# Center for Aerospace Technology

Contract No. FO86050-02-C-0018

Sub-Contract No. SRSIS-02-S-0018-002

RTI Report No. RTI/08360/103-13F

July 16, 2004



# T i t a n IV B-30 Downrange Risks

Final Report

Prepared for

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45th Space Wing (AFSPC)

Safety Office - 45 SW/SE

Patrick AFB, FL 32925

Distribution authorized to US Government agencies and their contractors to protect administrative/

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# Titan IV B-30 Downrange Risks

Final Report

Prepared by

Robb T. Laney

Brenda Wall

of

RTI International

Center for Aerospace Technology

Florida Office

Prepared for

Department of the Air Force

45th Space Wing (AFSPC)

Safety Office - 45 SW/SE

Patrick AFB, FL 32925

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14. ABSTRACT



# The Titan IV NUS vehicle configuration is described. Downrange risk estimates are based on th e B-30 mission. Trajectory data

was provided for launch from ER Pad 40 with an initial flight azimuth of 48.6 ° . Estimates are made for Stage 2 and payload

debris produced by aero-thermal breakup, and casualty a reas are computed. Population models for European and Middle

Eastern nations and surrounding islands are de scribed, along with estimates of shelteri ng provided by several structure types.

Using day sheltering assumptions and vary ing other key parameters, RTI's current es timates of downrange casualty expectation range from a low of 70 x 10 -6 

# to a high of 235 x 10 -6 

# . Within that range, the baseline estimates are 145 x 10 -6 

# for day and

152 x 10 -6 

# for night.

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# Titan IV B-30 Downrange Risks 07/16/2004



# Abstract

The Titan IV NUS (No Upper Stage) vehicle configuration is described. Downrange risk

estimates are based on the B-30 mission over Europe and the Middle East. Trajectory

data was provided for launch from ER (Eas tern Range) Pad 40 with an initial flight

azimuth of 48.6°. Estimates are made for Stag e 2 and payload debris produced by aerothermal breakup, and casualty areas are co mputed. Population models for European

and Middle Eastern nations and surrounding islands are described, along with estimates of sheltering provided by several structure types.

Hazards are simulated based on two types of failure responses: near trajectory (RTI

Mode 4) and sustained turn (RTI Mode 5) . Baseline risks (casualty expectations) are

computed for two population sheltering assu mptions: day and night. Due to uncertainty associated with various methods and in puts used, sensitivity of results to several

key assumptions are also investigated. Usin g day sheltering assumptions and varying

other key parameters, RTI's current estimate s of downrange casualty expectation range

from a low of 70 x 10 -6 

# to a high of 235 x 10 -6 

# . Within that range, the baseline estimates

are 145 x 10 -6 

# for day and 152 x 10 -6 

# for night.

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# Table of Contents

1. Introduc tion

............................................................................................................... ...............1

2. Vehicle Description

........................................................................................................ ..........3

3. Trajecto ry Description

..................................................................................................... ........4

4. Failure Probabilities

...................................................................................................... ...........6

5. Casualty Areas

............................................................................................................. .............7

5.1 Stage 2 Debris............................................................................................................ .........7

5.2 Payload Debris

............................................................................................................ .....10

5.3 Downrange Summary ....................................................................................................13

6. Population Models

.......................................................................................................... .......15

6.1 LandScan 2002

............................................................................................................. ....15

6.1.1 Coverage Details ......................................................................................................15

6.1.2 Cell Aggregation ......................................................................................................16

6.1.3 Shapefile Creation ...................................................................................................17

6.1.4 Land Area Adjustments .........................................................................................18

6.1.5 Attribute Table

......................................................................................................... 19

6.1.6 Cells Added by RTI .................................................................................................20

6.2 RTI Sheltering M o del

...................................................................................................... 20

6.3 Population Usage for B-30 An alysis .............................................................................21

6.3.1 Mode 4 Computations ............................................................................................21

6.3.2 Mode 5 Computations ............................................................................................21

7. Downrange Risk Estimation .................................................................................................2 3

7.1 Mode 4 Baseline Risks ....................................................................................................2 3

7.2 Mode 5 Baseline Risks ....................................................................................................2 6

7.3 Total Baseline Risks

...................................................................................................... ...32

8. Sensitivity of Risks to Assump tions ....................................................................................33

8.1 Sensitivity to Failure Probability ..................................................................................33

8.2 Sensitivity to Response Mode Allocation ....................................................................35

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# 8.3 Sensitivity to Cas u alty Areas .........................................................................................36

8.3.1 Sensitivity to Br eakup Model ................................................................................36

8.3.2 Sensitivity to Post-Impact Effects ..........................................................................37

8.4 Sensitivity to Population Counts ..................................................................................38

8.5 Sensitivity to Population She l tering .............................................................................39

8.6 Sensitivity to Mode 4 Dispersions ................................................................................40

8.7 Sensitivity to Wind Month .............................................................................................41

8.8 Use of Covariance Data ..................................................................................................41

8.9 HACK Version Comparison ..........................................................................................42

9. Summary and Conc lusions ...................................................................................................5 2

Appe ndix A: RT I She l ter i ng P e rc enta ge s as of 20 03

.............................................................. A

- 1

R e f e r e n c e s

..................................................................................................................... ........... R - 1

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# Table of Tables

Table 1. Event Times for Titan IV B-30 Mission ......................................................................4

Table 2 T i t a n I V S t a g e 2 D e b r i s C l a s s e s f o r D o w n r a n g e U s e ...................................................9

Table 3. B-30 Payload Debris Clas ses for Do wnrange Use – Part 1 ....................................11

Table 4. B-30 Payload Debris Clas ses for Do wnrange Use – Part 2 ....................................12

Table 5. Debris Model Totals for Downrange Use ................................................................13

Table 6. Ti tan IV B-30 Casualty Areas for Downrange Use .................................................14

Table 7. Po pulation D e nsity Thresholds .................................................................................17

Table 8. Criteria for Adjusting La nd Area ..............................................................................18

Table 9. Baseline Mode 4 Risks fo r Titan IV B-30 Mission ...................................................25

Table 10. B a seline Mode 5 Risks for Titan IV B-30 Mission .................................................28

Table 11. Day Sheltered Mode 5 Risks by Country ...............................................................29

Table 12. B a seline Downrange Risks for Titan IV B-30 Mission .........................................32

Table 13. B-30 Top-Down Failure Probabilities .....................................................................34

Table 14. B-30 Bottom-Up Failure Probabilities .....................................................................35

Table 15. Sensitivity o f Downrange Risks to Failure Probability ........................................35

Table 16. Sensitivity of Downrange Risks to Response Mode Allocation .........................36

Table 17. Alternative Breakup Models ....................................................................................37

Table 18. Sensitivity of Downrange Risks to Breakup Model .............................................37

Table 19. Alternative Post-Impact Assumptions ...................................................................38

Table 20. Sensitivity of Downrange Risks to Post-Impact Effects .......................................38

Table 21. Sensitivity of Downrange Risks to Population Counts .......................................39

Table 22. Sensitivity of Downrange Risks to Population Sheltering ..................................39

Table 23. Sensitivity of Downrange Risks to Mode 4 Dispersions .....................................40

Table 24. Sensitivity of Downrange Risks to Wind Month ..................................................41

Table 25. E ffect of Covariance Data on Downrange Risks ...................................................42

Table 26. Debris Class Index and Corresponding Clas s ID .................................................43

Table 27. Casualty Areas for HACK Version 3 ......................................................................44

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# Table of Figures

Figure 1. Titan IV NUS Vehicle

................................................................................................ ..3

Figure 2. Vacuum I m pact Trace fo r T i t a n I V B

- 3 0 M i s s i o n

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Figure 3. RTI’s 1 x 1 De gree Version of LandScan ................................................................22

Figure 4. Summary of Mode 4 Corridor for Titan IV B-30 Mission ....................................25

Figure 5. Downrange Gate for Titan IV B- 30 Mission ..........................................................26

Figure 6. Sample Mode 5 Impacts for Titan IV B-30 Mission ..............................................27

Figure 7. Selected Trends in Mode 5 Simulation Cases ........................................................27

Figure 8. Population Ex posed to Mode 5 Hazards ...............................................................31

Figure 9. Cumulative Risk for Mo de 5 Resp onse – Day Sheltering ....................................31

Figure 10. Casualty Areas per Fragment by Debris Class, Part 1 .......................................45

Figure 11. Casualty Areas per Fragment by Debris Class, Part 2 .......................................46

Figure 12. Casualty Areas per Fragment by Debris Class, Part 3 .......................................47

Figure 13. Casualty Areas per Fragment by Debris Class, Part 4 .......................................48

Figure 14. Casualty Areas per Fragment by Roof Type, Part 1 ...........................................49

Figure 15. Casualty Areas per Fragment by Roof Type, Part 2 ...........................................50

Figure 16. Casualty Areas per Fragment by Floor Location ................................................51

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# 1. Introduction

Using information about Stage 2 of the Tita n IVB vehicle and a specified payload, RTI

has developed a fragmentation model, esti mated downrange risks associated with

overflight of Europe and the Middle East, an d investigated sensitivities to selected

parameters and assumptions. Trajectory data were provided for the B-30 mission from

ER (Eastern Range) Pad 40, with an initial flight azimuth of 48.6 degrees. This work was

done for, and with selected direction from, ER Wing Safety (45 SW/SE).

Early in flight while a vehicle is still in or near the launch area, many types of failures

have the potential to endanger people or pr operty both inside and outside established

flight safety limits. Later in flight, th e nominal impact-point trace may intersect

downrange landmasses prior to the vehicle achieving orbital conditions. This downrange overflight is often necessary in orde r to conduct a normal mission. The risk, or

casualty expectation ( E C 

# ), associated with populations on downrange landmasses must

be estimated to ensure public safety criteria are met. During these overflight periods,

launch vehicle failures that cause significant divergence from the intended flight track

have the potential to endanger populations outs ide a normal overflight corridor. In fact,

some possibility exists for debris impacts anywhere within the vehicle’s range capability, which is a function of kinetic, potenti al, and chemical energies. Risks due to such

failure responses are reduced dramatically as long as a reliable FTS (Flight Termination

System) is installed and armed.

Most vehicle malfunctions cause behaviors th at lead to impacts within a normal overflight corridor. Termed **Mode 4** failure responses by RTI, th ese near-trajectory behaviors

result from failures such as a benign engine shutdown, tank rupture, turbine or turbopump failure, pressure rupture or burn-thr ough of a solid rocket motor, premature

stage separation or ignition, excessive angle of attack during periods of high dynamic

pressure, or a tumbling turn. It has been esti mated that more than three quarters of all

failures lead to vehicle breakup before the safety officer can send a destruct command. [1 ] 

# Although most vehicles do not maintain FTS capability beyond a certain point

in flight, known as the downrange gate, th is statistic helps support assumptions made

regarding the distribution of failure prob abilities between Mode 4 and other failure

responses.

To simulate the hazards posed by Mode 4 responses, RTI developed a software tool

c a l l e d C O R I D O R E

- G I S ( C o r r i d o r R i s k E s t i m a t i o n u s i n g a G e o g r a p h i c I nformation

S ystem). CORIDORE is hosted in ArcView GI S, a commercial software application from

ESRI (Environmental Systems Research Institut e, Redlands CA). In addition to providing a variety of electronic maps, polygon inte rsection routines, and a program execution

environment, ArcView allows for convenient maintenance of population datasets and

visualization of risk estimation results.

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# In contrast to Mode 4, some malfunctions may cause a vehicle to fly initially in an

erroneous direction, or after a period of no rmal flight, execute a sustained turn away

from the intended flight track. Such behaviors are referred to by RTI as **Mode 5** failure

responses. One response category of this ty pe is modeled by performing a series of

simulations using randomly generated, fixed directions of thrust; similar behaviors are

referred to here as random a ttitude responses. Although this category represents a

subset of those responses having the potenti al to yield impacts outside a normal overflight corridor, random attitude simulati ons can be shown to bound maximum possible

regions of impact, given that the vehicle ha s flown nominally for a specified period of

time. [1 ] 

# To estimate the hazards posed by Mode 5 failure responses, RTI developed two

software tools called RAFIP (Random Attitu de Failure Impact Points) and RAPIEC

(Random Attitude Probability of Impact and Expectation of Casualty). Currently, RTI

assumes that the impact distributions deri ved from random attitude simulations are

representative of impact distributions from all Mode 5 response categories. A more

involved research study would be needed to test the influence of this assumption on

risk analysis results.

Once a vehicle is out of the launch area , only Mode 5 failure responses have the

potential to yield impacts at significant di stances from a normal overflight corridor.

Nevertheless, the nature of Mode 5 density functions is such that impacts are most

likely inside the corridor, with impact pr obability decreasing as distance from the

corridor centerline increases. Mode 5 risks accumulated prior to reaching the downrange gate can usually be minimized by maintaining an FTS capability. Normally,

Mode 4 risks cannot be significantly mitigate d by an FTS, regardless of the gate time

(i.e., location). For some missions, Mode 4 ri sks can be somewhat reduced by rotating to

a slightly different launch azimuth, or perf orming mid-course steering maneuvers. In

most cases of launch vehicle ascent, howeve r, some downrange risk is unavoidable.

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# 2. Vehicle Description

The Titan IVB was first launched February 23, 1997. It is a derived configuration of

Titan IV that features upgraded solid rocket motors (SRMUs), a liquid propellant core

composed of two stages, and a payload fairing 16.7 feet in diameter. This system can be

flown with a wide-body Centaur upper stage, IUS (Inertial Upper Stage), or NUS (No

Upper Stage).

The B-30 mission will employ th e NUS vehicle variant, as shown in Figure 1. Because

the current analysis deals only with downra nge overflight, the vehicle components of

interest are Stage 2 and a large payload (see Section 5 for weight estimates). Note that

payload fairing jettison occurs prior to Stage 2 ignition.

Figure 1. Titan IV NUS Vehicle

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# 3. Trajectory Description

Downrange risk estimates are based on the proposed B-30 mission. Trajectory data were

provided by Lockheed Martin, via ER Wing Safety, for launch from ER Pad 40 with an

initial flight azimuth of 48.6 degrees. [2 ] 

# The nominal trajectory was designed to deliver

the spacecraft into a parking orbit (after St age 2 burn) at an inclination of 57.0 degrees,

with apogee and perigee heights of 383 and 258 nautical miles, respectively. Key event

times for the B-30 mission are listed in Table 1.

Table 1. Event Times for Titan IV B-30 Mission

E v e n t T i m e ( s e c )

Stage 0 ignition (SRMU)

Lift-off

Begin roll to flight azimuth

End roll to flight azimuth

Begin pitch-over

Begin zero-lift flight

Begin Stage 0 open-loop pitch rate

Stage 1 ignition

TCA jettison

SRMU separation

Payload fairing separation

Stage 1 shutdown command

Stage 1 shutdown

Stage 2 ignition

Step 1 / Stage 2 separation

Stage 2 shutdown command

Stage 2 shutdown

Park orbit injection 0.000

0.324

7.000

14.000

15.000

30.000

90.000

131.658

132.493

146.417

180.686

316.224

319.409

319.716

320.421

549.818

549.822

569.725

From the supplied trajectory, a Keplerian IIP (Instantaneous Impact Point) was found to

vanish at 540.851 seconds (to millisecond resoluti on). This IIP trace crosses into Ireland

at 461 seconds and exits Oman at 533 seconds, indicating a vacuum dwell time over

Europe and the Middle East of approximately 72 seconds (note that significant time is

spent over water).

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# A vacuum impact trace for the nominal B-30 mission azimuth of 48.6 degrees is shown

in Figure 2; selected time points are provided for reference. The downrange gate for this

mission is discussed later in this document (see Section 7.2).

Figure 2. Vacuum Impact Trace for Titan IV B-30 Mission

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# 4. Failure Probabilities

Failure probabilities used for this analysis, including the allocation between Mode 4 and

Mode 5, were assigned per direction of ER Wing Safety. [3 ] 

# A baseline failure probability of

0.02 was assumed for Stage 2, which has a bu rn time of 230.1 seconds. Assuming Stage 2

failure probability to be uniformly distribute d between ignition an d shutdown yields a

failure rate of 8.692 x 10 -5 

# per second. The baseline split between Mode 4 and Mode 5 was

assumed to be 95% and 5%, respectively.

I n summary,

Stage 2 Mode 4 Failure Probability = 0.0190

Stage 2 Mode 4 Failure Rate = 8.257 x 10 -5 

# /sec

Stage 2 Mode 5 Failure Probability = 0.0010

Stage 2 Mode 5 Failure Rate = 0.435 x 10 -5 

# /sec

Many liquid-propellant motors (e.g., Centaur) have higher failure rates during startup

than during steady-state operation. It has b een suggested that Centaur’s startup transients

lasts about four seconds following ignition. An investigation into the history of Titan

Stage 2 revealed that 8 out of 29 failures o ccurred during startup, including failure to

ignite. [4 ] 

# However, none of these were among “r epresentative” configurations; the most

recent Stage 2 startup failure occurred in 1965. Since Centaur history is not applicable, and

other information is scant, special steady-state failure rates were not computed.

Further discussions regarding failure probabilit ies will be presented later in this document

(see Section 8.1).

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# 5. Casualty Areas

Downrange risk analysis capabilities have been enhanced through the consideration of

population sheltering. It has been recognized that most people in downrange areas are

usually protected, to some unknown degree, by dwellings or other structures. For

computation of downrange risks, four sh eltering categories are considered: open

, light

, medium , and heavy

.

Sheltering categories are used to indicate levels of protection. Structural sheltering

provides protection from inert debris, pr imarily through roofing construction and

materials. Although explosions are not consid ered in the current study, sheltering also

provides protection from explosive debris, to a certain extent, through construction and

materials for both roofing and walls. Examples of light sheltering include typical houses

and mobile homes in the United States. An example of medium sheltering is the typical

single or two-story US office building with concrete roof. Examples of heavy sheltering

are the blockhouses used for launch control at Eastern Range and, for inert debris, the

lower floors of a multi-story office building.

Casualty areas for occupants of light, medi um, and heavy structures were computed

using HACK (Hazard Area Computational Kernel), a physics-based, roof penetration

algorithm developed by ACTA. [5 ] 

# Casualty areas for people in the open were computed

using RTI procedures. [6 ] 

# For baseline risk estimates, all debris were assumed to impact

on soft surfaces; impacts on hard surfaces were treated in a sensitivity analysis (see

Section 8.3). On average, soft surfaces were considered more likely than hard surfaces in

d o w n r a n g e a r e a s

. Unsheltered casualty areas were estimated based on the dimensions of

a fragment, dimensions of a person, impact path angle, and post-impact effects (i.e.,

bounce, splatter, and slide).

Debris data used for this study was orig inally documented in an RTI memorandum, [7 ]



# along with a preliminary estimate of total casualty area for people in the open.

**5.1 Stage 2 Debris**

Debris characteristics for T i t a n I V Stage 2 were provided by Lockheed Martin. [8 ] 

# This data

was used as the basis for RTI's Stage 2 debris model, which is listed in T a b l e 2

. Casualty

areas shown in the table are for people in th e open assuming impacts on soft surfaces.

In the table below, risk area refers to a f r a g m e n t ’ s projected area at impact. These values

were calculated based on debris geometry and center of gravity, as provided by Lockheed

Martin. For example, a side-on impact was as sumed for Class S2_01. It became necessary

to update the dimensions for "Piece #1" to reflect the presence of a payload adaptor. [9 ]



# Class S2_02 and Class S2_04 are not necessarily expected to stabilize; therefore, impact

orientation is uncertain. Risk area was calcul ated as the average of three face areas for

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# Class S2_02, and as a maximum fa ce area for Class S2_04. Due to center of gravity location,

an end-on impact was assumed for Class S2_03.

Ballistic coefficients and aerodynamic reference areas for Stage 2 debris are based on drag

coefficient (C D 

# ) versus Mach number curves provided by Lockheed Martin in their initial

data delivery. Those C D 

# vs. Mach curves were used to create reference C D 

# data tables for

use in estimating impact locations. It should be noted that the C D 

# vs. Mach curve for

"Piece #1" does not include effects of the payload (also indicated as space vehicle, or SV)

adaptor attached to its forward end.

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Export Controlled InformationTitan IV B-30 Downrange Risks07/16/2004Table2Titan IV Stage 2 Debris Classes for Downrange UseDebrisClassDescriptionNo. ofFragsWeight(lb)RiskArea (ft2)Aero RefArea (ft2)SubsonicBeta (psf)(M = 0.5)RefCDDataTableaFrangibilityCasualtyArea (ft2)(per fragment)S2_01SV Adaptor,Fwd OxidizerDome, etc.15,111215.1878.5476.56CD10.2286.83S2_02Fuel Barrel,OxidizerFeedline, etc.11,93984.7678.5437.98CD20.2131.64S2_03Engine11,40125.5925.5997.76CD30.270.94S2_04Panels2095.825.0025.006.39CD40.288.81a ReferenceCDdata table refers to drag vs. Mach curves used in estimatingimpact locations. Each class has it's own curve; for example,CD1 denotes the table for Class S2_01.For Official Use Only9RTI/08360/103-13FExport Controlled Information

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# 5.2 Payload Debris

Basic debris characteristics for the B-30 payload were provided by Lockheed Martin. [ 10]



# That data was used as the basis for RTI's initial payload debris model, as described in a

preliminary RTI memorandum. § 

# As additional data has become available, [ 11] [ 12] 

# RTI has

refined our payload debris model. The upda ted model now represents classes via two

distinct methodologies. The fi rst is the same one used for Stage 2 debris; drag data are

provided as a tabular function of C D 

# vs. Mach number for each class. The second uses a

single subsonic and a single supersonic C D 

# value for each class. Debris classes for which

additional data permitted refinement using C D 

# vs. Mach curves are listed in Table 3.

The remaining debris classes are presented in T a b l e 4

. A s b e f o r e

, c a s u a l t y areas shown

in these tables are for people in the open assuming impacts on soft surfaces.

The decision to refine a given class by using C D 

# vs. Mach curves was based on two

primary criteria: 1) fragment shape and di mension data were sufficiently clear, and

2) subsonic ballistic coefficient was greate r than approximately 30 pounds per square

foot. This value is somewhat arbitrary, but is intended as a reasonable compromise

between adequately characterizing hazardous fragments and the additional overhead

incurred by using C D 

# vs. Mach tables. Debris described by Lockheed Martin as "rectangular" were refined if ballistic coefficient was large enough. Debris described as

"cylindrical" were refined only if diameter was also clearly identified. Consideration of

diameter versus length is critic al in determining appropriate C D 

# vs. Mach curves for

cylinders. Also of importance is whether a cylin der is circular versus elliptical, or solid

versus hollow. As no insight on these co nsiderations was provided, RTI assumed all

cylinders were solid. Any cylinder for which diameter was not clearly identified was

treated as a tumbling box, which appears to be consistent with assumptions made by

Lockheed Martin. [ 13]



# "Piece #18" (which would have been Cla ss PL_18) was combined with "Piece #17"

(which became Class PL_17), as di rected by Lockheed Martin. [ 14] 

# Therefore, a separate

entry for "Piece #18" does not appear here . This combination made the characteristic

shape of the resulting, single debr is fragment unclear. Hence, a C D 

# vs. Mach curve

could not be assigned with co nfidence, even though fragment weight is significant at

906 pounds.

Considering the dimensions provided, Class PL_19 and Class PL_20 are both modeled

as tumbling plates.

§ B. Wall, 3 December 2003, superceded by Reference 7

For Official Use O n ly 

# 10 RTI/08360/103-13F

Export Controlled InformationTitan IV B-30 Downrange Risks07/16/2004Table 3. B-30 Payload Debris Classes for Downrange Use –Part 1DebrisClassDescriptionNo. ofFragsWeight(lb)RiskArea (ft2)Aero RefArea (ft2)SubsonicBeta (psf)(M = 0.25)RefCDDataTableaFrangibilityCasualtyArea (ft2)(per fragment)PL_02rectangular1546249.3374.0098.41TCUB0.2126.17PL_03rectangular13009.0313.5529.53TCUB0.228.79PL_08rectangular146016.6725.0024.53TCUB0.241.65PL_09rectangular13047.0610.5938.27TCUB0.226.22PL_10rectangular15389.0313.5552.96TCUB0.233.88PL_11rectangular14888.1612.2453.16TCUB0.232.00PL_12rectangular2141011.2616.89111.29TCUB0.249.20PL_13rectangular13909.3013.9437.29TCUB0.232.93PL_14rectangular14008.3712.5542.49TCUB0.231.15PL_15rectangular15449.0313.5553.55TCUB0.234.04PL_21cylindrical268818.441.4067.31TCYL210.247.92PL_24rectangular12757.0610.5834.65TCUB0.227.64PL_25rectangular18039.5414.3174.81TCUB0.242.51a ReferenceCDdata table refers to drag vs. Mach curves used in estimatingimpactlocations. TCUB is RTI's standard curve for a tumblingcube while TCYL21 is specifically for a tumbling, solid, rightcircular cylinder with diameter 16 inches and length 166 inches.In the above table, risk area refers toa fragment’s projected area at impact. These areas were calculated based solely ondebris geometry since center of gravityvalues were not provided by Lockheed Martin. Therefore, it was difficult to predict impact orientation. Risk area was calculated as the average of three face areas, exceptfor the cylindrical ClassPL_21,for which a side-on impact was assumed. Aerodynamic referencearea is, by definition, specific to the drag curve for agiven debris class.For Official Use Only11RTI/08360/103-13FExport Controlled InformationTitan IV B-30 Downrange Risks07/16/2004Table 4. B-30 Payload Debris Classes for Downrange Use –Part 2DebrisClassDescriptionNo. ofFragsWeight(lb)RiskArea (ft2)LMAverageArea (ft2)LMSubsonicBeta (psf)LMSupersonicBeta (psf)FrangibilityCasualtyArea (ft2)(per fragment)PL_04cylindrical12316.024.55101.5456.410.228.96PL_05cylindrical1821.711.21135.5475.30a0.217.89PL_06cylindrical127029.5828.7718.7710.430.278.92PL_07cylindrical1357.886.0011.676.480.245.60PL_16cylindrical49715.007.5912.7810.650.260.81PL_17rectangular190621.765.93152.78127.320.269.66PL_19dome8387.0710.473.633.020.246.82PL_20rectangular2369209.0078.184.723.930.2368.39PL_22cylindrical2834.713.6845.1125.060.219.06PL_23cylindrical272.252.236.283.490.231.50PL_26irregular154623.5733.5616.2713.560.272.49PL_27t-shape173813.5215.4295.7253.180.248.30PL_28rectangular655040.2040.1513.7011.420.2105.67PL_29irregular11814.944.4740.4933.740.221.16aSupersonic beta in LM (Lockheed Martin) data was 56.47psf. The CD(1.20) used to calculate this value is believed to be in error. Forconsistency with other data provided, the CDin question was changed to 0.90, which yields a beta of 75.30 psf.As in the previous tables, risk area refers to a fragment’sprojected area at impact. These areas were calculated as theaverage of three face areas for all debris classes listed in this table.For Official Use Only12RTI/08360/103-13FExport Controlled Information

# Titan IV B-30 Downrange Risks 07/16/2004



# 5.3 Downrange Summary

Debris model totals for Stage 2 and the B-30 mission payload are listed below in Table 5.

As modeled, this collection of inert fragments and components has been grouped into

31 classes for downrange risk analysis purposes.

Table 5. Debris Model Totals for Downrange Use

Component No. of

Frags Weight

(lb) Risk Area

(ft 2 

# )

Stage 2 23 10,366 825.53

P a y l o a d 4 7 2 2

, 0 5 9 1 1 0 0

. 6 4

T o t a l 7 0 3 2

, 4 2 5 1 9 2 6

. 2

Based on the debris characteristics presente d above, casualty areas were estimated for

people in the open (assuming impacts on soft su rfaces), and for sheltering categories of

light, medium, and heavy. Resulting values are listed below in Table 6.

The debris model presented for downrange ov erflight of B-30 reflects the assumption

that all liquid propellants leak out or boil off during breakup, leaving only inert weight

a t i m p a c t

. Due to a current lack of informat ion regarding survivability of debris, ER Wing

S a f e t y d i r e c t e d R T I t o a s s u m e a l l h a r d w a r e s u r v i v e s t o i m p a c t w i t h n o r e d u c t i o n f r o m aero-thermal ablation. If re-entry survivability effects were considered, total casualty areas

might be reduced significantly, depending on the time span for breakup and various

material properties.

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# Table 6. Titan IV B-30 Casualty Areas for Downrange Use

Casualty Areas per Fragment (ft 2 

# )

Debri s

Class Open (soft) Light Medium Heav y

S2_01

S2_02

S2_03

S2_04 286.57

131.54

70.18

88.99 559.95

231.10

159.37

6.86 543.42

187.85

125.52

0.00 61.95

0.00

24.23

0.00

PL_02

PL_03

PL_04

PL_05

PL_06

PL_07

PL_08

PL_09

PL_10

PL_11

PL_12

PL_13

PL_14

PL_15

PL_16

PL_17

PL_19

PL_20

PL_21

PL_22

PL_23

PL_24

PL_25

PL_26

PL_27

PL_28

PL_29 122.20

31.57

28.60

17.60

80.11

46.05

50.10

25.82

32.67

30.84

56.31

30.12

29.20

32.80

61.44

68.73

46.18

368.48

49.41

19.53

31.58

25.30

39.41

73.46

47.71

106.67

21.06 307.78

37.52

38.46

19.15

46.40

14.14

46.64

37.06

61.00

56.24

122.61

43.49

45.81

61.82

28.68

144.84

0.00

0.00

99.01

30.35

0.00

36.11

85.64

46.04

104.00

47.44

30.33 368.13

13.99

13.54

7.78

10.61

0.00

17.75

13.34

29.68

24.95

92.87

16.38

16.99

30.50

0.00

108.26

0.00

0.00

63.66

8.95

0.00

13.00

60.32

17.15

69.45

12.29

11.09 166.76

0.00

7.28

5.33

0.00

0.00

0.00

4.19

9.06

9.02

45.91

4.88

6.12

9.31

0.00

16.87

0.00

0.00

9.05

0.00

0.00

3.82

12.07

0.00

13.55

0.00

4.22

T o t a l 5 4 0 7

. 3 3 2 5 3

. 3 2 1 0 4

. 4 4 6 8

. 6

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# 6. Population Models

**6.1 LandScan 2002**

RTI has chosen LandScan 2002 Global Populati on Database, distributed by ORNL (Oak

Ridge National Laboratory), § 

# as a reasonable source for population count distributions

needed to estimate downrange risks. [ 15] 

# Raw LandScan data has been converted from

packed binary files to enhanced polygon cove rages, which are stored in ArcView shapefile format (see Section 1 for more info rmation on ArcView GIS). This process, [ 16] 

# which

results in a series of population cell polygons having multiple sizes, is discussed below

in more detail.

LandScan 2002 provides high-resolution populati on data with a quadrilateral cell size

of half-minute latitude or longitude (less th an one kilometer). Population is based on

the best available country census counts an d is augmented by employing an advanced

interpolation procedure. Designed by ORNL , this procedure is based on parameters

such as road locations, terrain slope, land vegetation, night-time lights, and coastlines.

The LandScan model covers the entire world fo r a consistent date, and it offers a single,

reliable data source for trans-continental applications such as overflight risk analysis.

Originally developed to support emergency response personnel, LandScan provides

ambient population estimates intended to accoun t for locations such as hotels, factories,

shopping areas, and highways where population may not be included in official census

counts. Since time of day for an emergency cannot be anticipated, a reasonable compromise is to estimate ambient population inte grated over 24 hours. Future versions of

LandScan are expected to address distinct ions between day and night populations, age

and gender variations by country, and improved resolution for urban areas.

**6.1.1 Coverage Details**

To accommodate usage of LandScan population counts in current RTI risk analysis

programs, modifications to raw LandScan da ta were necessary. Many programs require

a single polygon coverage to perform intersections and other computations, but the

original LandScan dataset is partitioned into more than one thousand raster coverages,

or tiles, each representing 5 x 5 degrees of latitude and longitude. Each tile contains

360,000 half-minute cells, thus producing a f ile size well over 20 megabytes when converted to shapefile format. The entire da taset would consume over 20 gigabytes of

storage space. To reduce this information to a more manageable size, RTI converts the

LandScan data into a series of multi-res (i.e., multiple resolution) population coverages

§ 

# http://www.ornl.gov/gist/landscan

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# by aggregating the half-minute cells accordin g to a fixed schedule, which is based on

population density. By design, this process maintains reasonable resolution near urban

concentrations. Land-water boundaries are also identified to maintain reasonable resolution along coastlines. Cells with a know n population count of zero are omitted to

further reduce storage requirements.

**6.1.2 Cell Aggregation**

To reduce the size of LandScan population coverages, raw half-minute grid cells are

merged based on fixed thresholds for popu lation density. The general aggregation

process can be illustrated as follows,

• Fill a 600 x 600 array with population counts from each half-minute cell in a

5 x 5 degree tile.

• Sum each 2 x 2 group of population count va lues to form a series of one-minute

grid cells.

• Split the resulting 300 x 300 array into a series of 60 x 60 arrays to preserve onedegree grid boundaries.

• For each 60 x 60 array, use an independent array to track resulting cell sizes.

• For each 60 x 60 array, use an independent array to store latitude-longitude

coordinates for each one-minute grid cell.

• Test each 2 x 2 group of one-minute cell values to determine if they should be

summed; if so, the result will be a two- minute grid cell. A group will not be

summed if,

1) any of the four densities match criteria (see Table 7)

2) it contains both a zero and a non-zero count (preserves coastlines)

3) the largest count is more than three times an average of the four values

• Similarly, test each 2 x 2 group of 2-mi nute cell values to determine if they

should be summed to form a 4-minute gr id cell; groups including un-merged,

1-minute cells should not be tested.

• Similarly, test each 3 x 3 group of 4-mi nute cell values to determine if they

should be summed to form a 12-minute gr id cell; groups including un-merged,

smaller cells should not be tested.

• Similarly, test each 2 x 2 group of 12-mi nute cell values to determine if they

should be summed to form a 24-minute gr id cell; groups including un-merged,

smaller cells should not be tested.

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# • After all 60 x 60 arrays have been processed, re-combine to produce three

300 x 300 arrays representing one 5 x 5 degree, multi-res tile.

• Combine data from these three arrays into a text file for input to ArcView.

Population density thresholds for each cell size are listed in Table 7. These criteria have

been selected to maintain appropriate resolu tion for risk analysis purposes, and they

are based solely on engineering judgment.

Table 7. Population Density Thresholds

Cell Size

(minutes) Min Pop

Density

(people/km 2 

# ) Max Pop

Density

(people/km 2 

# )

1 1 8 0 n o n e

2 6 0 1 8 0

4 2 0 6 0

1 2 5 2 0

2 4 0 5

**6.1.3 Shapefile Creation**

Each text file representing a 5 x 5 degree, multi-res tile is proc essed by an RTI script

hosted in the ArcView environm ent. This script reads latitude, longitude, cell size, and

population count, and then creates a polygon object for each multi-res grid cell. The

script also creates and partially populates an attribute table before calling another RTI

script to assign names and key codes based on political entities. A polygon coverage

based on DCW (Digital Chart of the World) was used to determine appropriate identity

codes and land area estimates for each ce ll. DCW was published by ESRI (Environmental Systems Research Institute), and it contains detailed polygons that provide a

reasonable representation of land-water boun daries on a global basis. Generally, DCW

is partitioned only to the level of individu al countries, but first-level administrative

districts and separate islands are distingu ished in selected cases. RTI also used

polygons from another ESRI coverage (Admin 98) to include islands missing from DCW

and update several obsolete political en tities, such as the former Yugoslavia.

Once a multi-res cell polygon is created, it is intersected with DCW land polygons. If an

intersection exists, the cell polygon is assi gned a GMI (Global Mapping International)

key code and the lowest level administrative district name that corresponds. If a cell

intersects more than one land polygon, identifi cation is based on the largest intersection

area. If a cell has no intersections with la nd polygons (e.g., is surrounded by water), a

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# search box, which is slightly larger than the cell polygon, is created. The search box is

then intersected with DCW in order to estab lish an identity. If needed, a search box will

be increased in size until an intersection is detected.

**6.1.4 Land Area Adjustments**

Unreasonably large population densities can occur when the intersection of multi-res

cells and DCW polygons returns very small land areas. Crosschecks in various parts of

the world using high resolution maps indicate that coastline definitions, as represented

by DCW polygons, are responsible. With that in mind, logic was introduced to modify

land area for selected cells based partially on population density. Table 8 lists criteria

used to make such adjustments. Note that en tries are presented in a specific order; the

first condition met would determine the only modification made for a given cell.

Table 8. Criteria for Adjusting Land Area

C r i t e r i a M o d i f i c a t i o n

Land Area < 0.01 km 2 

# Land Area = 0.01 km 2



# Land Area > Cell Area Land Area = Cell Area

Land Area > 50% of Cell Area None

(Pop / Land Area) > 8000 Land Area = Cell Area

(Pop / Land Area) > 1000 &

Pop < 1000 Land Area = Pop / 1000

(always < 1.0 km 2 

# )

(Pop / Land Area) > 1000 &

Pop > 1000 Land Area = linear interpolation between 50% and

100% of Cell Area based on log 10 

# (Pop) between

log 10 

# (1000) = 3.00 and log 10 

# (8000) = 3.95

These adjustments affected 48,730 out of nearly nine million cells, or approximately half

a percent of all multi-res grid cells. Those wi th less than 0.01 square kilometers of land

area accounted for 28,868 of the modified cells . After all multi-res processing, more than

7400 of the final population cells have a dens ity over 16,000 per square kilometer. These

cells are located in regions known to be de nsely populated and appear to be legitimate.

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# 6.1.5 Attribute Table

ArcView uses an attribute table to associ ate numerical and textual information with

polygons in a shapefile coverage. Physically, this information is stored in a database file

using a popular format called DBase IV (e.g., shape_name.dbf). Data fields from RTI’s

multi-res LandScan coverages are outlined below.

CELL_ID:

This field is provided to uniquely identify a multi-res grid cell. Latitude and

longitude are transformed to integer indices and then converted to text strings.

The two strings are joined with a period before being stored (e.g., - 89 degrees

latitude & - 178 degrees longitude yields “120.240”).

CENT_LAT (+N):

This field contains the latitude coordinate of a cell's centroid.

CENT_LON (+E):

This field contains the longitude coordinate of a cell's centroid.

CELL_SIZE (min):

This field contains cell size in minutes of latitude or longitude.

POP_2002:

This field contains the aggregated LandScan population count for year 2002.

SQKM_CELL (km2):

This field contains cell area in square kilometers. Polygon area was computed in

ArcView using an equal-area azimuthal projection, which was based on the

GRS80 ellipsoid.

SQKM_LAND (km2):

This field contains the land area of a cell in square kilometers. Land area was

estimated based on intersections with DCW polygons (see Section 6.1.3).

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# EFF_KEY:

This field contains an effective key code, which uniquely identifies the political

entity associated with a cell. Each key code is a text string composed of one to

three parts that are separated by hyphens. The first part is a country identifier,

and the second part is a first-level admini strative district, if available (e.g., the

state of Florida would be represented as “USA-FLA”). The third part may be a

second-level administrative district or an y other useful identifier. Total length is

limited to 17 characters.

REG_NAME:

This field contains a human readable region name based on the most specific part

of a cell’s effective key code. Total length is limited to 35 characters.

LAND_DIST:

This field contains a text string describing a cell’s land distribution among DCW

polygons. Up to three country identifiers are listed, along with a corresponding

percentage of cell area. The field is left bl ank if there were no intersections found.

Total length is limited to 25 characters.

**6.1.6 Cells Added by RTI**

The locations of multi-res LandScan cells we re compared to a list of known countries

and other political entities to ensure ad equate coverage. Although there are many

sources available, the CIA’s World Factbook 2002 [ 17] 

# was chosen as a baseline source for

information on political entities. There were a total of twelve entities found to have no

representation among multi-res population ce lls. Most of these discrepancies are military bases or research installations having large transient populations. Many population

counts for foreign military installations are estimated because exact values are difficult

to obtain from the respective host govern ments. Nevertheless, RTI added Diego Garcia

(3502 people), Wake Island (123 people), and Midway Island (39 people) for completeness. One-minute grid cells were created in appropriate locations, and population

counts were distributed uniformly based on la nd area in each cell. While the population

added is very small compared to LandScan’s 2002 world total of 6.23 billion, these three

islands are sometimes hazarded by launch vehicle overflight.

**6.2 RTI Sheltering Model**

RTI's population sheltering model was origin ally documented in a report concerning

only ER launches. [ 18] 

# Various types of demographic data were used to estimate percentages of populations in four sheltering categories: open, light, medium, and heavy.

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# Based on this information, three sets of shel tering assumptions were developed: one for

developing third-world nations, a second fo r more developed nations, and a third for

industrialized nations. A best-fit assumption set was identified for each country, and

sheltering percentages were determined for day and night time frames. Percentages by

country were assumed to apply to each comp utation cell associated with that country.

In a later effort, which was documented in an RTI memorandum, [ 19] 

# these procedures

were used to extend the model to most of th e political entities identified during LandScan processing (see Section 6.1).

Sheltering percentages for most political entiti es included in the curre nt model are listed in

A p p e n d i x A

. N o t e t h a t o n l y a s u b s e t w e r e used for this analysis. Some a d m i n i s t r a t i v e districts, such as U.S. states are not listed separately there.

**6.3 Population Usage for B-30 Analysis**

For this study, base year population estimate s were updated to an analysis year of 2005.

Annual growth rates from the CIA’s World Factbook 2002 [ 17] 

# were applied at a country

level, although many islands were treated separately.

**6.3.1 Mode 4 Computations**

RTI’s multi-res version of LandScan 2002 is de scribed above in Section 6.1. A subset of

this model was used as a source of populati on counts for Mode 4 computations over

Europe and the Middle East.

**6.3.2 Mode 5 Computations**

LandScan 2002 population counts, obtained from ORNL, were aggregated from raw

grid cells spanning a half-minute of lati tude or longitude to produce 17,034 sixtyminute (i.e., one-degree) cells. RTI added thr ee additional cells covering Diego Garcia,

Wake Island, and Midway Island (see Sect ion 6.1.6). The 2002 world population represented by RTI's 1 x 1 degree version of Land Scan totals 6.23 billion. Updated to reflect

three years of growth, the 2005 world population totals 6.46 billion. A subset of this

model was used as a source of population counts for Mode 5 computations.

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# 7. Downrange Risk Estimation

As described previously, RTI models downrange failure responses (and therefore risks)

using two methodologies: Mode 4 and Mode 5 (see Section 1). Casualty expectations

for each mode are estimated separately an d then summed. This is appropriate since

relative likelihood of occurrence has alread y been included in the failure probabilities

used. Regardless of response mode, risks are estimated by assuming failure occurs at

each of a series of time points during uppe r stage burn. Effects from simulated debris

impacts are determined based on various mode ls and combined on a probabilistic basis.

Many assumptions are made to facilitate this process, and they add uncertainty to the

result, beyond that due to uncertainty asso ciated with input data. Therefore, it is

important to interpret baseline risk estimate s with the assistance of a comprehensive

sensitivity analysis. Baseline values are intend ed to be good median estimates that are

consistent with scope and budget, sometimes conservative, yet reasonable enough to be

helpful in preparing for launch operations.

**7.1 Mode 4 Baseline Risks**

In order to estimate Mode 4 risks, RTI assumes impact probability (P I 

# ) may be modeled

as the product of a downrange component (P X 

# ), a crossrange component (P Y 

# ), and a

corridor-specific Mode 4 failure probability (P F4 

# ). Due to current modeling limitations,

P X 

# i s a s s u m e d t o b e p u r e l y a function of dwell time (based on drag-corrected impacts).

Dwell time is primarily a fu nction of the upper stage trajectory and aerodynamic

characteristics of a given debris class. P Y 

# is assumed to follow a class-specific Gaussian

distribution having a time-varying standard deviation. [ 20] 

# Factors influencing crossrange standard deviation include vehicle performance, potential guidance errors, postfailure behavior, breakup phenomena, and wind profiles. Geographic locations of

simulated debris impacts determine which people are hazarded versus time.

CORIDORE (see Section 1) was used to esti mate Mode 4 risks over Europe and the

Middle East. Based on the nominal trajectory, high beta (ballistic coefficient) debris

classes were found to generate impact traces that enter the west coast of Ireland around

462 seconds. Likewise, low beta classes were fo und to generate impact traces that exit

the south-east coast of Oman around 534 seco nds. Simulated debris impacts and associated dispersions were analyzed for assu med failures that bound this time span.

CORIDORE generates a series of small comp utation cells over downrange landmasses,

forming a corridor that envelopes the collectio n of impact traces for all debris classes

considered. This process is accomplished such that edges of the corridor extend at least

four sigma in both crossrange directions (rig ht and left), for all classes. Also, cells are

sized to be no wider than 40 percent of on e sigma for the smallest crossrange dispersion, for all classes. This is done to mini mize errors when computing area under the

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# probability density curve. For each computat ion cell, effective population density is

determined by intersecting the cell polygo n with appropriate population polygons, and

assuming a uniform density across each. Popula tion counts and casualty areas are both

tailored based on estimated sheltering percen tages (see Section 6.2). The following steps

are accomplished for each computation cell:

• find conditional impact probability for one fragment (P X 

# • P Y 

# )

• find conditional fragment casualty probability for each sheltering category

(multiply by ratio of effective casualty area to cell area)

• accumulate casualty probabilities for multiple fragments in a class

• accumulate casualty probabilities across debris classes

• multiply by P F4 

# to get an absolute casualty probability

• find a casualty expectation increment for each sheltering category

(multiply by product of cell area and effective population density)

• sum casualty expectation increments for all sheltering categories

Finally, casualty expectation increments for all computation cells are summed to yield

an estimate of Mode 4 risk.

For this study, RTI assumed a combination of immediate breakup at each potential fail

time and breakup after 90 degrees of tumblin g turn. For the tumble cases, end-of-turn

state vectors were approximated using an RT I software tool called TRAX (Trajectories

for overflight Risk Analysis), and tumble turn data provided by ER Wing Safety. [ 21] 

# This

data includes turn duration, turn angle of the velocity vector, and final speed at

assumed breakup. TRAX was derived from RTI program TRIAL (Trajectories for Risk

Investigations At Launch), but it includes a new tumble turn algorithm that is valid for

downrange overflight use. The cross-track com ponents of debris impact offsets attributable to tumble turns were root-sum-squared with three other dispersion components:

guidance and performance error, post-breaku p wind drift, and estimated effects of

aerodynamic lift. Due to current modeling lim itations, breakup induced components of

velocity were assumed zero (e.g., low energy breakup).

The left 3-sigma dispersed trajectory was used to estimate cross-track components of

debris impact offsets attributable to gu idance and performance error. Atmospheric

properties and wind effects over the region of interest were based on December winds

from NASA’s Global Reference Atmosphe ric Model 1999 (GRAM 99). In addition, a

3-sigma uncertainty factor was applied to ob tain wind dispersion components. A recent

upgrade to RTI’s software supports continuous interpolation of GRAM data as altitude

and location change. Because current aero dynamic models are based on continuum

flow assumptions, air density and wind speed were set to zero above an altitude of

350 thousand feet. Based on engineering judgme nt, a fixed lift-to-drag ratio of 0.2, and a

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# constant aerodynamic uncertainty of 25 percen t, were assumed for all debris classes.

Cross-track impact offsets attributable to aerodynamic lift were approximated using

reduced in-track offsets. Based on a limited number of test cases, drag variation was

reduced by an order of magnitude for this purpose. A more involved research study

would be needed to improve on that assumption.

Baseline results for Mode 4 are listed below in Table 9. The first row gives total time to

transit populated regions, and the second row gives Mode 4 failure probability over

that time span. Figure 4 shows a representa tion of the Mode 4 corridor. Each colored

block in this figure represents the summati on of risk increments from hundreds of

individual computation cells. Casualty expe ctations for day sheltering are depicted.

Table 9. Baseline Mode 4 Risks for Titan IV B-30 Mission

C r i t e r i a R e s u l t

Transit Time (sec) 72.5

Failure Probability 5.99 x 10 -3



# Exposed Population (millions) 128.2

Casualty Expectation

Day 142.483 x 10 -6



# Night 149.366 x 10 -6



# Figure 4. Summary of Mode 4 Corridor for Titan IV B-30 Mission

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# 7.2 Mode 5 Baseline Risks

The downrange gate time for this analysis (443 sec) was provided by ER Wing Safety, [ 22]



# as shown in Figure 5. Since no FTS is availabl e beyond the gate, this time is a reasonable

starting point for Mode 5 computations. Once orbit has been achieved, Mode 5 impacts

are no longer possible. Therefore, Stage 2 shutdown (approximately 550 sec for B-30) is

a reasonable ending point for Mode 5 co mputations. Consequently, Mode 5 failures

were considered from 443 through 550 seconds, a span of 107 seconds. This span was

divided into 19 failure intervals, and 100, 000 random attitude cases were simulated

from the mid-point of each interval. For ea ch simulation, Stage 2 thrust accelerations

were included through Stage 2 shutdown, regardless of assumed fail time.

Impact points for selected failure intervals, identified by mid-point time, are plotted in

Figure 6. Although 100,000 cases were simula ted for each interval, only 20,000 samples

per interval are plotted in this figure. Impa ct points corresponding to the assumed gate

time (shown below for a mid-point time of 446 sec) illustrate the maximum geographic

extents for Mode 5.

Panama Nicara gua Hondura s Cu b a Caribb ean A l geria

Maurit ania Wes t e r n

Sahara Morocc o

Mali

S e ne ga l

Gam b i a

G u in ea- B i ssau Gu i n e a Bu r k i n a Fa so A z or es

Ca na r y Is

. 

# Icelan d

D enm ar k



# Ire l an d Un i t ed

K i n g do m

Spain Fr ance

P o rt uga l B e lg iu m N e t h er l a nd s

Ma d e i r a

B e r m uda



# 10 0 20 0 30 0 40 0 45 0 46 0 47 0 48 0 49 0 44 3

Ga t e Tim e

B

- 30 N o minal Tra j

.



# Figure 5. Downrange Gate for Titan IV B-30 Mission

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# Figure 6. Sample Mode 5 Impacts for Titan IV B-30 Mission

0 10 20 30 40 50 60 70 80 90 100

440 450 460 470 480 490 50 0 510 52 0 5 3 0 540 5 5 0

Fai l Time M E T (sec)Monte Carlo Cases (%) 

# Or b i t a l %

Balli s t ic %



# Figure 7. Selected Trends in Mode 5 Simulation Cases

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# The percent of random attitude cases that achieved orbital conditions are plotted above

in Figure 7. All other cases, which are also plotted, resulted in ballistic impact; none of

the cases impacted while thrusting. Note th at all simulations beyond 547 seconds ended

in orbit rather than impact.

Baseline results for Mode 5 are listed below in Table 10. The first row gives the total

number of simulations performed, and the second row gives Mode 5 failure probability

over the time span considered. The fourth row gives an average density for all population cells involved.

Table 10. Baseline Mode 5 Risks for Titan IV B-30 Mission

Criteria 443 to 550 sec

Number of Simulations 1,900,000

Failure Probability 4.65 x 10 -4



# Exposed Population (millions) 1048.2

Population Density (/NM 2 

# ) 2 3 0

. 7

Casualty Expectation

Day 2.558 x 10 -6



# Night 2.592 x 10 -6



# Two countries in Europe (Germany and the Un ited Kingdom) together contribute about

23 percent of the total Mode 5 risk. Five a dditional countries (Iran, Ukraine, Turkey,

Poland, and France) contribute more than five percent each, and approximately 34 percent collectively. Day sheltered risk incremen ts are listed in Table 11 for each political

entity exposed. Figure 8 presents exposed popula tion as a function of failure time, while

Figure 9 presents cumulative casualty expectation for day sheltering.

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# Table 11. Day Sheltered Mode 5 Risks by Country

Country Casualty

Expectation Percent

Total

A f

g h a n i s t a n 7

. 9 0 3 E

- 1 0 0

. 0

A l b a n i a 7

. 2 2 9 E

- 0 9 0

. 3

A l g e r i a 8

. 7 3 7 E

- 0 9 0

. 3

A r m e n i a 9

. 9 1 5 E

- 0 9 0

. 4

A u s t r a l i a 1

. 8 2 7 E

- 1 3 0

. 0

A u s t r i a 3

. 1 5 3 E

- 0 8 1

. 2

A z e r b a i j a n 1

. 9 0 7 E

- 0 8 0

. 7

B e l a r u s 2

. 8 7 1 E

- 0 8 1

. 1

B e l g i u m 2

. 0 6 9 E

- 0 8 0

. 8

Bosnia and Herzegovina 7.480E-09 0.3

B u l g a r i a 2

. 2 0 9 E

- 0 8 0

. 9

C a n a d a 4

. 8 1 9 E

- 0 9 0

. 2

C r o a t i a 1

. 2 0 8 E

- 0 8 0

. 5

Cy p r u s 1

. 3 6 5 E

- 0 9 0

. 1

Czech Republic 3.477E-08 1.4

D e n m a r k 2

. 3 2 7 E

- 0 8 0

. 9

Diego G a rcia 5.284E-12 0.0

D j i b o u t i 4

. 1 8 7 E

- 1 1 0

. 0

Egy p t 6

. 9 1 2 E

- 0 8 2

. 7

E r i t r e a 1

. 4 2 2 E

- 0 9 0

. 1

E s t o n i a 1

. 9 0 3 E

- 0 9 0

. 1

E t h i o p i a 5

. 9 3 9 E

- 1 0 0

. 0

F a roe Isl 1.554E-10 0.0

F i n l a n d 4

. 3 0 8 E

- 0 9 0

. 2

F r a n c e 1

. 4 0 0 E

- 0 7 5

. 5

F r ench Poly nesia 4.119E-13 0.0

G e o r g i a 1

. 4 8 4 E

- 0 8 0

. 6

G e rmany 3

. 0 7 3 E

- 0 7 1 2

. 0

G r e e c e 1

. 3 5 0 E

- 0 8 0

. 5

G r e e n l a n d 2

. 4 9 8 E

- 1 3 0

. 0

Hungary 2

. 9 0 0 E

- 0 8 1

. 1

I c e l a n d 5

. 0 8 4 E

- 1 0 0

. 0

I n d i a 6

. 7 6 8 E

- 1 2 0

. 0

I r a n 1

. 8 8 0 E

- 0 7 7

. 3

I r a q 8

. 7 6 4 E

- 0 8 3

. 4

I r e l a n d 1

. 6 6 7 E

- 0 8 0

. 7

I s r a e l 1

. 0 1 4 E

- 0 8 0

. 4

Italy 9

. 1 7 6 E

- 0 8 3

. 6

Jersey 4

. 2 2 0 E

- 1 0 0

. 0

J o r d a n 7

. 5 5 5 E

- 0 9 0

. 3

K a z a k h s t a n 6

. 0 0 2 E

- 1 0 0

. 0

Kuw a i t 5

. 0 9 8 E

- 0 9 0

. 2



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Casualty Percent

Country Expectation Total

L a t v i a 5

. 6 7 0 E

- 0 9 0

. 2

L e b a n o n 6

. 8 8 8 E

- 0 9 0

. 3

Liby a 3

. 2 9 4 E

- 0 9 0

. 1

L i t h u a n i a 1

. 0 2 4 E

- 0 8 0

. 4

M a c e d o n i a 1

. 5 5 8 E

- 0 9 0

. 1

M a l d i v e s 4

. 9 4 6 E

- 1 0 0

. 0

M a l t a 3

. 2 9 3 E

- 1 0 0

. 0

M o l d o v a 1

. 0 0 6 E

- 0 8 0

. 4

M o n t e n e g r o 1

. 6 0 8 E

- 0 9 0

. 1

N e t h e r l a n d s 7

. 3 3 1 E

- 0 8 2

. 9

New Z ealand 2.123E-11 0.0

Norw ay 1

. 1 0 4 E

- 0 8 0

. 4

O m a n 7

. 9 6 4 E

- 0 9 0

. 3

P a k i s t a n 1

. 5 7 2 E

- 0 9 0

. 1

P o l a n d 1

. 7 0 7 E

- 0 7 6

. 7

P o r t u g a l 4

. 3 2 5 E

- 0 9 0

. 2

Q a t a r 1

. 9 5 5 E

- 0 9 0

. 1

R o m a n i a 7

. 9 7 6 E

- 0 8 3

. 1

R u s s i a 9

. 7 8 6 E

- 0 8 3

. 8

Saudi Arabia 3.090E-08 1.2

S e r b i a 2

. 3 5 7 E

- 0 8 0

. 9

S l o v a k i a 1

. 2 6 8 E

- 0 8 0

. 5

S l o v e n i a 2

. 5 7 8 E

- 0 9 0

. 1

S o m a l i a 1

. 5 1 4 E

- 0 9 0

. 1

S p a i n 2

. 8 7 4 E

- 0 8 1

. 1

St Pierre and Miguel 2.673E-11 0.0

S u d a n 6

. 8 3 8 E

- 1 0 0

. 0

Sw e d e n 2

. 2 7 1 E

- 0 8 0

. 9

Sw i t z e r l a n d 1

. 7 2 7 E

- 0 8 0

. 7

Sy r i a 3

. 8 1 9 E

- 0 8 1

. 5

T u n i s i a 6

. 6 0 0 E

- 0 9 0

. 3

T u rkey 1

. 8 0 3 E

- 0 7 7

. 0

T u r k m e n i s t a n 2

. 2 3 3 E

- 0 9 0

. 1

U k r a i n e 1

. 8 5 5 E

- 0 7 7

. 3

United Arab Emirat e s 9

. 0 6 2 E

- 0 9 0

. 4

United Kingdom 2.700E-07 10.6

U z b e k i s t a n 9

. 9 2 1 E

- 1 3 0

. 0

Y e m e n 1

. 3 7 2 E

- 0 8 0

. 5



# Total 2.558E-06 100

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T i t a n I V B

- 30 M i ssi on

Exposed P opul at i o n

0. 00E+00 2. 00E+08 4. 00E+08 6. 00E+08 8. 00E+08 1. 00E+09 1. 20E+09

440 45 0 460 470 480 490 500 510 52 0 530 540 550

M i ssi on El apsed T i m e ( sec)Exposed Population



# Figure 8. Population Exposed to Mode 5 Hazards

T i t a n I V B

- 30 M i ssi on

C asual t y Expect at i o n

0. 00E+00 5. 00E- 0 7 1. 00E- 0 6 1. 50E- 0 6 2. 00E- 0 6 2. 50E- 0 6 3. 00E- 0 6

440 450 460 470 480 490 500 510 520 530 540 550

M i ssi on El apsed T i m e ( sec)Casualty Expectation



# Figure 9. Cumulative Risk for Mode 5 Response – Day Sheltering

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# 7.3 Total Baseline Risks

Baseline downrange risks for the Titan IV B- 30 mission are listed in Table 12. Including

all assumptions discussed previously, the Mode 4 and Mode 5 casualty expectations are

repeated here. Total risks have been rounded to three significant figures as a reminder

of the uncertainty associated with these resu lts. The totals represent baseline estimates

for this study.

Table 12. Baseline Downrange Risks for Titan IV B-30 Mission

Casualty Expectations

Failure

Response Day N i g h t

Mode 4 142.48 x 10 -6 

# 149.37 x 10 -6



# Mode 5 2.56 x 10 -6 

# 2.59 x 10 -6



# Total 145 x 10 -6 

# 152 x 10 -6



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# 8. Sensitivity of Risks to Assumptions

Methods used for this analysis were sele cted from among several possibilities. As

acknowledged previously, the relationship between a random attitude, impact density

function and one based on all possible Mode 5 failure responses is unknown. It is not

possible, within the scope of this task, to quantify uncertainties in results associated

with such modeling decisions. However, a ge neral discussion of uncertainty in RTI risk

estimates is available. [ 23]



# Values used for a number of key parameters associated with th e models chosen are

uncertain. Their influence on risk estimates can be measured by varying values over an expected or reasonable range and re-computi ng. Sensitivity of results to the following

key parameters is presented in this section:

• failure probability and response mode allocation

• casualty areas, both breakup model and post-impact effects

• population counts

• sheltering percentages

• Mode 4 dispersions

• monthly winds

Baseline results presented previously have b een repeated here for convenience. Sensitivity analyses were performed for Day sh eltering only, unless otherwise specified.

Most of the issues addressed w e r e i n v e s t i g a t e d p e r d i r e c t i o n o f E R W i n g S a f e t y

. [ 24]



# 8.1 Sensitivity to Failure Probability

As part of this analysis, RTI was tasked by ER Wing Safety to provide estimates for

failure probabilities, including suggestions for alternate methodologies. RTI's proposed

Titan I V B-30 failure estimates were documented in an RTI memorandum. [4 ] 

# That

information has been used as the basis for se nsitivity analyses with respect to failure

probability. Two possible approaches were presented: top-down and bottom-up . The topdown approach begins with total mission failure probability and allocates it among

flight phases. Alternatively, the bottom-up approach begins with failure probabilities

for each flight phase and builds to a total mission failure probability. As compared to

the baseline probability of a failure during ov erflight, bottom-up yields a low estimate

and top-down yields a high estimate.

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# Top-Down Approach

With this approach, an overall mission failure probability of 0.12 is assumed and then

partitioned among flight phases 0

– 1, 2

- 3, and 4

- 5. The conditional failure probabilities for flight phases 2 and 3 are combined , based on the assumption that Stage 2, in

this case, has to do the work of typical flight phases 2 and 3. For example, if Stage 1 or

Stage 2 under-performed, the deficit in ener gy could possibly be overcome in vehicles

with a phase 3. Similarly, phases 4 and 5 we re combined because the payload, in this

case, has to do the work typically accomplis hed by those two phases in Titan configurations with upper stages. Values for the t op-down approach are summarized in Table 13.

Table 13. B-30 Top-Down Failure Probabilities

P a r a m e t e r V a l u e

Titan overall mission failure probability 0.12

Conditional failure probability for flight phases 2 - 3 0.27 a



# Absolute failure probability for flight phases 2 - 3 0.0324

Conditional f ailure proba bilitie s for Mode 4

Mode 5 0.9500 b



# 0.0500 b



# Absolute fail ure probabili ties for: Mode 4

Mode 5 0.0308

0.0016

Mode-4 absolute failure rate

(probability per second) (0.0308 / 230.1)

1.338 x 10 -4



# Mode-5 absolute failure rate

(probability per second) (0.0016 / 230.1)

7.0404 x 10 -6



# a Sum of conditional probabilities for flight phases 2 and 3

b As directed by ER Wing Safety



# Bottom-Up Approach

With this approach, an enhanced phase 2 conditional probability is used, and the

phase 3 conditional probability is discarded . Overall mission failure probability is

effectively reduced from 0.12 to 0.0948. Althou gh not used for this analysis, the conditional probabilities for phases 4 and 5 could be combined into phase 5. Values for the

bottom-up approach are summarized in Table 14. It should be noted that a more comprehensive bottom-up approach is possible.

Risk estimates that are based on failure pr obabilities derived from both bottom-up and

top-down approaches are listed in Table 15.

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# Table 14. B-30 Bottom-Up Failure Probabilities

P a r a m e t e r V a l u e

Titan overall mission failure probability 0.12

Conditional failure probability for flight phase 2 0.08 a



# Absolute failure probability for flight phase 2 0.0096

Conditional f ailure proba bilitie s for Mode 4

Mode 5 0.9500 b



# 0.0500 b



# Absolute fail ure probabili ties for: Mode 4

Mode 5 0.0912

0.0005

Mode-4 absolute failure rate

(probability per second) (0.0912 / 230.1)

3.963 x 10 -4



# Mode-5 absolute failure rate

(probability per second) (0.0005 / 230.1)

2.086 x 10 -6



# a Adjusted for Titan configurations with no upper stage

b As directed by ER Wing Safety



# Table 15. Sensitivity of Downrange Risks to Failure Probability

Casualty Expectation

Failure

Probability Mode 4 Mode 5 Total

0.0096 (low) 68.39 x 10 -6 

# 1.23 x 10 -6 

# 69.6 x 10 -6



# 0.0200 (baseline) 142.48 x 10 -6 

# 2.56 x 10 -6 

# 145.0 x 10 -6



# 0.0324 (high) 230.89 x 10 -6 

# 4.14 x 10 -6 

# 235.0 x 10 -6



# 8.2 Sensitivity to Response Mode Allocation

Varying response mode allocation provides insight into risk contributions associated

with each mode. Total risk is the sum of Mode 4 and Mode 5 values, which are each

proportional to mode-specific failure probabilit ies. First, the baseline estimates for each

mode are adjusted based on updated prob ability levels. Then, the new estimates for

each mode are summed to yield a total sensit ivity value. Results of this process for

selected alternate allocations are presented in Table 16.

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# Table 16. Sensitivity of Downrange Risks to Response Mode Allocation

Casualty Expectation

Response Mode

Allocation (M4 - M5) Mode 4 Mode 5 Total

85% - 15% 127.49 x 10 -6 

# 7.67 x 10 -6 

# 135.2 x 10 -6



# 90% - 10% 134.98 x 10 -6 

# 5.12 x 10 -6 

# 140.1 x 10 -6



# 95% - 5% (baseline) 142.48 x 10 -6 

# 2.56 x 10 -6 

# 145.0 x 10 -6



# 99% - 1% 148.49 x 10 -6 

# 0.51 x 10 -6 

# 149.0 x 10 -6



# Intuition would suggest that the relative perc entage of Mode 5 failures should be small

given modern guidance systems and variou s redundancies. However, launch vehicle

history can be used to support a range of estimates, some higher than 20 percent. [ 25]



# Baseline and alternate allocations presented here were requested by ER Wing Safety.

**8.3 Sensitivity to Casualty Areas**

Casualty areas depend on extent of vehicle breakup, presence or absence of explosive

debris, aero-thermal survivability of fragment s to impact, population sheltering effects,

and modeling procedures used. Estimates fo r occupants of light, medium, and heavy

sheltering were computed using HACK (Ver sion 2), while those for people in the open

were computed using RTI procedures (see Se ction 5). For baseline risk estimates, all

debris were assumed to impact on soft surfaces, a n d p o s t

- i m p a c t e f f e c t s w e r e i n c l u d e d

.

Currently, RTI has no way of determining wh ich fragments may be partially or totally

consumed during re-entry. Therefore, ER Wing Safety directed an assumption that all

hardware survives to impact, and no sens itivity computations were performed for

debris survivability. As mentioned previous ly, all liquid propellants in Stage 2 and the

payload are assumed to leak out or boil off be fore impact. If propellants were to remain,

explosions could occur, thus increasing effe ctive casualty areas. However, RTI believes

that retention of significant quantities of liquid propellant is implausible.

**8.3.1 Sensitivity to Breakup Model**

Extent of breakup can dramatically influenc e total casualty areas. RTI's baseline downrange debris model includes 70 fragments, wh ich total an inert weight of 32,425 pounds.

The Stage 2 portion of this model comprises only 23 pieces with a total weight of 10,366

pounds. Of these, 20 pieces are small, and the remaining three pieces are much larger.

In order to conduct a previous quick-look analysis for B-30, [ 26] 

# RTI assumed a more

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# extensive breakup of Stage 2. This model in cludes 96 fragments with a total weight of

9728 pounds, and it has been used for sensitiv ity purposes in the current study. Due to

a lack of data regarding the payload, th at portion of the baseline model was not

updated to reflect a more extensive breaku p. At the other extreme, if Stage 2 and

payload each survived intact (with loss of liquid propellants only), there could be as few as two components at impact.

From the above discussion, casualty areas for less extensive breakup were computed

assuming two intact components that total 32,425 pounds. Casualty areas for more extensive breakup were computed assuming 143 fragments that total 31,787 pounds.

Resulting total casualty areas for four shel tering categories are listed in Table 17.

Corresponding risk estimates are listed in Tabl e 18. In the third row of each table, note

that sheltering becomes more effective with a larger number of smaller fragments.

Table 17. Alternative Breakup Models

Sheltered Casualty Areas (ft 2 

# )

Breakup

Model Number

of Frags O p e n L i g h t M e d i u m Heavy

Two Pieces 2 1433 2283 2474 1520

B a s e l i n e 7 0 5 4 0 7 3 2 5 3 2 1 0 4 4 6 9

Many Pieces 143 9811 2779 1374 434

Table 18. Sensitivity of Downrange Risks to Breakup Model

Casualty Expectation

Breakup

Model Mode 4 Mode 5 Total

Two Pieces 108.76 x 10 -6 

# 1.87 x 10 -6 

# 110.6 x 10 -6



# Baseline 142.48 x 10 -6 

# 2.56 x 10 -6 

# 145.0 x 10 -6



# Many Pieces 141.51 x 10 -6 

# 2.60 x 10 -6 

# 144.0 x 10 -6



# 8.3.2 Sensitivity to Post-Impact Effects

Impacts on hard surfaces may be a more reasonable assumption for unsheltered people

in urban areas. On the other hand, the extent of post-impact effects, as modeled by RTI,

has been questioned occasionally by other analysts. Consequently, casualty areas for

people in the open were computed based on ha rd surfaces to yield a high estimate, and

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# excluding post-impact effects to yield a low estimate. RTI has no way of varying HACK

computations for people in shelters. Resulting total casualty areas are listed in Table 19,

and corresponding risk estimates are listed in Table 20.

Table 19. Alternative Post-Impact Assumptions

Sheltered Casualty Areas (ft 2 

# )

Post-Impact

Effects O p e n L i g h t M e d i u m Heavy

O f f 3 6 3 0 3 2 5 3 2 1 0 4 4 6 9

Soft (baseline) 5407 3253 2104 469

H a r d 9 5 9 4 3 2 5 3 2 1 0 4 4 6 9

Table 20. Sensitivity of Downrange Risks to Post-Impact Effects

Casualty Expectation

Post-Impact

Effects Mode 4 Mode 5 Total

Off 134.92 x 10 -6 

# 2.41 x 10 -6 

# 137.3 x 10 -6



# Soft (baseline) 142.48 x 10 -6 

# 2.56 x 10 -6 

# 145.0 x 10 -6



# Hard 163.74 x 10 -6 

# 2.97 x 10 -6 

# 166.7 x 10 -6



# 8.4 Sensitivity to Population Counts

Population counts are uncertain, especially given the movement of refugees in regions

torn by natural disasters and civil war. In addition, it is typically assumed that population values are for indigenous peoples only, and do not include refugees or tourists.

Populations for a year of interest (2005 for baseline risks) depend on estimated counts

for some base year (2002 in this case) and estimated growth rates for subsequent years.

Growth rates are also uncertain. Therefore, it seems reasonable that population estimates for year 2005 could easily be in error by as much as 20% (high or low). To provide

further insight, five years of additional gr owth can be illustrated by updating to year

2010. Including these adjustments, correspondi ng risk estimates are listed in Table 21.

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# Table 21. Sensitivity of Downrange Risks to Population Counts

Casualty Expectation Population

Year and

Adjustment Mode 4 Mode 5 Total

2005 – 20% 113.99 x 10 -6 

# 2.10 x 10 -6 

# 116.1 x 10 -6



# 2005 (baseline) 142.48 x 10 -6 

# 2.56 x 10 -6 

# 145.0 x 10 -6



# 2005 + 20% 170.98 x 10 -6 

# 3.15 x 10 -6 

# 174.1 x 10 -6



# 2010 144.70 x 10 -6 

# 2.62 x 10 -6 

# 147.3 x 10 -6



# 8.5 Sensitivity to Population Sheltering

RTI's day and night sheltering percentages ar e intended as baseline values. Since it is

difficult to predict the distribution of pe ople among sheltering categories with much

precision, two additional sheltering scenari os were investigated. To assess sensitivity

for an upper threshold on effectiveness, RTI created a fictitious sheltering scenario that

will be referred to here as substantial . Based on engineering ju dgment, fixed sheltering

percentages for open, light, medium, and heavy categories were assigned as 0, 50, 30,

and 20 percent, respectively, for all population cells. To assess sensitivity in the opposite

sense, an unsheltered scenario, where all people are assu med in the open, was used. Risk

estimates for substantial and unsheltered scena rios are compared to the baseline results

for day and night in Table 22.

Table 22. Sensitivity of Downrange Risks to Population Sheltering

Casualty Expectation

Sheltering

Scenario Mode 4 Mode 5 Total

Substantial 117.64 x 10 -6 

# 2.02 x 10 -6 

# 119.7 x 10 -6



# Day (baseline) 142.48 x 10 -6 

# 2.56 x 10 -6 

# 145.0 x 10 -6



# Night (baseline) 149.37 x 10 -6 

# 2.59 x 10 -6 

# 152.0 x 10 -6



# Unsheltered 262.30 x 10 -6 

# 4.65 x 10 -6 

# 266.9 x 10 -6



# For Official Use O n ly 

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# 8.6 Sensitivity to Mode 4 Dispersions

As discussed in Section 7.1, four dispersion factors were considered for the Mode 4

portion of this study: guidance and pe rformance (G&P) error, tumbling turn deviations, debris wind drift, and aerodynamic lif t effects. Cross-track impact offsets were

estimated for each debris class based on thes e dispersion factors. Total impact dispersion for each class was then determined by root-sum-squaring the four components at

each downrange location of interest. Assumi ng a Gaussian distri bution in the crosstrack direction, each section of the Mode 4 corridor (see Figure 4) was constructed to

extend at least four sigma for any debris cla ss. It should be noted that relative contributions from each of the four components change with ballistic coefficient (beta).

To assess sensitivity with respect to Mode 4 dispersions, RTI chose to vary total crosstrack offset by first halving each compon ent of dispersion and then doubling each

component. Results show the effect of changi ng all four components simultaneously, as

presented in rows two and three of Table 23. For this combination of vehicle configuration and mission trajectory, risk appears to increase as Mode 4 dispersions increase;

however, that trend could reverse for larg er multipliers. Each dispersion component

was also doubled independently to illustrate its relative influence on risk. It can be seen

in the table below that tumble and lift have little influence for this particular mission

(e.g., debris characteristics, impact point ra tes, population distribution, etc.). The negative change for tumble indicates that slig htly reducing impact probability near the

centerline yields more effect than slightly increasing corridor width.

Table 23. Sensitivity of Downrange Risks to Mode 4 Dispersions

Casualty Expectation

Mode 4

Dispersion Mode 4 Total a 

# % C h a n g e

Baseline 142.483 x 10 -6 

# 145.04 x 10 -6 

# 0

. 0

Total 0.5x offset 138.084 x 10 -6 

# 140.64 x 10 -6



# - 3

. 0

Total 2.0x offset 151.975 x 10 -6 

# 154.53 x 10 -6 

# 6

. 5

G&P 2.0x offset 151.602 x 10 -6 

# 154.16 x 10 -6 

# 6

. 3

Tumble 2.0x offset 142.420 x 10 -6 

# 144.98 x 10 -6



# - 0

. 0 4 3

Wind 2.0x offset 142.544 x 10 -6 

# 145.10 x 10 -6 

# 0

. 0 4 2

Lift 2.0x offset 142.485 x 10 -6 

# 145.04 x 10 -6 

# < 0

. 0 1

a All totals include a constant Mode 5 risk estimate of 2.558 x 10 -6



# .

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# 8.7 Sensitivity to Wind Month

For baseline risk estimates, wind effects ov er the region of interest were computed

using December wind data from GRAM 99 (see Section 7.1). To assess sensitivity versus

seasonal variations, risks were also comput ed using wind data for October and June.

These results are presented below in Table 24. Since Mode 5 risk estimates are not a

function of winds, this sensitivity is applicable to Mode 4 only.

Table 24. Sensitivity of Downrange Risks to Wind Month

Casualty Expectation

Wind

Month Mode 4 Mode 5 a 

# T o t a l

October 141.63 x 10 -6 

# 2.56 x 10 -6 

# 144.2 x 10 -6



# December (baseline) 142.48 x 10 -6 

# 2.56 x 10 -6 

# 145.0 x 10 -6



# June 141.66 x 10 -6 

# 2.56 x 10 -6 

# 144.2 x 10 -6



# a Monthly wind variations have no effect on Mode 5 risk estimates.

**8.8 Use of Covariance Data**

In an effort separate from this study, RTI investigated B-30 covariance data provided by

Lockheed Martin. [ 27] 

# A series of 6 x 6 matrices representing G&P (guidance and performance) error were processed to generate a tr ajectory (i.e., a collection of state vectors)

that is similar to the 3-sigma dispersed traj ectories typically used for risk estimation

purposes. Details of this covariance proce ssing are described in an interim RTI memorandum. [ 28] 

# The alternative G&P trajectory was then used to compute Mode 4 risk

estimates for comparison to baseline results, as shown in Table 25.

Covariance data is based on a large numb er of Monte Carlo simulations, whereas a

standard 3-sigma trajectory is constructed us ing a small number of selected simulation

cases. Since these few cases are usually chosento model extreme behaviors, it is reasonable to expect that covariance processing should yield smaller G&P dispersions. For the

B-30 mission, that translates into lower Mode 4 risk estimates.

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# Table 25. Effect of Covariance Data on Downrange Risks

Casualty Expectation

G&P

Trajectory Mode 4 Mode 5 a 

# T o t a l

Left 3-sigma

(baseline) 142.48 x 10 -6 

# 2.56 x 10 -6 

# 145.0 x 10 -6



# Covariance

3-sigma 138.67 x 10 -6 

# 2.56 x 10 -6 

# 141.2 x 10 -6



# a G&P trajectory variations have no effect on Mode 5 risk estimates.

**8.9 HACK Version Comparison**

RTI first tested and implemented what we refer to as HACK Version 2 (“HACK2”,

developed by ACTA) in the February to March 2000 time frame. As used for downrange risk analysis, this roof penetration model includes four sheltering categories

— people in the open (assuming impacts on soft surfaces) and people in three structure

types: light (e.g., a typical house), medium (e.g., a typical office), and heavy (e.g., a

range blockhouse). RTI has a global populati on sheltering model that depicts distribution of people into these four categories on a country-level basis (see Section 6.2). Our

model also includes four distinct population distribution scenarios: day, night, everyone in the open, and substantial shelteri ng. The latter two scenarios are used for

sensitivity analysis purposes (see Section 8.5) . To estimate baseline casualty areas, RTI

procedures were used for people in the ope n (soft), and HACK2 was used for people in

light, medium, and heavy structures (see Section 5).

RTI first tested and implemented what we re fer to as HACK Version 3 (“HACK3”, also

developed by ACTA) in the November to De cember 2003 time frame. This roof penetration model is more complex than HACK2 an d includes sixteen sheltering categories

— open (soft), open (hard), and people in fourteen combinations of roof type and location

by floor: composite laminate (one floor), pr e-engineered metal (“pre-metal”; one floor),

steel (top floor, next floor down = “mid”, and all lower floors = “low”), concrete (top,

mid, and low), wood (top, mid, and low), and w ood with tile (“tile”; top, mid, and low).

RTI was provided a population sheltering model, developed by ACTA for the B-30

mission, [ 29] 

# that depicts distribution of people into these sixteen categories on a countrylevel basis. This model includes two independ ent sheltering fractions for each category:

one for urban regions and one for rural region s. In addition, separate parameter sets

were provided for six distribution scenari os: day summer weekday, day summer weekend, day winter weekday, day winter weekend, night weekday, and night weekend. To

estimate casualty areas, RTI procedures were used for open (soft) and open (hard)

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# categories, and HACK3 was used for the fourt een structural combinations. It should be

noted that modifications needed for risk estimation have not yet been made to programs CORIDORE or RAPIEC (see Section 1).

Casualty areas produced by HACK3, for a si ngle fragment in each debris class, are

listed in Table 27. The corresponding casual ty areas produced by HACK2 were listed

previously in Table 6 (see Section 5.3). It is interesting to note that the HACK3 responses

for wood and tile roof types are quite simila r for the debris examined in this study.

Figure 10 presents a visual illustration of ca sualty areas from both versions of HACK.

Casualty areas per fragment, for each roof type, are shown in Figure 14 and Figure 15.

Values corresponding to individual floors have been averaged for each HACK3 roof

type. Note that effective weighting would not be uniform if population fractions were considered. To enhance readability, debris class names were replaced with numeric

indices in these figures. Debris class index designations are assigned to debris class

names in Table 26.

Casualty areas per fragment, for each floor lo cation, are shown in Figure 16. Values corresponding to individual roof types have b een averaged for each HACK3 floor location.

Composite and pre-metal roof types are includ ed in the "Top Only" results. Note that

light, medium, and heavy structure types do not correspond to specific floors.

Table 26. Debris Class Index and Corresponding Class ID

I n d e x C l a s s I D I n d e x C l a s s I D I n d e x C l a s s I D I n d e x C l a s s I D

1 S 2

_ 0 1 9 P L

_ 0 6 1 7 P L

_ 1 4 2 5 P L

_ 2 3

2 S 2

_ 0 2 1 0 P L

_ 0 7 1 8 P L

_ 1 5 2 6 P L

_ 2 4

3 S 2

_ 0 3 1 1 P L

_ 0 8 1 9 P L

_ 1 6 2 7 P L

_ 2 5

4 S 2

_ 0 4 1 2 P L

_ 0 9 2 0 P L

_ 1 7 2 8 P L

_ 2 6

5 P L

_ 0 2 1 3 P L

_ 1 0 2 1 P L

_ 1 9 2 9 P L

_ 2 7

6 P L

_ 0 3 1 4 P L

_ 1 1 2 2 P L

_ 2 0 3 0 P L

_ 2 8

7 P L

_ 0 4 1 5 P L

_ 1 2 2 3 P L

_ 2 1 3 1 P L

_ 2 9

8 P L

_ 0 5 1 6 P L

_ 1 3 2 4 P L

_ 2 2

For Official Use O n ly 

# 43 RTI/08360/103-13F

Export Controlled InformationTitan IV B-30 Downrange Risks07/16/2004SteelConcreteWoodTileClassOpen(hard)CompositePre-metalTopMidLowTopMidLowMidLowTopMidLowS2_01407.91505.55689.88260.4922.57406.028.0297.67540.02567.32529.42568.56530.64S2_02254.05160.79453.64276.1534.1527.757.400.00435.53452.48438.49430.83453.89436.64S2_03144.23121.47361.73166.9289.2125.6581.2012.56296.04316.65301.59302.92316.86301.57S2_04149.430.000.000.000.000.000.000.0011.280.001.940.000.00PL_02266.71184.12475.67487.28220.35344.20116.8536.63369.30384.15353.68371.92385.45PL_0387.6521.81107.4413.965.990.0010.190.00185.43125.3335.78172.92124.8835.87PL_0427.0294.8321.068.623.9311.112.390.00186.10113.73172.83164.21111.30PL_0537.039.5344.106.093.827.141.820.0084.1385.2556.1077.2050.87PL_06164.5216.04185.3011.290.000.000.000.00Table 27. Casualty Areas for HACK Version 3Top724.77543.030.003.388.460.00353.86357.080.0056.80171.088.0279.490.00202.609.270.00199.789.310.00PL_0787.8711.130.000.000.000.000.000.000.0025.790.000.000.000.000.00PL_080.00119.2330.71209.9420.773.1812.200.000.00226.85170.4540.04217.12169.3740.30PL_0972.5622.26100.6915.078.061.5612.970.000.00177.87150.1373.88176.81147.8376.09PL_1060.4535.52132.5534.1613.235.3210.887.000.00211.62199.94173.60206.56200.59173.07PL_1157.6433.07122.5130.0411.645.1110.486.200.00203.51193.53162.84200.37194.51164.57PL_12127.7699.67211.96120.0489.6744.8965.2517.057.28238.54246.20230.24241.64248.46230.79PL_1382.1927.20117.9420.057.331.5714.090.000.00201.50171.5996.68191.10170.02100.29PL_1472.2828.11113.0821.577.892.8712.531.630.00199.23175.69124.89190.96175.01128.15PL_1559.7035.86133.2434.7913.595.4710.937.310.00211.93200.88175.47207.07201.65174.86PL_16117.3013.153.780.000.000.000.000.000.0089.060.000.0058.090.000.00PL_17156.8992.59345.67136.6161.1714.1755.4711.752.38292.28304.53292.28293.45304.87290.52PL_1980.240.950.000.000.000.000.000.000.000.000.000.000.000.000.00PL_20505.800.000.000.000.000.000.000.000.000.000.000.000.000.000.00PL_2186.5560.00271.9768.5418.163.2416.824.610.00263.96276.09254.99268.43279.72247.42PL_2255.269.7939.9013.120.000.000.000.000.00108.0353.874.2797.1148.212.40PL_2355.530.000.000.000.000.000.000.000.000.000.000.000.000.000.00PL_2475.9920.1298.0713.467.840.4512.150.000.00180.16134.0351.47176.49131.1152.27PL_2578.9058.63145.4960.5834.0713.0618.927.034.12222.37225.78207.32224.43224.43207.64PL_26146.6632.45242.7121.030.000.004.060.000.00237.09152.236.04232.94149.105.99PL_2797.6962.75233.0377.1138.8410.3021.559.142.16246.72251.24237.33248.64253.49235.46PL_28197.3924.07254.8310.460.000.000.0035.230.00279.8750.600.79260.7248.940.81PL_2961.3615.2474.0011.337.330.009.280.000.00152.01110.1142.49137.65103.4737.79Total9594.22264.77108.22637.31188.2432.51257.3453.8160.98570.26007.64496.88024.75974.54477.1For Official Use Only44RTI/08360/103-13FExport Controlled InformationTitan IV B-30 Downrange Risks07/16/2004Figure 10. Casualty Areas per Fragment by Debris Class, Part 1For Official Use Only45RTI/08360/103-13FExport Controlled InformationTitan IV B-30 Downrange Risks07/16/2004Figure 11. Casualty Areas per Fragment by Debris Class, Part 2For Official Use Only46RTI/08360/103-13FExport Controlled InformationTitan IV B-30 Downrange Risks07/16/2004Figure 12. Casualty Areas per Fragment by Debris Class, Part 3For Official Use Only47RTI/08360/103-13FExport Controlled InformationTitan IV B-30 Downrange Risks07/16/2004Figure 13. Casualty Areas per Fragment by Debris Class, Part 4For Official Use Only48RTI/08360/103-13FExport Controlled InformationTitan IV B-30 Downrange Risks07/16/2004Figure 14. Casualty Areas per Fragment by Roof Type, Part 1For Official Use Only49RTI/08360/103-13FExport Controlled InformationTitan IV B-30 Downrange Risks07/16/2004Figure 15. Casualty Areas per Fragment by Roof Type, Part 2For Official Use Only50RTI/08360/103-13FExport Controlled InformationTitan IV B-30 Downrange Risks07/16/2004Figure 16. Casualty Areas per Fragment by Floor LocationFor Official Use Only51RTI/08360/103-13FExport Controlled Information

# Titan IV B-30 Downrange Risks 07/16/2004



# 9. Summary and Conclusions

A downrange overflight risk analysis has b een performed for the Titan IV B-30 mission

using the methodology described in this docu ment and currently available data. Including all assumptions discussed previously, RTI has estimated baseline total casualty

expectation as 145 x 10 -6 

# for day sheltering and 152 x 10 -6 

# for night sheltering. The night

value is larger because the model has more people inside structures at night, and

simulated debris impacts are, in effect, colla psing at least some portion of the roofs.

Varying only sheltering assumptions for compar ison, total risk was found to increase by

84 percent ( unsheltered ) and decrease by 17 percent ( substantial ) as compared to the baseline value for day sheltering.

Using day sheltering assumptions and vary ing other key parameters, RTI's current

estimates of downrange casualty expe ctation range from a low of 70 x 10 -6 

# t o a h i g h o f

235 x 10 -6 

# . Both of these extremes result from va riations in failure probability. Within

this range, increasing population coun t resulted in an estimate of 174 x 10 -6 

# , and reducing the extent of simulated breakup resulted in an estimate of 111 x 10 -6 

# . Variations in

several Mode 4 parameters, including wind mont h, had little effect. It should be noted,

however, that sensitivity to these paramete rs could change for other vehicle configurations and mission trajectories.

Variations in failure probability and population counts have a direct scaling effect on

downrange risks. This stems from the equati ons used to estimate casualty expectation

for both failure response modes considered by RTI. Therefore, it should be expected

that results will be sensitive to changes in these parameters.

Risk estimates presented in this study ar e based on ACTA's HACK2 roof penetration

model. Casualty areas from their new HACK 3 roof penetration model were compared

to HACK2 results, as discussed in the previo us section. For most debris examined, the

HACK3 model generates a larger casualty area than HACK2. It should be noted that

RTI has not yet developed an independent population sheltering model which could

support HACK3 data requirements.

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Export Controlled InformationTitan IV B-30 Downrange Risks07/16/2004**Appendix A: RTI Sheltering Percentages as of 2003**DayNightCountryOpenLightMediumHeavyOpenLightMediumHeavyAfghanistan42.6247.528.281.585.5289.514.720.25Albania20.9759.6116.213.210.1785.0912.372.37Algeria11.7569.4616.212.580.0585.4211.982.55American-Samoa10.6157.0924.427.891.4280.0013.894.69Andorra7.8244.7335.7511.701.7276.6416.664.98Angola50.5640.437.521.496.2589.563.970.22Anguilla9.9267.5018.144.440.0185.6711.372.95Antarctica100.000.000.000.00100.000.000.000.00Antigua and Barbuda12.1365.7217.544.610.0585.0411.633.28Argentina8.2547.9627.8615.931.3477.5716.095.00Armenia22.8559.6714.672.810.2085.6012.192.01Aruba7.0336.6045.0611.311.7475.7017.565.00AscensionIsl10.6557.7221.3110.320.0184.9410.104.95Australia8.3845.9634.4211.241.6477.3116.164.89Austria7.9645.5734.0712.401.6576.8716.514.97Azerbaijan15.4565.4916.003.060.0985.4311.972.51Bahamas10.4165.9818.425.190.0284.6911.613.68Bahrain9.0766.0119.695.230.0085.0411.523.44BakerIsl10.6557.7221.3110.320.0184.9410.104.95Bangladesh20.9562.4714.522.070.1884.8212.682.32Barbados12.2064.6218.135.050.0485.5611.323.08Belarus14.3066.5415.893.270.0786.5911.312.03Belgium8.2547.9332.8111.011.6877.1916.174.96Belize11.1055.1225.448.341.3380.5313.614.54Benin39.3349.629.121.935.3389.345.000.33Bermuda7.9844.6536.7910.581.7176.8216.514.96Bhutan35.2452.0811.501.170.3883.5513.922.14Bolivia16.8064.0316.402.770.1284.6112.552.72For Official Use OnlyA - 1RTI/08360/103-13FExport Controlled InformationTitan IV B-30 Downrange Risks07/16/2004**Appendix A: RTI Sheltering Percentages as of 2003**DayNightCountryOpenLightMediumHeavyOpenLightMediumHeavyBosnia and Herzegovina11.4272.3014.301.980.0387.1811.161.63Botswana37.8152.398.451.355.0489.655.130.18BouvetIsl100.000.000.000.00100.000.000.000.00Brazil17.3960.0818.174.360.1285.0112.062.81Brunei8.9846.4132.2012.411.4378.1515.634.80Bulgaria16.9060.6717.764.670.1186.2211.342.33BurkinaFaso46.1044.837.821.255.8889.804.220.10Burma28.7555.0813.602.570.2984.5212.932.27Burundi44.7946.457.731.035.7389.894.330.05Cambodia35.0647.1515.042.750.3883.0314.012.58Cameroon48.0641.238.542.176.1289.044.360.48Canada8.0043.8936.3011.811.6676.8616.554.93CanaryIsl15.6464.7515.434.180.0985.9511.302.66Cape Verde Isl10.2071.4016.332.070.0384.8712.342.76CaymanIsl7.4839.5240.9512.041.6676.2817.104.96Central African Republic40.4050.807.850.955.2689.944.770.03Chad42.5348.088.151.245.5589.804.550.10ChesterfieldIsl100.000.000.000.00100.000.000.000.00ChilcottIsl100.000.000.000.00100.000.000.000.00Chile9.4850.2630.1510.111.5378.6015.114.76China13.7453.2723.929.071.3482.9112.003.75ChristmasIsl8.3845.9634.4211.241.6477.3116.164.89ClippertonIsl100.000.000.000.00100.000.000.000.00CocosIsl8.3845.9634.4211.241.6477.3116.164.89Colombia15.8362.7117.633.820.1085.0112.112.79Comoros48.2743.717.130.895.9789.944.060.03Congo,Democratic Republic40.8848.728.751.655.4689.504.790.25Congo,Republic36.0045.0112.956.045.3787.046.111.48For Official Use OnlyA - 2RTI/08360/103-13FExport Controlled InformationTitan IV B-30 Downrange Risks07/16/2004**Appendix A: RTI Sheltering Percentages as of 2003**DayNightCountryOpenLightMediumHeavyOpenLightMediumHeavyCookIsl39.8546.2811.272.605.4586.396.351.81CostaRica10.0050.5529.919.541.4879.1714.704.65CoteD’Ivoire41.3748.548.521.575.4689.564.760.22Croatia9.2353.0628.309.411.6878.2415.234.85Cuba10.2851.8128.239.671.5879.3314.534.56Cyprus13.2761.8519.355.530.0685.3411.453.15CzechRepublic8.2845.5034.6911.531.6977.1316.294.89Denmark7.9642.4436.9412.661.6476.8116.654.90Diamond and Tregosse100.000.000.000.00100.000.000.000.00DiegoGarcia10.6557.7221.3110.320.0184.9410.104.95Djibouti26.2457.0914.582.090.2683.7313.432.58Dominica16.6363.5216.713.140.1185.6012.012.28DominicanRepublic11.8066.9117.773.530.0585.2311.932.80EastTimor18.6763.0615.452.820.1485.1812.262.42Ecuador14.5664.3617.643.440.0984.8612.242.81Egypt15.4965.4716.322.720.1085.1712.242.49ElSalvidor9.2274.8514.861.060.0186.0111.952.02EquatorialGuinea40.8843.8710.884.375.6488.085.320.96Eritrea43.3947.387.941.295.5789.724.570.14Estonia8.9145.3534.4411.301.6477.7915.844.73Ethiopia42.3148.208.211.285.5389.744.600.13EuropaIsl10.6557.7221.3110.320.0184.9410.104.95FalklandIsl17.3066.8011.943.961.4686.458.943.15FaroeIsl13.7856.3723.276.581.4782.8811.843.81Fiji47.3943.457.801.365.8989.124.550.44Finland8.6746.5733.6111.151.6677.5715.954.82France8.5748.6231.9710.841.6677.5315.914.90FrenchGuiana12.1767.3317.133.380.0585.5111.792.65For Official Use OnlyA - 3RTI/08360/103-13FExport Controlled InformationTitan IV B-30 Downrange Risks07/16/2004**Appendix A: RTI Sheltering Percentages as of 2003**DayNightCountryOpenLightMediumHeavyOpenLightMediumHeavyFrenchPolynesia35.7547.8512.503.905.2686.486.501.76FrenchSouthern100.000.000.000.00100.000.000.000.00Gabon49.1839.428.782.626.1888.434.610.78Gambia42.6347.788.221.375.5589.634.640.18GazaStrip9.9567.8118.933.310.0383.9012.643.43Georgia23.5655.4416.504.500.2185.2212.032.54Germany8.1146.0833.4212.391.6577.0316.384.94Ghana41.9247.888.551.655.4989.354.840.32Gibraltar7.8144.5734.3913.231.5876.8016.625.00GloriosoIsl10.6557.7221.3110.320.0184.9410.104.95Greece9.9852.7127.939.381.6778.9414.754.64Greenland9.8564.3120.375.470.0185.3411.423.23Grenada15.3061.9218.374.410.1084.0712.393.44Guadeloupe11.2270.5715.562.650.0386.4111.412.15Guam8.2343.8835.5712.311.4477.3716.254.94Guatemala41.9646.639.292.135.5988.835.000.59Guernsey8.2044.5437.379.891.7876.9516.394.88Guinea44.0746.627.941.375.6489.684.520.16GuineaBissau45.0145.617.931.455.7289.604.480.20Guyana14.7064.3317.323.650.0885.4711.872.58Haiti42.6747.748.221.375.5289.474.750.27HeardIsl100.000.000.000.00100.000.000.000.00Honduras14.8562.7418.563.840.0983.9912.623.30HongKong10.8450.8728.1710.121.5679.8414.214.39HowlandIsl100.000.000.000.00100.000.000.000.00Hungary8.8849.9330.6010.591.6777.8415.644.85Iceland9.6446.7332.5611.071.5478.6315.224.61India16.3867.8914.261.470.1185.6712.251.97For Official Use OnlyA - 4RTI/08360/103-13FExport Controlled InformationTitan IV B-30 Downrange Risks07/16/2004**Appendix A: RTI Sheltering Percentages as of 2003**DayNightCountryOpenLightMediumHeavyOpenLightMediumHeavyIndonesia18.6763.062.820.1485.182.42Iran69.1216.932.6985.6711.892.40Iraq39.5350.371.565.2489.520.24Ireland46.1433.4011.6877.7615.864.80Israel8.4347.7110.931.5677.524.95Italy50.9829.4510.7377.7815.654.87Jamaica14.0964.044.130.0885.122.92JanMayen57.7221.3110.3284.9410.104.9515.4512.2611.260.048.545.008.781.5832.9315.978.841.7017.7411.8810.650.01Japan8.0943.2135.6613.041.6576.9316.554.87JarvisIsl0.000.000.000.000.00Jersey36.7744.1811.431.7476.244.84JohnstonAtoll10.6557.72100.000.00100.007.6217.1821.3110.320.0184.9410.104.95Jordan9.3171.7816.482.430.0185.3911.962.64Juan De Nova Isl10.6557.7221.3110.320.0110.104.95Kazakhstan16.2259.165.260.1011.873.10Kenya43.737.931.315.6089.700.15Kiribati36.7852.989.011.2389.225.360.39Korea,North11.5153.5484.9419.3684.9347.034.555.0325.379.581.4480.6913.554.32Korea,South47.1533.049.1310.671.6078.094.74Kuwait61.5421.260.0183.8511.514.6346.1742.169.152.5288.464.830.77Laos44.5545.721.725.6989.664.490.17Latvia15.569.757.45Kyrgyzstan5.948.019.4745.0531.8913.591.5078.45Lebanon10.3318.644.220.0285.7111.502.7741.8049.587.650.975.3589.884.71Liberia40.4750.278.131.135.3015.474.5866.81Lesotho0.0689.744.830.13Libya37.6850.275.1488.625.55Liechtenstein6.8532.6142.8317.711.5017.924.939.682.370.6975.65For Official Use OnlyA - 5RTI/08360/103-13FExport Controlled InformationTitan IV B-30 Downrange Risks07/16/2004**Appendix A: RTI Sheltering Percentages as of 2003**DayNightCountryOpenLightMediumHeavyOpenLightMediumHeavyLithuania10.0048.9929.911.5678.9914.894.56Luxembourg7.6939.3711.921.6976.4816.904.9311.1041.02Macau7.0636.7241.3114.911.5575.9317.525.00Macedonia11.0469.5216.423.022.09MacquarieIsl100.000.000.00100.000.000.000.00Madagascar46.3744.018.041.585.904.320.23Malawi44.8146.100.0386.5911.290.0089.557.841.255.7289.764.40Malaysia9.5946.6712.221.3578.8215.164.67Maldives71.4216.741.980.0284.8312.422.73Mali42.3148.138.221.345.5389.740.1231.539.864.600.13Malta8.7050.0731.0077.6815.744.917.8942.8437.1312.141.6716.684.93MarshallIsl41.508.381.135.5089.744.6310.231.67Man,Isle of76.7248.990.13Martinique11.2568.150.0386.1111.31Mauritania39.2150.089.061.655.315.040.35Mauritius13.1560.2920.820.0685.2011.603.14Mayotte16.2816.823.782.5589.305.7467.1415.201.380.1284.4512.932.50100.000.000.00100.000.000.000.00Mellish Reef100.000.000.00100.000.000.000.0010.3849.9730.109.551.4379.5714.45Micronesia,Federated States13.4067.8316.122.640.07McDonaldIsl0.000.00Mexico4.5584.6712.372.89MidwayIsl10.6510.320.0184.944.95Moldova17.7564.8214.692.7486.0411.772.07Monaco5.8826.2713.491.8274.2418.945.00Mongolia49.459.560.680.4583.8913.961.7057.7221.3110.100.1254.3640.31Montenegro11.2670.4115.702.630.0387.05Montserrat13.0061.776.130.0684.7211.483.7418.9262.9315.532.620.1584.872.4911.151.7719.10Morocco12.49For Official Use OnlyA - 6RTI/08360/103-13FExport Controlled InformationTitan IV B-30 Downrange Risks07/16/2004**Appendix A: RTI Sheltering Percentages as of 2003**DayNightCountryOpenLightMediumHeavyOpenLightMediumHeavyMozambique43.5047.457.885.5889.724.560.14Namibia47.509.382.395.4788.945.06Nauru7.0533.5344.8814.5576.0917.545.00NavassaIsl100.001.1740.730.531.370.000.000.00100.000.000.000.0044.887.661.145.824.370.19Netherlands8.2946.4333.991.6777.2016.224.91Netherlands Antilles8.2933.899.911.6677.2716.094.98Nepal46.3289.6211.2947.91NewCaledonia34.8049.3912.103.715.1286.896.441.55NewZealand33.8611.481.5777.864.77Nicaragua17.6962.4716.733.1184.2912.662.92Niger36.9653.738.8745.7915.800.138.350.965.0190.004.990.00Nigeria5.6788.864.900.5742.6446.419.201.75Niue39.8546.2811.272.605.4586.396.351.81NorfolkIsl8.3834.4211.241.6477.3116.164.89Northern Mariana Isl62.4420.896.971.6778.9514.394.997.6738.6340.3213.381.6176.4817.034.88Oman8.7169.6518.523.120.0185.0412.08Pakistan41.3146.669.652.385.5688.580.71Palau,Republic60.7432.375.711.186.933.290.21PalmyraAtoll10.6557.7221.3110.3245.969.70Norway2.875.1589.570.0184.9410.104.95Panama12.8365.7917.533.850.0685.2311.8414.641.650.1384.9812.602.29ParacelIsl10.6557.7221.3110.3284.9410.104.95Paraguay11.6756.0124.667.661.3781.0413.204.40Peru9.8518.063.660.0285.1811.842.96Philippines19.0760.5616.923.440.1584.3712.602.88PitcairnIsl53.6040.625.360.426.1190.003.890.00Poland10.4453.5226.749.301.6014.354.562.87Papua NewGuinea17.6066.110.0168.4379.49For Official Use OnlyA - 7RTI/08360/103-13FExport Controlled InformationTitan IV B-30 Downrange Risks07/16/2004**Appendix A: RTI Sheltering Percentages as of 2003**DayNightCountryOpenLightMediumHeavyOpenLightMediumHeavyPortugal8.8746.8032.5011.831.6277.8015.814.77PuertoRico8.7651.8130.089.351.6577.8215.574.96Qatar9.0771.1816.932.820.0086.6211.312.07Reunion9.8770.4716.742.920.0285.7611.682.54Romania18.9859.8417.104.080.1486.1711.592.10Russia9.5150.1429.6110.741.6178.5015.194.70Rwanda49.4741.977.331.236.1289.754.010.12Samoa46.0444.837.831.305.7589.204.650.40SanMarino7.4939.9337.6614.931.5976.3417.134.94Sao Tome and Principe10.9970.4216.621.970.0484.5112.612.84SaudiArabia9.6270.1517.572.660.0284.8812.252.85Senegal40.1050.028.541.345.3389.564.890.22Serbia72.9010.6914.631.780.0287.4411.111.43Seychelles10.0371.7215.812.440.0286.2211.542.22SierraLeone41.3448.858.431.385.4489.564.780.22Singapore9.1047.1232.2111.581.6178.0415.624.73Slovakia8.6544.3634.5512.441.5977.5616.074.78Slovenia8.3547.4332.1712.051.6577.2816.154.92SolomonIsl41.3448.858.431.385.4489.564.780.22Somalia45.9943.848.411.765.9089.274.460.37SouthAfrica13.8266.6016.632.950.0785.5211.962.45S.Georgia and Sandwich Isl10.6557.7221.3110.320.0184.9410.104.95Spain8.9751.6929.1510.191.7077.9315.514.86SpratlyIsl10.6557.7221.3110.320.0184.9410.104.95SriLanka15.3067.3914.912.400.0986.1211.752.04StHelena10.3870.9215.493.210.0286.7211.032.23StKittsand Nevis Isl11.9464.4518.535.080.0484.7211.723.52StLucia15.9566.0615.552.440.1085.4012.172.33For Official Use OnlyA - 8RTI/08360/103-13FExport Controlled InformationTitan IV B-30 Downrange Risks07/16/2004**Appendix A: RTI Sheltering Percentages as of 2003**DayNightCountryOpenLightMediumHeavyOpenLightMediumHeavyStPierre and Miquel8.0445.9132.0514.011.4677.1816.365.00StVincentand Grenada16.9559.7918.464.800.1284.3112.243.33Sudan46.7943.847.861.515.9289.654.250.18Suriname11.1969.9316.302.580.0485.8411.782.35Svalbard7.7744.7434.9612.531.6676.6616.685.00Swaziland42.4546.049.122.395.6389.164.790.42Sweden7.9444.5435.5811.941.6776.8216.564.95Switzerland8.0942.9736.1012.841.6376.9616.544.87Syria15.2465.0816.892.790.1084.5712.542.79Taiwan8.7346.7432.2912.251.5577.7515.874.83Tajikistan41.4847.159.182.195.5288.965.000.52Tanzania,United Republic46.3644.837.631.185.8589.824.240.09Thailand14.3254.2323.557.901.3783.4411.563.63Togo41.6048.498.571.345.5089.464.770.27Tokelau27.8159.1012.260.840.2884.1313.492.10Tonga15.8662.3518.163.640.1184.1612.613.12Trinidad and Tobago11.7163.4919.535.270.0485.4211.443.10TromelinIsl10.6557.7221.3110.320.0184.9410.104.95Tunisia12.8766.9516.913.270.0685.6211.792.53Turkey10.4774.7413.830.960.0286.8211.571.59Turkmenistan41.9346.079.522.485.5788.545.160.73Turks and Caicos Isl12.1469.7815.442.640.0585.4111.902.64Tuvalu17.8464.2815.082.800.1384.8012.362.71Uganda11.4060.2721.976.360.0484.6311.693.64Ukraine15.5662.3517.694.400.0986.2411.352.32United Arab Emirates43.7546.538.361.365.7389.684.430.16UnitedKingdom7.8143.7936.8811.521.6976.6616.684.97United States ofAmerica7.9442.8936.8512.321.6276.8316.624.93For Official Use OnlyA - 9RTI/08360/103-13FExport Controlled InformationTitan IV B-30 Downrange Risks07/16/2004**Appendix A: RTI Sheltering Percentages as of 2003**DayNightCountryOpenLightMediumHeavyOpenLightMediumHeavyUruguay9.4550.2630.849.451.6178.4815.174.75Uzbekistan21.7956.6617.544.010.1983.9512.803.06Vanuatu27.7856.3213.552.350.2883.9113.232.59Venezuela11.8264.8418.894.450.0484.9311.873.15Vietnam25.7457.3514.472.440.2484.6412.872.25VirginIsl9.1566.5819.265.010.0085.3511.413.24WakeIsl10.6557.7221.3110.320.0184.9410.104.95Wallis and Futuna17.3566.3814.851.420.1384.8612.752.26WestBank10.3467.6018.623.440.0384.3012.403.27WesternSahara36.9453.318.641.115.0689.885.000.06Willis Group100.000.000.000.00100.000.000.000.00Yemen41.6547.738.941.685.5689.264.810.37Zambia48.4442.177.911.486.1389.604.070.20Zimbabwe44.6645.438.301.615.7289.264.650.37For Official Use OnlyA - 10RTI/08360/103-13FExport Controlled Information

# Titan IV B-30 Downrange Risks 07/16/2004



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