.3-1. Effect of Shadowing and Reduced Criticality on a Typical 8-Foot Cable 

O-9. Assessing exposed wire bundle MMOD risk is complex, encompassing hard-toquantify failure contributions associated with variables such as wire redundancy, 

bundle size, criticality, and failure modes. This results in a high level of uncertainty 

for the critical failure probability prediction due to MMOD for JPSS-1. 

Task 2: Evaluating an Enhanced MMOD Blanket Over Zenith Deck Wires 

Based in part on the high computed risk of a critical wire bundle failure from the generic 

approach (Task 1), JPSS decided to implement an enhanced MMOD protection design consisting 

of betacloth-reinforced MLI suspended at a 5 cm standoff over a seven layer betacloth and 

Kevlar® blanket, draped over the exposed wire bundles, as shown in Figure 7.3-6. It is 

noteworthy that 99.5 percent of orbital debris approaches from within the X-Y (orbital) plane, 

Shadowing Factors Criticality 

Factor 

(assumes 

redundant 

wires)

Cable # Length

(ft)

Baseline 

no. of 

fails

Portside Front 

(0°)

45° Bundle Adjusted 

no. of 

fails

1 2 0.446 0.5 0.46 0.84 1 0.5 0.043

2 2 0.446 0.5 0.46 0.84 0 0.5 0

3 2 0.446 0.5 0.46 0.84 1 0.5 0.043

4 2 0.446 0.5 0.46 0.84 0 0.5 0

Total 1.784 Total 0.086

Fails per Foot 0.223 Fails per Foot 0.011

Ratio of baseline no. of fails to adjusted no. of fails 20.7
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and that orbital debris approaching from the Y-axis (from the “front” as viewed by the spacecraft 

or ram direction) makes up nearly 50 percent of this flux. The impact velocity would be 

14.6 km/sec and impact the blanket at 75° obliquity relative to the exposed wires on the zenith 

deck. The ultimate objective was to develop a design that prevented penetration of 3 mm 

aluminum spheres from the worst-case impact conditions shown in Figure 7.3-7.

Figure 7.3-6. Enhanced Shield Configuration for Defeat of 3mm Aluminum Orbital Debris 

Particles 

As shown in Figure 7.3-7, SPHC analyses showed that the enhanced shield was capable of 

preventing penetration of a 3 mm aluminum and 2.12 mm stainless steel orbital debris particle at 

the stated conditions. 

Figure7.3-7. Hydrocode Evaluation of Enhanced MMOD Shield for Steel and Aluminum Orbital 

Debris 
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Once the ballistic limit for the worst-case orientation (and highest orbital debris flux) was 

determined using SPHC, the exposed area for the blanket (and wiring beneath it) was calculated 

using the configuration shown in Figure 7.3-8. This area includes all regions where a wiring 

harness might be present, not just the critical harness bundles themselves, which could not be 

estimated reliably at the time of this assessment. As shown, the JPSS-1 spacecraft features 

radiators on the sides of the spacecraft that block much of the orbital debris from approaching the 

spacecraft from angles at 15° or more from the velocity vector. 

Figure 7.3-8. Calculation of Exposed Areas for Wiring Harnesses by Approach Angle 

Table 7.3-2 shows that for steel orbital debris particles, there is a 4.3 percent probability that one 

or more orbital debris penetrations of the enhanced shield over the zenith deck wiring will occur 

in the expected 7-year operation of the JPSS-1 spacecraft. Similarly, there is a 1.0 percent risk 

for aluminum particles, resulting in a combined risk of 5.3 percent. Most of this risk results from 

penetration by stainless steel particles due to their lower ballistic limit and higher flux on the 

enhanced wiring shield. 
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Table 7.3-2. Orbital Debris Penetration Risk for Enhanced Wiring Shield (Ppen = Probability of 

Penetration) 

Conclusions 

Two approaches were pursued to evaluate the risk from orbital debris penetration of exposed 

JPSS-1 wiring. In the first case, a generic approach considering normal impact of the baseline 

MLI over wires resulted in an evaluation of wire damage that was very conservative in that it did 

not consider the effects of obliquity and shadowing by other spacecraft components and 

adjoining wiring, and could not be sufficiently refined to account for the exact wiring bundle 

design, including redundancy. 

In the second case, an enhanced orbital debris shield was evaluated to provide less than a 

5.3 percent probability of penetration in 7 years when placed over the exposed wires. However, 

shield penetration should not be equated to critical wire failure; the figure of 5.3 percent “risk” of 

shield penetration is an upper bound for critical wire failure risk for the following reasons: 

 Actual wire coverage is less than the coverage of the MMOD blanket (lowering critical 

wire risk).

 There is a higher ballistic limit of the shield at other approach angles since that debris 

approaches at a lower velocity. 

 Shield penetration does not necessarily imply wire damage or failure. 

Steel Projectiles (Ballistic Limit = 2.12 

mm)

Aluminum Projectiles (Ballistic Limit = 3.00 

mm)

Approach 

Angle 

(deg.)

Penetration 

Flux 

(pens./m2-yr)

Area 

(m2)

Number of 

Penetrations 

in 7 years

Penetration 

Flux 

(pens./m2-yr)

Area 

(m2)

Number of 

Penetrations 

in 7 years

5 0.003448 0.562 0.0136 0.000678 0.562 0.0027

15 0.001757 0.488 0.0060 0.000505 0.488 0.0017

25 0.000942 0.272 0.0018 0.000259 0.272 0.0005

35 0.000688 0.098 0.00047 0.000184 0.098 0.000126

45 0.000549 0.042 0.00016 0.000149 0.042 4.38E-05

55 0.000484 0.013 4.4E-05 0.000129 0.013 1.17E-05

65 0.000462 0.005 1.62E-05 0.000116 0.005 4.06E-06

75 0.000471 0.0006 2.0E-06 0.000114 0.0006 4.8E-07

85 0.0000955 0.0002 1.34E-07 6.57E-05 0.0002 9.2E-08

Total Port 0.0221 Total Port 0.0051

Total Starboard 0.0221 Total Starboard 0.0051

Total Number of Penetrations 0.0431 Total Number of 

Penetrations

0.0101
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 There are inadequate impact test data for wiring harnesses available to correlate the 

hydrocode simulations to actual performance. 

 Not every wire is critical, and many wires are redundant. 

 Many of the critical wires are placed below other wires, so more shadowing is likely than 

was accounted for in this assessment. 

Considering these factors, the probability of critical wire failure on the zenith deck may be less 

than 1 percent. 

Additional details for this assessment are included as Appendix E. 

O-10. Preliminary NESC orbital debris risk assessments for representative wire bundles 

leading from reentry-critical elements on the zenith surface of the JPSS-1 covered 

only by baseline MLI predicted that a critical failure penetration per year might be 

expected for every 100 feet of exposed cables, considering blockage from other 

JPSS-1 wiring and equipment. This first order analysis was intended to give an 

estimate of the order of magnitude risk for the wire bundles. This baseline risk 

could be reduced through heavier MLI blanket/shielding protection over these 

zenith bundles. 

7.4 Debris Shape Assumptions 

Prior NESC assessments of MMOD risk for the Constellation Program [ref. 30] and ISS [ref. 31] 

noted that NASA risk assessments make the assumption that all orbital debris is spherical, with 

the same outer dimension as the characteristic length called out in ORDEM 3.0 and previous 

models. In reality, most orbital debris particles are not spherical. The characteristic length is a 

description of the average of three major dimensions of any shape particle, and the use of a 

spherical debris particle results in particles that are much heavier than the average particles 

(which are also more representative of fragments from upper stage explosions or breakups). 

NASA’s own Standard Breakup Model (SBM) is used to determine orbital debris reentry life, 

and arguments have been made to include it or other models in future debris penetration risk 

assessments (see charts from reference 30 in Figures 7.4-1 and 7.4-2). These prior studies, 

considering such disparate structural designs as aluminum single walls, Whipple shields, and 

thermal protection systems, indicate that the use of spherical debris assumptions is overly 

conservative, arriving at orbital debris risk assessments that are higher by a factor of ~2 

compared to SPH-based models. 
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Figure 7.4-1. Fragment Shapes from Impact Experiments [ref. 30] 
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Figure 7.4-2. SBM “Flake” Compared to Sphere Debris Shape Assumption (Current Assumption) 

The following finding and observation are related to orbital debris shape. 

F-24. Assuming a spherical debris shape is a source of uncertainty in the MMOD risk 

assessment, some studies suggest assuming a spherical shape (versus cubes or flat 

plates of the same critical length) may overestimate risk by a factor of ~2. 

However, not all potential debris shapes have been examined for their effect on 

risk. 

O-11. The ORDEM 3.0 environment does not explicitly describe the expected shape, or 

range of shapes possible in the orbital debris environment. It accounts for nonspherical particles when determining debris populations from the radar sources, 

while assuming spherical particles when associating particle sizes with crater 

sizes (in situ environment). 
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7.5 Pressure Vessel Failure Criteria 

When the NNO BLE is applied to pressurized elements of the JPSS-1 (e.g., the propellant tank), 

the wall thickness of the tank, which is the inner wall of a dual-wall (i.e., Whipple) shield, is 

assumed to be 50 percent of its actual thickness based on the following argument: 

1. Inner wall “failure” as used in development of the NNO BLE is defined as detached rearside spall, even without an actual through-hole or perforation of the inner wall. 

2. According to reference 32, to avoid rear-side detached spall, the thickness of a thin plate 

must be between 1.8 and 2.2 times the maximum expected crater depth in the thin plate. 

3. If an average value of 2.0 is used, this means that implicit in the NNO BLE is a 

maximum allowable crater penetration depth of 50 percent before inner wall failure is 

said to occur. 

4. According to reference 33, to prevent pressurized tank failure, maximum crater 

penetration depth cannot exceed 25 percent of the tank wall thickness. 

5. Therefore, in order for the NNO BLE to be used in the prediction of the failure response 

of a pressurized tank, only 50 percent of the actual rear wall thickness is to be used as an 

input value. 

The implementation of this “25 percent thickness rule” is illustrated in Figure 7.5-1. This figure 

also shows the difference between the initial design parameters of the JPSS-1 tank and those 

used in the initial Bumper runs. First, the MLI blanket, with an areal density of 0.32 g/cm2, has 

been equivalenced to a thin aluminum bumper, and then the thickness of the titanium wall has 

been reduced by 50 percent (although the wall material, in this particular case, was kept as 

titanium and not equivalenced to aluminum). 
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Figure 7.5-1. Application of the “25 Percent Thickness Rule” for the Exposed Portion of the JPSS 

Propellant Tank 

While the above argument regarding the “25 percent thickness rule” appears to be selfconsistent, several important points must be taken into consideration if the NNO BLE is to be 

applied to pressurized structural elements in this fashion. 

1) The actual allowable thickness rule or criterion in [ref. 33] consists of two parts: 

a. “The concentrated damage area should not exceed 1 inch in diameter.

b. The reduction in cross-sectional area (crater and spall) must not exceed 

25 percent of the wall thickness.”

At present, it is not clear as to whether or not the first part of the criterion has been discussed or 

checked to determine if it has been met for the geometries and impact conditions of interest on 

JPSS-1. Furthermore, the wording in the second part may be confusing as written. This second 

part of the criterion speaks to a reduction in cross-sectional area, not a reduction in crosssectional thickness (although the allowable reduction in area is related to a fraction of the initial 

wall thickness). First of all, area cannot be reduced by a length. Secondly, unless one bounds 

the edges of a region, how can initial and final cross-sectional areas be compared? 

2) The allowable thickness rule in reference 33 was based on a series of seven tests, all 

performed under the following nearly identical conditions: 
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“... 0.32-cm-diameter glass spheres were impacted on shielded tanks at 7.0 km/sec. 

The tanks were made of type 6Al-4V titanium alloy and were pressurized with 

alcohol to 960 lb/in2 to develop a hoop stress of 90,000 lb/in2. Aluminum shields 

were placed 1 inch from the tank walls. In seven tests, no tanks were ever ruptured, 

and there was no spallation inside the tank. The front-surface cratering was 

nominal, and the total reduction in cross-sectional area was less than 25 percent. 

Concentrated damage was confined to a 1/2-inch circular area.”

No documentation has been found indicating that in the 45 years since this rule first appeared, it 

has ever been checked for applicability to materials and impact conditions other than those from 

which it was developed. Further, these seven tests resulted in no spallation or rupture. So they 

represent conservative acceptance criteria, not necessarily a definition of a failure condition. 

Although mentioned briefly in Section 4.2.3.2 of NASA-SP-8042 (Space Vehicle Design 

Criteria: Meteoroid Damage Assessment), a review of more current NASA standards reveals that 

neither NSS 1740.14, nor NASA-STD 8719.14A, nor NPR 8715.6A, nor the DAS 2.0 User’s 

Guide offer any information on the subject of how to design pressurized volumes intended to 

operate in the MMOD environment. 

To determine what modification DAS 2.0.2 performs regarding allowable penetration depth for 

pressure vessels, several runs were performed using DAS 2.0.2 and a generic pressure vessel. 

The orbit was not considered to be a contributor, so simple default parameters of 500 km 

circular, 28.5° inclination, 1 year, 100 kg, and 0.01 m2/kg were used in the Mission Editor. A 

post-mission disposal maneuver was specified to limit the assessment period to 1 year in all 

cases. The generic tank used had a 4 g/cm2

areal density and a 1 m2critical surface area, with no 

additional shielding. This baseline tank was initially specified as pressurized. DAS was then run 

again using parameters and tanks identical to this first run tank, except in subsequent runs (1) the 

tanks were not pressurized, and (2) a variety of areal densities were used to simulate different 

allowable penetration depths. All tank critical surfaces in all of the runs were oriented directly 

into the ram direction to maximize the risk result (and therefore to better highlight any 

differences). The MMOD risk for each case was calculated by DAS and is shown in Table 7.5-1. 
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Table 7.5-1. MMOD Risk Assessment Results Calculated by DAS for a Generic Pressure Vessel 

Critical 

Surface

Areal 

Density 

(g/cm2)

Surface 

Area (m2)

Pressurized?

(yes/no)

DAS 2.0.2 

Calculated

MMOD Risk

Tank - Full 

Thickness 4 1 yes 0.018997

Tank - 1/2 

Thickness 2 1 no 0.001599

Tank - 1/4 

Thickness 1 1 no 0.009470

Tank - 1/8 

Thickness 0.5 1 no 0.075316

Tank - 1/5 

Thickness 0.8 1 no 0.018977

As shown in Table 7.5-1, the reported risk for the initial baseline case (with a 4 g/cm2

areal 

density) was 0.018997 (~1.9 percent). Furthermore, the results for the case where the tank was 

unpressurized and where its wall thickness was simulated as one-fifth as thick as the baseline 

wall thickness (or put another way, for the case where the maximum allowable penetration depth 

was 20 percent of the original wall thickness) exactly matched the baseline case. This indicates 

that when a component is specified as pressurized, DAS 2.0.2 multiplies the areal density by 

0.20 (and not 0.25), effectively dividing the tank wall thickness by a factor of five (i.e., allowing 

a penetration depth of only 20 percent of the original wall thickness). By adding an identical 

MLI layer to all cases and running DAS again, it was confirmed that this compensation factor 

applies only to the pressurized vessel wall itself. This was confirmed by examination of the 

actual DAS program, which revealed the following lines of code: 

IF (PRESS) THEN !ONLY NEED TO PENETRATE 20% OF 

 SIGMATOT=0.2D0*SIGMABW !PRESSURE WALL TO CAUSE 

FAILURE 

ELSE 

 SIGMATOT=SIGMABW 

ENDIF 

Since (1) areal density is equal to material bulk density multiplied by the wall thickness, and 

(2) DAS considers wall failure to occur when the rear side of a wall is breeched (that is, DAS 

considers a penetration depth of 100 percent of the wall thickness to be a failure, and not just a 

50 percent penetration depth as does Bumper), this reduction by DAS in the areal density and, by 

implication, a corresponding reduction in the thickness, of a pressurized tank wall to 

20 percent = one-fifth of its original value would be equivalent to specifying a maximum 
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allowable penetration depth of 40 percent in Bumper (instead of the currently assumed and used 

value of 50 percent in Bumper). 

This use of a 20 percent reduction factor (instead of 25 percent, which would correspond to a 

maximum allowable penetration depth of 50 percent in Bumper) may be an attempt to add an 

additional layer of conservatism to the DAS calculation process. 

F-25. Apollo-era failure criteria for pressure vessels are being applied to modern era 

spacecraft without tests or analysis to confirm their applicability. 

7.6 Updated JPSS Risk Assessment 

As the JPSS design evolved during the course of this NESC activity, members of the NESC team 

participated in telecons among NASA and contractor JPSS personnel to give real time 

recommendations to improve MMOD shielding and assessed risk posture. These 

recommendations played a part in improving the component-level MMOD risk assessments, 

along with improvements already underway. The critical component risk assessments as of the 

conclusion of this activity (October 2014) are shown in Table 7.6-1 for comparison to the risk 

numbers at the beginning of this activity (March 2014). Each of the components saw varying 

degrees of improvement in values for assessed risk. Most of the components received additional 

MMOD protection from the addition of either layers to MLI or MMOD blankets. A protective 

cover was added to the propulsion tank. The results of these modifications combined with a 

better understanding of the configuration and operational conditions is displayed in Table 7.6-1. 

JPSS did not perform risk assessments for those components that were not part of the reentry 

package (e.g., the science instruments). Consequently, the NESC team did not consider how 

MMOD risk would affect mission success. 
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Table 7.6-1. Risk Assessments for JPSS-1 Critical Components for Designs as of May 2014 

compared to October 2014 

Component Probability of Penetration

ORDEM2000/

SSP 30425 (May 

2014)

ORDEM 3.0/

MEMR2 (May 

2014)

ORDEM 3.0/

MEMR2 (Oct. 

2014)

Propellant Tank 1.1% 26.0% 0.22%

Propellant Lines 3.2% 49.7% included with 

Propellant Tank

Battery #1 (Stbd) 1.2% 40.9% 0.84%

Battery #2 (Port) 1.3% 43.4% 0.64%

Command and Telemetry Unit (CTU) 0.5% 9.7% 0.3%

PCDU #1 (Stbd) 0.1% 0.3% 0.21%

PCDU #2 (Port) 0.1% 0.3% not assessed

SCP #1 (Forward) 0.7% 14.5% 0.81%

SCP #2 (Aft) 0.2% 2.9% 0.29%

The following observations are related to the JPSS-1 risk assessment process. 

O-12. Preliminary assessments of risk are most often characterized by a conservative 

initial approach, including use of the minimum standoff and density of shielding, 

minimum thickness of rear wall material, and use of the lowest applicable ballistic 

limit relationship. The JPSS Project has followed this practice for JPSS-1, refined 

subsequently by program inputs in locations where the MMOD risk was seen to 

be a driver (e.g., the propulsion tank). Continued refinement of shielding 

characteristics and failure criteria could lead to a reduction in assessed risk. 

O-13. The fidelity of the penetration risk modeling improved throughout the assessment 

as more details became available and analysts refined the model. 

• The spacecraft construction is complex, and representing the applicable 

details of the construction in the model accurately is difficult. 

O-14. The required MMOD risk assessment for safe reentry has been performed, but risk 

assessment for mission success has not. 

• There is no NASA requirement to perform MMOD risk assessment for 

mission success. 
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7.7 Correlation of MMOD Environment Models to Failure History 

The NESC team considered the possibility of using historical data from satellite failures to 

evaluate the orbital debris models. In other words, how close do any of the orbital debris models 

come to predicting either actual failures or observed penetrations? 

The first element of a comparison is to determine what type of data is available. In situ data 

exists only for spacecraft components that have been returned to Earth. The Space Shuttle 

orbiter window and radiator impact data were used to develop ORDEM2000 and ORDEM 3.0. 

So, as expected, the MMOD risk assessments produced using these models agree relatively well 

with the impact damage. LDEF was used for ORDEM2000 and was considered too old to be 

useful for ORDEM 3.0. Other impact data were similarly rejected for being out of date including 

solar array panels from the HST (one recovered in 1993 and one in 2002), Solar Max hardware 

flown from 1980-1984, the EuReCa from 1993, and other MIR and ISS witness plates. All of 

these sources represent exposure at altitudes from 400 to 600 km because they flew at an altitude 

that allowed recovery via the Space Shuttle and therefore they would not provide any in situ

information for the altitudes of greatest interest where the models are predicting higher orbital 

debris fluxes (e.g., JPSS-1’s 824 km altitude). 

MMOD impacts may also present themselves as anomalies occurring in operational satellites. 

There are several challenges in using these data to “validate” orbital debris models. Much of the 

data on satellite failures that has been collected is unavailable due to reluctance on the part of 

satellite owners and operators to sharing data relative to failures. These data are sometimes 

considered proprietary information, or the operators are concerned about maintaining reputation. 

Information on DoD assets is also usually unavailable due to national security concerns. For 

failure data that do exist, it can be difficult to pinpoint the root cause for a given failure as one 

due to an MMOD impact. Satellite operators typically place a higher priority on returning the 

spacecraft to active service over troubleshooting the root cause. Also, there may not be enough 

information to identify whether a specific anomaly was caused by an MMOD impact. This 

places a high level of uncertainty on establishing an MMOD-related failure rate for a given 

satellite population. 

If a failure rate is obtained, there are further challenges to correlating that rate to the environment 

models. There is sometimes a misconception that the MMOD environment models directly 

result in a risk of penetration. In reality, the environment models are only one of several 

contributors to an MMOD risk assessment, which is produced using a software application such 

as DAS or Bumper. Any perceived disconnect between the calculated MMOD-related satellite 

anomaly rate and MMOD risk assessment is due to other factors along with the MMOD 

environment models. These other factors include the failure criteria, the fidelity of the risk 

assessment, and the BLEs chosen. In addition, very few detailed MMOD risk assessments have 

been performed for robotic spacecraft, limiting the efficacy of a statistically significant 

correlation. JPSS-1 may present a unique opportunity to gain some insight into its risk 

predictions. The predecessor to JPSS-1 in the JPSS constellation, S-NPP, uses the same general 

design as JPSS-1, with some reorientation of components and a few different subsystem designs. 
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JPSS-1 has undergone a detailed MMOD risk assessment that could be assumed to apply to 

S-NPP, with some modification (the S-NPP assessment performed before launch was not as 

detailed or as rigorous as the JPSS-1 assessment). For over 3 years, S-NPP has been in the same 

orbit as JPSS-1. Comparing the anomaly record of S-NPP to the risk for JPSS-1 may provide 

some useful insight into the risk assessment process for JPSS-1. The NESC team considered 

applying Bayesian statistical analysis to the S-NPP anomaly history to quantify uncertainty in 

ORDEM 3.0, but determined that the uncertainties in the analysis were too great to achieve 

confidence (See Section 9.0 for an alternate viewpoint). In addition, the S-NPP mission reported 

anecdotally that no on-orbit anomalies had occurred to that time that could be attributed to 

MMOD strikes. 

The following finding and observation are related to comparisons of satellite historical data to 

MMOD risk assessments for the purpose of risk assessment validation. 

F-26. It is difficult to correlate recorded spacecraft failure rates with those predicted by MMOD 

models. Reasons for this include: 

1. Spacecraft operators are either reluctant to share failure data or unable to discern the 

cause of failures so it is problematic to obtain MMOD-induced failure rates for 

operational spacecraft. 

2. Failure criteria may drive the risk assessment for MMOD as much, if not more, than 

variations in the environment model. 

3. Most operational spacecraft have not undergone MMOD risk assessments to predict 

the expected failure rate. 

O-15. S-NPP has a similar design and operates in the same orbit as JPSS-1. The risk of 

MMOD-induced damage on JPSS-1 can, to some extent, be compared to the performance 

of S-NPP. However, that comparison may become statistically meaningful only after a 

long period with no failures or after experiencing many failures. 

8.0 Findings, Observations, and NESC Recommendations

8.1 Findings 

The following findings were identified: 

F-1. The degree of uncertainty present in the prediction of the 3 mm orbital debris flux by any 

orbital debris model at altitudes higher than 600 km has not been characterized, but is 

expected to be large because of the lack of direct measurements of orbital debris particles 

smaller than 3 mm at altitudes higher than where the Space Shuttle orbiter operated. 

F-2. For the flux for particles < 3 mm, orbital debris model validation for altitudes above 

600 km is most effective using in situ data. 

F-3. The four models that were compared (ORDEM 3.0, ORDEM2000, MASTER-2009, and 

the current version of ADEPT) agree within a factor of ~2 for most debris sizes larger 
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than 3 mm, where ground radar has provided data describing the environment over a 

large range of altitudes. 

F-4. The models disagree significantly for particles < 3 mm, which is also the size that poses 

the highest penetration risk to most spacecraft. 

F-5. A higher 1 mm particle flux and assumptions behind the source of those particles 

constitute the primary drivers for the differences in flux between ORDEM 3.0 and 

ORDEM2000. 

F-6. There are four factors contributing to the divergence of flux values between 

ORDEM2000 and ORDEM 3.0 from 400 to 600 km up to the JPSS orbital altitude: 

1. In ORDEM2000, high-eccentricity intact objects were assumed to have a 

significantly higher particle production rate relative to low eccentricity intact objects. 

ORDEM 3.0 does not make a distinction between the rate of production from higheccentricity and low-eccentricity intact objects, so its 1 mm population has a higher 

relative contribution from circular orbits than does ORDEM2000. 

2. The impact data for ORDEM 3.0 identify a higher proportion of particles as stainless 

steel/high density at the higher altitudes, the source of which is assumed to come 

from intact objects (e.g., spacecraft and rocket bodies) in low eccentricity/high 

inclination orbits. 

3. ORDEM 3.0 includes high density particles, which are not included in ORDEM2000. 

The higher density particles, with a lower area to mass ratio, will decay less rapidly 

than medium density particles assumed for ORDEM2000, contributing to a greater 

total flux at higher altitudes. 

4. Surface degradation model particle emanation rates in ORDEM2000 and ORDEM 

3.0 are significantly different. 

F-7. The greater JPSS MMOD risk using ORDEM 3.0 compared to ORDEM2000 is due to 

two factors: 

1. ORDEM 3.0 has a ~13x higher particle flux than ORDEM2000 at the JPSS orbital 

altitude. 

2. ORDEM 3.0 contains high density particles. Smaller high density particles cause the 

same amount of damage as larger medium density particles and are more numerous. 

ORDEM2000 does not model high density particles. 

F-8. Because all the orbital debris models are consistent at all altitudes for debris fluxes of 

particles ~3 mm and larger, designing spacecraft to withstand impacts by particles up to 

3 mm in diameter will provide the least uncertainty in orbital debris risk. 

F-9. The orbital debris risk for JPSS-1 would be reduced if the orbital altitude were <800 or 

>1000 km. 
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 The results of the orbital debris model comparisons showed the highest flux 

between 800 and 1000 km for all of the orbital debris models analyzed. 

F-10. In spite of the identified uncertainties, ORDEM 3.0 possesses several advantages over 

ORDEM2000, MASTER-2009 and the current version of ADEPT: 

1. ORDEM 3.0 incorporates the most extensive use of Space Shuttle impact data, 

including residue chemical analysis. As a result of safety concerns about the orbiters, 

radiator and window surfaces represent the most complete, thoroughly examined 

returned spacecraft surfaces. 

2. MASTER-2009 and ADEPT do not predict the 1 mm particle flux reflected by the 

Space Shuttle impact data. 

3. ORDEM 3.0 contains high density particles that are not included in the other models, 

but are present in the Space Shuttle impact data. 

4. ORDEM 3.0 includes an updated large-object catalog, including contributions from 

the FY-1C event, the Iridium-Cosmos collision, and other recent debris-producing 

events. ORDEM2000 includes none of these, MASTER-2009 includes only the 

FY-1C debris, and ADEPT includes FY-1C and Iridium-Cosmos debris. 

F-11. No source for 1 to 3 mm steel debris particles in ORDEM 3.0 has been definitively 

identified. Possible contributors include: 

1. Surface degradation (e.g., collisions with MMOD) of stainless steel rocket stages with 

thicknesses near 1 mm. 

2. Explosions of non-passivated stages. 

3. Ejecta from non-catastrophic collisions. The debris population model in LEGEND 

does not include ejecta from impacts from particles from 1 to 10 cm, nor a groundtested non-catastrophic collision model that may apply to upper stages. 

F-12. ORDEM 3.0 has no technical documentation describing the validation and verification or 

user guide information detailing the best uses and limitations. 

1. Data for this assessment was provided to the NESC team by ODPO in the form of 

presentations, spreadsheets, and verbal exchanges. 

F-13. The assessed risk associated with meteoroids for individual JPSS components is roughly 

two orders of magnitude smaller on average than the assessed risk from orbital debris 

when using ORDEM 3.0 and MEMR2. The difference is roughly one order of magnitude 

when using ORDEM2000 and SSP 30425. 

F-14. The meteoroid flux for 0.1 to 10 mm diameter sizes given by MEMR2 is within an order 

of magnitude of that produced by the Divine-Staubach model, which is used by 

MASTER-2009.
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F-15. MEM was reviewed by the NESC in 2009 [ref. 15] and found to be appropriate for use in 

MMOD risk assessments. MEMR2 differs from MEM primarily in the graphical user 

interface. Neither MEM nor MEMR2 assess uncertainties, which was discussed in the 

2009 NESC report. 

F-16. Over 90 percent of the orbital debris impacting the JPSS-1 spacecraft have a velocity 

above 7 km/s, with an average velocity over 12 km/s. Because of this, the BLE Region 

III curve requires critical understanding. 

F-17. The choice of 9.1 km/s as the Region II to Region III transition velocity for steel-onaluminum uses valid reasoning and assumptions and realistically models the 

phenomenology associated with the HVI of a steel projectile on a thin aluminum plate. 

However, this value may not be an appropriate choice for other bumper materials 

(e.g., MLI). 

F-18. There are no BLEs available that were developed specifically for the JPSS-1 structural 

elements. As a result, risk assessments are being performed using equations that were not 

developed for the materials or wall configurations used on JPSS-1. Using the modified 

NNO, adjusted for materials used by JPSS, may be predicting smaller critical diameters 

(i.e., increased assessed risk) than would BLEs purposely developed for actual JPSS-1 

configurations. 

F-19. The BLEs used for JPSS were developed for manned spacecraft and take a conservative 

approach to model spacecraft penetrations beyond a 7 km/s normal impact velocity. 

1. HVI test results suggest that the actual Region III BLE may provide higher 

performance and yield less assessed risk than currently assumed in the modified NNO 

BLE.

2. Using a modified Wilkinson BLE (80 percent of original Wilkinson BLE, as 

recommended by Elfer and Bjorkman) resulted in a factor of ~3.4 lower probability 

of penetration for a 1 meter cube at the JPSS orbit than the NNO BLE. 

F-20. Use of the NNO BLE for fixed shield thickness and a range of impactor diameters, as 

specified by an environment model like ORDEM 3.0, will result in an over-prediction of 

damage fluence (i.e., increased conservatism) by smaller impactors. 

F-21. The JPSS configuration used to perform the MMOD risk assessment resulted in a 

9.7 percent failure risk for the CTU contained configuration inaccuracies. These 

deviations tended to underestimate the assessed risk: 

1. The risk assessment assumed a 5 cm (2-inch) stand-off between the MLI blanket and 

the CTU, while the actual stand-off varies and is less than 5 cm in some locations. 

2. The risk assessment used a MLI density of 0.091 g/cm2, while the actual blanket has a 

density of 0.034 g/cm2

.
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F-22. Higher fidelity analyses of the internal structure and expected failure modes for critical 

electronics box interior elements aboard JPSS-1 (i.e., examining the resistance of interior 

critical elements to penetration within each box) may result in a lower MMOD assessed 

risk for those boxes (compared to assuming any box penetration is a failure). 

1. This examination must be balanced with long-term concerns over interior box 

element contamination from penetrations. 

2. This is a first-order assessment and thorough risk evaluation would require HVI 

testing to verify hydrocode results. 

F-23. Exterior wire bundles, especially those on the most-exposed zenith side, on JPSS-1 have 

not been considered in the risk assessment, and their inclusion in future risk assessments 

can be expected to increase the assessed MMOD risk. 

F-24. Assuming a spherical debris shape is a source of uncertainty in the MMOD risk 

assessment, some studies suggest assuming a spherical shape (versus cubes or flat plates 

of the same critical length) may overestimate risk by a factor of ~2. However, not all 

potential debris shapes have been examined for their effect on risk. 

F-25. Apollo-era failure criteria for pressure vessels are being applied to modern era spacecraft 

without tests or analysis to confirm their applicability.

F-26. It is difficult to correlate recorded spacecraft failure rates with those predicted by MMOD 

models. Reasons for this include: 

1. Spacecraft operators are either reluctant to share failure data or unable to discern the 

cause of failures, so it is problematic to obtain MMOD-induced failure rates for 

operational spacecraft. 

2. Failure criteria may drive the risk assessment for MMOD as much, if not more, than 

variations in the environment model. 

3. Most operational spacecraft have not undergone MMOD risk assessments to predict 

the expected failure rate. 

8.2 Observations 

The following observations were identified: 

O-1. The NASA standard break-up model used by LEGEND does not account for the 1 mm 

population observed in Space Shuttle impact data. 

O-2. The surface degradation model in ORDEM 3.0 dominates the population particles smaller 

than 3 mm. 

1. This is based on a default assumption that intact object surface-degradation generates 

particles that caused the Space Shuttle impacts. 

2. Because of the orbital distribution of the source intact objects, most debris-producing 

surfaces are located in near-circular orbits between 700 and 1000 km altitude. 
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O-3. The generation of 1 mm stainless steel debris via surface degradation in ORDEM 3.0 is 

assumed to continue into the future beyond the dates of Space Shuttle impact data. 

O-4. The credibility assessment methodology in NASA-STD-7009 (NASA Standard for 

Models and Simulations) was useful in providing a high-level evaluation of ORDEM 3.0, 

highlighting opportunities for overall improvement and confidence building in the model. 

This was especially the case for orbital debris particle regimes <3 mm, where focus areas 

include validation, input pedigree, results uncertainty, results robustness, and model and 

simulation management. OSMA has determined that this standard is not mandatory for 

ORDEM 3.0, and an evaluation of this decision was outside the scope of this assessment. 

O-5. MEMR2 is calibrated to ground-based radar observations and the data from which the 

Grun flux was derived. 

O-6. ESA’s iMEM model is based on modeling and limited in situ measurements. ESA has 

recently concluded that iMEM is not representing the meteoroid environment adequately 

and is initiating an extensive rework of the model. The Divine-Staubach model (used in 

MASTER-2009) is also no longer used by ESA. 

O-7. NASA TM-2009-214789 (Ref 24) appears to apply the Reimerdes BLE dual wall-tosingle wall transition factor, , differently than specified by Reimerdes et al. [ref. 22] 

and verified mathematically by the NESC team. Reference 9 applies to the critical 

particle diameter, while the prescribed method in reference 22 is to apply to the 

critical wall thickness. 

O-8. There appears to be an error in the algorithm that defines the ratio of critical wall 

thickness from the single wall BLE to that from the NNO BLE ( ) in the Reimerdes 

BLE definition set forth by Reimerdes et al. in reference 22. The specification in 

reference 22 to evaluate the parameter only at the high velocity region boundary 

results in a BLE curve (impactor diameter versus critical wall thickness) that exceeds the 

single wall curve as the impactor diameter increases, instead of approaching the single 

wall curve asymptotically as would be expected. Evaluating the parameter at the 

actual normal component of impact velocity appears to rectify the problem. 

O-9. Assessing exposed wire bundle MMOD risk is complex, encompassing hard-to-quantify 

failure contributions associated with variables, such as wire redundancy, bundle size, 

criticality, and failure modes. This results in a high level of uncertainty for the critical 

failure probability prediction due to MMOD for JPSS-1. 

O-10. Preliminary NESC orbital debris risk assessments for representative wire bundles leading 

from reentry-critical elements on the zenith surface of the JPSS-1 covered only by 

baseline MLI predicted that a critical failure penetration per year might be expected for 

every 100 feet of exposed cables, considering blockage from other JPSS-1 wiring and 

equipment. This first order analysis was intended to give an estimate of the order of 

*

F2

*

F2

*

F2

DS

r /

DS

r /

DS

r /
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magnitude risk for the wire bundles. This baseline risk could be reduced through heavier 

MLI blanket/shielding protection over these zenith bundles. 

O-11. The ORDEM 3.0 environment does not explicitly describe the expected shape, or range 

of shapes possible in the orbital debris environment. It accounts for non-spherical 

particles when determining debris populations from the radar sources, while assuming 

spherical particles when associating particle sizes with crater sizes (in situ environment). 

O-12. Preliminary assessments of risk are most often characterized by a conservative initial 

approach, including use of the minimum standoff and density of shielding, minimum 

thickness of rear wall material, and use of the lowest applicable ballistic limit 

relationship. The JPSS Project has followed this practice for JPSS-1, refined 

subsequently by program inputs in locations where the MMOD risk was seen to be a 

driver (e.g., the propulsion tank). Continued refinement of shielding characteristics and 

failure criteria could lead to a reduction in assessed risk. 

O-13. The fidelity of the penetration risk modeling improved throughout the assessment as 

more details became available and analysts refined the model. 

1. The spacecraft construction is complex, and representing the details of the 

construction in the model accurately is difficult. 

O-14. The required MMOD risk assessment for safe reentry has been performed, but risk 

assessment of mission success has not. 

1. There is no NASA requirement to perform MMOD risk assessment for mission 

success. 

O-15. S-NPP has a similar design and operates in the same orbit as JPSS-1. The risk of 

MMOD-induced damage on JPSS-1 can, to some extent, be compared to the performance 

of S-NPP. However, that comparison may become statistically meaningful only after a 

long period with no failures or after experiencing many failures. 

8.3 NESC Recommendations 

The following NESC recommendations were identified and directed towards the JPSS, ODPO, 

HVIT, MEO, and NASA/OSMA unless otherwise identified: 

Directed to JPSS 

R-1. Use MEMR2 as the meteoroid model in JPSS-1 MMOD risk assessments. (F-15)

R-2. Use ORDEM 3.0 as the orbital debris model in JPSS-1 MMOD risk assessments, 

recognizing that there are large uncertainties associated with the unknown source of 

stainless steel particles. (F-10) 

R-3. Assess the MMOD risk for JPSS-1 wire bundles, include in the overall JPSS-1 risk 

assessment, and add additional protection if necessary. (F-23, O-9, O-10, O-14) 
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R-4. Develop BLEs using materials (especially MLI) that are specific to JPSS-1. 

(F-18, F-19, F-17)

R-5. Conduct trades to determine MLI thickness, spacing, and other elements (e.g., Kevlar®

blankets) that will optimize penetration risk for electronics boxes such as the CTU, SCP, 

and PCDU. (F-22, O-15)

R-6. Add spacers to maintain the desired stand-off between the MLI and critical elements 

(e.g., CTU). (F-22, F-18, O-15)

R-7. Explore methods to modify the NNO BLE to account for the impeding effect of shield 

thickness larger than the critical value. (F-18, F-19, F-20)

Directed to ODPO/HVIT 

R-8. Concentrate efforts toward characterizing and reducing the uncertainty in ORDEM 3.0. 

(F-1, F-3) 

1. Recommendations R-9, R-10, R-11, and R-19 will help to bound and define the 

uncertainty. 

2. Sensitivity studies that vary assumptions and contributors will also help to bound the 

model uncertainty. 

3. Allowing for periodic updates to the model for solar flux intensity will help to 

reduce uncertainty. 

R-9. Work to identify the source of 1 mm stainless steel particles. This includes creating a 

comprehensive database of satellite/rocket body material composition and investigating 

rocket engine firings as a possible source. (F-1, F-3, F-4, F-5, F-6, F-11, O-2, O-3)

R-10. Pursue ground testing to improve break-up models, which would include explosion/HVI 

testing of complex surfaces representing spacecraft and rocket body structures. 

(F-1, F-3, F-4, F-5, F-6, O-1)

R-11. Use available in situ impact data to help validate ORDEM 3.0 at 400-600 km. 

(F-1)

1. Impact data from the HST Wide Field Planetary Camera is a possible data source. 

These impact data have just been received by the ODPO and still need to be 

formatted for use. 

2. Two HST solar arrays have been returned and examined by ESA for impacts. To 

date, the raw data have not been available. 

3. Materials ISS Experiment data may be a data source. 

4. These data sources may not include chemical analyses to identify impactor material 

composition. 

5. Look for variations in the data based on altitude. 
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R-12. Document the data analysis and modeling in ORDEM 3.0 and its validation and 

verification. Incorporate in the user’s guide information detailing best uses and 

limitations of the model. (F-12)

R-13. Perform HVI testing (especially in Region III) to produce and refine BLEs for steel and 

aluminum projectiles on typical materials and configurations used by robotic satellites. 

(F-18, F-19, F-17, F-16, O-15)

R-14. Perform HVI testing to interrogate effect of shape (e.g., cube and flake-shaped particles) 

on the orbital debris model and penetration risk. (F-24, O-11)

R-15. Update pressure vessel HVI failure criteria for structural materials and impact conditions 

that metallic spacecraft pressurized elements will likely encounter based on existing data 

and HVI testing. Use the Apollo-era maximum allowable pressure vessel penetration 

depth criterion of 25 percent of the pressure shell thickness until the updated assessments 

are available. (F-25)

R-16. Document guidelines for selection and use of BLEs and failure criteria (e.g., limits for 

stand-offs, obliquity cut-off, and non-aluminum materials). 

(F-18, F-19, F-17, F-16, F-25)

R-17. Include internal component configuration inside electrical boxes when assessing MMOD 

failure criteria, which includes consideration of post-penetration contamination. (F-22)

Directed to MEO 

R-18. Incorporate uncertainties in MEMR2 revisions. (F-15)

Directed to NASA/OSMA 

R-19. Increase efforts to directly characterize the debris environment, especially at altitudes 

above 600 km for which there is currently no in situ data. (F-1, F-2, F-4, F-5, F-6, F-7) 

 Note: Possible ways in which this could be accomplished include examining impact 

craters on spacecraft at higher altitudes using advanced telescopic imaging, or 

dedicated missions to directly measure orbital debris at 600+ km altitudes (e.g., the 

Debris Resistive/Acoustic Grid Orbital Navy-NASA Sensor (DRAGONS) in 

development to detect MMOD impacts) -- including sample retrieval to analyze the 

impacts for material composition. As an example, the three Pegasus missions in the 

1960's obtained direct in situ measurements of the meteoroid environment in LEO. 

Those measurements have provided an essential description of the meteoroid 

environment for the last 45 years. 

R-20. Create a database of spacecraft anomalies to track and understand MMOD-induced 

failures. (F-25) 
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R-21. Issue guidance regarding the conditions for incorporation of orbital debris models into 

MMOD risk assessments for both new and existing designs. (F-3, F-10)

1. Specific guidance is needed for the use of the DAS assessment tool in the interim 

until ORDEM3.0 is incorporated into it. Guidance is also needed not only on when 

ORDEM3.0 is to be phased in, but also how it is to be used. 

R-22. Ensure that independent and external peer reviews of technical content and software are 

performed prior to all new orbital debris model releases. (F-1, F-6, F-11, F-12)

R-23. Use NASA-STD-7009 during the development of new orbital debris model releases to 

help focus on areas of weakness related to the model and its intended usage. (O-4) 

9.0 Alternate Viewpoints

Alternate Viewpoint #1 

Alternate Opinion Relative to Observation 8 by Dr. William Vesely 

Alternate Opinion to the Observation that Long Time Periods or Many Observed Failures 

May be needed to Provide Meaningful Statistical Estimates 

Dr. William Vesely 

Technical Risk Manager 

Office of Safety and Mission Assurance 

September 30, 2014 

Overview 

There are two important issues regarding the uncertainty in a risk prediction using ORDEM 3.0 

that need to be addressed: 1) the characterization of the uncertainty in an MMOD risk prediction 

using ORDEM 3.0 to allow updating and revision of the predicted risk, and 2) the effective use 

of observational data to update and revise an MMOD risk prediction using ORDEM 3.0 or any 

other code. Having worked for many years in using data to update risk estimates, I want to 

provide an alternate opinion to the observation in the main body that indicates that a long time 

period or many failures are necessary to use observation data to update an MMOD risk 

prediction. Long time periods are not necessarily needed even if no events are observed if the 

uncertainty is appropriately characterized and effective statistical analysis techniques are used. 

This applies to effectively using S-NPP observational data to update and revise the MMOD risk 

prediction for JPSS-1. It more generally applies to using relevant observational data for any 

other spacecraft. 

ORDEM 3.0 has a strong basis in its use of a comprehensive analysis of measured stainless steel 

debris impacts on the Space Shuttle. The measured impacts are then extrapolated to a postulated 

stainless steel flux contribution that gave rise to the impacts. The postulated flux contribution is 

assumed to still exist. The postulated flux contribution in turn results in a significant risk 

contribution particularly at higher altitudes, (e.g., greater than 600 km), which corresponds to the 
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altitude of the JPSS-1 mission. There is a potentially large uncertainty in the postulated flux 

contribution and associated risk contribution because the postulated flux contribution has not 

been measured nor validated. Also, none of the assumptions have been validated which are used 

to extrapolate the measured shuttle impacts to the postulated flux contribution. 

In spite of these weaknesses, there is a strong motivation to use ORDEM 3.0 because of its 

strong shuttle analysis basis and because of the implications of the predicted increased flux and 

risk contribution. However, because of the potentially large uncertainty in the predicted risk the 

uncertainty needs to be bounded and be characterized in a necessary manner for risk informed 

decision making and for incorporation of observational data. Without appropriate 

characterization of the uncertainty, there is no information as to how conservative or nonconservative the predicted risk value may be. As importantly, by appropriately characterizing 

the uncertainty, observational data can be effectively used to update and revise the predicted risk 

value and associated uncertainty. This observational data includes data that can be collected not 

only in the long term, but data that are presently available and can be made available in the 

shorter term. It includes periods of no failure-causing MMOD hits as well as periods in which 

MMOD hits occurred. By appropriately characterizing the uncertainty and using appropriate 

statistical techniques, long observation times and large amounts of data are not needed to provide 

useable updates and revisions of the predicted risk and associated uncertainty. Approaches for 

the necessary characterization of the uncertainty in the predicted risk value using ORDEM 3.0, 

and for effectively using observational data are described in the following sections. These 

approaches are direct and follow NASA guidelines. Examples and references are given. 

The Modeling Uncertainty in ORDEM 3.0 

There are two basic uncertainty contributions in ORDEM 3.0 - the parameter uncertainty 

contribution and the model uncertainty contribution. The parameter uncertainty contribution 

involves the uncertainty in the measured parameters used in ORDEM 3.0, such as the density 

and composition of the stainless steel debris impacts analyzed on the Space Shuttle radiators. 

The model uncertainty contribution in ORDEM 3.0 involves the uncertainty in the modeling and 

extrapolations used to postulate a new stainless steel flux contribution giving rise to the 

measured debris impacts on the Space Shuttle. The dominant uncertainty contribution in an 

ORDEM 3.0 risk prediction is the modeling uncertainty contribution. 

The important difference in ORDEM 3.0 from ORDEM2000 and other debris codes is the new, 

postulated flux contribution containing high density, stainless steel debris. This new flux 

contribution results in a high risk contribution at higher altitudes, which for the JPSS-1 mission 

results in the predicted MMOD risk being of the order of a factor of 10 higher than predicted 

using ORDEM2000. The postulated flux contribution used in ORDEM 3.0 is not unreasonable 

and is even plausible. However, the new flux contribution in ORDEM 3.0 has not been validated 

by observation and measurement. Also, the model used to postulate the flux contribution is 

based on specific assumptions which include the assumption of the stainless steel debris fluxes 

continuing after the Space Shuttle impacts occurred several years ago, the assumption of 

particular inclinations and eccentricities for the stainless steel debris flux that determine the flux 
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patterns and that cause high risks, and the assumption that the stainless steel debris is continually 

being emitted from surfaces of orbiting spacecraft containing stainless steel. None of these 

assumptions have been validated. Furthermore, a plausible mechanism by which the stainless 

steel debris is emitted has not been able to be identified. 

Ways to Address the Modeling Uncertainty in ORDEM 3.0 

The modeling uncertainty in ORDEM 3.0 due to the extrapolation of the Space Shuttle 

measurements to a postulated flux contribution is not itself a justification for not using ORDEM 

3.0 since it has the strongest foundation in terms of being based on the most detailed analyses of 

Space Shuttle debris impacts. Furthermore, the implications of the new flux contribution and 

new risk contribution potentially existing are important enough that they cannot be dismissed. 

This does not mean, however, that the associated uncertainty in a predicted risk value using 

ORDEM 3.0 should be ignored since it can be large. Instead, it is critical that the uncertainties 

be addressed. There are three ways to do this: 1) take detailed in situ measurements of fluxes in 

relevant environments, 2) characterize the predicted risk uncertainty to provide a framework for 

risk-informed decision making and to allow observational data to be effectively used, and 3) use 

observational data to update and revise the predicted MMOD risk estimate and associated 

uncertainty. Taking in situ measurements has been addressed in the main body. The other two 

important ways are addressed in the following. 

Characterization of the Uncertainty Range for a Risk Prediction Using ORDEM 3.0 

To appropriately characterize the uncertainty associated with a predicted risk value, an 

uncertainty range and associated coverage probability need to be estimated. The coverage 

probability is the probability of the risk value lying in the range. In addition, a probability 

distribution needs to be associated with the uncertainty range. Important points are that only 

approximate values are needed and that methods have been developed that are standardly used in 

NASA probabilistic risk analysis and NASA risk-informed decision making (refs. WV-1, WV-2, 

WV-3, and WV-4). The other important point is that the uncertainty characterization is only an 

initial estimate which is subsequently updated and revised as additional observational data and 

knowledge are obtained. Methods and software for doing this for risk estimates are described in 

the NASA guidelines given in the references. The methods are straightforward and involve the 

use of probabilistic approaches to characterize uncertainties. 

An example of the application of the guidelines to the characterization of the uncertainty in an 

MMOD risk estimate for JPSS is useful. It is not as thorough as it would be for an application, 

but it illustrates the basic processes and rationale used. As an initial assessment, it can be 

reasoned that because the predicted risk value for JPSS using ORDEM 3.0 differs by 

approximately a factor of 10 from that using ORDEM2000, therefore approximately a factor 10 

uncertainty needs to be assigned to the predicted MMOD risk value using ORDEM 3.0. This 

treats the difference in the risk predicted value using ORDEM 3.0 as an uncertainty because of 

the present lack of validation for ORDEM 3.0. It also allows for the possibility that the predicted 

MMOD risk value using ORDEM2000 is applicable. A moderate coverage probability (i.e., a 

moderate confidence, such as 75 percent) is associated with the uncertainty range to reflect the 
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situation that there is not a high confidence in the range. To complete the uncertainty 

description, an uncertainty distribution is associated with the uncertainty range to allow for use 

in risk assessments and to allow updating and revision with observational data. A gamma 

distribution or log normal distribution is commonly used as risk uncertainty distributions in risk 

predictions. Each has its own features. Both could be used and differences be included in the 

final uncertainty characterization. 

The above is only a preliminary assessment, but it provides a framework for refinement for an 

actual application. For refinement, a more consensual basis for the uncertainty characterization 

would be carried out. Also, a more analytical basis for estimating the uncertainty range could be 

carried out by evaluating variations in fluxes and risks that result from different extrapolations in 

ORDEM 3.0, such as by using different eccentricities and inclinations for the new flux. 

Plausibilities would be assigned the different fluxes based on expert knowledge and judgment. 

The focus again would be on general sizes and not precise values. Significant differences in the 

uncertainty sizes using different assumptions can be incorporated in the overall uncertainty 

assessment. The procedures and tools identified in the NASA references allow these evaluations 

to be efficiently carried out. This uncertainty characterization is as important as the prediction of 

a specific risk value and this work needs to be done. 

Use of Observational Data to Update and Revise a Risk Prediction Using ORDEM 3.0 

Observational data consists of observed numbers of MMOD hits on relevant exposed spacecraft 

surfaces in given time periods. The hits can be failure causing or non-failure causing. The 

observational data can also consist of periods of no hits or no failure hits. The relevant 

spacecraft are those that are exposed to potential MMOD hits at similar altitudes as the 

spacecraft whose risk is to be predicted. Differences in exposed areas are handled by 

appropriately proportioning the exposure probability by the exposed area. Difference in failure 

criteria can also be handled by appropriately weighting the events. These are common ways of 

modifying observed data to be applicable to the particular spacecraft of interest and are not the 

focus here. The focus here is how to systematically use observational data to update an MMOD 

risk prediction using ORDEM 3.0. 

Consistent with NASA risk assessment guidelines, Bayes’s rule is applied to update the predicted 

risk value using observational data. Bayes’s rule is a basic probability relationship and its use in 

analyzing data is termed Bayesian statistical analysis. To apply Bayes’s rule to updating an 

MMOD risk prediction let

R

be the MMOD predicted occurrence rate for failure (i.e., the 

occurrence rate due to an MMOD fatal hit). The occurrence rate

R

is in units of per time period 

such as per year. Let 

D

be a set of observational data such as a number of MMOD occurrences, 

or no occurrence, in a given time period. Bayes’s rule is used to give the updated probability of 

the MMOD occurrence rate having a specific value incorporating both the model prediction and 

observational data. 
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For updating an MMOD risk prediction, application of Bayes’s rule gives:

)(

)()/(

)/(

DP

RPRDP

DRP 

(1) 

where 

DRP )/( 

the updated probability that the MMOD occurrence rate has value 

R

 (2) 

including the observed data 

D

and the ORDEM 3.0 prediction 

RDP )/(

= the observational probability for the data 

D

using an (3) 

MMOD occurrence rate with value 

R

RP )( 

the probability that the MMOD occurrence rate has value R (4) 

based on the ORDEM 3.0 prediction

and

DP )( 

the probability of the observed data over all possible values (5) 

of the MMOD occurrence rate. 

DP )(

is a normalization factor and is the sum (or integral) of the numerator of Bayes’s rule over 

all possible occurrence rate values. 

The key factor in Bayes’s rule is the uncertainty characterization

RP )(

, of the MMOD 

occurrence rate which is the probability of the predicted occurrence rata having a specific value. 

As indicated this probability is commonly represented as a gamma distribution or log normal 

distribution over the uncertainty range. The observational data probability 

RDP )/(

is the 

standard Poisson probability of observing a given number of MMOD occurrences or zero 

occurrences in a time period. This is the standard probability used for occurrences of discrete 

events and is used in ORDEM 3.0. Substitution of the probabilities for a particular MMOD 

prediction and for given observational data yields the applicable updated predicted probability. 

Examples are given in the next section. 

Before the examples, it is useful to contrast the use of Bayes’s rule with the use of a purely 

empirical statistical estimate, which is also termed a classical statistical estimate. The empirical 

statistical estimate only uses the Poisson observational data probability 

RDP )/(

and ignores the 

model prediction

RP )(

. The predicted model occurrence rate and associated uncertainty are thus 

not updated in any systematic manner. The observational probability alone is used to obtain a 

best estimate of the occurrence rate and bounds on the occurrence rate. When there is sparse 

data as there often is in a risk assessment then the best estimate and bounds obtained are not 

generally useable since the prediction from the model is not used to direct and constrain the 

estimate. This is illustrated in the next section. When there is a preponderance of data 

consisting of a large number of events then the model predicted occurrence rate and associated 
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bounds using only observational data will be essentially the same as those obtained using 

Bayes’s rule. However, this will generally take a long time and in the meantime the empirical 

estimates obtained are not useable. This again is illustrated in the next section. The fact that the 

use of Bayes’s rule provides the most information in a timely manner is why it is standardly used

for updating a risk prediction in NASA probabilistic risk and reliability assessments. 

Examples of Risk Prediction Updates When No MMOD Events Are Observed 

Figure 1 illustrates the application of Bayes’s rule to determine the factor reduction in the model 

predicted MMOD occurrence rate for failure when no such MMOD occurrences are observed for 

a given time period. The factor reduction is the model predicted occurrence rate divided by the 

updated predicted occurrence rate accounting for the observation of no occurrences. The 

predicted occurrence rate is taken to be the expected value. The factor reduction in the model 

predicted occurrence rate versus time period without any observed occurrences is plotted for a 

model predicted occurrence rate of 0.01 per year for different sizes of the characterized 

uncertainty. The characterized uncertainty in the model predicted occurrence rate is described by 

the coefficient of variation (CV) which is the standard deviation in the predicted occurrence rate 

divided by the expected value of the occurrence rate. The coefficient of variation is also 

commonly called the error factor. A gamma distribution is used here for the uncertainty 

distribution and a Poisson distribution is used for the observational probability. 

As seen from Figure 1 the factor reduction is always greater than one for any non-zero 

observation time period since the updated predicted occurrence rate is less than the initial model 

predicted occurrence rate. For a large uncertainty in the prediction (e.g., for CV=5), the factor 

reduction is relatively large and hence there is a significant reduction in the model predicted 

value when there are no observed occurrences. This shows the importance of accounting for the 

uncertainty in the model predicted occurrence rate in effectively using observational data to 

update the prediction. As also observed, moderate differences in the uncertainty characterization 

do not cause large differences in the reduction factor for smaller observation time periods. 

Larger differences in the uncertainty characterization that cannot be resolved can be 

accommodated by using appropriate bounds on the reduction factor. Figure 2 shows similar 

plots for a model predicted occurrence rate of 0.1 per year. The factor reductions are now 

significantly larger for the same time period especially when the uncertainty in the predicted 

occurrence rate is large. This makes sense since smaller times without occurrences are needed to 

provide significant reduction when the model predicted occurrence rate is relatively large and 

has large uncertainty. More details of the evaluations used for Figures 1 and 2, which are meant 

to be illustrative, are given in Reference WV-5. Using the software described in the NASA 

guide given in Reference WV-1, similar updates can be efficiently evaluated for any number of 

observed occurrences for different characterizations of the uncertainty and using different 

uncertainty distributions. 
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Figure 1. Factor Reduction in the Predicted Occurrence Rate: Model Predicted Value=0.01 

Figure 2. Factor Reduction in the Predicted Occurrence Rate: Model Predicted Value=0.1 

0

2

4

6

8

10

12

14

16

18

20

0 2 4 6 8 10 12 14 16 18 20 Factor Reduction in Predicted Occurence Rate

Time Period Without Occurrence (years)

Factor Reduction in Initial Predicted Event Occurrence Rate Versus EventFree Time Period (Initial Predicted Occurrence Rate = 0.01 per year)

1

2

3

4

5

6

7

8

9

10

Coefficient

of Variation in 

Predicted 

Occurrence Rate

0

20

40

60

80

100

120

140

160

180

200

220

0 2 4 6 8 10 12 14 16 18 20 Factor Reduction in Predicted Occurence Rate

Time Period Without Occurrence (years)

Factor Reduction in Initial Predicted Event Occurrence Rate Versus EventFree Time Period (Initial Predicted Occurrence Rate = 0.1 per year)

1

2

3

4

5

6

7

8

9

10

Coefficient

of Variation in 

Predicted 

Occurrence Rate
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In concluding this section, it is instructive to compare the informative results in Figures 1 and 2 

with the purely empirical statistical estimates using only the Poisson observational probability 

and ignoring the model predicted value and uncertainty. Since no occurrences were observed in 

any of the time periods, the empirical best estimate for the occurrence rate is zero for any 

observational time period. The empirical 95 percent upper bound for the occurrence rate for any 

observational time period is 3.7 divided by the time period. This upper bound is generally not 

usable because of its large value. Even after 20 years with no observed occurrence there is little 

useful information obtained for updating the model predicted occurrence rate as long as it is 

below 3.7/20=.18 per year regardless of the size of uncertainty associated with the prediction. 

This lack of information from the empirical statistical estimates arises because the model 

predicted occurrence rate and associated uncertainty are not used to provide important 

information in the updating. This lack of information occurs for any observational data that does 

not encompass a long observation time or numerous occurrences; it is why Bayesian statistical 

analysis is standardly used in probabilistic risk and reliability predictions. 

Summary 

A long time observation time period is not necessary to effectively update and revise an MMOD 

risk prediction using ORDEM 3.0 if appropriate statistical analysis is used. The appropriate 

statistical analysis is Bayesian analysis and is standardly used in NASA probabilistic risk and 

reliability predictions. NASA guideline documents give methods and tools for carrying out this 

analysis. These methods involve probabilistically characterizing the uncertainty in the predicted 

risk so that observational data can be effectively used to update and revise the risk prediction. 

By using these methods relatively short observation time periods and presently available data can 

be used to update and revise the predicted risk using ORDEM 3.0. A risk prediction using 

ORDEM 3.0 has large uncertainty due to the lack of validation of the assumptions used. It is 

therefore important and necessary to probabilistically characterize the uncertainty to not only 

meaningfully bound the risk value, but to effectively use presently available observational data to 

update and revise the risk prediction. The other alternatives are to take in situ measurements 

which is direct but costly and longer term or to wait and observe for many years until there are 

numerous MMOD events on present spacecraft so that meaningful empirical statistical estimates 

can be obtained. There is a better way in the near term. 

References 

WV-1. Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis, 

NASA/SP-2009-569, June 2009. 

WV-2. NASA Risk-Informed Decision Making, NASA/SP-2010-576, April 2010. 

WV-3. NASA Risk Management Handbook, NASA/SP-2011-3422, November 2011. 

WV-4. Probabilistic Risk Assessment Procedures Guide for NASA Managers and 

Practitioners, NASA/SP-2011-3421, December 2011. 

WV-5. Bill Vesely, Updating Factors for the Model Predicted Occurrence Rate from 

Observational Experience, NESC team paper, August 2014. 
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Alternate Viewpoint #2 

Alternate Opinion Relative to Recommendation 2 by Scott Hull 

There is incomplete agreement regarding Recommendation 2 (R-2), which recommends that 

JPSS use ORDEM 3.0 as the orbital debris model for MMOD assessments, recognizing (and 

accepting) the large uncertainties associated with the unknown source of high density particles 

(assumed to be stainless steel). Because this source is neither identified nor characterized over 

time or altitude (see Findings F-1 and F-11), inclusion of the assumed effects of this high density 

particle population is premature for orbits higher than 600 km altitude. 

All physical data, which suggest the currently assumed surface degradation mechanism, have 

been collected from Space Shuttle missions between 400 km and 600 km and prior to 2012 (F-1). 

Slower decay of high density particles drives the model toward a higher flux of these particles at 

altitudes above those shuttle missions (see F-6), where no data exists. The ORDEM 3.0 model 

uses a default surface degradation assumption (see O-2) that generation of stainless steel 

particles will continue, and even increase, into the foreseeable future as additional launch 

vehicles accumulate (O-3). Three other potential stainless steel particle sources identified in 

F-11, though, would each result in different long-term evolution characteristics, potentially 

yielding far fewer stainless steel particles over time, especially at altitudes higher than 600 km. 

All of this results in uncertainty potentially as much as an order of magnitude for these higher 

altitude orbits (F-1). 

If the effect of this high density population were to only marginally increase the assessed risk, 

then in the interests of conservatism the responsible course of action would be to accept this 

assumed additional risk, and modify hardware designs accordingly. For the JPSS example, 

though, the overall risk using ORDEM 3.0 is approximately 30 times higher than with the 

previous model, mostly driven by the introduction of the high density population (F-7). In fact, 

this new high density population accounts for approximately 90 percent of the risk in most 

spacecraft regions examined in detail, effectively increasing the assessed risk by an order of 

magnitude. At this time, the evidence for the assumed source of the high density stainless steel 

population is insufficient to justify inclusion of a mechanism with such a strong effect on the 

assessed risk. 

ORDEM 3.0 includes several important advantages and advances over other orbital debris 

models, including previous versions of ORDEM (F-10). Since the high density population 

effects on the ORDEM 3.0 environment have greatest impact above the 400 km to 600 km 

region (F-5), it is recommended that ORDEM 3.0 be used as-is for missions with apogee below 

600 km. However, until additional in situ information can be collected to characterize the high 

density population at higher altitudes, missions with apogee above 600 km (such as JPSS-1) 

should omit the high density particle contribution when using ORDEM 3.0 as the orbital debris 

model for MMOD risk assessments. This can be accomplished easily using the existing Bumper 

III risk assessment tool. 
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Efforts should continue to identify and characterize the source of the stainless steel particles 

observed on returned surfaces (R-4). Such information will reduce the uncertainty associated 

with the ORDEM 3.0 model in the 600 km to 2000 km region, and allow future versions of 

ORDEM to be used with greater confidence for missions operating in this region. Until that 

time, such missions should use ORDEM 3.0 without the high density contribution as the orbital 

debris environment model. 

10.0 Other Deliverables

No unique hardware, software, or data packages, outside those contained in this report, were 

disseminated to other parties outside this assessment. 

11.0 Lessons Learned

No applicable lessons learned were identified for entry into the NASA Lessons Learned 

Information System (LLIS) as a result of this assessment. 

12.0 Recommendations for NASA Standards and Specifications

No recommendations for NASA standards and specifications were identified as a result of this 

assessment. 

13.0 Definition of Terms 

Corrective Actions Changes to design processes, work instructions, workmanship practices, 

training, inspections, tests, procedures, specifications, drawings, tools, 

equipment, facilities, resources, or material that result in preventing, 

minimizing, or limiting the potential for recurrence of a problem. 

Finding A relevant factual conclusion and/or issue that is within the assessment 

scope and that the team has rigorously based on data from their 

independent analyses, tests, inspections, and/or reviews of technical 

documentation.

Lessons Learned Knowledge, understanding, or conclusive insight gained by experience 

that may benefit other current or future NASA programs and projects. 

The experience may be positive, as in a successful test or mission, or 

negative, as in a mishap or failure. 

Observation A noteworthy fact, issue, and/or risk, which may not be directly within the 

assessment scope, but could generate a separate issue or concern if not 

addressed. Alternatively, an observation can be a positive 

acknowledgement of a Center/Program/Project/Organization’s operational 

structure, tools, and/or support provided. 

Problem The subject of the independent technical assessment. 
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Proximate Cause The event(s) that occurred, including any condition(s) that existed 

immediately before the undesired outcome, directly resulted in its 

occurrence and, if eliminated or modified, would have prevented the 

undesired outcome. 

Recommendation A proposed measurable stakeholder action directly supported by specific 

Finding(s) and/or Observation(s) that will correct or mitigate an identified 

issue or risk.

Root Cause One of multiple factors (events, conditions, or organizational factors) that 

contributed to or created the proximate cause and subsequent undesired 

outcome and, if eliminated or modified, would have prevented the 

undesired outcome. Typically, multiple root causes contribute to an 

undesired outcome. 

Supporting Narrative A paragraph, or section, in an NESC final report that provides the detailed 

explanation of a succinctly worded finding or observation. For example, 

the logical deduction that led to a finding or observation; descriptions of 

assumptions, exceptions, clarifications, and boundary conditions. Avoid 

squeezing all of this information into a finding or observation 

14.0 Acronyms List

ADEPT Aerospace Debris Environment Projection Tool 

BLC Ballistic Limit Curve 

BLE Ballistic Limit Equation 

CAD Computer Aided Design 

CAS Credibility Assessment Scale 

CORDS Center for Orbital and Reentry Debris Studies 

CTU Command and Telemetry Unit 

CV Coefficient of Variation 

DAS Debris Assessment Software 

DoD Department of Defense 

DRAGONS Debris Resistive/Acoustic Grid Orbital Navy-NASA Sensor 

EOS Equation of State 

ESA European Space Agency 

EuReCa European Retrievable Carrier 

FEM Finite Element Model 

FY-1C Fengyun-1C 

GSFC Goddard Space Flight Center 

HAX Haystack Auxiliary Radar 

HD High Density 

HST Hubble Space Telescope 

HVIT Hypervelocity Impact Technology 
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IADC Inter-Agency Debris Coordination Committee 

IMEM Interplanetary Meteoroid Engineering Model 

ISS International Space Station 

JPSS Joint Polar Satellite System 

JSC Lyndon B. Johnson Space Center 

LaRC Langley Research Center 

LD Low Density 

LDEF Long Duration Exposure Facility 

LEGEND LEO-to-GEO Environment Debris Model 

LEO Low Earth Orbit 

LVLH Local Vertical Local Horizontal 

MASTER Meteoroid and Space Debris Terrestrial Environment Reference 

MD Medium Density 

MEM Meteoroid Engineering Model 

MEMR2 Meteoroid Engineering Model Release 2 

MEO Meteoroid Environment Office 

MLI Multi-layer Insulation 

MMOD Micrometeoroid and Orbital Debris 

MSFC George C. Marshall Space Flight Center 

NaK Sodium-potassium 

NESC NASA Engineering and Safety Center 

LaRC Langley Research Center 

NNO New Non-optimum [BLE] 

NOAA National Oceanographic and Atmospheric Administration 

ODAR Orbital Debris Assessment Report 

ODPO Orbital Debris Program Office 

OM Orbital Module 

ORDEM Orbital Debris Engineering Model 

OSMA Office of Safety and Mission Assurance 

PCDU Power Control and Distributor Unit 

RBD Reliability Block Diagram 

RCS Radar Cross-Section 

RORSAT Radar Ocean Reconnaissance Satellite 

SBM Standard Breakup Model 

SCP Spacecraft Control Processor 

SEI Standard Environment Interface 

S-NPP Suomi National Polar-orbiting Partnership 

SPENVIS ESA Space Environment Information System 

SPH Smooth Particle Hydrodynamic 

SPHC SPH in C 

SRM Solid Rocket Booster 

SSN Space Surveillance Network 
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SSP Space Station Program 

STK Systems Tool Kit 

15.0 References

1. E.G. Stansbery, C.C. Pitts, G. Bohannon, et al., Size and Orbit Analysis of Orbital Debris 

Data Collected Using the Haystack Radar, NASA JSC-25245, NASA Johnson Space 

Center, Houston, Texas, 1991. 

2. R.P. Bernhard and M.E. Zolensky, NASA TM-104784, Analysis of Impactor Residues in 

Tray Clamps from the Long Duration Exposure Facility, Part 1: Clamps from Bay B, 

1994.

3. Orbital Debris Program Office presentation, ORDEM 3, part of a review package, Aug, 

2012.

4. Orbital Debris Program Office Excel data file, ShuttleImpaceDB_June14.xlsb. 

5. Briefing by the ODPO at JSC, July 9-10, 2014. 

6. JPSS – PID List – 8May2014_withComments.xlsx, spreadsheet supplied by Eric 

Christiansen via email, June 25, 2014. 

7. Space Environment Information System (SPENVIS), European Space Agency, website 

operated by Belgian Institute for Space Aeronomy, 

https://www.spenvis.oma.be/help/background/metdeb/metdeb.html#MWBL 

8. JPSS BLEs_BumperSubroutines.docx, word document supplied by Eric Christiansen via 

email, July 2, 2014. 

9. Ryan, S., Christiansen, E.L., “Micrometeoroid and Orbital Debris (MMOD) Shield 

Ballistic Limit Analysis Program,” NASA TM-2009-214789, February 2010. 

10. Jenkin, A.B., Sorge, M.E., McVey, J.P., Peterson, G.E., Yoo, B.B., “MEO Debris 

Environment Projection Study,” Proceedings of the Sixth European Conference on Space 

Debris, Darmstadt, Germany, April 22-25, 2013 (ESA SP-723, August 2013). 

11. Jer-Chyi Liou, Mark J. Matney, Phillip D. Anz-Meador, Donald Kessler, Mark Jansen, 

Jeffery R. Theall, NASA/TP-210780, The New NASA Orbital Debris Engineering 

Modekl ORDEM2000, May 2002. 

12. Alan B. Jenkin, Possible Sources of Stainless Steel Orbital Debris, Aerospace NESC 

JPSS MMOD Review, July 9, 2014. 

13. Glenn E. Peterson, Consideration of Rocket Body Events as a Possible Source of High 

Density Particles in ORDEM 3.0, Released to NESC study group only, August 18, 2014. 

14. Standard for Models and Simulations, NASA-STD-7009, July 11, 2008. 
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15. Squire, M. D, et al.: Independent Review of Constellation (Cx) Orion Vehicle 

Micrometeoroids and Orbital Debris (MMOD) Risk Analysis. NASA/TM-2009-215912, 

August 2009. 

16. Christiansen, E.L., 1993. "Design and Performance Equations for Advanced Meteoroid 

and Debris Shields," Int. J. Impact Eng. Vol. 14, pp 145-156, 1993.

17. Bohl, W, et al, 2010. Orion Spacecraft MMOD Protection Design and Assessment,” 

Hypervelocity Impact Symposium Paper, Freiburg, Germany, April 11-15, 2010. 

18. Kalinski, M. (2004): “Hypervelocity Impact Analysis of International Space Station 

Whipple and Enhanced Stuffed Whipple Shields.” Master’s Thesis, Naval Post Graduate 

School, California. 

19. Squire, M. D, et al.: Micrometeoroid and Orbital Debris (MMOD) Design and Analysis 

Improvements. NASA/TM−2014-218268/Volume I, May 2014. 

20. Bjorkman, M.: Ballistic Limit Equation Updates for BUMPER Analyses Using the 

ORDEM 3.0 Orbital Debris Environment, JSC-66619, September 2013. 

21. Elfer, N.C., “Structural Damage Prediction and Analysis for Hypervelocity Impacts –

Handbook,” NASA Contractor Report 4706, February, 1996.

22. Reimerdes, H.G., Nölke, D., Schäfer, F., “Modified Cour-Palais/Christiansen Damage 

Equations for Double-Wall Structures,” International Journal of Impact Engineering 33 

(2006) 645-654.

23. Christiansen, E. L.: Handbook for Designing MMOD Protection. NASA/TM–2009–

214785, June 2009. 

24. Ryan, S.; and Christiansen, E. L.: Micrometeoroid and Orbital Debris (MMOD) Shield 

Ballistic Limit Analysis Program. NASA/TM2009-214789, February 2010.

25. Frost, V.C., “Meteoroid Damage Assessment,” NASA SP-8042, 1970.

26. bleReimerdesNNOwithMLI – Copy_JSC_HVIT.docx, Word document supplied by Eric 

Christiansen via email, October 6, 2014. 

27. NASA Engineering and Safety Panel, 2008. “James Webb Space Telescope (JWST) 

Integrated Science Module (ISIM) Electrical Cable Protection,” NASA RP-08-59, June 8, 

2008.

28. NASA Presentation, 2008, “AXAF Harness Meteoroid Protection,” January 4, 2008.

29. Matney, et al, 2014. “Orbital Debris Environment Model, ORDEM 3.0,” NASA John 

Space Center Presentation to NESC, March 2014. 

30. Williamsen, J. et al, 2007. “A Comparison of NASA, DoD, and Hydrocode Ballistic 

Limit Predictions for Spherical and Non-spherical Shapes versus Single and Dual Wall 

Targets, and Their Effect on Orbital Debris Penetration Risk”, Hypervelocity Impact 

Symposium Paper, Williamsburg, VA, September 23, 2007. 
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31. Williamsen, J. and Schonberg, W. “Effect of Orbital Debris Mass and Shape Variations 

on Crew Escape Vehicle Risk,” Presentation to NESC, 7 November 2008.

32. Cour-Palais, B.: “Hypervelocity Impact Investigations and Meteoroid Shielding 

Experience Related to Apollo and Skylab.” NASA Conference Publication 2360 (1982) 

page 247. 

33. Cour-Palais, B.: “Meteoroid Protection by Multiwall Structures.” AIAA Paper AIAA-69-

372. Presented at the Hypervelocity Impact Conference, Cincinnati, Ohio, April 30-May 

2, 1969.

16.0 Appendices

Appendix A. Evidence Network Evaluation 

Appendix B. Examination of Possible Sources of High-Density 1-mm Particles 

Appendix C. Delta and Atlas Centaur Tanks as Possible Sources of Stainless Steel Orbital 

Debris

Appendix D. CTU Risk Assessment 

Appendix E. JPSS-1 Zenith Deck Orbital Debris Wire Harness Failure Assessment 
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Appendix A. Evidence Network Evaluation

An evidence network is a tool used by a wide variety of organizations to perform assurance 

assessments. In an evidence network, also referred to as a credibility assessment, several 

evidence sources can be identified and then integrated to give an overall level of assurance. 

Each individual source can be graded according to its identified level of importance, and even a 

lack of information can be addressed by assigning different support levels to different scenarios. 

The support levels are combined to give an overall assurance level with an associated 

uncertainty. Evidence networks are flexible. The number of levels of support can be as large 

and detailed as required, and levels of support may be expressed in quantitative or qualitative 

terms. Several software applications are available that produce evidence network evaluations. 

The charts in this appendix present an example of how an evidence network can be used to 

evaluate the basis for the 1-mm range stainless steel debris model in ORDEM 3. The example 

shown uses the AgenaRisk tool.
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Appendix B. Examination of Possible Sources of High-Density 1-mm 

Particles 

Introduction 

ORDEM 3.0 is showing a large flux of high-density particles (i.e., steel) at 1 mm size. There are 

at least 3 possible physical sources of these 1-mm particles: 1) Surface degradation. This is 

assumed as the current model in ORDEM 3.0, and is likely a conservative assessment 

(i.e., maximum) given that it assumes all intact objects continually produce particles at a rate 

dependent on the on-orbit surface area; 2) Explosions. This is a temporally erratic source in the 

sense that historical explosions do not occur at a uniform rate; 3) Liquid engine firings. Given 

conversations with propulsion experts, this is not likely a significant source, but these might also 

produce small stainless steel particles per discussion with ULA regarding their tests of a new RCS 

thruster. Also, there is little knowledge regarding what foreign rocket engines would produce onorbit. Residue from firings is likely conservative in the other direction (i.e., minimum) since, once 

an upper stage has deposited the payload, it will no longer fire and therefore will not contribute 

any further to the debris environment. In reality, the production of the high-density small particles 

is likely a combination of the three effects and possibly others. 

Figure 1 shows the ORDEM 3.0 1-mm flux profile in the year 2010 as a function of altitude broken 

down into the high density (steel) and medium density (aluminum) components. High-density 

particles dominate in the 900-1000 km altitude range but are fairly comparable to the aluminum 

particles elsewhere. As these particles decay, they cross the higher altitude orbit of satellites such 

as JPSS and contribute extensively to the survivability risk results. Particles originating lower than 

~850 km altitude do not last long enough on orbit to cause long-term issues for orbiting satellites 

at JPSS altitude. 
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Figure 1: ORDEM 3.0 1-mm Flux as a Function of Altitude for Year 2010 by Source 

Figure 2 shows the 1-mm ORDEM 3.0 flux at 950 km altitude as a function of time with the 

medium and high-density components called out explicitly. The high-density flux continues to 

grow with time over the ORDEM 3.0 model time span (2010-2035). This is consistent with a 

surface degradation model that is based on all intact objects emitting high-density particles. As 

more objects are launched, the overall surface area grows, and the more particles are released. The 

medium density flux does not grow with time due to decay counteracting the increased surface 

area, and actually shows a slight decrease during the next anticipated solar cycle maximum. In 

2035, the high-density flux at 950 km averages about 41% higher than the value in 2011 (end of 

STS in situ data). 
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Figure 2: ORDEM 3.0 1-mm Flux at 950 km Altitude as a Function of Time 

Surface Degradation 

The source of the steel particles is unknown but is currently modeled to be coming from the 

degradation of exposed surface area of all intact orbiting objects, which implies that the small 

particles are starting their life in near-circular, high inclination orbits (commensurate with the onorbit distribution of intact objects). This is a reasonable first estimate. However, there is little 

exposed steel in satellites and most rocket bodies use aluminum tanks. Atlas/Centaurs did (and 

still do) use stainless steel tanks, but Centaurs are mostly on high eccentricity, low inclination 

orbits (Figure 3). Older Delta 1 and Delta 2s also had steel tanks on their upper stages that were 

very similar to each other but, taken in totality, were split between different types of orbits: high 

eccentricity/low inclination, low eccentricity/low inclination, and low eccentricity/high inclination 

orbits. Given the material properties of the two types of tanks (Centaurs: ~355-640 kg in steel 

tanks depending on the configuration with 0.25-0.5 mm thickness1; Delta P/TR-201: ~230-250 kg 

of steel in main propellant tank, 1-2 mm thickness), then approximately 62% of the steel mass and 

68% of the surface area from the Centaurs and Deltas is in high eccentricity/low inclination orbits 

and only 17% of the steel mass and 13% of the surface area is in low eccentricity/high inclination 

orbits. If the steel particles are coming from surface degradation off the Centaurs and Deltas, then 

the majority of these particles should be in high eccentricity/low inclination orbits. 
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Figure 3: Centaur and Delta Upper Stage Orbit Element Distribution 

Figure 4 shows the approximate rocket body mass of the various types of major upper stages 

beyond the Centaurs and Deltas. Information on the actual material composition of many foreign 

upper stages is difficult to come by. Therefore, the types of rocket bodies have been grouped by 

whether their tanks are known to be aluminum, are unknown (but likely aluminum), and known 

stainless steel. Three columns of data are shown for each upper stage: total mass launched, the 

current mass that is on-orbit, and the normalized on-orbit mass that passes through LEO 

(i.e., rocket bodies in GEO, like the Block DM-SL or the SL-12, would not show in this column). 

The largest contributor to the upper stage mass that is currently both on-orbit and crossing LEO 

comes from the SL-8 system. However, it is known that the SL-8s used aluminum tanks, not 

stainless steel. The known stainless steel tanks (Centaur and Delta) are a small part of the overall 

rocket body mass. This would indicate that, unless the unknowns are mostly steel, then aluminum 

particles should be produced at a much greater rate than stainless steel particles if surface 

degradation is the source mechanism. 
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Figure 4: Approximate Mass of Rocket Bodies by Type and Likely Tank Material 

Figure 5 shows estimates of the mass and surface area present in the currently orbiting rocket 

bodies as a function of altitude. The only stainless steel tanks known are those of the Centaur and 

Delta 1 & 2 upper stages, which do not reside in the 950 km regime therefore they cannot be the 

dominant source for the large reservoir seen in Figure A.1. There is little available information on 

engine material composition, but based on older RL-10 data, approximately 6-15% of empty stage 

weight would be high-density material if the tanks were not stainless steel (mostly in the thrust 

chamber). There is a large spike at ~950 km altitude corresponding to the peak observed in the 

ORDEM 3.0 flux plot. There are smaller peaks at lower Sun-synchronous altitude (many types of 

upper stages) and at ~775 km (mostly SL-8) and ~625 km (mostly SL-14). 

Figure 5 also shows estimates of the mass and surface area present in the currently orbiting 

satellites as a function of altitude. The plots for the satellites are on the same scale as those for the 

rocket bodies to facilitate comparison. There is little available information on how much high 

density material is in satellites, but it is thought to be minimal (1-5% by mass) due to the 

dominance of lighter weight aluminum. As with the rocket bodies, there is a large spike at ~950 

km altitude corresponding to the peak observed in the ORDEM 3.0 flux plot. There are also 

smaller peaks at lower Sun-synchronous altitude (many types of satellites), at ~775 km (Iridium) 

and at ~500-600 km (Soviet/Russian Tselina-D). The spike at ~900-1000 km is comparable in 

total mass, but smaller in surface area, for the satellites than the rocket bodies. 
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Figure 5: Mass and Surface Area of On-orbit Satellites and Rocket Bodies 

The public catalog was then examined to determine what objects lie near 950 km, subject to the 

following conditions: semi-major axis altitude had to be between 900-1000 km (i.e., near-circular 

at the right mean altitude), the perigee altitude had to be above 800 km (or any small particles 

given off with zero relative velocity would decay too rapidly), and only intact satellites and rocket 

bodies were included (i.e., fragmentation debris was excluded). This left 421 objects (255 

satellites, 182 rocket bodies) remaining. Figure 6 below shows the breakdown. The Kaur-1 bus 

(Soviet) dominates the satellites (145 out of 255), while the SL-8 upper stages (also Soviet) 

dominate the rocket bodies (165 out of 182). 
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Figure 6: Number of Intact objects near 950 km Altitude by Type 

Figure 7 shows the mass and exposed surface area for the examined objects. The SL-8 rocket 

bodies dominate both the mass and surface area in the reservoir region (47% of the total mass 

(91% of the rocket body mass) and 56% of the total surface area (87% of the rocket body surface 

area)). The Kaur-1 bus satellites were used widely on multiple constellations; the Parus, 

Nadezhda, Tsiklon, Tsikada, and Sfera satellites are all at ~900-1000 km. Other Kaur-1 bus 

satellites are the GEO-IK at 1500 km and Strela-2 at 785 km. The Kaur-1 bus is a cylindrically 

shaped satellite with solar cells attached to the body (no panels, except for GEO-IK); thermostatic 

temperature regulators are also on the surface. It is about 2 meters in length with a diameter of 1 

meter and a 600-800 kg mass depending upon configuration and specific mission. By percent, the 

Kaur-1 bus dominates the satellite mass and surface area (27% of the total mass (58% of the 

satellite mass) and 25% of the total surface area (65% of the satellite surface area)). (As a side 

note: the RORSAT satellites are the source of the NaK population due to nuclear core ejections. 

A casing of stainless steel surrounded the pre-ejected core consisting of ~38 kg of steel per satellite. 

So, some small amount of steel could have been released by the RORSATs, but not nearly enough 

to account for the stainless steel particles predicted by ORDEM 3.0.). If the reservoir at 950 km 

exists and the SL-8s have aluminum tanks, then the Kaur-1 bus should be investigated as the most 

likely possible physical source of steel. 
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Figure 7: Mass and Surface Area of Intact Objects near 950 km Altitude 

Figure 8 shows the number of SL-8/Kaur-1 launches to the ~950 km altitude as a function of time. 

The last SL-8 launch was in 2010 (to any altitude, not just ~950 km). Long-term models like 

ORDEM 3.0 and ADEPT assume a future launch model that mimics recent historical activity 

(2000-2009 time frame). However, if there have been no SL-8 launches to the 950 km regime 

since 2010, then assuming such activity in the future launch model is not appropriate, and the 

increase in the 950 km reservoir due to the existing surface degradation model should actually be 

a constant. It should be noted that the Chinese are placing satellite(s) at ~1100 km with the rocket 

body from the launch being left in an elliptical orbit crossing 950 km (about 1 per year since 2010). 

Figure 8: SL-8 Launches to 950 km Altitude 
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On-orbit Explosions 

Another possible source of the 1-mm particles is on-orbit explosions of upper stages. While only 

some upper stages have steel propellant tanks, all have steel parts in their engines (mostly in the 

thrust chamber) so that the rocket engines may be composed of one-half high-density material by 

mass. While the thrust chambers are protected from surface degradation, explosions could release 

that material. The Atlas/Centaur upper stages have not had any known explosive debris producing 

events, but the older Deltas have experienced at least eight such events (7 in 1973-1981, 1 in 1991). 

A mass balancing is here performed to determine whether the upper stage explosions could have 

produced enough 1-mm particles to account for the observed in situ STS flux. 

The mass balancing was performed in two parts. First, determine the high-density mass that is 

predicted to be on-orbit at the start of the JPSS mission (roughly the current status) based on the 

ORDEM 3.0 model. Then an estimate is made on how much high density mass has re-entered 

based on the ORDEM 3.0 model. These two mass values (currently on-orbit + re-entered) together 

will yield the total amount of high-density small-particle mass that would have been produced if 

the ORDEM 3.0 predictions were correct. 

Then the problem is approached from the other direction. An estimate is made of the amount of 

high–density mass that would be available from the upper stage events. This estimate will be a 

conservative upper bound since it only looks at the mass capability of the upper stages and does 

not include time or only partial disintegration, but it does provide for a check on whether the events 

could possibly have contributed to the high-density environment. Finally, the temporal variation 

(decay) of the upper stage event particles is examined to determine if they could have been present 

at STS/ISS altitude during the 1995-2011 timeframe of the returned in situ data. 

The computational process to determine the ORDEM predicted high-density mass consists of the 

following steps: 1) the input data (flux and median velocity) was determined from ORDEM 3.0 

runs at 25 km intervals from 400-1600 km for 2017; 2) the spatial object density was computed 

from the flux and velocity; 3) the volume for each 25 km shell was computed; 4) the number of 

particles present in each shell is computed through the spatial object density times the volume of 

the shell; 5) the mass of a 1-mm particle is determined; 6) the total mass of high-density particles 

in each shell is computed as the number times the mass; 7) the cumulative mass is the sum of the 

individual shell masses. Three particle shapes will be examined: spheres (representing steel that 

melted during an explosion and then re-formed), cubes (representing chunks from a deteriorating 

thin (~1 mm) propellant tank skin), and octahedrons (representing nuggets of material that did not 

melt). All of these shapes are Platonic solids, which has the benefit that their area-to-mass ratios, 

for the same characteristic length (Lc), will differ only by a constant. For each of the three shapes, 

the mass for an individual particle was determined using a material density value of 8000 kg/m^3. 

Figure 9 shows the amount of high-density mass as a function of altitude in 2017. The plot to the 

left shows the amount of mass in each of the 25 km shells (note the log scale for the y-axis). The 

amount of high-density mass at JPSS altitude ranges from 72 to 227 kg. The plot to the right shows 

the cumulative high density mass. Accumulation was performed from the bottom up (the values 

in the plot show the total mass at and below a given altitude). For example, the total mass of steel 
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1-mm particles at or below the JPSS altitude (824 km) ranges from 413 to 1297 kg. Approximately 

half of the mass is below 950 km, about half above. The total amount of high-density mass in 

LEO ranges from 1700 to 5200 kg. 

Figure 9: High-density Mass as a Function of Altitude Predicted from ORDEM 3.0 

To compute the amount of particles that decay in each year, the 1-mm high-density ORDEM 3.0 

flux is adjusted back to the time frame of the rocket body events (i.e., 1970, 1974, etc.). Then for 

each year, sample 1-mm particles are propagated to establish the altitude at which decay takes 

1 year. The flux at and below this altitude gets removed for that year, and finally the mass 

equivalent to the flux that decayed is computed. 

The ORDEM 3.0 model does not allow for the selection of years prior to 2010. To account for 

this, it is noted that the 1-mm high-density flux in the model assumes the small particles are 

produced as a function of on-orbit intact surface area. Therefore, when computing how much 

high-density mass has decayed since the upper stage explosions, the ORDEM 2010 flux was scaled 

by the historical time-varying surface area curve. For example, since the surface area in LEO in 

1995 is approximately one-half of the current surface area, then the 1-mm high-density flux in 

1995 is one-half of the current flux. 

In any given year, the effect of decay will depend upon the level of atmospheric activity. The 

atmospheric activity profiles used are the historical values from Marshall Space Flight Center. 

Roughly speaking and noting that there is a slight difference between the three particle shapes, 

then for an average atmosphere, the maximum STS altitude of 600 km is where a 1-mm particle 

will have a lifetime of 1 year. 

The resulting amount of 1-mm high-density mass that has decayed out of the environment will 

depend upon the level of atmospheric activity with annual values ranging from 14-256 kg per year 

(Figure 10). When summing over time, ~2200-4300 kg of high density mass should have reentered between 1970 and 2017. When adding what is currently on orbit to what has decayed, the 

amount of high-density mass that ORDEM 3.0 predicts to have been created since 1970 is 

~3900-9500 kg. 
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Figure 10: High-density Mass Loss due to Decay 

The other side of the mass balance equation consists of the amount of mass that could have been 

generated by upper stage explosive events. To determine the amount of high-density mass, the 

most dominant events (i.e., those events that produced the most cataloged debris) were examined2. 

These events were included on the assumption that the events that produced the greatest amount 

of large tracked debris are also those that produced the greatest amount of small, untracked debris. 

Table 1 shows the specific upper stage events that were examined along with an estimate of their 

possible high density mass. The largest contributors were the 8 Delta upper stages and the 2 SL16s. Using a mass estimate for the Centaur RL-10 engine3,4, it is estimated that approximately 1/2 

of the mass for a rocket engine is high-density material with most of that in the thrust chamber 

(~1/3 of the mass) and the rest in mountings, cables, etc. From Table 1, the total amount of 

available high density mass is ~4800 kg, which is within the range of mass predicted by ORDEM 

(3900-9500 kg). However, this estimate assumes all the steel was available to create 1-mm 

particles (i.e., complete disintegration to the 1-mm size). This is highly unlikely and not born from 

the presence of many objects that are of tracked size (>10 cm), but it does provide a conservative 

upper bound on how much these events could have affected the environment and is at the lower 

end of the predicted mass range (3900-9500 kg). 
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Table 1: Examined Upper Stage Explosive Events 

Upper stage satellite launch 

date

event 

date

num 

of 

objs

apo 

(km)

per 

(km)

inc 

(deg)

mass 

(kg)

engine 

type

engine 

mass 

(kg)

potential 

for high 

density 

mass 

(kg)

DELTA 1 Landsat 1 7/23/72 5/22/75 226 910 635 98.3 919 TR-201 113 306.5

DELTA 1 NOAA 3 11/673 12/28/73 197 1510 1500 102.1 919 TR-201 113 306.5

DELTA 1 Landsat 2 1/22/75 2/9/76 207 915 740 98.8 919 TR-201 113 306.5

DELTA 1 Nimbus 6 6/12/75 5/1/91 268 1105 1095 99.6 919 TR-201 113 306.5

DELTA 1 NOAA 5 7/29/76 12/24/77 162 1520 1505 102 919 TR-201 113 306.5

DELTA 1 Landsat 3 3/5/78 1/27/81 210 910 900 98.8 919 TR-201 113 306.5

DELTA 1 Himawari 7/14/77 7/14/77 172 2025 535 29 919 TR-201 113 306.5

DELTA 1 NOAA 4 11/15/74 8/20/75 146 1460 1445 101.7 919 TR-201 113 306.5

SL-16 Cosmos

2227

12/25/92 12/30/92 226 855 845 71 8300 RD-120 

/RD-8

1505 752.5

SL-16 Cosmos 

2237

3/26/93 3/28/93 84 850 840 71 8300 RD-120 

/RD-8

1505 752.5

SL-8 Cosmos 61 3/15/65 3/15/65 147 1825 260 56.1 1407 11D49 185 92.5

SL-14 Cosmos 

1703

11/22/85 5/4/06 50 640 610 82.5 1407 S5M/RD861123 61.5

SL-14 Meteor 2-

16

8/18/87 2/15/98 83 960 940 82.6 1407 S5M/RD861123 61.5

PSLV TES/Proba1/Bird-210/22/01 12/19/01 346 675 550 97.9 920 PSLV-4 150 75.0

CZ-4 Fengyun 

1B

9/3/90 10/4/90 84 895 880 98.9 1000 YF-40 120 60.0

CZ-4 CBERS1/SACI-110/14/99 3/11/00 345 745 725 98.5 1000 YF-40 120 60.0

ARIANE 1 Spot 1 / 

Viking

2/22/86 11/13/86 489 835 805 98.7 1457 HM7-A 149 74.5

PEGASUS STEP 2 5/19/94 6/3/96 713 820 585 82 176 Hercules 104 52.0

THORAD 

AGENA D

Nimubs 4 4/8/70 10/17/70 385 1085 1065 99.9 673 Bell 8096 132 66.0

THORAD 

AGENA D

OPS 7613 9/30/69 10/4/69 261 940 905 70 673 Bell 8096 132 66.0

THOR 

ABLESTAR

Transit 4a 6/29/61 6/29/61 296 995 880 66.8 450 AJ10-104 90 45.0

TITAN 3C 

TRANSTAGE

OV-2 10/15/65 10/15/65 473 520 210 48.4 1950 AJ10-138 

(x2)

110 110.0
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However, while the overall mass does balance to within acceptable limits, there is still an issue as 

to when the particles cross STS altitude and so could have impacted the STS windows and 

radiators. Many of the events listed in Table 1 occurred years before the beginning of the 1995-

2011 in situ data; if the particles decayed before 1995, then they could not have been detected 

through STS impacts. To determine the amount of high-density mass that could have been present 

during the time and at the altitude of the STS in situ data, propagations were performed using a 

sample data set to represent the 1-mm particles that would have been created by each explosion. 

This starting population consisted of taking the tracked objects from the catalog for each event and 

assigning an area-to-mass ratio consistent with a 1-mm particle. Only cataloged objects that 

appeared within 1 year of each event were included to avoid objects that had already gone through 

significant decay. This population is hereafter called the “sample population.” The goal then was 

to propagate the sample population forward using the historical solar cycle activity and determine 

how many of these objects were still in orbit in 1995 and in 2011 (start and end of STS data). 

Figure 11 gives a graphical description of the computational process. 

Figure 11: Graphical Description of Computational Process 

The assumption here is that the cataloged object population is representative of the orbit element 

distribution of the 1-mm high-density particles. However, given the limited number of tracked 

objects from each of these events, the extreme tails of the 1-mm distribution would not be 

represented. To account for this, when summing to determine the high-density mass at STS 

altitude, each particle in a given event’s sample population was weighted so that the sum of the 
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sample population’s mass would be equivalent to the potential high-density mass for that event 

(Table 1). Examining the sample population during 1995 and 2011 will give an approximate idea 

as to how much of the event-created 1-mm population was still in orbit at those times. 

Figure 12 shows the initial conditions of each of the Delta events used in the propagation. The 

plot on the left shows the number of cataloged objects available for analysis with a total sample 

population of 1289 objects. The plot on the right shows the spread in the perigee altitude for each 

event of the cataloged objects. It should be noted that six of the events had some objects with 

initial perigees in the STS range. These 1289 objects were given 1-mm area-to-mass ratios and 

propagated forward. 

Figure 12: Initial Conditions of the Delta Sample Data Sets 

Figure 13 shows the perigee histogram of the sample Delta particle population that was still onorbit in 1995 and 2011 (dotted line shows the original spread). By 1995, that portion of the original 

population residing at lower altitudes was largely gone (largely the Landsat and Himawari Deltas).

At higher altitudes (Nimbus and NOAA Deltas), little decay had occurred. By 1995, most of the 

Delta particles that were going to decay have, and those that were too high to experience decay by 

1995 still had not decayed by 2011. Between 1995 and 2011, 54 sample particles passed through 

STS altitude out of the original 1289 (or only about 4%). The conclusion here is that the Delta 

events are unlikely to have contributed many steel particles to the observed STS flux during 1995-

2011.
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Figure 13: Perigee Altitude Histogram of Delta Sample Particles 

Figure 14 shows the ratio of the high-density fluxes in the STS altitude (400-600 km) range for 

both all of the debris-producing events along with the few that contributed the most (Deltas, SL16, and SL-14). The divisor of the ratio is the temporally adjusted high-density flux from 

ORDEM 3.0. The high-density small particle mass from the Deltas is largely gone by 1995, but 

the mass from the two SL-16 breakups would have been present at STS altitude from 1993-2002. 

During the years 2006-2010, the SL-14 breakup at ~635 km dominated the system. After ~2012, 

the sample particles are largely gone from the environment. The values here imply that while the 

Delta explosive events cannot account for the ORDEM 3.0 modeled flux, the SL-16 and SL-14 

events could have contributed a noticeable fraction of the flux to at most a couple tens of percent 

(again, this assumes complete disintegration). 
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Figure 14: Ratio of High-density Fluxes in the 400-600 km Altitude Range 

A comparison was then performed between the ODPO spreadsheet and the predicted high-density 

mass at STS altitude from the preceding analysis. The spreadsheet’s raw steel impact counts were 

normalized by the number of days the STS was on-orbit for that year (yielding impacts per day). 

The results are shown in Figure 15. From 1990-1995, all the curves are low; from 95-02, all are 

high, from 05-11, all are middling. While the shapes of the curves match fairly well, the absolute 

values of the fluxes do not (Figure 14). 

Figure 15: Results of Comparison with ODPO-provided Spreadsheet of STS Impacts 
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Liquid Rocket Engine Firings in the 950 km Reservoir 

A third possible source of small steel particles is residue from liquid engine firings. This source 

is not likely, but given a lack of knowledge of how foreign engines work (i.e., the SL-8s at 950 

km), comments by ULA that a new RCS engine may be emitting small steel particles during test 

firings, and solid rocket motors giving off aluminum slag and dust, the possibility should be 

considered. 

If the source of the particles were liquid rocket engine firings, then once the upper stage has 

deposited its payload there would be no further particle production. Since there have been no 

launches to that regime since 2010 (Figure 8), the ~950 km reservoir should actually be decreasing 

as the particles decay. This in turn, would cause a general decrease in the amount of flux that 

spacecraft at lower altitudes would face in the future. To investigate the effect this would have on 

the reservoir, then for every SL-8 launch, a 1-mm particle was released with zero relative velocity 

and then followed through time as the particle decays. The issue then is: how long does it take for 

the particles to decay past either the 900 km point (out of the reservoir) or past JPSS altitude? The 

thought being that if the firings are the source of the high-density particles in the reservoir, then 

once they have decayed past 900 km, they are no longer part of the reservoir, and when they decay 

past 824 km, they are no longer a threat to satellites like JPSS. The base data set consisted of 

single 1-mm particles released from the actual post-mission parent SL-8 upper stage orbit right 

after launch. Two sets of area-to-mass ratios were examined: spheres and cubes. Since there were 

155 SL-8 launches to ~950 km altitude, 155 sample particles were present in each of the two data 

sets. 

Figure 16 shows the results of the propagation. There are three curves on each of the plots below. 

The first is the number of sample particles that are on-orbit in any given year, the second is the 

number of sample particles that are both on-orbit, AND have apogees higher than 800 km 

(potentially affecting a satellite at JPSS altitude), and the third is the number of sample particles 

that are both on-orbit, AND have apogees higher than 900 km (still in the reservoir). During the 

2017-2024 time frame, only about 12.5% of the cubic particles and 34% of the spherical particles 

are still in the reservoir. Compare this to the ~33% increase in the flux as seen in Figure 2 averaged 

over that same time span. 
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Figure 16: Projected Presence of Small Particles from Rocket Engine Firings 

If the source of the high density particles at ~950 km are the SL-8/Kaur-1 objects, then that source 

is no longer being added to through new launches since the last SL-8 launch was in 2010. If 

production is due to SL-8/Kaur-1 surface degradation, then the new particles should be added at a 

constant rate since the SL-8/Kaur-1 surface area is constant. If production was due to SL-8 rocket 

engine firings, then no new particles are being added to the environment and once the particles 

decay past a given satellite’s altitude, they are no longer a threat.

As a consequence, the ORDEM 3.0 future predictions of the 1-mm high-density flux at the 

reservoir altitude may be an over-prediction during the JPSS 2017-2024 time frame as compared 

to the 2011 reservoir flux. The current surface degradation model shows an increase in the highdensity flux when going into the future (~33%), but no new launches to ~950 km implies a 

reservoir flux equal to the 2011 value. Rocket engine firings (spherical) imply that ~34% of the 

particles remain during the JPSS mission while rocket engine firings (cubic) imply that only 

~12.5% of the particles remain. Correctly identifying the source of the high-density particles is 

crucial and viable source models could change future predictions by a large amount. 

Conclusions 

The total amount of >1-mm high-density mass predicted by ORDEM 3.0 to be on-orbit in LEO in 

2017 ranges from 1700 to 5200 kg. Decay predictions imply that an additional ~2200-4300 kg of 

high density mass re-entered between 1970 and 2017. This gives a total amount of created highdensity mass of 3900-9500 kg. 

The total amount of high-density mass available from upper stage explosive events is ~4800 kg if 

complete disintegration to particles of 1-mm size occurred. Since it is known from the direct 

observation of tracked objects that complete disintegration did not occur, it is highly unlikely that 

upper stage explosions can account for all of the predicted steel particles. However, it is also 

possible that the events can account for a noticeable fraction of the steel. The shapes of the 

impacts/day curve from the STS data (sparse as the data may be) and the STS-altitude HD eventdriven mass correspond fairly well. 
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SL-8/Kaur-1s are the most likely source of the reservoir of particles at 950 km, but there have been 

no SL-8 launches since 2010, and furthermore there have been no launches at all to ~950 km since 

the last SL-8 launch. Therefore, the 950 km reservoir should be either constant (no-new-SL-8 

surface degradation model) or decreasing (engine firings) rather than increasing (current surface 

degradation model). 

References 

1Reusable Centaur Study, General Dynamics, 1974. 

2History of On-orbit Satellite Fragmentations, 14th ed., NASA ODPO, June, 2008 

3RL-10 Liquid Rocket Installation Handbook, Pratt & Whitney, 1966 

4Design Report for RL10A-3-3 Rocket Engine, PWA FR-1769, Pratt & Whitney, 1966 
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Appendix C. Delta and Atlas Centaur Tanks as Possible Sources of 

Stainless Steel Orbital Debris 

Introduction 

Chemical analysis of impact craters on space shuttle windows and perforations on radiator 

surfaces indicate that some impacting particles contain stainless steel (ref. 1). Impacts by these 

particles as large as several millimeters were found. The source of these particles has not yet 

been determined. 

A query about which historically launched upper stages and satellites contain stainless steel was 

submitted to Dr. Mike Weaver of the Fluid Mechanics Department at The Aerospace 

Corporation (Aerospace). Dr. Weaver has extensive experience performing atmospheric reentry 

survivability analyses for a wide range of satellites and upper stages. These analyses require 

detailed information on satellite or upper stage components, including material composition. Dr. 

Weaver provided the following list of objects that are definitively known to be made of stainless 

steel: Delta I and II second stage propellant tanks; and Centaur stage propellant tanks. 

The objective of this analysis is to briefly investigate the possibility that the Delta I and II second 

stage propellant tanks and Centaur propellant tanks could be sources of the stainless steel debris 

that was observed on space shuttle surfaces. In particular, the number of meteoroid impacts that 

may have created stainless steel ejecta was estimated. 

Catalog Information 

The unclassified USSTRATCOM catalog of resident space objects (SATCAT) dated June 24, 

2014, was examined for information on Delta I and II second stages and Centaur stages. Catalog 

data showed that as many as 157 Delta I second stages had been flown from 1960 to 1990. As 

many as 139 Delta II second stages had been flown from 1989 to 2011. As many as 138 Centaur 

upper stages had been flown on a variety of mostly Atlas and some Titan missions since 1963. 

Centaur upper stages continue to be flown on Atlas V missions. 

A series of Delta I upper stages exploded through 1984. A total of 1882 debris objects had been 

correlated to Delta I upper stages as of June 24, 2014 (excluding yo-yo weights used for 

despinning). Of these, 1013 were still on orbit. Only 64 debris objects had been correlated to 

Delta II second stages, of which 28 were still on orbit. Only 22 debris objects had been 

correlated to Centaur upper stages, of which eight were still on orbit. 

Mechanisms for Generating Stainless Steel Debris 

Two possible mechanisms for release of stainless steel debris from intact propellant tanks were 

briefly considered. 

 Generation during upper stage breakups. 

 Gradual release of particles from intact tanks due to surface degradation. 
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Peterson modeled the orbital decay of hypothetical millimeter-sized stainless steel objects from 

the orbits of cataloged fragments from the Delta I second stages that exploded. Results showed 

that the majority of such particles would have either reentered before most of the impacts on the 

shuttle surfaces occurred or not have decayed into the shuttle altitude range by then (see 

appendix by Peterson). 

There are several possible causes of surface degradation. Atomic oxygen and ultra violet 

radiation are known to cause deterioration of paint, plastics, etc. The likelihood that they could 

cause stainless steel particles to be released was not considered here. Thermal cycling is also 

known to cause material cracking, but again the likelihood that this could result in the release of 

stainless steel particles from stainless steel surfaces was not considered here. 

Impacts by orbital debris and meteoroids can produce ejecta. The generation of ejecta due to 

impacts by meteoroids is considered in this analysis. Ejecta from impacts by other orbital debris 

particles is plausible but not considered in this analysis due to limited scope of the study. 

Meteoroid Impact Analysis

The objective of the analysis was to determine the total number of meteoroid impacts on Delta I, 

II, and Centaur stainless steel tanks over time. Such impacts may have produced stainless steel 

ejecta that were observed by the shuttle and that may pose a risk to JPSS. To accomplish this 

analysis, an estimate of the total on-orbit tank area vs. time was generated. The area at each time 

was then multiplied by the meteoroid flux at several size levels. The resulting rate of impacts 

was then integrated over time. 

The determination of total on-orbit tank area was performed as follows. In an analysis 

performed in late 2011 by Bernard Yoo of the Astrodynamics Department at Aerospace, orbital 

element time histories for intact objects (satellites and rocket bodies) in the SATCAT from end 

of 2010 were created from time series of two-line element sets obtained from SpaceTrack. org. 

In the current analysis, the on-orbit object counting tool in the ADEPT suite (Ref. 2) was run on 

these orbital element histories for the Delta I second stages, Delta II second stages, and Centaur 

stages to determine the number, total mass, and total area of known stainless steel tanks on orbit 

versus time from start of the Space Age until end of 2010. This tool determines the effective 

number of objects at each time point within a specified altitude range using an equation derived 

by Don Kessler (Ref. 3). Three altitude ranges were considered: 

 200 km to 1000 km: This range contains some atmosphere, and particles may decay into 

the shuttle altitude range. 

 800 km to 1000 km: This range more tightly fits the JPSS orbit. There is effectively no 

atmosphere above 1000 km, so particles are unlikely to decay down to JPSS orbit. 

Particles may eventually cross the JPSS orbit due to eccentricity growth from solar 
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radiation pressure, but the time spent at JPSS orbit will be diluted due to associated 

increase in apogee. 

 200 km to 6000 km: More comprehensive, but some objects will be retained that are far 

from the shuttle altitude range and JPSS orbit. 

The following stainless steel tank mass and surface area estimates were used for the analysis. 

 Delta I 2nd stage: mass = 229 kg, area = 28.87 m2

. Estimated from data on Delta 1000s 

at www.astronautix.com; reliability of data unknown. Tank mass prorated from stage 

mass using Delta II tank to stage mass ratio. 

 Delta II 2nd stage: mass = 266.7 kg, area = 14.8 m2. Determined from data provided by 

Mike Weaver. 

 Centaur stage: mass from contractor data used, area = 136 m2. Area determined from 

data supplied by Mike Weaver for previous upper stage lifetime analyses. This is very 

approximate; there are several Centaur variants. 

Figures 1-3 show the effective number, total mass, and total area, respectively, of stainless steel 

tanks in the 200 km to 1000 km altitude range vs. year. Figures 4-6 show the same plots for the 

800 km to 1000 km altitude range. Figures 7-9 show the same plots for the 200 km to 6000 km 

altitude range. From these figures, it can be seen that the amount of total stainless steel tank 

mass and area grew steadily from 1960 to 2010. Approximately 14000 kg of mass and 1600 m2

of area were effectively available between 200 km and 1000 km by 2010. Approximately 3200 

kg of mass and 350 m2

of area were effectively available between 800 km and 1000 km by 2010. 

Approximately 30000 kg of mass and 4000 m2

of area were effectively available between 

200 km and 6000 km by 2010. As a result, it can be inferred that approximately 16000 kg of 

mass and 2400 m2

of area were effectively available between 1000 km and 6000 km by 2010, 

i.e., there is approximately as much mass and area in the 1000 to 6000 km altitude range as in the 

200 to 1000 km altitude range. 
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Figure 1. Effective number of Delta I, II, and Centaur stainless steel tanks in the altitude 

range 200 km to 1000 km vs. time. 

Figure 2. Effective total mass of Delta I, II, and Centaur stainless steel tanks in the altitude 

range 200 km to 1000 km vs. time. 
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Figure 3. Effective total area of Delta I, II, and Centaur stainless steel tanks in the altitude 

range 200 km to 1000 km vs. time.

Figure 4. Effective number of Delta I, II, and Centaur stainless steel tanks in the altitude 

range 800 km to 1000 km vs. time. 
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Figure 5. Effective total mass of Delta I, II, and Centaur stainless steel tanks in the altitude 

range 800 km to 1000 km vs. time. 

Figure 6. Effective total area of Delta I, II, and Centaur stainless steel tanks in the altitude 

range 800 km to 1000 km vs. time.
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Figure 7. Effective number of Delta I, II, and Centaur stainless steel tanks in the altitude 

range 200 km to 6000 km vs. time. 

Figure 8. Effective total mass of Delta I, II, and Centaur stainless steel tanks in the altitude 

range 200 km to 6000 km vs. time. 
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Figure 9. Effective total area of Delta I, II, and Centaur stainless steel tanks in the altitude 

range 200 km to 6000 km vs. time.

The meteoroid flux was computed using the Gruen meteoroid flux vs. mass model with Earth 

shielding and gravitational focusing factors (Ref. 4) evaluated at a single altitude of 500 km 

altitude. The use of flux at a single altitude is acceptable for this analysis because the variation 

with altitude in LEO is relatively small. The resulting flux was multiplied by the total on-orbit 

area at each time point to yield the impact rate vs. time. The impact rate was then integrated 

over time to yield cumulative number of impacts vs. time. 

The Gruen flux was evaluated at three mass thresholds. The primary interest in this analysis is in 

impacting particles with enough mass to generate stainless steel ejecta particles of size 1 mm. 

The impacting particle mass thresholds were selected to correspond to equivalent stainless steel 

particle diameters of 0.1 mm (4.14 x 10-6 g), 1 mm (4.14 x 10-3 g), and 1 cm (4.14 g). The 

assumption here is that the ejected stainless steel particles will have masses which lie in a range 

that has an upper bound just above the mass of the impacting meteoroid. 

Figures 10-12 show cumulative meteoroid impacts on effective total stainless steel tank surface 

area in the 200 km to 1000 km altitude range vs. year for equivalent stainless steel size 

thresholds of 0.1 mm, 1 mm, and 1 cm, respectively. Figure 12 shows the 1 cm curve on a log 

scale because the mean cumulative impacts are much less than 1 (essentially a probability instead 

of a count). The 0.1 mm and 1 mm curves are included to illustrate how much lower the 1 cm 

curve is. Figures 13-15 show the same plots for the 800 km to 1000 km altitude range. Figures 

16-18 show the same plots for the 200 km to 6000 km altitude range. From these figures, it can 

be seen that the number of impacts on collective Delta I, II, and Centaur stainless steel tanks only 
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becomes significant for an equivalent stainless steel particle size threshold between 0.1 mm and 

1 mm. The number of impacts at the 1 mm threshold is approximately 27 between 200 km and 

1000 km, approximately 5 to 6 between 800 km and 1000 km, and approximately 62 between 

200 km and 6000 km. The number of impacts at the 0.1 mm threshold is approximately 100,000 

between 200 km and 1000 km, approximately 21,000 between 800 km and 1000 km, and 

approximately 240,000 between 200 km and 6000 km. To yield enough 1 mm stainless steel 

ejecta to produce a likely hit on the shuttle, impactors would have to generate larger ejecta 

fragments than themselves. Ejecta models, such as the one used by MASTER, could be used to 

further investigate this possibility. 

Figure 10. Cumulative meteoroid impacts on Delta I, II, and Centaur stainless steel tanks 

vs. time. Results are shown for an equivalent stainless steel size threshold of 0.1 mm and 

the altitude range 200 km to 1000 km. 
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Figure 11. Cumulative meteoroid impacts on Delta I, II, and Centaur stainless steel tanks 

vs. time. Results are shown for an equivalent stainless steel size threshold of 1 mm and the 

altitude range 200 km to 1000 km. 

Figure 12. Cumulative meteoroid impacts on Delta I, II, and Centaur stainless steel tanks 

vs. time. Results are shown for equivalent stainless steel size thresholds of 1 cm, 1 mm, and 

0.1 mm, and the altitude range 200 km to 1000 km. 
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Figure 13. Cumulative meteoroid impacts on Delta I, II, and Centaur stainless steel tanks 

vs. time. Results are shown for an equivalent stainless steel size threshold of 0.1 mm and 

the altitude range 800 km to 1000 km. 

Figure 14. Cumulative meteoroid impacts on Delta I, II, and Centaur stainless steel tanks 

vs. time. Results are shown for an equivalent stainless steel size threshold of 1 mm and the 

altitude range 800 km to 1000 km. 
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Figure 15. Cumulative meteoroid impacts on Delta I, II, and Centaur stainless steel tanks 

vs. time. Results are shown for equivalent stainless steel size thresholds of 1 cm, 1 mm, and 

0.1 mm, and the altitude range 800 km to 1000 km. 

Figure 16. Cumulative meteoroid impacts on Delta I, II, and Centaur stainless steel tanks 

vs. time. Results are shown for an equivalent stainless steel size threshold of 0.1 mm and 

the altitude range 200 km to 6000 km. 
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Figure 17. Cumulative meteoroid impacts on Delta I, II, and Centaur stainless steel tanks 

vs. time. Results are shown for an equivalent stainless steel size threshold of 1 mm and the 

altitude range 200 km to 6000 km. 

Figure 18. Cumulative meteoroid impacts on Delta I, II, and Centaur stainless steel tanks 

vs. time. Results are shown for equivalent stainless steel size thresholds of 1 cm, 1 mm, and 

0.1 mm, and the altitude range 200 km to 6000 km.
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Ejecta Model

The size distribution of cone ejecta resulting from a typical meteoroid impact was briefly 

investigated. The references for this are the description of the ejecta model implemented in 

MASTER-2009 (Ref. 5) and a report on hypervelocity impact ejecta prepared by the IADC 

Working Group 3 (Ref. 6). 

References 5 and 6 state that there are three types of ejecta: cone ejecta, jetting ejecta, and 

spallation ejecta. Both references indicate that jetting ejecta account for only 1% of the total. In 

addition, spallation only occurs for brittle surfaces. Since stainless steel is not brittle, only 

coning ejecta were considered here. 

The model used by MASTER-2009 (Ref. 5) was implemented in a spreadsheet. The impactor 

was assumed to be a meteoroid with mass of a 1 mm size stainless steel particle (4.14 mg). The 

meteoroid density was assumed to be 1 g/cc, and the corresponding meteoroid size was 1.99 mm. 

The impact was assumed to occur at 20 km/s at an angle normal to the surface. The target 

surface parameters were selected to represent stainless steel. These include selecting model 

parameters for a ductile surface and a density of 7.9 g/cc. It is noted that the model information 

presented in Ref. 6 was essentially the same as the MASTER-2009 model, but with variations in 

some parameter values derived from experimental data processed by various authors. 

Figure 19 shows the resulting reverse cumulative distribution of ejecta fragments over size. The 

total mass ejected computed by the model was 12.9 times the impactor mass. The maximum 

fragment size computed by the model was 0.051 mm, which is much less than the equivalent 

stainless steel size of the impacting meteoroid. The figure shows that the ejecta mass consists of 

a large number of fragments that are much smaller than the impactor. 

Since only 5 to 60 impacts by meteoroids of equivalent stainless steel size 1 mm and larger are 

expected to have occurred on all Delta I, II, and Centaur stainless steel tanks, and since the 

resulting ejecta are much smaller than the impactor due to the ductile nature of stainless steel, it 

appears unlikely that a significant population of stainless steel fragments has been created by 

meteoroid impacts on these tanks. 

Whether impacts by existing orbital debris particles could have created significant amounts of 

stainless steel ejecta has not been determined here. 

It is separately noted that Refs. 5 and 6 note that brittle surfaces can release multiple spall 

fragments that are larger than the impactor. This implies that meteoroid and debris impacts on 

spacecraft solar arrays or other brittle surfaces may generate a significant amount of spall ejecta 

of the same material. 
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Figure 19. Reverse cumulative distribution of stainless steel ejecta fragments over size 

resulting from an impact by a meteoroid with equivalent stainless steel size of 1 mm with 

impact 20 km/s and normal incidence.

Evidence of Impacts on Stainless Steel Tanks

A reentered Delta II 2nd stage tank on display at the Aerospace main campus shows evidence of 

multiple impacts. This tank was on orbit from April 1996 to June 1997 for a total of 273 days. 

There are 15 impact craters as well as two perforations with diameters of approximately 1 to 

1.5 mm. Consistent with statements in Refs. 5 and 6, none of these craters or perforations shows 

any evidence of spallation. No analysis has been performed to determine whether the impactors 

were likely meteoroids or orbital debris.

Conclusions

The possibility that Delta I and II second stage propellant tanks and Centaur propellant tanks 

could be sources of the stainless steel debris observed on space shuttle surfaces was investigated. 

It was found that the amount of total on orbit stainless steel tank mass and area available for 

creation of debris has steadily grown from 1960 to 2010. Approximately 14000 kg and 1600 m2

was effectively available between 200 km and 1000 km altitude by 2010. 

The number of meteoroid impacts on these tanks that may have produced stainless steel ejecta 

was estimated. The associated number of debris impacts on these tanks was not considered due 

to limited study scope. It was determined that the number of impacts only becomes significant 

for an equivalent stainless steel particle size threshold between 0.1 mm and 1 mm. To generate 

significant amounts of 1 mm stainless steel ejecta, meteoroid impactors smaller than 1 mm 

equivalent stainless steel particle size would have to generate larger ejecta fragments than 
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themselves. The MASTER-2009 ejecta model was used to investigate the ejecta created by a 

meteoroid equivalent to a 1 mm stainless steel particle. It was found that the largest ejecta 

fragment was much smaller than the impactor due to the ductile nature of stainless steel. 

Spallation can generate fragments larger than the impactor, but this occurs only on brittle 

surfaces. Evidence of actual impacts is shown on a reentered Delta II 2nd stage tank at Aerospace 

that was on orbit 273 days, but they showed no evidence of spallation. As a result, it appears 

unlikely that a significant population of stainless steel fragments has been created by meteoroid 

impacts on known stainless tanks.

Whether impacts by existing orbital debris particles could have created significant amounts of 

stainless steel ejecta has not been determined here. 

References 

1. “ORDEM 3.0 (Orbital Debris Engineering Model),” NASA Orbital Debris Program Office 

briefing package, August 2012. 

2. Jenkin, A.B., Sorge, M.E., McVey, J.P., Peterson, G.E., Yoo, B.B., “MEO Debris Environment 

Projection Study,” Proceedings of the Sixth European Conference on Space Debris, Darmstadt, 

Germany, April 22-25, 2013 (ESA SP-723, August 2013). 

3. Kessler, D.J., “Derivation of the Collision Probability between Orbiting Objects: The Lifetimes 

of Jupiter’s Outer Moons,” Icarus 48, 1981, pp. 39-48.

4. “Natural Orbital Environment Guidelines for Use in Aerospace Vehicle Development,” NASA 

Technical Memorandum 4527, June 1994. 

5. “Final Report – Maintenance of the ESA MASTER Model,” Revision 1.1, June 7, 2011.

6. “Characterization of Ejecta from HVI on Spacecraft Outer Surfaces,” IADC-11-05, April 

2013, accessible at http://www.iadc-online.org. 
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Appendix D. CTU Risk Assessment 
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Appendix E. JPSS-1 Zenith Deck Orbital Debris Wire Harness 

Failure Assessment 
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REPORT DOCUMENTATION PAGE Form Approved

OMB No. 0704-0188

2. REPORT TYPE

Technical Memorandum

4. TITLE AND SUBTITLE

Joint Polar Satellite System (JPSS) Micrometeoroid and Orbital Debris 

(MMOD) Assessment

5a. CONTRACT NUMBER

6. AUTHOR(S)

Squire, Michael D.; Cooke, William J.; Williamsen, Joel; Kessler, Donald; 

Vesely, William E.; Hull, Scott H.; Schonberg, William; Peterson, Glenn 

E.; Jenkin, Alan B.; Cornford, Steven L.

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

NASA Langley Research Center

Hampton, VA 23681-2199

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration

Washington, DC 20546-0001

8. PERFORMING ORGANIZATION

REPORT NUMBER

L-20595 NESC-RP-14-00948

10. SPONSOR/MONITOR'S ACRONYM(S)

NASA

13. SUPPLEMENTARY NOTES

12. DISTRIBUTION/AVAILABILITY STATEMENT

Unclassified - Unlimited

Subject Category 16 Space Transportation and Safety

Availability: NASA STI Program (757) 864-9658

19a. NAME OF RESPONSIBLE PERSON

STI Help Desk (email: help@sti.nasa.gov)

14. ABSTRACT

The Joint Polar Satellite System (JPSS) Project requested the NASA Engineering and Safety Center (NESC) conduct an 

independent evaluation of the Micrometeoroid and Orbital Debris (MMOD) models used in the latest JPSS MMOD risk 

assessment. The principal focus of the assessment was to compare Orbital Debris Engineering Model version 3 (ORDEM 3.0) 

with the Meteoroid and Space Debris Terrestrial Environment Reference version 2009 (MASTER-2009) and Aerospace 

Debris Environment Projection Tool (ADEPT) and provide recommendations to the JPSS Project regarding MMOD 

protection. The outcome of the NESC assessment is contained in this report.

15. SUBJECT TERMS

Micrometeoroid and Orbital Debris; NASA Engineering and Safety Center; Joint Polar Satellite System; MASTER-2009, 

ORDEM; ADEPT

18. NUMBER

OF

PAGES

225

19b. TELEPHONE NUMBER (Include area code)

(757) 864-9658

a. REPORT

U

c. THIS PAGE

U

b. ABSTRACT

U

17. LIMITATION OF

ABSTRACT

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Prescribed by ANSI Std. Z39.18

Standard Form 298 (Rev. 8-98)

3. DATES COVERED (From - To)

April 2015 - April 2015

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER

5e. TASK NUMBER

5f. WORK UNIT NUMBER

869021.05.07.05.32

11. SPONSOR/MONITOR'S REPORT

NUMBER(S)

NASA/TM-2015-218780

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1. REPORT DATE (DD-MM-YYYY)

01- 07 - 2015