- 

Office of Nuclear Regulatory Research 

State-of-the-Art Reactor 

Consequence Analyses (SOARCA) 

Project 

Sequoyah Integrated Deterministic 

and Uncertainty Analyses 

Draft Technical Report 
ii 

TABLE OF CONTENTS 

Section Page 

TABLE OF CONTENTS ................................................................................................................ ii

LIST OF FIGURES ...................................................................................................................... vi

LIST OF TABLES ....................................................................................................................... xiv

EXECUTIVE SUMMARY .......................................................................................................... xviii

ACKNOWLEDGMENTS ........................................................................................................... xxix

ABBREVIATIONS AND ACRONYMS ....................................................................................... xxxi

1. Introduction .................................................................................................................... 1-1

1.1 Background ........................................................................................................ 1-1

1.2 General Sequoyah Plant and Site Information ................................................... 1-4

1.3 Sequoyah SOARCA Scope ................................................................................ 1-5

1.4 Approach for Uncertainty Considerations ........................................................... 1-6

1.5 Outline of Report ................................................................................................ 1-8

2. Accident Scenario Development .................................................................................... 2-1

2.1 Background ........................................................................................................ 2-1

2.2 Scenario Selection ............................................................................................. 2-1

2.2.1 Short-term Station Blackout (STSBO) .................................................... 2-1

2.2.2 Long-term Station Blackout (LTSBO) ..................................................... 2-3

2.3 Mitigative Measures ........................................................................................... 2-4

3. Sequoyah MELCOR Model ............................................................................................ 3-1

3.1 Sequoyah MELCOR Model ................................................................................ 3-1

3.1.1 Reactor Core and Vessel ........................................................................ 3-4

3.1.2 Reactor Coolant System ....................................................................... 3-12

3.1.3 Secondary System ................................................................................ 3-15

3.1.4 Emergency Core Cooling System ......................................................... 3-16

3.1.5 Natural Circulation Modeling ................................................................. 3-16

3.1.6 Containment ......................................................................................... 3-16

3.1.7 Containment Leakage and Rupture ...................................................... 3-19

3.1.8 Hydrogen Combustion .......................................................................... 3-23

3.1.9 Operator Actions ................................................................................... 3-26

3.1.10 Mitigative Actions .................................................................................. 3-27
iii 

3.1.11 Modeling Practices ............................................................................... 3-28

3.2 Selection of Uncertain MELCOR Parameters .................................................. 3-31

3.3 Primary SV Stochastic Failure to Close (priSVcyles) ....................................... 3-34

3.4 Primary SV Open Area Fraction (SV_frac) ....................................................... 3-37

3.5 Effective Temperature of the Eutectic Reaction for Zircaloy Oxide 

and Uranium Oxide (EU_melt_T) ..................................................................... 3-38

3.6 Hydrogen Ignition Criteria ................................................................................. 3-41

3.7 Barrier Seal ...................................................................................................... 3-42

3.8 Ice Bed Lower Plenum Doors (AJAR) .............................................................. 3-46

3.9 Dynamic Shape Factor (shape_factor) ............................................................. 3-47

3.10 Containment Rupture Pressure (rupture) ......................................................... 3-49

4. Accident Progression Analysis ....................................................................................... 4-1

4.1 Short Term Station Blackout (STSBO) Base Case ............................................ 4-1

4.1.1 Short-Term Station Blackout with Igniters ............................................ 4-12

4.2 STSBO Source Term Parameter Uncertainty Analysis .................................... 4-13

4.2.1 Uncertainty Evaluations with No Random Ignition ................................ 4-15

4.2.2 Uncertainty Evaluations with Random Ignition ..................................... 4-59

4.3 Analysis of Select MELCOR Individual Realizations ........................................ 4-93

4.4 LTSBO Accident Progression Analysis .......................................................... 4-117

4.4.1 Unmitigated Long-Term Station Blackout Base Case ........................ 4-117

4.4.2 Unmitigated Long-Term Station Blackout Sensitivity 

Calculations ........................................................................................ 4-128

4.4.3 Long-Term Station Blackout with Igniters ........................................... 4-132

5. Sequoyah MACCS Model .............................................................................................. 5-1

5.1 Introduction ......................................................................................................... 5-1

5.2 Site Location and Surrounding Area .................................................................. 5-2

5.3 Site Demographics and Economic Data ............................................................. 5-5

5.4 Site Topography and Land Cover ...................................................................... 5-6

5.5 Meteorological Data ........................................................................................... 5-6

5.5.1 Selection of Representative Weather Data ............................................. 5-8

5.5.2 Sequoyah Site Meteorological Features ................................................. 5-9

5.5.3 Weather Sampling ................................................................................ 5-12

5.6 Atmospheric Transport and Dispersion ............................................................ 5-13

5.6.1 Radiological Release Information ......................................................... 5-14

5.6.2 Surface Roughness .............................................................................. 5-14

5.6.3 Atmospheric Dispersion ........................................................................ 5-15
iv 

5.6.4 Dry and Wet Deposition ........................................................................ 5-15

5.7 Protective Action Parameters ........................................................................... 5-16

5.7.1 Cohort Definitions ................................................................................. 5-16

5.7.2 Emergency Response Timelines .......................................................... 5-20

5.7.3 Shielding From Dose ............................................................................ 5-22

5.7.4 Hotspot Relocation Time (TIMHOT) ..................................................... 5-23

5.7.5 Normal Relocation Time (TIMNRM) ..................................................... 5-23

5.7.6 Intermediate and Long-Term Phase ..................................................... 5-24

5.8 Dosimetry ......................................................................................................... 5-25

5.8.1 Exposure and Commitment Periods ..................................................... 5-26

5.8.2 Dose-Response Models ....................................................................... 5-26

5.9 Uncertain MACCS Input Parameters and Distributions .................................... 5-28

5.9.1 Wet Deposition Model (CWASH1) ........................................................ 5-32

5.9.2 Dry Deposition Velocities (VDEPOS) ................................................... 5-35

5.9.3 Shielding Factors (GSHFAC and PROTIN) .......................................... 5-38

5.9.4 Early Health Effects (EFFACA, EFFACB, EFFTHR) ............................ 5-45

5.9.5 Latent Health Effects (DDREFA, CFRISK, Long-Term 

Inhalation Dose Coefficients) ................................................................ 5-49

5.9.6 Dispersion (CYSIGA, CZSIGA) ............................................................ 5-55

5.9.7 Weather Forecast (KEYFORCST) ........................................................ 5-59

5.9.8 Time-Based Crosswind Dispersion Coefficient (CYCOEF) .................. 5-60

5.9.9 Emergency Response (Evacuation and Relocation) ............................ 5-61

6. Offsite Consequence Analysis ....................................................................................... 6-1

6.1 Introduction ......................................................................................................... 6-1

6.2 Sequoyah Source Terms .................................................................................... 6-2

6.3 Deterministic Analysis of Individual Latent Cancer Fatality Risk ........................ 6-6

6.3.1 Summary of Deterministic Results .......................................................... 6-6

6.3.2 STSBO Base Case Scenario .................................................................. 6-9

6.3.3 STSBO Early Release Scenario ........................................................... 6-12

6.3.4 LTSBO Scenario ................................................................................... 6-14

6.4 Uncertainty Analysis – STSBO without Random Ignition ................................. 6-16

6.4.1 LCF Risks for the Unmitigated STSBO Scenario without 

Random Ignition.................................................................................... 6-16

6.4.2 Regression Analysis of STSBO w/o Random Ignition 

Latent Cancer Fatality Risk ................................................................... 6-19

6.4.3 Results for Early Fatality Risks ............................................................. 6-27

6.5 Uncertainty Analysis - STSBO with Random Ignition ....................................... 6-28
v 

6.5.1 Results for LCF Risks ........................................................................... 6-28

6.5.2 Regression Analysis of STSBO with Random Ignition 

Latent Cancer Fatality Risk ................................................................... 6-32

6.5.3 Regression Analysis of Early Fatality Risk Assuming 

Random Ignition.................................................................................... 6-38

6.6 MACCS Results from Selected UA Realizations .............................................. 6-39

6.7 Sensitivity Analysis ........................................................................................... 6-44

6.8 Summary .......................................................................................................... 6-47

7. Conclusions .................................................................................................................... 7-1

8. References ..................................................................................................................... 8-1

Appendix AUncertainty Quantification and Propagation ........................................................... A-1

Appendix BInput Parameters for Consequence Analysis ......................................................... B-1

Appendix CRoadway Capacity Analysis ................................................................................... C-1

 
vi 

LIST OF FIGURES 

Figure Page 

Figure ES-1 Illustration of the effect of hydrogen igniters in a typical Sequoyah 

SOARCA unmitigated STSBO accident progression .......................................... xx

Figure ES-2 Iodine release to the environment for variations of unmitigated 

STSBO and LTSBO ........................................................................................... xxii

Figure ES-3 Cesium release to the environment for variations of unmitigated 

STSBO and LTSBO .......................................................................................... xxiii

Figure ES-4 Individual latent cancer fatality risk conditional on unmitigated 

STSBO (per event) ............................................................................................ xxv

Figure ES-5 Comparison of Peach Bottom, Surry, and Sequoyah SOARCA 

unmitigated scenarios in terms of conditional (per event) individual 

latent cancer fatality risk within 10 mile radius using LNT doseresponse and mean values over weather variations ........................................ xxvii

Figure 1-1 Typical Containment Volumes and Design Pressures (Figure 4.1-1 

from NUREG/CR-6042, Rev. 2 [14]) .................................................................. 1-4

Figure 1-2 View of Sequoyah from the north ....................................................................... 1-5

Figure 1-3 Diagram of code information flow for uncertainty analysis ................................. 1-8

Figure 3-1 MELCOR COR/CVH nodalization ...................................................................... 3-6

Figure 3-2 Upper RPV CVH/FL nodalization ....................................................................... 3-8

Figure 3-3 MELCOR radial nodalization of Sequoyah core ............................................... 3-10

Figure 3-4 Sequoyah middle of cycle decay heat curve .................................................... 3-11

Figure 3-5 Natural circulation flow patterns in a PWR ....................................................... 3-12

Figure 3-6 Sequoyah reactor coolant system hydrodynamic nodalization ......................... 3-14

Figure 3-7 Sequoyah steam generator lumped Loop 2 ..................................................... 3-15

Figure 3-8 Containment nodalization ................................................................................. 3-17

Figure 3-9 Sequoyah ice condenser nodalization .............................................................. 3-18

Figure 3-10 Three-dimensional finite element model of the Sequoyah 

containment ...................................................................................................... 3-20

Figure 3-11 Flammability limits for hydrogen for Air-H2-H2O systems [30] .......................... 3-24

Figure 3-12 Combustion of hydrogen and air mixtures near lean limits for 

upward propagation of the flame [30] ............................................................... 3-25

Figure 3-13 Parameter storyboard used to capture key information for each 

parameter investigated ..................................................................................... 3-34

Figure 3-14 Possible transitions in the 3 SV pressurizer pressure relief system 

considering FTO and FTC valve conditions ..................................................... 3-37

Figure 3-15 Mean CDF for number of cycles at which any SV of a 3-SV 

system fails open compared to the mean CDF for number of 

cycles at which all 3 valves fail ......................................................................... 3-37

Figure 3-16 CDF for the SV thermal FTC open area fraction (SVfrac) ................................ 3-38

Figure 3-17 CDF of effective temperature of the eutectic reaction temperature .................. 3-41

Figure 3-18 Uncertainty distribution for ignition propagation direction ................................. 3-42

Figure 3-19 Barrier seal section ........................................................................................... 3-43

Figure 3-20 Seal break area probability ............................................................................... 3-45

Figure 3-21 CDF of failure pressure difference.................................................................... 3-45

Figure 3-22 Lower plenum doors characteristics (developed from NUREG/CR5586) ................................................................................................................ 3-46
vii 

Figure 3-23 Fraction of LP doors stuck open ....................................................................... 3-47

Figure 3-24 Dynamic shape factor compared to number of spheres within a 

chain ................................................................................................................. 3-49

Figure 3-25 CDF of the dynamic shape factor ..................................................................... 3-49

Figure 4-1 STSBO base case primary and secondary pressure history .............................. 4-4

Figure 4-2 STSBO base case SG water level and SV cycling ............................................. 4-5

Figure 4-3 STSBO base case primary system water level .................................................. 4-5

Figure 4-4 STSBO base case fuel and debris temperature history ..................................... 4-6

Figure 4-5 STSBO base case in-vessel hydrogen production ............................................. 4-6

Figure 4-6 STSBO base case containment dome hydrogen, oxygen, and 

steam mole fractions .......................................................................................... 4-7

Figure 4-7 STSBO base case fraction of ice melted ............................................................ 4-8

Figure 4-8 STSBO base case containment dome pressure ................................................ 4-9

Figure 4-9 Base case containment ablation during STSBO with random 

ignition ................................................................................................................ 4-9

Figure 4-10 STSBO base case iodine fission product distribution history ........................... 4-11

Figure 4-11 STSBO base case cesium fission product distribution history ......................... 4-11

Figure 4-12 STSBO base case environmental release fraction history ............................... 4-12

Figure 4-13 Containment Pressure for the STSBO with and without hydrogen 

igniters .............................................................................................................. 4-13

Figure 4-14 CDFs of containment rupture time with and without random 

ignition .............................................................................................................. 4-14

Figure 4-15 Containment rupture time versus RPV breach time colored by 

whether rupture was immediate to a deflagration – without random 

ignition .............................................................................................................. 4-20

Figure 4-16 Containment rupture time versus RPV breach time colored by 

number of pressurizer SV cycles – without random ignition ............................. 4-21

Figure 4-17 Relationship of priSVcycles to SV_Frac colored by containment 

failure timing – without random ignition ............................................................ 4-22

Figure 4-18 Containment rupture time versus in-vessel hydrogen production – 

without random ignition .................................................................................... 4-23

Figure 4-19 Containment rupture time versus in-vessel hydrogen production 

up to the RPV breach time – without random ignition ...................................... 4-24

Figure 4-20 Iodine release fractions over 72 hours with mean, median, 5th and 

95th percentiles ................................................................................................. 4-26

Figure 4-21 Scatterplot of iodine release fraction versus SV_Frac ...................................... 4-29

Figure 4-22 Radionuclide mass in the pressurizer relief tank for Rlz 133 ............................ 4-30

Figure 4-23 Radionuclide mass in the pressurizer relief tank for Rlz 225 ............................ 4-30

Figure 4-24 Scatterplot of iodine release fraction versus priSVcycles ................................. 4-31

Figure 4-25 Scatterplot of iodine release fraction versus rupture ........................................ 4-32

Figure 4-26 Scatterplot of iodine release fraction versus eutectic melt 

temperature ...................................................................................................... 4-34

Figure 4-27 Cesium release fractions over 72 hours with mean, median, 5th

and 95th percentiles .......................................................................................... 4-36

Figure 4-28 Comparison of Iodine and Cesium Compound Vapor Pressures ..................... 4-37

Figure 4-29 Expanded view of the cesium release fractions over the first 

20 hours of the simulation ................................................................................ 4-37

Figure 4-30 Cumulative probabilities for the fraction of cesium release for 

selected time intervals ...................................................................................... 4-38

Figure 4-31 Scatterplot of cesium release fraction versus SV_Frac .................................... 4-42

Figure 4-32 Radionuclide mass in the pressurizer relief tank for Rlz 133 ............................ 4-42
viii 

Figure 4-33 Radionuclide mass in the pressurizer relief tank for Rlz 225 ............................ 4-43

Figure 4-34 Scatterplot of cesium release fraction versus priSVcycles ............................... 4-44

Figure 4-35 Scatterplot of cesium release fraction versus shape factor .............................. 4-45

Figure 4-36 Scatterplot of cesium release fraction versus Ajar ........................................... 4-46

Figure 4-37 Lower door percentage open for Rlz 133 ......................................................... 4-47

Figure 4-38 Typical ex-vessel gas production (Rlz 133) ...................................................... 4-49

Figure 4-39 Total hydrogen production over 72 hours with mean, median, 5th 

and 95th percentiles ......................................................................................... 4-50

Figure 4-40 Cumulative probabilities for the quantity of hydrogen produced for 

selected time intervals to 72 hours ................................................................... 4-50

Figure 4-41 Cumulative probabilities for the quantity of hydrogen produced for 

selected time intervals to 20 hours ................................................................... 4-51

Figure 4-42 Scatterplot of in-vessel hydrogen production versus SV 72 hour 

post-calculated open fraction............................................................................ 4-53

Figure 4-43 Scatterplot of in-vessel hydrogen production versus eutectic melt 

temperature input values .................................................................................. 4-54

Figure 4-44 Scatterplot of in-vessel hydrogen production versus SV cycles to 

failure values .................................................................................................... 4-55

Figure 4-45 Scatterplot of rupture time versus the combined open fraction of 

the pressurizer SV system ............................................................................... 4-57

Figure 4-46 Scatterplot of rupture time versus rupture ........................................................ 4-58

Figure 4-47 Scatterplot of rupture time versus priSVcycles ................................................. 4-59

Figure 4-48 Containment rupture time versus RPV breach time colored by 

whether the rupture was immediate to a deflagration – with 

random ignition ................................................................................................. 4-63

Figure 4-49 Containment rupture time versus RPV breach time colored by 

number of pressurizer SV cycles – with random ignition .................................. 4-64

Figure 4-50 Time of containment rupture versus in-vessel hydrogen production 

– with random ignition ...................................................................................... 4-65

Figure 4-51 Iodine release fractions over 72 hours with mean, median, 5th and 

95th percentiles ................................................................................................. 4-67

Figure 4-52 Scatterplot of iodine release fraction versus rupture ........................................ 4-69

Figure 4-53 Scatterplot of iodine release fraction versus priSVcycles ................................. 4-70

Figure 4-54 Scatterplot of rupture time versus priSVcycles ................................................. 4-70

Figure 4-55 Scatterplot of iodine release fraction versus SV_Frac ...................................... 4-72

Figure 4-56 Scatterplot of iodine release fraction versus Ajar ............................................. 4-73

Figure 4-57 Scatterplot of rupture time versus Ajar ............................................................. 4-73

Figure 4-58 Scatterplot of iodine release fraction versus EU_melt_T .................................. 4-74

Figure 4-59 Cesium release fractions over 72 hours with mean, median, 5th

and 95th percentiles .......................................................................................... 4-76

Figure 4-60 Expanded view of the cesium release fractions over the first 

20 hours of the simulation ................................................................................ 4-77

Figure 4-61 Scatterplot of cesium release fraction versus SV_Frac .................................... 4-78

Figure 4-62 Scatterplot of rupture time versus SV_Frac ...................................................... 4-78

Figure 4-63 Scatterplot of cesium release fraction versus priSVcycles ............................... 4-80

Figure 4-64 Total hydrogen production over 72 hours with mean, median, 5th 

and 95th percentiles ......................................................................................... 4-82

Figure 4-65 Cumulative probabilities for the quantity of hydrogen produced for 

selected time intervals through 72 hours .......................................................... 4-83

Figure 4-66 Cumulative probabilities for the quantity of hydrogen produced for 

selected time intervals to 20 hours ................................................................... 4-84
ix 

Figure 4-67 Scatterplot of in-vessel hydrogen production versus SV 72 hour 

post-calculated open fraction............................................................................ 4-86

Figure 4-68 Scatterplot of in-vessel hydrogen production versus eutectic melt 

temperature input values .................................................................................. 4-87

Figure 4-69 Scatterplot of in-vessel hydrogen production versus priSVcycles .................... 4-88

Figure 4-70 Scatterplot of rupture time versus rupture ........................................................ 4-90

Figure 4-71 Scatterplot of rupture time versus SV_Frac ...................................................... 4-91

Figure 4-72 Scatterplot of rupture time versus burn direction .............................................. 4-92

Figure 4-73 Scatterplot of rupture time versus Ajar ............................................................. 4-93

Figure 4-74 Time of containment rupture versus time of RPV breach for 

selected realizations without random ignition ................................................... 4-95

Figure 4-75 Burn energy and containment pressure for Rlz 338 w/o random 

ignition .............................................................................................................. 4-97

Figure 4-76 Burn energy and containment pressure for Rlz 171 w/o random 

ignition .............................................................................................................. 4-97

Figure 4-77 In-vessel hydrogen production for Rlzs 338 and 171 w/o random 

ignition .............................................................................................................. 4-98

Figure 4-78 Integral safety valve hydrogen flow for Rlzs 338 and 171 w/o 

random ignition ................................................................................................. 4-99

Figure 4-79 Hydrogen mole fraction and burn energy for Rlz 338 w/o random 

ignition ............................................................................................................ 4-100

Figure 4-80 Steam mole fraction and burn energy for Rlz 338 w/o random 

ignition ............................................................................................................ 4-100

Figure 4-81 Conditions in containment dome with respect to burn propagation 

for Rlz 338 w/o random ignition ...................................................................... 4-101

Figure 4-82 Hydrogen mole fraction and burn energy for Rlz 171 w/o random 

ignition ............................................................................................................ 4-102

Figure 4-83 Steam mole fraction and burn energy for Rlz 171 w/o random 

ignition ............................................................................................................ 4-102

Figure 4-84 Conditions in containment dome with respect to burn propagation 

for Rlz 171 w/o random ignition ...................................................................... 4-103

Figure 4-85 Lower containment conditions for Rlz 171 ..................................................... 4-104

Figure 4-86 Burn energy and containment pressure for Rlz 142 w/o random 

ignition ............................................................................................................ 4-105

Figure 4-87 Burn energy and containment pressure for Rlz 133 w/o random 

ignition ............................................................................................................ 4-106

Figure 4-88 Reactor cavity conditions for Rlz 133 w/o random ignition ............................. 4-106

Figure 4-89 Lower containment conditions (CV 8) for Rlz 133 w/o random 

ignition ............................................................................................................ 4-107

Figure 4-90 Lower containment (CV 9) for Rlz 133 w/o random ignition ........................... 4-107

Figure 4-91 Lower annulus (CV 11) for Rlz 133 w/o random ignition ................................ 4-108

Figure 4-92 In-vessel hydrogen production for RLZs 225 and 469 w/o random 

ignition ............................................................................................................ 4-109

Figure 4-93 Cesium distribution for Rlz 174 w/o random ignition ...................................... 4-110

Figure 4-94 Cesium distribution for Rlz 142 w/o random ignition ...................................... 4-110

Figure 4-95 Containment temperature for Rlz 142 w/o random ignition ............................ 4-111

Figure 4-96 Burn energy and containment pressure for Rlz 174 w/o random 

ignition ............................................................................................................ 4-111

Figure 4-97 Containment temperature for Rlz 174 w/o random ignition ............................ 4-112

Figure 4-98 Pressurizer pressure responses for Rlz 551 and 148 .................................... 4-114

Figure 4-99 Core swollen water level responses for Rlz 551 and 148 ............................... 4-114
x 

Figure 4-100 Total accumulator water injection responses for Rlz 551 and 148 ................. 4-115

Figure 4-101 Creep rupture index and lower head temperature responses for 

Rlz 551 and 148 ............................................................................................. 4-115

Figure 4-102 Rlz 469 no random ignition, lower containment CV-8 conditions 

with respect to a burn starting within the CV, as opposed to 

propagating to the CV .................................................................................... 4-116

Figure 4-103 Rlz 469, no random ignition, conditions in the containment dome 

with respect to a burn being propagated to the dome .................................... 4-117

Figure 4-104 Unmitigated LTSBO base case primary and secondary pressure 

history ............................................................................................................. 4-119

Figure 4-105 Unmitigated LTSBO base case secondary system water level and 

valve history ................................................................................................... 4-120

Figure 4-106 Unmitigated LTSBO base case primary system water level history ............... 4-121

Figure 4-107 Unmitigated LTSBO base case peak fuel and debris history ......................... 4-122

Figure 4-108 Unmitigated LTSBO base case core degradation and relocation 

history ............................................................................................................. 4-123

Figure 4-109 Unmitigated LTSBO base case containment pressure history ....................... 4-124

Figure 4-110 Unmitigated LTSBO base case ice melting history ........................................ 4-125

Figure 4-111 Unmitigated LTSBO base case concrete ablation history .............................. 4-125

Figure 4-112 Unmitigated LTSBO base case Iodine fission product distribution 

history ............................................................................................................. 4-127

Figure 4-113 Unmitigated LTSBO base case Cesium fission product distribution 

history ............................................................................................................. 4-127

Figure 4-114 Unmitigated LTSBO base case environmental release fraction 

history ............................................................................................................. 4-128

Figure 4-115 Unmitigated LTSBO reactor pressure sensitivity cases ................................. 4-130

Figure 4-116 Unmitigated LTSBO containment pressure sensitivity cases ......................... 4-130

Figure 4-117 Unmitigated LTSBO in-vessel hydrogen sensitivity cases ............................. 4-131

Figure 4-118 Unmitigated LTSBO iodine environmental release fraction 

sensitivity cases ............................................................................................. 4-131

Figure 4-119 Unmitigated LTSBO cesium environmental release fraction 

sensitivity cases ............................................................................................. 4-132

Figure 4-120 LTSBO containment pressure history with igniters......................................... 4-133

Figure 4-121 LTSBO containment hydrogen mole fraction history with igniters .................. 4-134

Figure 4-122 LTSBO containment oxygen mole fraction history with igniters ..................... 4-134

Figure 4-123 LTSBO containment steam mole fraction history with igniters ....................... 4-135

Figure 5-1 Sequoyah Nuclear Power Plant site with 50-mile radius [50] ............................. 5-3

Figure 5-2 Sequoyah 2 mile, 5 mile and 10 mile radial distances and EPZ 

sectors [50] ......................................................................................................... 5-4

Figure 5-3 Sequoyah site boundary and aerial layout [50] .................................................. 5-5

Figure 5-4 Monthly precipitation by year (inches) ................................................................ 5-9

Figure 5-5 2012 stability class frequency annually and by time of day .............................. 5-11

Figure 5-6 Sequoyah combined Windrose plots for 2012 and 2008 – 2012 ...................... 5-12

Figure 5-7 Wind speed bins for 2012 ................................................................................. 5-12

Figure 5-8 Keyhole evacuation area with a wind shift........................................................ 5-19

Figure 5-9 Sequoyah emergency response timeline and travel speeds (mph) .................. 5-21

Figure 5-10 CDF of the linear coefficient in the MACCS wet deposition model, 

CWASH1 .......................................................................................................... 5-34

Figure 5-11 CDF of dry deposition velocities for mass median diameters 

representing MACCS aerosol bins ................................................................... 5-38
xi 

Figure 5-12 Cumulative distribution functions of GSHFAC for normal activity, 

sheltering, and evacuation based on expert elicitation data ............................. 5-40

Figure 5-13 Cumulative distribution function of GSDE ........................................................ 5-41

Figure 5-14 CDF of GSHFAC for evacuation accounting for uncertainty in 

GSDE ............................................................................................................... 5-41

Figure 5-15 CDFs of PROTIN for normal activity, sheltering, and evacuation ..................... 5-44

Figure 5-16 CDFs of EFFACA for specified health effects .................................................. 5-47

Figure 5-17 CDFs of EFFACB for specified health effects .................................................. 5-48

Figure 5-18 CDFs of EFFTHR for specified health effects .................................................. 5-49

Figure 5-19 Normalized PDFs for DDREFA for breast and other cancers .......................... 5-50

Figure 5-20 CDFs of DDREFA for breast and other cancer types ....................................... 5-51

Figure 5-21 CDF for CFRISK for each of the organs included in the analysis 

(Nate, please update – remove SOARCA value) ............................................. 5-52

Figure 5-22 Cs-137 lifetime inhalation dose coefficient distributions for organs 

included in the consequence analysis .............................................................. 5-54

Figure 5-23 Pu-241 long-term inhalation dose coefficient distributions for 

organs included in the consequence analysis .................................................. 5-55

Figure 5-24 CDFs of CYSIGA for individual stability classes............................................... 5-58

Figure 5-25 CDFs of CZSIGA for individual stability classes ............................................... 5-59

Figure 5-26 CDF of KEYFORCST ....................................................................................... 5-60

Figure 5-27 CDF of CYCOEF .............................................................................................. 5-61

Figure 5-28 CDF of DLTEVA for each cohort ...................................................................... 5-66

Figure 5-29 CDFs of ESPEED for each cohort .................................................................... 5-69

Figure 5-30 Assumed hotspot and normal relocation areas ................................................ 5-71

Figure 5-31 CDF of TIMHOT ............................................................................................... 5-71

Figure 5-32 CDF of TIMNRM ............................................................................................... 5-73

Figure 5-33 CDF of DOSHOT .............................................................................................. 5-74

Figure 5-34 CDF of DOSNRM ............................................................................................. 5-75

Figure 6-1 Cumulative environmental release fraction of cesium and iodine 

as a function of time for the STSBO base case scenario ................................... 6-4

Figure 6-2 Cumulative environmental release fraction of cesium and iodine 

as a function of time for the STSBO early release scenario ............................... 6-5

Figure 6-3 Cumulative environmental release fraction of cesium and iodine 

as a function of time for the LTSBO base case .................................................. 6-6

Figure 6-4 Mean long term and emergency phase individual LNT, LCF risks 

for residents within 0 - 10, 10 - 20, and 0 - 50 mile intervals of the 

plant .................................................................................................................... 6-9

Figure 6-5 Mean individual LCF risk from the Sequoyah STSBO base case 

for residents within 0 - 10, 10 - 20, and 0 - 50 mile intervals of the 

plant showing contribution from different accident phases ............................... 6-10

Figure 6-6 Mean individual LCF risk as a function of risk quantification 

threshold for the STSBO base case scenario .................................................. 6-11

Figure 6-7 Mean individual LCF risk from the Sequoyah STSBO Early 

Release scenario for residents within 0 - 10, 10 - 20, and 0 - 50 

mile intervals of the plant showing contribution from different 

accident phases ............................................................................................... 6-13

Figure 6-8 Mean individual LCF risk as a function of risk quantification 

threshold for the STSBO Early Release scenario ............................................ 6-14

Figure 6-9 Mean individual LCF risk from the Sequoyah LTSBO base case 

for residents within 0 - 10, 10 - 20, and 0 - 50 mile intervals of the 

plant showing contribution from different accident phases ............................... 6-15
xii 

Figure 6-10 Mean individual LCF risk as a function of risk quantification 

threshold for the LTSBO scenario .................................................................... 6-16

Figure 6-11 Complimentary cumulative distribution function of mean, 

population-weighted LCF risk (w/o random ignition) within five 

intervals centered on Sequoyah ....................................................................... 6-18

Figure 6-12 Complimentary cumulative distribution function of mean, 

population-weighted LCF risk (w/o random ignition) within five 

intervals centered on Sequoyah ....................................................................... 6-18

Figure 6-13 Complimentary cumulative distribution function of fraction of 

mean, population-weighted LCF risk (w/o random ignition) from 

the emergency phase for residents within five intervals centered 

on Sequoyah .................................................................................................... 6-19

Figure 6-14 Scatter plot of mean, individual, LCF risk within a 0 - 50 mile 

interval for all realizations versus safety valve open area fraction, 

SV_frac, conditional on an STSBO accident occurring .................................... 6-25

Figure 6-15 Scatter plot of mean, individual, LCF risk within a 0 – 50 mile 

interval for all realizations versus CFRISK(8), conditional on an 

STSBO accident occurring ............................................................................... 6-25

Figure 6-16 Scatter plot of mean, individual, LCF risk within a 0 – 50 mile 

interval for all realizations versus CFRISK(7), conditional on an 

STSBO accident occurring ............................................................................... 6-26

Figure 6-17 Scatter plot of mean, individual, LCF risk within a 0 – 50 mile 

interval for all realizations versus DDREFA(8), conditional on an 

STSBO accident occurring ............................................................................... 6-26

Figure 6-18 Scatter plot of mean, individual, LCF risk within a 0 – 50 mile 

interval for all realizations versus groundshine shielding factor, 

GSHFAC(2), conditional on an STSBO accident occurring .............................. 6-27

Figure 6-19 Complimentary cumulative distribution function of mean, 

population-weighted early fatality risk (STSBO w/o random 

ignition) within six distance intervals centered on Sequoyah ........................... 6-28

Figure 6-20 Complimentary cumulative distribution function of mean, 

population-weighted LCF risk (assuming a random ignition source) 

within five intervals centered on Sequoyah ...................................................... 6-30

Figure 6-21 Complimentary cumulative distribution function of mean, 

population-weighted LCF risk (assuming a random ignition source) 

within five intervals centered on Sequoyah ...................................................... 6-30

Figure 6-22 Complimentary cumulative distribution function of fraction of 

mean, population-weighted LCF risk (assuming a random ignition 

source) from the emergency phase for residents within five 

intervals centered on Sequoyah ....................................................................... 6-31

Figure 6-23 Scatter plot of mean, individual, LCF risk within a 0 – 50 mile 

interval for all realizations versus CFRISK(8), conditional on an 

STSBO accident occurring ............................................................................... 6-35

Figure 6-24 Scatter plot of mean, individual, LCF risk within a 0 – 50 mile 

interval for all realizations versus safety valve open area fraction, 

SV_frac, conditional on an STSBO accident occurring .................................... 6-36

Figure 6-25 Scatter plot of mean, individual, LCF risk within a 0 – 50 mile 

interval for all realizations versus DDREFA(8), conditional on an 

STSBO accident occurring ............................................................................... 6-36
xiii 

Figure 6-26 Scatter plot of mean, individual, LCF risk within a 0 – 50 mile 

interval for all realizations versus CFRISK(7), conditional on an 

STSBO accident occurring ............................................................................... 6-37

Figure 6-27 Scatter plot of mean, individual, LCF risk within a 0 – 50 mile 

interval for all realizations versus CFRISK(7), conditional on an 

STSBO accident occurring ............................................................................... 6-37

Figure 6-28 Complimentary cumulative distribution function of mean, 

population-weighted EF risk (with random ignition) within five 

intervals centered on Sequoyah ....................................................................... 6-39

Figure 6-29 Cesium release histories for the maximum cesium release and 

maximum risk realizations ................................................................................ 6-40

Figure 6-30 Iodine release histories for the maximum cesium release and 

maximum risk realizations ................................................................................ 6-40

Figure 6-31 Complimentary cumulative distribution function of mean, 

population-weighted LCF risk (without random ignition source) for 

the maximum-cesium-release realization within five intervals 

centered on Sequoyah. Probability represents weather variability. .................. 6-41

Figure 6-32 Complimentary cumulative distribution function of mean, 

population-weighted LCF risk (without random ignition source) for 

the maximum-cesium-release realization within five intervals 

centered on Sequoyah. Probability represents weather variability ................... 6-42

Figure 6-33 Complimentary cumulative distribution function of mean, 

population-weighted, LCF risk (without random ignition source) for 

the maximum-risk realization within five intervals centered on 

Sequoyah. Probability represents weather variability. ...................................... 6-43

Figure 6-34 Complimentary cumulative distribution function of mean, 

population-weighted, LCF risk (without random ignition source) for 

the maximum-risk realization within five intervals centered on 

Sequoyah. Probability represents weather variability. ...................................... 6-43

Figure 6-35 Summary of sensitivity analysis long term and emergency phase 

mean, individual, LNT, LCF risk results for the 0 - 10 mile region .................... 6-45
xiv 

LIST OF TABLES 

Table Page 

Table ES-1 SBO scenario variations and modeling approach .............................................. xxi

Table ES-2 Uncertain MELCOR parameters used in unmitigated STSBO UA ..................... xxi

Table ES-3 Uncertain MACCS parameter groups used in unmitigated STSBO 

UA ..................................................................................................................... xxiv

Table 1-1 Sequoyah SOARCA scenarios, variations, and approach for 

considering uncertainty ...................................................................................... 1-7

Table 3-1 Important design parameters for Sequoyah. ...................................................... 3-4

Table 3-2 MELCOR rings and number of fuel assemblies. ................................................ 3-9

Table 3-3 Sequoyah ice condenser input parameters. ..................................................... 3-19

Table 3-4 Best estimate of rupture pressures for the Sequoyah containment 

from various NRC-sponsored studies............................................................... 3-21

Table 3-5 MELCOR sampled parameters ........................................................................ 3-32

Table 3-6 SV failure data and associated epistemic uncertainty distributions 

for probability of occurrence on demand for FTO and FTC. ............................. 3-36

Table 3-7 VERCORS test results for collapse temperature. ............................................. 3-39

Table 4-1 Timing of key events for base case unmitigated STSBO. .................................. 4-2

Table 4-2 Post-calculated regression parameters. ........................................................... 4-15

Table 4-3 Summary of the number of realizations that were unsuccessful in 

the without random ignition set. ........................................................................ 4-16

Table 4-4 Regression analysis of iodine release fraction at 72 hours (with no 

random ignition). ............................................................................................... 4-27

Table 4-5 Regression analysis of cesium release fraction (with no random 

ignition). ............................................................................................................ 4-39

Table 4-6 Regression analysis of hydrogen production at 72 hours (with no 

random ignition). ............................................................................................... 4-51

Table 4-7 Regression analysis of rupture time for all realizations (with no 

random ignition). ............................................................................................... 4-56

Table 4-8 Realizations that were unsuccessful with random ignition assumed 

to occur. ............................................................................................................ 4-60

Table 4-9 Regression analysis of iodine release fraction at 72 hours (with 

random ignition). ............................................................................................... 4-68

Table 4-10 Regression analysis of cesium release fraction (with random 

ignition). ............................................................................................................ 4-77

Table 4-11 Regression analysis of hydrogen production at 72 hours (with 

random ignition). ............................................................................................... 4-85

Table 4-12 Regression analysis of rupture time for all realizations (with 

random ignition). ............................................................................................... 4-89

Table 4-13 Single realizations for detailed analysis............................................................ 4-94

Table 4-14 Select realizations sampled parameters........................................................... 4-95

Table 4-15 Selected realization results............................................................................... 4-96

Table 4-16 Timing of key events for unmitigated LTSBO base case. ............................... 4-118

Table 4-17 Description of unmitigated LTSBO sensitivity cases. ..................................... 4-128

Table 4-18 Description of mitigated LTSBO sensitivity cases with igniters. ..................... 4-132

Table 5-1 Seasonal diurnal mixing heights for Tennessee (m). ......................................... 5-7

Table 5-2 Meteorology data completion rates. ................................................................... 5-8
xv 

Table 5-3 Summary of precipitation by year (inches). ........................................................ 5-9

Table 5-4 Summary of 2012 meteorological statistics. ..................................................... 5-10

Table 5-5 Surface roughness. .......................................................................................... 5-15

Table 5-6 Sequoyah evacuation cohorts. ......................................................................... 5-17

Table 5-7 Shielding and Protection Factors for Evacuating Cohorts. ............................... 5-22

Table 5-8 Dose responses modeled. ................................................................................ 5-27

Table 5-9 Sequoyah MACCS model uncertain parameters.............................................. 5-28

Table 5-10 Uncertain MACCS parameters applied in the analysis. .................................... 5-30

Table 5-11 Dry deposition velocities. .................................................................................. 5-37

Table 5-12 Simplified groundshine shielding factor (GSHFAC) for Sequoyah. .................. 5-42

Table 5-13 Inhalation protection factor (PROTIN) for Sequoyah. ....................................... 5-44

Table 5-14 Radionuclides treated as having uncertain inhalation dose 

coefficients. ...................................................................................................... 5-53

Table 5-15 Log-triangular values for dispersion parameter CYSIGA for each 

stability class. ................................................................................................... 5-57

Table 5-16 Log-triangular values for dispersion parameter CZSIGA for each 

stability class. ................................................................................................... 5-59

Table 5-17 MACCS uncertain parameters – evacuation delay........................................... 5-65

Table 5-18 MACCS uncertain parameters – evacuation speeds. ....................................... 5-68

Table 5-19 MACCS correlated parameters. ....................................................................... 5-75

Table 6-1 Source term releases for Sequoyah accident scenarios. ................................... 6-3

Table 6-2 The Sequoyah core damage frequency and conditional scenariospecific individual LCF risks for the 0 – 10 mile radial interval 

assuming LNT dose response. ........................................................................... 6-7

Table 6-3 Summary of the mean emergency and long term phase individual 

LNT, LCF risks for residents within 0 - 10, 10 - 20, and 0 - 50 mile 

intervals of the plant. .......................................................................................... 6-8

Table 6-4 Individual LCF risk calculated using alternate low dose risk 

quantification approaches for the STSBO base case scenario. ....................... 6-10

Table 6-5 Mean individual LCF risk calculated using low dose risk 

quantification approaches for the STSBO Early Release scenario. ................. 6-13

Table 6-6 Total LCF risk as a function of dose threshold for the LTSBO 

scenario. ........................................................................................................... 6-15

Table 6-7 Mean individual LCF risk conditional on the STSBO w/o random 

ignition accident occurring (per event) for five intervals centered 

on Sequoyah. ................................................................................................... 6-17

Table 6-8 Mean individual LCF risk conditional on the STSBO w/o random 

ignition accident occurring (per event) for four intervals centered 

on Sequoyah. ................................................................................................... 6-17

Table 6-9 Mean contribution of emergency phase to overall risk. .................................... 6-19

Table 6-10 Fraction of STSBO w/o random ignition MACCS realizations in 

which the emergency phase contributions to dose exceed those of 

the long term phase. ......................................................................................... 6-19

Table 6-11 Mean, individual, LCF risk regression results within a 0 – 10 mile 

interval for all realizations based on LNT. ........................................................ 6-21

Table 6-12 Mean, individual, LCF risk regression results within a 10 - 20 mile 

interval for all realizations based on LNT. ........................................................ 6-22

Table 6-13 Mean, individual, LCF risk regression results within a 0 – 50 mile 

interval for all realizations based on LNT. ........................................................ 6-23
xvi 

Table 6-14 Mean individual early fatality risk without random ignition, 

conditional on accident (per event) for the MACCS uncertainty 

analysis for seven distance intervals centered on Sequoyah. ......................... 6-27

Table 6-15 Mean, individual STSBO with random ignition LCF risk, conditional 

on accident (per event) for the MACCS uncertainty analysis for 

five intervals centered on Sequoyah. ............................................................... 6-29

Table 6-16 Mean, individual STSBO with random ignition LCF risk, conditional 

on accident (per event) for the MACCS uncertainty analysis for 

four intervals centered on Sequoyah. ............................................................... 6-29

Table 6-17 Mean contribution of emergency phase to overall risk, STSBO 

with random ignition. ........................................................................................ 6-31

Table 6-18 Fraction of STSBO with random ignition MACCS realizations in 

which the emergency phase contributions to dose exceed those of 

long term phase. ............................................................................................... 6-31

Table 6-19 Mean, individual, LCF risk regression results within a 0 – 10 mile 

interval for all realizations based on LNT. ........................................................ 6-33

Table 6-20 Mean, individual, LCF risk regression results within a 10 – 20 mile 

interval for all realizations based on LNT. ........................................................ 6-34

Table 6-21 Mean, individual, LCF risk regression results within a 0 – 50 mile 

interval for all realizations based on LNT. ........................................................ 6-35

Table 6-22 Mean, individual EF risk with random ignition (STSBO-WRI), 

conditional on accident (per event) for the MACCS uncertainty 

analysis for five distance intervals centered on Sequoyah ............................... 6-38

Table 6-23 Integral release fractions for the maximum cesium release and 

maximum risk realizations. ............................................................................... 6-41

Table 6-24 Mean, individual LCF risks for the maximum-cesium-release 

realization, conditional on accident (per event) for the MACCS 

uncertainty analysis for five intervals centered on Sequoyah. .......................... 6-42

Table 6-25 Mean, individual LCF risks for the maximum-cesium-release 

realization, conditional on accident (per event) for the MACCS 

uncertainty analysis for four intervals centered on Sequoyah. ......................... 6-42

Table 6-26 Mean, individual LCF risks for the maximum risk realization, 

conditional on accident (per event) for the MACCS uncertainty 

analysis for five circular areas centered on Sequoyah. .................................... 6-44

Table 6-27 Mean, individual LCF risks for the maximum risk realization, 

conditional on accident (per event) for the MACCS uncertainty 

analysis for four intervals centered on Sequoyah............................................. 6-44

Table 6-28 Inhalation and skin protection factors of the sheltering sensitivity 

analysis. ........................................................................................................... 6-45

Table 6-29 Sensitivity analysis results for emergency phase LCF risks for the 

0 – 10 mile interval, the emergency phase fraction of total LCF 

risk, and the emergency phase risks normalized to the STSBO 

early release risk. ............................................................................................. 6-46

Table 6-30 Sensitivity analysis conditional early fatality risk results for 

selected radial intervals near Sequoyah. ......................................................... 6-47

 
xvii 

 
xviii 

EXECUTIVE SUMMARY 

The U.S. Nuclear Regulatory Commission (NRC), the nuclear power industry, and the domestic 

and international nuclear energy research community have devoted considerable research over 

the last several decades to examining severe reactor accident phenomena and offsite 

consequences. Following the terrorist attacks of 2001 in the United States (U.S.), an NRC 

initiative reassessed severe accident progression and offsite consequences in response to 

security-related events. An insight gained from these security assessments was that the NRC 

needed updated analyses of severe reactor accidents to reflect realistic estimates of the more 

likely outcomes, considering the current state of plant design and operation and the advances in 

understanding of severe accident behavior. 

The NRC initiated the State-of-the-Art Reactor Consequence Analyses (SOARCA) project to 

develop best estimates of the offsite radiological health consequences for potential severe 

reactor accidents. The initial SOARCA work considered two pilot plants: the Peach Bottom 

Atomic Power Station in Pennsylvania and the Surry Power Station in Virginia. Peach Bottom is 

generally representative of U.S. operating reactors using the General Electric boiling-water 

reactor (BWR) design with a Mark I containment. Surry is generally representative of U.S. 

operating reactors using the Westinghouse pressurized-water reactor (PWR) design with a 

large, dry (subatmospheric) containment. The SOARCA project evaluates plant improvements 

and changes not reflected in earlier NRC publications such as NUREG/CR-2239, “Technical 

Guidance for Siting Criteria Development, NUREG-1150, “Severe Accident Risks: An 

Assessment for Five U.S. Nuclear Power Plants,” and WASH-1400, “Reactor Safety Study: An 

Assessment of Accident Risks in U.S. Commercial Nuclear Power Plants”. SOARCA includes 

system improvements, improvements in training and emergency procedures, offsite emergency 

response, and security-related improvements, as well as plant changes such as power uprates 

and higher core burnup. The collection of SOARCA reports helps the NRC to communicate its 

current understanding of severe-accident-related aspects of nuclear safety to Federal, State, 

and local authorities, licensees, international counterparts, and the general public. Quantitative 

SOARCA results are specific to the plants and accident sequences evaluated in these studies. 

The insights derived from the SOARCA analyses may be generally applicable to plants with 

similar designs, but additional work would be needed to confirm this because differences exist in 

plant-specific designs, procedures, and emergency response characteristics. 

After completing the Peach Bottom and Surry SOARCA analyses, the Commission approved 

the staff’s recommendations to complete an analysis of station blackouts at the third pilot plant, 

Sequoyah, a PWR with an ice condenser containment. Staff recommended this study because 

ice condenser containments have a lower design pressure than other U.S. nuclear power plant 

containment types and are therefore potentially more susceptible to early failure from hydrogen 

combustion during a severe accident. Ice condensers are pressure suppression containments 

and use ice to absorb heat in a severe accident. Unlike the BWR Mark I and Mark II pressure 

suppression containments, ice condensers are not inerted with nitrogen. Therefore they use 

alternating current (AC) powered igniters to introduce sparks to burn hydrogen in a controlled 

manner before the hydrogen can accumulate to levels that could result in a highly energetic 

deflagration or detonation that could challenge the containment, should the postulated accident 

occur. The SOARCA project sought to focus its resources on the more important severe 

accident scenarios for Sequoyah, and in particular focused on issues associated with 

containment response for ice condenser containments. 
xix 

The staff used updated and benchmarked standardized plant analysis risk (SPAR) models and 

available plant-specific external events information to identify the most probable of the very low 

probability station blackout (SBO) scenario variations for analysis. Similar to the Peach Bottom 

and Surry analyses, this group of scenarios includes the short-term station blackout (STSBO) 

and the long term station blackout (LTSBO). Both types of SBOs involve a loss of all AC power. 

SBO scenarios can be initiated by external events such as a fire, flood, or earthquake. The 

Sequoyah SOARCA analysis assumes that an SBO is initiated by a low probability severe 

seismic event because this is an extreme case in terms of timing and equipment failure. For the 

LTSBO, AC power is lost but the turbine-driven auxiliary feedwater pump (TDAFW) is available 

until batteries deplete. The contribution to core damage frequency for the LTSBO was 

estimated at one event per approximately 100,000 years of reactor operation. For the STSBO, 

AC power is lost and the turbine-driven auxiliary feedwater pump is not available, and therefore 

the postulated scenario proceeds to core damage more rapidly (hence “short term”). The 

contribution to core damage frequency for the STSBO is lower, and was estimated at one event 

per approximately 500,000 years of reactor operation. 

SOARCA analyses were performed primarily with two computer codes, MELCOR for accident 

progression and MACCS for offsite consequences. MELCOR models the thermal-hydraulic 

response in the reactor coolant system, reactor cavity, containment, and confinement buildings; 

core heatup, degradation, and relocation; core-concrete attack; hydrogen production, transport, 

and combustion; fission product transport and release to the environment. MACCS models the 

atmospheric transport and deposition of radionuclides released to the environment as well as 

emergency response and long-term protective actions, exposure pathways, dosimetry, and 

health effects for the affected population. The Peach Bottom and Surry SOARCA studies were 

comprised of deterministic base case analyses using point estimates for input parameter values, 

followed by probabilistic uncertainty analyses (UAs) which sampled distributions representing 

input uncertainty to generate multiple results to represent the range of potential outcomes. For 

the Sequoyah SOARCA analysis, staff integrated probabilistic consideration of uncertainty into 

accident progression and offsite consequence analyses in parallel with deterministic 

calculations. 

The Sequoyah containment is a free-standing steel containment consisting of a cylinder topped 

by a hemispherical dome. The vessel has been determined as nominally capable of resisting 

an internal pressure of 67 psig before rupturing. Upon overpressure failure, a tear in the 

containment (estimated to be on the order of a few square feet in area) is envisioned to open in 

the cylindrical portion, resulting in the potential for release of radioactive material to the 

atmosphere. Understanding the impact of the igniters as well as random sources of ignition on 

accident progression was a primary focus of the Sequoyah SOARCA analysis. As illustrated in 

Figure ES-1, Sequoyah SOARCA analyses continue to confirm that successful use of igniters 

can control hydrogen accumulation through earlier deflagrations resulting in smaller pressure 

spikes that avert early containment failure (long-term containment over-pressurization from 

ongoing steam production, unabated fission product decay heating and noncondensable gas 

generation associated with core-concrete interaction can still lead to containment failure). 
xx 

Figure ES-1 Illustration of the effect of hydrogen igniters in a typical Sequoyah 

SOARCA unmitigated STSBO accident progression 

The critical time frame for igniter operation is prior to the accumulation of hydrogen in the 

containment (e.g., < 3 hours for the STSBO and 10-14 hours after direct current (DC) battery 

failure for the LTSBO). Igniters can be useful until the containment oxygen concentration falls 

below the flammability limit (i.e., >18 hours for the STSBO and >30 hours for the LTSBO) due to 

burning of hydrogen by the igniters and gas generation from core concrete interaction, at which 

point igniters are no longer needed. 

Several variations of the STSBO were evaluated. The base case scenario was an evaluation of 

an unmitigated (i.e., without igniters) STSBO scenario. A sensitivity case of a mitigated STSBO 

was evaluated in which hydrogen igniters are modeled as operable. Two unmitigated (i.e., 

igniters are assumed inoperable) STSBO variations were run probabilistically with and without 

the presence of random ignition sources. Random ignition sources refer to the potential of the 

accident to lead to sparks possibly caused by structural materials sliding against or falling on 

other materials, static discharge, etc., but the timing and location of the spark(s) is inherently 

unknown. The staff modeled hundreds of variations of the STSBO with and without random 

ignition sources, each of which used different values of important input parameters1 selected 

from distributions that account for parameter uncertainty in both accident progression 

(MELCOR) and offsite consequences (MACCS). For the LTSBO scenario, staff has 

 

1 Because the unmitigated STSBO does not credit human actions, the UA also does not address human actions. 
xxi 

deterministically analyzed accident progression for a scenario without igniters or random ignition 

sources, and performed a number of deterministic sensitivity calculations to assess the impact 

of features including battery duration, hydrogen ignition criteria, and safety valve (SV) behavior. 

A sensitivity case of a mitigated LTSBO was also evaluated in which hydrogen igniters are 

modeled as operable. The scenario variations and approach are listed in Table ES-1. 

Table ES-1 SBO scenario variations and modeling approach 

SBO Mitigation Variation Approach

STSBO 

Mitigated (igniters available) Deterministic sensitivity 

analysis 

Unmitigated (igniters not available)

No random ignition 

sources modeled 

Integrated UA 

(Deterministic and 

probabilistic analyses) 

Random ignition 

sources modeled 

Integrated UA 

(Probabilistic analyses) 

LTSBO 

Mitigated (igniters available) 

Deterministic sensitivity 

Unmitigated (igniters not available) analysis 

Hydrogen ignition 

criteria 

Battery duration 

Safety valve behavior

Based on the results of the previous Peach Bottom and Surry uncertainty analyses, several 

MELCOR parameters that were shown to be important were selected for sampling in the 

integrated UA for the Sequoyah unmitigated (i.e., igniters inoperable) STSBO scenarios. In 

addition, new parameters were added specific to the ice condenser containment. The uncertain 

MELCOR input parameters affecting accident progression are identified in Table ES-2. 

Table ES-2 Uncertain MELCOR parameters used in unmitigated STSBO 

UA 

Sequence Issues • Number of primary SV cycles to failure 

• Failed SV flow area 

In-Vessel Accident Progression • Melting temperature of the eutectic 

formed between ZrO2 and UO2

Ex-vessel Accident Progression 

and Containment behavior 

• Containment fragility (failure pressure) 

• Ice condenser inlet door response 

• Containment barrier seal failure pressure 

and area 

Hydrogen Combustion 

• Flammability (flame propagation and 

direction) 

• Ignition source 

Aerosol transport and deposition • Dynamic shape factor 

For the unmitigated STSBO (i.e., igniters inoperable), there are two potential containment 

outcomes: early failure (~3-12 hours from initiating event) due to hydrogen combustion or late 

failure (> 30 hours) from more gradual overpressure. If igniters are not available because of the 

loss of power, containment can fail soon after hot leg rupture or at the time of lower head failure 
xxii 

due to hydrogen deflagration. However, if a random spark triggers ignition prior to reactor 

coolant system breach, the accident is less likely to proceed to early containment failure. An 

early containment failure can result in the potential for radionuclide release to the environment 

before the 0-10 mile EPZ population has time to complete evacuation; however, the 

consequence analysis shows that even under such conditions, health risk to the public is low. 

The uncertainty in the release magnitude and timing is illustrated in Figure ES-2 for iodine and 

Figure ES-3 for cesium. These are the results assuming that an unmitigated SBO accident has 

occurred (in other words, the conditional results). Each gray curve represents an individual 

scenario simulation of an unmitigated STSBO and each black curve represents a deterministic 

variation of the unmitigated LTSBO. These figures show that there is a large variation in 

release timing and magnitude for the unmitigated STSBO. With respect to release timing, a 

bifurcation is observed between simulations leading to early failure (~ 3-12 hrs) and simulations 

leading to late overpressure failure (> 30 hrs). The unmitigated LTSBO variations fall within the 

unmitigated STSBO variations, however for the unmitigated LTSBO, there are no cases of early 

containment failure. The use of TDAFW to extend core cooling in the unmitigated LTSBO 

delays containment failure to greater than about 18 hours following the initiating event. 

Figure ES-2 Iodine release to the environment for variations of unmitigated STSBO 

and LTSBO 
xxiii 

Figure ES-3 Cesium release to the environment for variations of unmitigated STSBO 

and LTSBO 

The base case STSBO and LTSBO analyses indicate that a near complete melting of ice can 

occur as a result of flow of steam and hot gases following the failure of the reactor coolant 

system pressure boundaries. Complete melting of the ice has the beneficial effect of lowering 

containment pressure in the long term however it can make hydrogen combustion more likely in 

some situations. Igniters would delay but not alleviate potential containment overpressure 

failure following lower head failure if no other mitigation systems are available. 

Regression and single-realization analysis results show that uncertain accident progression 

parameters (of those included in the analysis) most important to release timing are those 

affecting the timing of the containment failure, which naturally included the containment rupture 

pressure. An early containment failure was most likely if the hydrogen was distributed 

throughout the containment prior to the development of a strong ignition source, which was 

influenced by an early failure of the pressurizer safety valve (low number of safety valve cycles) 

and a large flow area for failed safety valve. Uncertain accident progression parameters most 

important to release magnitude (cesium and iodine release fraction) are the ones that affect the 

timing of the containment failure and events that contribute to fission product revaporization, 

which included the number of safety valve cycles and the failed safety valve flow area. 

Uncertain accident progression parameters most important to hydrogen production include the 

melting temperature of the eutectic formed from fuel (UO2) and oxidized cladding (ZrO2), and 

the parameters that lead to reactor coolant system depressurization (i.e., few pressurizer safety 

valve cycles before failure and the subsequent flow area of the failed safety valve). 
xxiv 

For the LTSBO, sensitivity calculations show that hydrogen combustion-induced failure occurs 

~18-24 hours after accident initiation. The TDAFW system is very important in extending core 

cooling and allowing more time for implementation of additional mitigation. 

The MACCS model was developed using information from the site-specific emergency 

preparedness programs and State emergency response plans to reflect timing of offsite 

protective action decisions. Scenarios that are assumed to be initiated by a seismic event 

consider the earthquake’s impact on infrastructure (i.e., loss of bridges). As with the accident 

progression analyses, key uncertain parameters were varied as part of the integrated MACCS 

uncertainty analyses. There were over three hundred individual parameters that were varied, 

within the parameter groups2 identified in Table ES-3. 

Table ES-3 Uncertain MACCS parameter groups used in unmitigated 

STSBO UA 

Atmospheric 

Dispersion 

• Crosswind Dispersion Linear Coefficient 

• Vertical Dispersion Linear Coefficient 

• Time-Based Crosswind Dispersion Coefficient 

Deposition • Wet Deposition 

• Dry Deposition Velocities 

Emergency 

Response 

• Evacuation Delay 

• Evacuation Speed 

• Hotspot Relocation Time and Dose Criterion 

• Normal Relocation Time and Dose Criterion 

Shielding Factors • Groundshine Shielding Factors 

• Inhalation Protection Factors 

Early Health Effects 

• Lethal dose to 50% of the population (LD50) 

• Shape factor in hazard function 

• Threshold dose 

Latent Health Effects 

• Dose and Dose Rate Effectiveness Factor 

• Lifetime Cancer Fatality Risk Factors 

• Long Term Inhalation Dose Coefficients 

Aleatory Uncertainty • Weather Trials 

Similar to the results from the Peach Bottom and Surry SOARCA analyses, the Sequoyah 

analyses show essentially zero individual early fatality risk and a very low individual risk of fatal 

cancer for the population close to the plant. The relatively slow releases allow effective 

protective actions to limit exposures to offsite populations. Even for STSBO variations leading 

to early containment failure in which the release to the environment begins prior to the 

completion of the 0-10 mile emergency planning zone (EPZ) evacuation, there is essentially 

zero individual early fatality risk and the individual cancer fatality risk to residents within ten 

miles of the plant is very low – a lifetime risk of fatal cancer following an accident of 

approximately 0.07 percent. When the very low likelihood of the accident occurring is also 

considered, which has been estimated to be on the order of one per several hundred thousand 

 

2 Some parameter groups represent multiple individual parameters. For example, latent health effects 

parameters are radionuclide-specific and organ-specific. 
xxv 

years of reactor operation, the overall risk is much lower. For comparison, the average lifetime 

risk of fatal cancer to a member of the US population is approximately 20 percent. 

Figure ES-4 shows for selected deterministic cases of the unmitigated STSBO, conditional (on 

the occurrence of the core damage scenario) scenario-specific probabilities of a latent cancer 

fatality (LCF) for an individual located within the EPZ (0-10 miles from the plant), an individual 

residing outside the EPZ (10-20 miles from the plant), and within the local region (i.e., within 50 

miles of the plant). Calculations illustrated in this figure use the linear no threshold (LNT) dose 

response model that assumes all dose, no matter how small, results in health risk. In 

Figure ES-4, the cross-hatched sections of the bars represent the individual LCF risk due to 

emergency phase exposures (in the first week of the accident) while the solid sections represent 

the individual LCF risk due to doses experienced in the intermediate and long-term phases of 

the accident (from one week to potentially decades). This figure shows that individual LCF risk 

calculations are generally dominated by long-term exposure to small annual doses (below 2 rem 

in the year of the accident and below 500 mrem per year in subsequent years corresponding to 

the habitability criterion) for evacuees and relocated populations returning to their homes after 

the accident and being exposed to residual radiation over a long period of time. The use of dose 

response models that truncate annual doses below certain levels results in a further reduction to 

the individual LCF risks. 

Figure ES-4 Individual latent cancer fatality risk conditional on unmitigated STSBO 

(per event) 

Regression analysis results show that uncertain input parameters (of those included in the 

analysis) that show importance to individual LCF risk in the STSBO cases include: failed safety 

0.E+00

1.E-04

2.E-04

3.E-04

4.E-04

5.E-04

6.E-04

7.E-04

8.E-04

9.E-04

 0-10 miles 10-20 miles 0-50 miles

Conditional Individual LCF Risk 

Assuming LNT (per event)

Area around Sequoyah

Individual LCF Risk 

from Emergency 

Phase Exposures 

(cross-hatched) 

Individual LCF Risk 

from Intermediate 

and Long-Term 

Phase Doses (solid) 

Start of Significant 

Release to 

Environment

Total Cs 

Release at 

72 hrs

Total I 

Release at 

72 hrs

STSBO Base Case with no Ignition Sources 52 hrs 6.3% 6.9%

STSBO Early Release Example 3 hrs 3.9% 8.4%

Legend
xxvi 

valve flow area (and number of safety valve cycles experienced, in some cases), the cancer 

fatality risk factor for the “residual” organ3 (and sometimes other organs), dose and dose rate 

effectiveness factor for the “residual” organ, the groundshine shielding factors, and the 

containment rupture pressure. 

Figure ES-5 shows conditional (per event) individual LCF risk calculations for the 0-10 mile EPZ 

population for unmitigated SOARCA scenarios for Peach Bottom, Surry, and Sequoyah. This 

figure provides deterministic base case results as well as UA results for scenarios analyzed 

(Sequoyah unmitigated STSBO in this report, Peach Bottom unmitigated LTSBO in 

NUREG/CR-7155, and Surry unmitigated STSBO and STSBO with thermally induced steam 

generator tube rupture (TISGTR) in a forthcoming NUREG/CR). While there are differences in 

the modeling of each scenario, this shows that Sequoyah results in terms of individual LCF risk 

are comparable to those calculated for the other SOARCA pilot plants. The individual LCF risk 

is often in the range of 1E-4 to 1E-3 which can be considered small given that the severe 

accidents have a contribution to core damage frequency typically on the order of 1E-5 per year 

of reactor operation or lower. Sequoyah SOARCA unmitigated STSBO results show that the 

range in individual LCF risk due to uncertainty in the selected MELCOR and MACCS input 

parameters is not very large—approximately one order of magnitude separates the 5th and 95th

percentile values. 

 

3 MACCS uses eight cancer sites (organs), seven of which are specific (lung, red bone marrow, bone, breast, 

thyroid, liver, and colon) and the last of which (“residual”) is used as a surrogate for the remaining soft 

tissues not explicitly modeled. 
xxvii 

Figure ES-5 Comparison of Peach Bottom, Surry, and Sequoyah SOARCA 

unmitigated scenarios in terms of conditional (per event) 

individual latent cancer fatality risk within 10 mile radius 

using LNT dose-response and mean values over weather 

variations 

In summary, this analysis reinforces the results of past analyses of ice condenser containments 

showing that successful use of igniters is effective in averting early containment failure. The 

relatively slow releases allow effective protective actions to limit exposures to offsite 

populations. Even for scenarios resulting in early containment failure (radioactive release to the 

environment prior to completion of evacuation for the 0-10 mile EPZ), resulting individual fatal 

cancer risks are very small. Although early releases were calculated, under these specific 

conditions, for the Sequoyah SOARCA STSBO, health risks are projected to be low, with 

essentially zero individual early fatality risk. Quantitative Sequoyah SOARCA results are 

specific to Sequoyah and the scenarios evaluated herein. The insights derived from these 

analyses may be generally applicable for other PWRs with ice condenser containments, but 

additional work would be needed to confirm this because differences exist in plant-specific 

designs, procedures, and emergency response. 

 

1.E-06

1.E-05

1.E-04

1.E-03

LTSBO STSBO LTSBO STSBO STSBO

with

TISGTR

ISLOCA LTSBO STSBO

Individual Latent Cancer Fatality Risk, 0-10 mi, 

Conditional on unmitigated SOARCA scenario, 

per event, LNT dose-response, weather-means

95th

percentile

base case

mean

median

5th

percentile

Peach 

Bottom Surry Sequoyah
xxviii 

 
xxix 

ACKNOWLEDGMENTS 

Numerous NRC and Sandia National Laboratories (Sandia) managers and technical staff 

contributed to the production of the Sequoyah SOARCA deterministic and uncertainty analyses. 

The NRC manager, Patricia Santiago, and project managers, Jonathan Barr, Salman Haq, and 

AJ Nosek provided the leadership to ensure this project met the objectives of the program and 

ensured sufficient resources were available. Tina Ghosh served as the NRC technical lead for 

the uncertainty applications. Additional NRC staff developed computer code models, conducted 

accident progression and offsite consequence simulations, and analyzed and documented 

results. Sandia technical staff, including Kyle Ross, Nathan Bixler, Joseph Jones, Matt 

Denmen, Aubrey Eckert, Dusty Brooks, Doug Osborn, and Jeff Cardoni performed research in 

developing technical bases for parameters, enhanced the state of the MELCOR model, and 

advanced the regression techniques used in the analysis. Mr. KC Wagner of dycoda, Inc. 

supported model development and analysis of results. The Sequoyah SOARCA project team 

also acknowledges the feedback and guidance provided by the project’s technical advisory 

group consisting of senior level experts throughout NRC. The project team also acknowledges 

the participation of Tennessee Valley Authority Sequoyah staff who supported site visits, 

provided information to NRC staff, and who have reviewed the report and provided a “fact 

check” of the project’s modeling of Sequoyah.

 
xxx 
xxxi 

ABBREVIATIONS AND ACRONYMS 

ACRS Advisory Committee on Reactor Safeguards 

AFW auxiliary feedwater 

ANS American Nuclear Society 

atm atmosphere 

ARV atmospheric relief valve 

BEIR Biological Effects of Ionizing Radiation 

BWR boiling water reactor 

CCP centrifugal charging pump 

CCDF complementary cumulative distribution function 

CCI core-concrete interaction 

CDF cumulative distribution function 

CFC containment fragility curve 

CMTR certified material property test reports 

CPI Consumer Price Index 

CPI-U-RS Consumer Price Index for urban consumers 

CST condensate storage tank 

CV control volume 

CVH control volume hydrodynamics 

CVCS chemical and volume control systems 

DCF dose conversion factor 

DDREF dose and dose rate effectiveness factor 

DF decontamination factor 

EAB exclusion area boundary 

EAL emergency action level 

EAS emergency alert system 

EF early fatality 

ECCS emergency core cooling systems 

ECST emergency condensate storage tank 

EDG emergency diesel generator 

ELAP extended loss of AC power 

EOP emergency operating procedure 

EPA Environmental Protection Agency 

EPZ emergency planning zone 

ETE evacuation time estimate 

FGR Federal Guidance Report 

FP flow path 

FSAR final safety analysis report 

FSI FLEX support instructions 

FTC failure to close 

FTO failure to open 

GE general emergency 

gpm gallons per minute 

GSDE ground shine deposited energy 

GSI generic safety issue 

Gy gray 

HMS hydrogen mitigation system 

HPS Health Physics Society 

HTC heat transfer coefficient 
xxxii 

IPE individual plant examination 

IPEEE Individual Plant Examination for External Events 

LCF latent cancer fatality 

LD50 lethal dose to 50% of population 

LHS Latin hypercube sampling 

LET linear energy transfer 

LOOP loss of offsite power 

LNT linear no threshold 

LTSBO long-term station blackout 

LWR light water reactor 

m meter 

MACCS MELCOR Accident Consequence Code System 

MARS Multivariate Adaptive Regression Splines 

MCCI molten core concrete interactions 

MD-AFW motor-driven auxiliary feedwater 

mph miles per hour 

MSIV main steam isolation valve 

MSL main steam line 

MSS main steam system 

NRC Nuclear Regulatory Commission 

NTTF Near Term Task Force 

NWS National Weather Service 

ORO offsite response organization 

PAG protective action guideline 

PAR protective action recommendation 

PDF probability density function 

PGA peak ground acceleration 

PORV power operated relief valve 

PRA probabilistic risk assessment 

PRT pressure relief tank 

PRY per reactor year 

PWR pressurized water reactor 

QHO quantitative health objective 

RCP reactor coolant pump 

RCS reactor coolant system 

RPV reactor pressure vessel 

SAE Site Area Emergency 

SBO station blackout 

scfh standard cubic feet per hour 

SG steam generator 

SGTR steam generator tube rupture 

SME subject matter expert 

SNL Sandia National Laboratories 

SOARCA State-of-the-Art Reactor Consequence Analyses 

SPAR Standardized Plant Analysis Risk 

SPAR-AHZ SPAR All Hazards 

SRRC standardized rank regression coefficients 

SRS simple random sampling 

SSC systems, structures, and components 

STSBO short-term station blackout 

SV safety valve 
xxxiii 

TDAFW turbine driven auxiliary feedwater 

TVA Tennessee Valley Authority 

UA uncertainty analysis 

USBGR U.S. background 

 
xxxiv 
1-1

1. INTRODUCTION 

This document describes the severe accident analyses performed for the Sequoyah Nuclear 

Plant (Sequoyah) as part of the U.S. Nuclear Regulatory Commission’s (NRC’s) State-of-the-Art 

Reactor Consequence Analyses (SOARCA) project. 

1.1 Background 

The NRC initiated the SOARCA project to develop best estimates of the offsite radiological 

health consequences for potential severe reactor accidents. The project initially envisioned 

analyzing all U.S. nuclear power plants but then chose to analyze pilot plants generally 

representative of the major plant and containment design types. The first two pilot plants are the 

Peach Bottom Atomic Power Station (Peach Bottom) in Pennsylvania and the Surry Power 

Station (Surry) in Virginia. Peach Bottom is generally representative of U.S. operating reactors 

using the General Electric boiling-water reactor (BWR) design with a Mark I containment. Surry 

is generally representative of U.S. operating reactors using the Westinghouse pressurized-water 

reactor (PWR) design with a large, dry (subatmospheric) containment. The third pilot plant, 

Sequoyah, was selected as a representative of the ice condenser containment design and is the 

subject of this report. 

The Peach Bottom and Surry SOARCA analyses evaluated plant improvements and changes 

that were not reflected in earlier NRC publications. SOARCA included system improvements; 

improvements in training and emergency procedures; offsite emergency response; 

improvements codified in 10 CFR 50.54(hh)(2) [1]; and plant changes, such as power uprates 

and higher core burnup. To provide a perspective between SOARCA results and more 

conservative offsite consequence estimates, SOARCA results were compared to NUREG/CR2239 (December 1982), “Technical Guidance for Siting Criteria Development” (also referred to 

as the 1982 Siting Study) [2]. The SOARCA analysis and reports help the NRC communicate its 

current understanding of severe-accident-related aspects of nuclear safety to stakeholders, 

including Federal, State, and local authorities; licensees; and the general public. 

A systematic approach was implemented in SOARCA to calculate the potential consequences 

of the analyzed severe accidents. The project team first decided it could learn more by 

rigorously and realistically analyzing a relatively small number of important accident scenarios 

instead of carrying out less detailed modeling of many scenarios. Therefore, the team selected 

a core damage frequency threshold to help select scenarios to analyze (greater than 1E-7 per 

operating year for bypass accidents and greater than 1E-6 per operating year for non-bypass 

accidents). Accident progression calculations used the MELCOR computer code. For scenarios 

leading to an offsite release of radioactive material, SOARCA analyzed atmospheric dispersion, 

emergency response, and potential health consequences using the MELCOR Accident 

Consequence Code System (MACCS) computer code. 

SOARCA’s key results for Peach Bottom and Surry include the following: 

• When operators are successful in using available onsite equipment during the accidents 

analyzed in SOARCA, they can prevent the reactor from melting or can delay or reduce 

releases of radioactive material to the environment. 

• SOARCA analyses indicate that all modeled accident scenarios, even if operators are 

unsuccessful in stopping the accident, progress more slowly and release much smaller 
1-2

amounts of radioactive material than calculated in earlier studies such as the 1982 Siting 

Study [2]. 

• As a result, public health consequences from severe nuclear power plant accidents 

modeled in SOARCA are smaller than previously calculated. 

• The delayed releases calculated provide more time for emergency response actions, 

such as evacuating or sheltering for affected populations. For the scenarios analyzed, 

SOARCA shows that emergency response programs, if implemented as planned and 

practiced, reduce the risk of public health consequences. 

• Both mitigated (operator actions are successful) and unmitigated (operator actions are 

unsuccessful) cases of all modeled severe accident scenarios in SOARCA cause 

essentially no risk of death to the public from ionizing radiation during or shortly after the 

accident. 

• SOARCA’s calculated longer term cancer fatality risks for the accident scenarios 

analyzed are millions of times lower than the general U.S. cancer fatality risk from all 

causes. 

The SOARCA analysis of Peach Bottom and Surry is documented in a series of NUREG 

reports. NUREG-1935, “State-of-the-Art Reactor Consequence Analyses (SOARCA) Report,” 

was published in 2012 and provides a summary of the project’s objectives, methods, results, 

and conclusions [3]. NUREG/CR-7110, “State-of-the-Art Reactor Consequence Analyses 

Project,” Volume 1, “Peach Bottom Integrated Analysis,” and Volume 2, “Surry Integrated 

Analysis,” provide additional technical details on the analyses conducted for each of the pilot 

plants [4][5]. In addition, the staff developed an information brochure, NUREG/BR-0359, 

“Modeling Potential Reactor Accident Consequences,” to facilitate communication with 

stakeholders, including the public [6]. The staff documented best modeling practices as applied 

in SOARCA for the MELCOR and MACCS codes in NUREG/CR-7008 and NUREG/CR-7009, 

respectively [7][8]. 

NRC staff completed an uncertainty analysis (UA) of the SOARCA unmitigated long term station 

blackout (LTSBO) at Peach Bottom to develop insights into the overall sensitivity of SOARCA 

results to uncertainty in inputs; to identify the most influential input parameters for releases and 

consequences; and to demonstrate a UA methodology that could be used in future source term, 

consequence, and site level 3 probabilistic risk assessment (PRA) studies. Integrated analyses 

using about 40 independent MELCOR and MACCS parameter groups corroborate the overall 

SOARCA results and conclusions for the selected accident scenario. The Peach Bottom UA is 

documented in NUREG/CR-7155 [9]. 

Following the completion of the SOARCA analyses of the first two pilot plants, the NRC staff 

issued a memorandum to the Commission, SECY-12-0092 [10], recommending completion of 

SOARCA analyses of the third pilot plant, Sequoyah, a PWR with an ice condenser 

containment. This analysis, as well as a UA of a SOARCA Surry scenario, was recommended to 
1-3

inform agency activities such as implementation of the Fukushima4 Near Term Task Force 

(NTTF) Tier 3 recommendations related to hydrogen control and reliable hardened vents for 

containment designs other than BWR Mark I and Mark II and the site level 3 PRA project for 

Vogtle Units 1 and 2. Both analyses were also recommended to expand on the body of 

knowledge on severe accident progression and consequences developed in the SOARCA pilot 

plant analyses. The SOARCA analysis of an ice condenser plant was recommended because 

its relatively low design pressure and reliance on hydrogen igniters makes it potentially 

susceptible to early failure from hydrogen combustion during a severe accident. 

In December 2012, the Commission approved the staff’s recommendations. Prior to this 

recommendation, Sequoyah had been the third SOARCA pilot plant after Peach Bottom and 

Surry. Plant and site information had been gathered and the MELCOR plant model was being 

updated. These Sequoyah activities were paused in 2009 in order to focus on completion of the 

Peach Bottom and Surry analyses. Because the staff had already started the process of 

analyzing Sequoyah, it was the logical candidate for the follow-on analyses recommended in 

SECY-12-0092. Sequoyah is generally representative of the fleet of ice condenser containment 

plants in the U.S. although there are differences from unit to unit and site to site. With ten 

units5, nine of which are operational6, the PWR ice condenser design is the third most prevalent 

after the PWR large dry containment and the BWR Mark I containment. 

Figure 1-1 shows that the typical design pressure of a PWR ice condenser containment is the 

lowest among U.S. nuclear power plant containment types, around 12 psig (83 kPa), slightly 

below that of the BWR Mark III containment. Ice condensers are pressure suppression 

containments and use ice to absorb heat in a severe accident. Unlike the BWR Mark I and 

Mark II pressure suppression containments, ice condensers are not inerted with nitrogen. 

Therefore they use alternating current (AC) powered igniters to introduce sparks that burn 

hydrogen in a gradual, controlled manner before it can rise to levels that could result in a highly 

energetic deflagration or detonation that could challenge the containment. The susceptibility of 

ice condenser (and Mark III) containments to early failure from hydrogen combustion during a 

severe accident was analyzed in the NRC’s generic safety issues (GSI) program as GSI-189. 

The GSI-189 technical assessment led staff to recommend the addition of a backup power 

supply for the igniters for plants with an ice condenser or Mark III containment. Licensees of 

these plants committed to voluntarily develop and maintain the capability to provide backup 

power to the containment hydrogen igniters during severe accidents and certain security 

scenarios. The staff accepted these commitments and verified acceptable implementation of 

the commitments as of January 2013 [11]. 

 

4 On March 11, 2011, a 9.0-magnitude earthquake struck Japan and was followed by a 45-foot tsunami, 

resulting in extensive damage to the nuclear power reactors at the Fukushima Dai-ichi facility. The NRC has 

taken significant action to enhance the safety of reactors in the United States based on the lessons learned 

from this accident. NRC has developed a webpage (http://www.nrc.gov/reactors/operating/opsexperience/japan-dashboard.html) to serve as a navigation hub to follow the NRC's progress in 

implementing the many different lessons-learned activities. 

5 There are 10 reactors at five sites with ice condenser containments: Catawba Units 1 & 2 in South Carolina, 

DC Cook Units 1 & 2 in Michigan, McGuire Units 1 & 2 in North Carolina, and Sequoyah Units 1 & 2 and 

Watts Bar Units 1 & 2 in Tennessee. 

6 Watts Bar Unit 2 is scheduled to start commercial operation in 2016 [16]. 
1-4

The ice condenser containment design was also analyzed in NUREG-1150 in which the staff 

conducted a level 3 PRA of five plants representing different design types [12]. The Sequoyah 

analysis was documented in greater detail in NUREG/CR-4551, Volume 5 [13]. 

Figure 1-1 Typical Containment Volumes and Design Pressures (Figure 

4.1-1 from NUREG/CR-6042, Rev. 2 [14])

1.2 General Sequoyah Plant and Site Information 

Sequoyah is located near Soddy-Daisy in Hamilton County, Tennessee, about 18 mi (29 km) 

northeast of Chattanooga along the Tennessee River. Sequoyah is owned and operated by 

Tennessee Valley Authority (TVA). Sequoyah has two Westinghouse 4-loop PWRs with ice 

condenser containments. Units 1 and 2 received their operating license in 1980 and 1981, 

began commercial operation in 1981 and 1982, and received their renewed licenses in 2015 to 

operate until 2040 and 2041, respectively. Both units are licensed at 3455 MWth and have 

operated at a 92% (Unit 1) and 89% (Unit 2) average annual capacity factor for years 2008-

2014 [15]. Spent fuel is kept at Sequoyah in a spent fuel pool and in dry cask storage. An 

aerial view of Sequoyah is shown in Figure 1-2 looking from the north to the south. 
1-5

Figure 1-2 View of Sequoyah from the north 

1.3 Sequoyah SOARCA Scope 

Similar to the Peach Bottom and Surry SOARCA analyses [4][5], the priority of the Sequoyah 

analysis is on detailed analytical modeling of a set of important severe accident scenarios 

considering both likelihood and potential consequences. Like Peach Bottom and Surry, the 

scope is on single unit reactor accident scenarios postulated to be caused by seismic events. 

The Sequoyah analysis does not consider multiunit accidents, spent fuel pool or dry cask 

storage accidents, accidents during shutdown or low-power modes, extreme seismic events that 

lead directly to gross containment failure with simultaneous reactor core damage, or accidents 

caused by malevolent acts7. The Sequoyah SOARCA analysis is not a PRA, and it does not 

attempt to quantify the frequency of each modeled variation in the accidents analyzed. While 

the Peach Bottom and Surry SOARCA analyses [4][5] considered a variety of scenarios 

including station blackouts (SBOs), steam generator tube ruptures (SGTRs), and interfacing 

systems loss of cooling accidents (ISLOCAs), the Sequoyah SOARCA analysis is focusing 

specifically on short-term SBO (STSBO) and long-term SBO (LTSBO). These scenarios involve 

an immediate loss of offsite and onsite AC power. In the STSBO variation, early failure of the 

 

7 The NRC’s Site Level 3 PRA of Vogtle Units 1 & 2 includes within its scope multiunit accidents, spent fuel 

pool and dry cask accidents, and accidents during shutdown and low-power modes. Information on the 

objectives and status of the Site Level 3 PRA is available on the NRC’s website from the 2015 Regulatory 

Information Conference: http://www.nrc.gov/public-involve/conferencesymposia/ric/past/2015/docs/abstracts/sessionabstract-10.html
1-6

turbine driven auxiliary feedwater (TDAFW) is assumed and direct current (DC) power is also 

immediately unavailable; thus, the accident can progress to core damage in the “short term”. In 

the LTSBO variation, DC power is available until station batteries deplete and thus the accident 

can progress to core damage in the “long term”. These are important scenarios for an ice 

condenser plant because of its reliance on AC-powered igniters for hydrogen control. The Surry 

SOARCA analyses revealed insights on SGTRs and ISLOCAs and because those are not 

expected to progress much differently in an ice condenser containment plant, they are excluded 

from the Sequoyah analysis. 

1.4 Approach for Uncertainty Considerations 

The initial SOARCA studies were comprised of a deterministic base case or “best estimate” 

analysis (documented in NUREG-1935 and NUREG/CR-7110 Volumes 1 and 2) for the Peach 

Bottom and Surry plants. An integrated uncertainty analysis8 for one of the Peach Bottom 

accident scenarios was begun before the conclusion of the initial SOARCA project, and 

subsequently completed and documented in NUREG/CR-7155. A second uncertainty analysis 

for a Surry accident scenario, the unmitigated STSBO, was also subsequently completed and 

will be documented as a NUREG/CR report [17]. At the conclusion of the original SOARCA 

project, one of the recommendations of the NRC’s Advisory Committee on Reactor Safeguards 

(ACRS) was to conduct future deterministic “best estimate” studies and associated uncertainty 

analyses in parallel. 

For the Sequoyah SOARCA analysis, staff integrated consideration of uncertainty into accident 

progression and offsite consequence analyses from the beginning of the project. The approach 

used to consider uncertainty varied by accident scenario. Because this study had a particular 

focus on the potential for early containment failure, and ice condenser containment-specific 

issues, an integrated uncertainty analysis was included for the two scenarios with highest 

likelihood of early containment failure: the unmitigated STSBO9 cases with, and then without, 

the presence of random ignition sources. The analysis of remaining scenarios applied 

sensitivity analyses to examine variations in system response and results. The different 

scenarios, their variations, and the approach to considering uncertainty are listed in Table 1-1. 

 

8 The original SOARCA study did not include a formal human reliability analysis within its scope. The Peach 

Bottom Uncertainty Analysis also did not include human actions within the scope of its integrated 

assessment. It did, however, document the results of a sensitivity analysis varying the timing of the two 

operator actions that are credited in the unmitigated LTSBO. 

9 Because the unmitigated STSBO does not credit human actions, the UA also does not address human 

actions. 
1-7

Table 1-1 Sequoyah SOARCA scenarios, variations, and approach for 

considering uncertainty 

SBO Type Variation Approach

STSBO 

• Unmitigated (igniters not available 

and no random ignition sources 

modeled) 

Integrated UA 

(Deterministic and probabilistic 

analyses) 

• Unmitigated (igniters not available but 

random ignition sources modeled) 

Integrated UA 

(Probabilistic analyses) 

• Mitigated (igniters available) Deterministic sensitivity analysis 

LTSBO 

• Igniters Deterministic sensitivity analysis 

• Hydrogen ignition criteria Deterministic sensitivity analysis 

• Battery duration Deterministic sensitivity analysis 

• Safety valve behavior Deterministic sensitivity analysis 

For the MELCOR accident progression analysis, the parameters selected for sampling in the 

integrated UA were based on insights from previous UAs, and new ice condenser containmentspecific and hydrogen-specific considerations. Insights from the Surry SOARCA UA [17] were 

leveraged heavily to identify a reduced set of parameters to include in the Sequoyah integrated 

UA. Ice condenser containment-specific and hydrogen-specific considerations added new 

parameters to the list, resulting in the selection of 11 MELCOR parameters to vary in the 

integrated UA. These parameters are further described in Chapter 3. 

For the MACCS offsite consequence analysis, the set of uncertain parameters was selected 

based on insights from previous UAs and based on a review to identify any new site-specific 

considerations. Parameters varied as part of the Sequoyah integrated UA are the same as 

those in the Surry SOARCA UA, with the addition of a time-based crosswind dispersion 

coefficient in Sequoyah. The selected parameter groups are further described in Chapter 5. 

For a more complete discussion of the UA methodology used in this study, the reader is 

directed to Chapter 3 of the Surry SOARCA UA [17] and Appendix A of the Peach Bottom 

SOARCA UA [9] which describe the probabilistic methodology in even more detail. The reader 

is also referred to the “Glossary of Uncertainty Analysis Terms” that is contained as an appendix 

to both of these earlier SOARCA UAs. 

The Sequoyah integrated UA for the two variations of unmitigated STSBO (with and without 

random ignition sources) again employed a Monte Carlo approach as described in Appendix A 

of this report. Uncertainty in chosen MELCOR and MACCS parameters was described by 

assigning distributions of possible values. The uncertainty in these input parameters was then 

propagated to results of interest using Monte Carlo simulation with simple random sampling in 

both the MELCOR uncertainty engine and WinMACCS, the Windows-based user interface for 

MACCS. The Monte Carlo results were post-processed and analyzed using four regression 

techniques – linear rank regression, quadratic regression, recursive partitioning, and 

multivariate adaptive regression splines (MARS) – as well as scatter plots and investigation of 

interesting single realizations (the outcome of one vector of sampled input values of the ‘n’ 

successful Monte Carlo realizations). The purpose of the regression analysis and scatter plots 

is to determine the contributions of uncertain inputs to the uncertainty in the analysis results. 

The single realization analyses further help construct phenomenological explanations of 
1-8

interesting variations in system behavior (e.g., variations in containment failure time) that are 

dependent on uncertain inputs. 

MELCOR, MelMACCS, and MACCS are the three primary codes used in the integrated UA. 

These codes are continually enhanced, updated, and maintained as part of the NRC research 

program. As improvements or fixes are implemented, the codes are updated. The codes were 

designed for outputs from one code to become inputs for the next code to support deterministic 

analyses. The codes have been integrated for use in analyzing uncertainty in the same manner 

as implemented for the Peach Bottom UA [9] and Surry UA [17], as illustrated in Figure 1-3. 

Figure 1-3 Diagram of code information flow for uncertainty analysis 

1.5 Outline of Report 

Chapter 2 of this report describes the severe accident scenarios modeled, their estimated 

frequency, and the seismic event postulated to initiate them. Chapter 3 describes the MELCOR 

model of Sequoyah used in the analyses and the approach used to develop it. Chapter 3 also 

discusses the MELCOR input parameters which are modeled using a distribution of values to 

account for epistemic model uncertainty. Chapter 4 describes the results of MELCOR accident 

progression calculations. Chapter 5 describes the MACCS model of Sequoyah, the approach 

used to develop it, and the uncertain MACCS input parameters. Chapter 6 describes the offsite 

consequence results in terms of individual early and latent cancer fatality risks along with the 

regression analysis used to assess the importance of MELCOR and MACCS uncertain input 

parameters on the results. Chapter 7 provides a brief conclusion of the study’s results and 

insights gained. References are listed in Chapter 8. Three appendices provide additional detail 
1-9

on UA methodology (Appendix A), MACCS input parameters (Appendix B), and an evacuation 

roadway capacity analysis (Appendix C). 
2-1

2. ACCIDENT SCENARIO DEVELOPMENT 

2.1 Background 

Ice condenser plants such as Sequoyah are unique because of low containment design 

pressure (12 psig for Sequoyah) and a relatively small volume containing a four loop PWR 

reactor coolant system (RCS) having a power rating of 3455 MWt. A severe accident in such a 

plant can generate high concentrations of hydrogen which can potentially lead to early 

containment failure during an SBO. The scope of this study is limited to SBO scenarios [10]. 

2.2 Scenario Selection 

Information from the NRC’s Standardized Plant Analysis Risk (SPAR) model for Sequoyah was 

used to assist the selection of SBO scenarios as candidates to be studied in this project. Loss 

of offsite power (LOOP) events contribute significantly to plant core damage frequency. A 

further examination of the failure combinations that lead to core damage shows that SBO 

contributions are dominant. 

SBO events may arise from different hazards, such as internal events, seismic events, internal 

fire events, external flooding events, etc. Moreover, their occurrence frequencies are plant and 

site specific. Based on insights from available studies, high-pga seismic events (0.5g pga and 

above) are chosen as the representative initiators for the severe accident SBO scenarios to be 

analyzed in this study. SBO events stemming from other hazard categories would provide 

different additional event frequencies, recovery and mitigation opportunities. The two selected 

SBO scenario variations are (1) an early failure of TDAFW (STSBO) which includes loss of DC 

power systems and (2) a late failure of TDAFW (LTSBO). 

Directly below is a description of the initiating event for both scenarios. Sections 2.2.1 and 2.2.2 

define the STSBO and LTSBO accident scenarios, respectively. Section 2.3 discusses 

mitigation measures for the LTSBO scenario. 

The initiating event for both accident scenarios in this study is a beyond design basis 

earthquake resulting in a LOOP due to a damaged transmission line or switchyard components. 

The initiating event is similar to that in Surry and Peach Bottom SOARCA analyses. The 

emergency diesel generators (EDGs) also fail to start or the emergency AC power system 

becomes inoperable, and the event is classified as an SBO, i.e., loss of all onsite and offsite AC 

power. In PWRs this can lead to loss of cooling water in the steam generators (SGs) and loss 

of water inventory in the RCS that covers and cools the core. 

2.2.1 Short-term Station Blackout (STSBO) 

A seismically initiated STSBO has an estimated contribution to the CDF of approximately one in 

500,000 years of reactor operation. For Sequoyah, STSBO scenarios include AFW unavailable 

due to a loss of DC power, failure of a TDAFW10 system component, or loss of the water supply 

 

10 The authors note that TVA has upgraded and performed modifications to the TDAFW system to better cope with 

external events such as earthquakes. However, the NRC had not finished a review of these upgrades at the time of 

this report and thus a discussion of this new capability is not included.
2-2

due to the seismic initiating event. The TDAFW uses DC power to open the motor-driven 

trip/throttle valve that supplies steam to the turbine and to the control governor valve. However, 

the plant emergency procedures include actions to manually perform those functions. The 

normal supply of water to the TDAFW is the two non-seismically-qualified condensate storage 

tanks (CSTs). The CSTs are assumed to fail in a large seismic event. There are two backup 

(seismic Category I) essential raw cooling water (ERCW) system header feeds to each AFW 

electric pump as an alternate water supply. The AFW turbine pump can be remote-manually 

aligned to receive water from the ERCW header. However, the AFW is unavailable in the 

STSBO due to (a) component failure (e.g., batteries/DC power), (b) failure to manually open the 

steam valve to the TDAFW, or (c) failure to establish a water supply. 

A review of the survivability of systems, structures, and components (SSCs) of interest for the 

accident progression analysis permits an assessment of the assumptions made in that analysis. 

Ice Condenser – The Individual Plant Examination for External Events (IPEEE) report for 

Sequoyah reports a high confidence in low probability of failure (HCLPF) of 0.3 g for the ice 

condenser using design allowable stresses as acceptance criteria for the estimation. Such 

criteria are judged to be a conservative indicator of the condenser functionality. A walkdown of 

the ice condenser for a plant with similar characteristics recently completed in 2014 confirms 

that the structure of the ice condenser would undergo some structural damage and some ice 

might drop for the seismic event. It was considered that this damage would not significantly 

prevent the ice condenser from performing its function. The large number of doors in the ice 

condenser and its open internal structure is assumed to provide redundancy to establish cooling 

and condensing pathways. 

Auxiliary Feedwater Systems – The natural sources of water for the TDAFW are the two CSTs 

which were not designed and built as safety-related structures. Using the seismic fragility 

derived by Nie (2011) for similar steel CST tanks designed for a safe-shutdown earthquake 

(SSE) similar to that used in the design of Sequoyah, the HCLFPF and median fragility for these 

tanks are about 0.4g and 0.9g, respectively. This means that there is high confidence that the 

CST tanks would survive a seismic event with a PGA of 0.4 g and about a 50% likelihood that 

they would survive a seismic event with a PGA of 0.9 g. According to the Sequoyah final safety 

analysis report (FSAR), water loss from CST would accumulate in the sump of the turbine 

building and in the plant holding pond. The plant operator upgraded the auxiliary feedwater 

(AFW) system and the CST tanks to better cope with external events such as earthquakes but 

details of that upgrade were not yet reviewed by the NRC at the time of the Sequoyah SOARCA 

study. Review of those details or related additional information from the plant operator will 

permit confirmation or revision of this survivability review. 

Containment structure and containment isolation system – The IPEEE report for Sequoyah 

confirms that the steel containment structure has a high seismic capacity and there is high 

confidence that it would survive the seismic event. Fragility estimates of the isolation systems 

for one other ice condenser plant with similar characteristics, assuming that the systems for that 

plant are adequate surrogates for the systems in the Sequoyah plant, indicates that there is high 

confidence that the isolation systems would survive the seismic event. 

The following list shows the key events at the plant for the STSBO scenario, with unavailability 

of the AFW due to a beyond design basis earthquake. These define the boundary conditions for 

the unmitigated STSBO base case MELCOR analyses. 

• LOOP initiating event, a beyond design basis earthquake, occurs. 
2-3

• The reactor protection system will automatically actuate upon a LOOP and terminate the 

fission power reaction – reactor is tripped. 

• The EDG, emergency AC, will receive a start signal but will fail. 

• The reactor coolant pumps (RCPs) will coast down to stop within few minutes and all 

other pumps normally running (e.g., chemical and volume control system (CVCS) 

charging pumps) stop due to loss of AC power. 

• Valves designed to fail-close, close to isolate systems and the containment (e.g., Main 

Steam Isolation Valves (MSIVs)). 

• Emergency AC power is not available due to SBO, therefore Motor-Driven AFW (MDAFW) pumps fail to start. 

• TDAFW is inoperable. 

• Fission product decay heat continues to heat the water in the reactor vessel. 

• The safety valves (SVs) on primary and secondary system open to control the pressure, 

which decreases the RCS and SG water inventory, respectively. 

• The RCP seals start to leak initially at a nominal rate of 21 gpm per pump due to the 

failure of the pump seal cooling injection. 

• AC power recovery prior to core damage is unsuccessful (both offsite and onsite). 

2.2.2 Long-term Station Blackout (LTSBO) 

A seismically initiated LTSBO has an estimated contribution to core damage frequency of one in 

approximately 100,000 years of reactor operation. The system availabilities following the 

seismic initiating event are the same as those noted for STSBO, with one exception – the 

TDAFW is initially operable in the LTSBO due to the initial availabilities of DC power and one of 

the AFW water supplies discussed in Section 2.2.1 above. 

The following list shows the key events at the plant for LTSBO, due to a beyond design basis 

earthquake. These define the boundary conditions for the unmitigated LTSBO base case 

MELCOR analyses. 

• LOOP initiating event, a beyond design basis earthquake, occurs. 

• The reactor protection system will automatically actuate upon a LOOP and terminate the 

fission power reaction – reactor is tripped. 

• The EDG, emergency AC, will receive a start signal but will fail. 

• The RCPs will coast down to stop within few minutes and all other pumps normally 

running (e.g., CVCS charging pumps) stop due to loss of AC power. 
2-4

• Valves designed to fail-close, close to isolate systems and the containment (e.g., 

MSIVs). 

• Because DC power is available, the AFW system automatically receives a start signal. 

• Emergency AC power is not available due to SBO, therefore MD-AFW pumps fail to 

start. 

• Fission product decay heat continues to heat the water in the reactor vessel. 

• The SVs on primary and secondary system are available to control the pressure. 

• The RCP seals start to leak initially at a nominal rate of 21 gpm per pump due to the 

failure of the pump seal cooling injection. 

• The supply valve to the AFW steam turbine automatically opens and DC power is 

available, which starts AFW injection into steam generators. 

• The TDAFW pump will automatically maintain the water level in the steam generators, 

which will remove decay heat from the core using natural convection heat transfer and 

prevent core damage as long as water inventory is available. The operators will 

simultaneously attempt to mitigate the accident by restoring AC power or aligning dieseldriven portable pumps. 

• Manual DC deep load shedding and secondary depressurization is successful. The 

secondary depressurization stops at 160 psig to maintain adequate steam pressure for 

the TDAFW. 

• Battery runs out after 8 hours (i.e., nominally with successful load shedding) and all 

attempts to restore AC power restoration is unsuccessful (both offsite and onsite). 

• The AFW steam turbine control valve fully opens following the loss of battery power and 

increases AFW injection. The AFW steam turbine fails when the SG overfills. No credit 

is given for local operator actions to keep the AFW pump running, although it is 

considered in the NRC’s SPAR model. 

• The atmospheric relief valve closes following the loss of battery power and the SVs open 

to control pressure. 

2.3 Mitigative Measures 

Sequoyah operators are expected to follow the emergency procedures, such as ECA-0.0, 

“LOSS OF ALL AC POWER” during an SBO event. For example, if the TDAFW is inoperable 

due to failure of DC power, then the operators can start the system by manually opening the 

steam line valve to start the turbine and control it manually to avoid a STSBO. Recently added 

FLEX equipment provide many additional options to the operators to restore power, or add 

components like pumps, alternate sources of water, and piping connections to steam generators 

or the RCS. Following confirmation of the LOOP and failure of the EDGs, which can take about 

15 minutes, the declaration of extended loss of AC power (ELAP) is made typically within one 
2-5

hour of the SBO. If cooling water level in SG or RCS is not restored, then the peak core 

temperature can reach 1200 °F within two to three hours and a technical support center 

becomes operational to start implementation of severe accident mitigation guidelines (SAMGs) 

and FLEX support instructions (FSIs). Using the plant specific SAMGs and FSIs, the 

recommended mitigating actions are identified for plant operators. If these actions are 

successful in time, then the RCS breach can be prevented or containment failures can be 

delayed long enough to allow emergency evacuation plans to be implemented. 

For this project, the SAMGs and recently developed FSIs were reviewed for mitigating actions. 

The actions identified in the FSIs and the supporting document including time duration for 

adding mitigating equipment, have been recently submitted by TVA in response to the NRC 

order EA-12-049 [ML14064A295]. As in past studies, the significance of operable igniters, 

using backup power generators, was identified. If these igniters have power prior to a 

significant accumulation of hydrogen into the containment, then early containment failure during 

an STSBO can be avoided, which allows time to implement emergency protective actions. The 

generation of a significant amount of hydrogen can be prevented if operators can successfully 

complete mitigating actions, e.g., maintaining the SG water level and maintaining the water level 

in the reactor above the top of active fuel. 

An unmitigated STSBO will progress quickly, to fuel failure within a few hours, resulting in steam 

and hydrogen blowdown into containment. This can ultimately lead to containment failure if 

operator mitigating actions are unsuccessful. Two variations of the unmitigated STSBO are 

analyzed in this study – one without the presence of random ignition sources, and one with the 

presence of random ignition sources. An integrated uncertainty analysis is performed for both 

of these unmitigated STSBO cases. The only potential mitigation that is modeled for the 

STSBO scenario is successfully supplying power to the igniters. Chapter 4 discusses further 

details of these STSBO cases. 

The LTSBO progresses more slowly and RCS failure is delayed as long as the AFW maintains 

the steam generator water level. The LTSBO benefits from the initial cooling provided by 

TDAFW. In the unmitigated11 LTSBO base case scenario, two emergency operating procedure 

(EOP)-based operator actions are credited. First, the operators must complete load shedding to 

extend DC battery life to 8 hrs. Second, the operators can use the steam generator 

atmospheric relief valves and with the TDAFW injection, perform feed and bleed steps that cool 

down the RCS and lower pressure to about 160 psig. This will allow accumulators to passively 

inject water into the RCS. These operator actions of cooling the RCS and adding water from 

accumulators further delay the failure of the RCS and potential containment failure. Except for 

the time delay, the unmitigated LTSBO accident progression is similar to the STSBO. Additional 

mitigation is examined for the LTSBO scenario in sensitivity cases, including examination of the 

benefits of timely restoration of power to the igniters to mitigate against early failure of the 

containment due to hydrogen burns. Chapter 4 discusses further details of these LTSBO cases. 

 

11 “Unimitigated” here is used in the same way as the original SOARCA study [3], meaning SAMGs, FLEX, 10 CFR 

50.54(hh)(2) measures are not credited, but EOPs are credited. 
2-6
3-1

3. SEQUOYAH MELCOR MODEL 

3.1 Sequoyah MELCOR Model 

The Sequoyah MELCOR model is equally representative of the two functionally identical units at 

TVA’s Sequoyah Nuclear Plant. Both Sequoyah units comprise a Westinghouse 4-loop PWR in 

an ice-condenser containment. The reactors each have an operating power of 3,455 MWth. The 

Sequoyah model is current with respect to the steam generator replacements accomplished at 

the plant. 

During multiple site visits, substantial information was obtained for updating the model including 

detailed characteristics of replacement steam generators. The model description below 

identifies the activities in building the Sequoyah MELCOR input deck. 

The Sequoyah model was modified primarily in three areas. The first area was the reactor core 

where the core representation was upgraded to be consistent with MELCOR 1.8.6 and 2.1. This 

upgrade included the following: 

1. Replacing the cylindrically-walled flat-bottomed geometry of the lower head with a 

hemispherical geometry; 

2. Adding core former and core baffle structures that become fully integrated into core 

degradation modeling which includes treatment of debris in the bypass region between 

the core barrel and the core shroud; 

3. Activating logic for simulating the formation of molten pools in the lower plenum and the 

core, crust formation, convection in molten pools, stratification of molten pools into 

metallic and oxide layers, and partitioning of radionuclides between stratified molten 

pools; 

4. Making use of a reflood quench model that separately tracks the component quench 

front, quench temperature, and unquenched temperatures; 

5. Activating a control rod silver release model; 

6. Applying the CORSOR-Booth fission product release model; and 

7. Updating core degradation modeling practices. 

The second area included numerous plant design features and safety systems while updating 

and converting the model from MELCOR Version 1.8.6 to MELCOR Version 2.1. Existing 

modeling was enhanced and new modeling was added to broaden the capabilities of the 

Sequoyah model in addressing a wide range of severe accident sequences. These 

enhancements included: 

1. Largely redefining the SG representations using data supplied by the plant; 

2. Revising containment overpressure failure modeling; 
3-2

3. Revising the operation of the pressurizer and secondary system pressure relief valves 

such that they fail open after a certain number of cycles based on the Surry UA [17]; 

4. Revising containment floor areas to better predict flooding of the lower compartments; 

5. Accounting for heat loss from the reactor to the containment; 

6. Adding motor and TDAFW models with control logic representing both automatic and 

manual operation; 

7. Accounting for steam draw by the TSAFW, flooding failure of the pump’s drive turbine, 

and variable performance of the pump with changing steam pressure; 

8. Accounting for nitrogen discharge from the accumulators as they empty; 

9. Updating core fission product inventory, decay heat, and axial and radial peaking 

factors; 

10. Adding fission product tracking; 

11. Adding better representation of natural circulation in the hot legs and steam generators 

and associated prediction of creep rupture; 

12. Adding gaps around the inlet and exit doors of the ice condenser for better 

representation of flow leakage through the ice condenser; 

13. Managing the ignition of hydrogen deflagrations with additional mechanistic 

considerations; 

14. Adding a representation of the auxiliary building (although this enhancement was not 

used in the subject SBO calculations); 

15. Updating the ice condenser door modeling (including lower and upper plenum door, and 

the intermediate deck doors) based on the information provided in [18]; and 

16. Revising the characteristics of RCP seal leakage to be consistent with current best 

modeling practice and Surry UA [17]. 

The third area of enhancements was representation of operator and mitigative actions. Logic 

was added to manage the following activities: 

1. The planned operator action at Sequoyah in the case of a LTSBO where the RCS will be 

cooled and depressurized by venting one of the four steam generators to 300 psig, is 

represented in the MELCOR model. 

2. The only identified mitigative actions in STSBO and LTSBO scenarios involved 

activation of the igniters by the time hydrogen is generated and released to the 

containment. 
3-3

Lastly, modifications to the Surry model in support of the Surry UA [17] were made to the 

Sequoyah model, where appropriate, and some final items specific to Sequoyah were added or 

modified. The additions and modifications involved: 

• Adding multiple control volumes (both axially and radially) to better represent the ice 

chests, and inlet and outlet plenum compartments; 

• Adding detailed SG nodalization; 

• Updating drains with check valves; 

• Updating all control function initial conditions; 

• Adding and updating sensitivity coefficients per current Sandia National Laboratories 

(SNL) best practices; 

• Adding the hot leg creep rupture modeling (two-part criteria) as applied in the Surry UA; 

• Adding the latest MELCOR CAV package heat transfer modeling practices that capture 

better heat transfer from core-concrete debris to an overlying pool of water; 

• Updating the fuel failure and eutectic temperature; 

• Adding changes to containment condensation per accepted historical work 

accomplished with the CONTAIN code [19]; 

• Updating the aerosol shape factors; 

• Limiting maximum time step size to 100 ms; 

• Adding pressurizer relief valve stochastic failures to open/close after a variable number 

of cycles as configured in the Surry UA model; 

• Added the same for MSL relief valves; 

• Implementing a variable Zr breakout temperature and a variable melting temperature for 

the eutectic formed between UO2 and ZrO2; and 

• Disabling mechanistic pump seal failure modeling and instead applying discrete leakage 

values per Westinghouse Owners Group. 

For convenience, many of the enhancements cited above were made to the MELCOR 1.8.6 

version of the Sequoyah SOARCA model. The model was then converted to MELCOR Version 

2.1 and improvements remaining to be made were accomplished. Because multiple modelers 

had contributed over a significant period to the enhancements, the model was compared 

electronically (applying the MELCOR differences function) to a converted version of the original 

1.8.6 Sequoyah SOARCA model. This process was implemented to identify all enhancements 

made in the current Sequoyah work. Important model design parameters for Sequoyah are 

identified in Table 3-1. 

 
3-4

Table 3-1 Important design parameters for Sequoyah. 

Parameter Value Units 

Rated core power 3,455 MWth

Number of fuel assemblies 193 - 

Fuel rod array size 17 x 17 - 

Fuel rods per assembly 264 - 

Fuel (UO2) mass 102,130 kg 

Zircaloy mass as fuel cladding 23,127 kg 

RPV inner diameter 2.202 m 

RPV inner height 12.294 m 

Pressurizer PORV capacity 2 x 22.55 @ 16.20 kg/s @ MPa(g)

Pressurizer safety valve capacity 3 x 52.92 @ 17.13 kg/s @ MPa(g)

PRT volume 52.13 kL 

PRT initial inventory 25.60 kL 

PRT burst disk rupture pressure 6.90 bar(g) 

Reactor inlet/outlet temperature 289/325 °C 

RCS coolant flow 16,907 kg/s 

Nominal RCS pressure 15.41 Mpa(g) 

Number of steam generators 4 - 

Steam generator recirculation ratio* 11.2 - 

Steam generator heat transfer area 5,139 m2

Secondary pressure 5.890 MPa(g) 

Steam generator secondary water mass 192,512 kg 

Steam generator secondary side volume 710 kL 

CST water volume 908.50 kL 

RWST inventory 1,341.49 kL 

TDAFW capacity 55.52 @ 6.932 L/s @ MPa(g) 

Motor-driven AFW capacity 2 x 27.8 L/s 

Containment design pressure 0.745 bar(g) 

Containment free volume 33,613 kL 

Containment operating pressure 0 bar(g) 

Containment operating temperature 30.0 upper 

38.1 lower 

1.9 ice 

°C 

Accumulator combined water volume 124.8 kL 

Accumulator pressure 4.302 MPa(g) 

High head safety injection max flow 41.0 L/s 

Low head safety injection max flow 378.5 L/s 

Ice mass 1,165,760 kg 

* Ratio of riser flow to steam flow 

3.1.1 Reactor Core and Vessel 

The active height of the reactor core was represented with five concentric rings and 12 axial 

levels. An additional six levels were included to represent core lower support structure and a 

single level was included to represent the fuel assembly upper tie plate (for a COR nodalization 

comprised of a total 19 axial levels). A control volume hydrodynamics (CVH) nodalization which 

is axially coarser than the COR nodalization was employed where a single control volume was 
3-5

used to represent several axial core cells. The MELCOR COR and CVH nodalizations for the 

active fuel and lower plenum regions of the reactor vessel are shown in Figure 3-1. Reactor 

operating power was assumed to be 3,455 MWth. Fission product inventories and decay powers 

were defined per a mid-time in cycle ORIGEN calculation made for a representative Sequoyah 

core comprised of 193 total 17 x 17 fuel assembles. Representative radial and axial power 

shapes were applied. 

The core support structures are modeled as distinct structures in the MELCOR COR package, 

and failure is calculated based on temperature and stress criteria, as specified in the MELCOR 

COR User’s Guide and Reference Manual [20][21]. The core support and diffuser plates of a 

Westinghouse reactor are column-supported, and the assemblies load the plates between the 

columns. These plates are modeled as grid supported plates per SNL MELCOR modeling best 

practice. The lower core plate (also known as the lower forging) is modeled as an edgesupported plate. Self-supporting column structures are located between the plates. 
3-6

Figure 3-1 MELCOR COR/CVH nodalization 

0.1445 m

1.1610 m

1.9481 m

2. 2365 m

2.7595 m

2. 8103 m

3. 1151 m

3. 4199 m

3.7247 m

4. 0295 m

4. 3343 m

4.6391 m

4.9439 m

5. 2487 m

6.1631 m

6.4679 m

6. 6377 m

108 208 308 408 508

109 209 309 409 509

110 210 310 410 510

111 211 311 411 511

112 212 312 412 512

113 213 313 413 513

114 214 314 414 514

115 215 315 415 515

116 216 316 416 516

107 207 307 407 507

105 205 305 405 505

103 203 303 403 503

102 202 302

603

101 201 301

402 502

Instrument

Thimble Tubes

(MELCOR SS -

COLUMN)

Lower Support Columns

(MELCOR SS -

COLUMN)

Upper Support 

Columns and 

Flow Mixer Plate

(MELCOR SS -

PLATEG )

Lower Core Plate

(MELCOR SS -

PLATEG)

Active Fuel and 

Control Rods 

(Core Shroud 

and Former in 

Ring 5)

Westinghouse 4-Loop PWR with Ice 

Condenser Containment

Core Nodalization

R1 = 

0.4353 m

R2 = 

0.9116 m

R3 = 

1.2373 m

R4 = 

1.4739 m

R6 = 

2.202 m

Core Cells

Lower Head 

Nodalization

Flow Path Opens 

at Shroud Failure

Flow Path 

Controlled by 

COR Blockage 

Model

Legend

0.5160 m

DZLH = 

0.1397 m

104 204 304 404 504

Lower Core 

Plate and 

Instrument

Thimble Tubes

(MELCOR SS -

PLATE)

HLST = 

1.9481 m

5.8583 m

5. 5535 m

118 218 318 418 518

R5 =

1.8796 m

117 217 317 417 517

Un-fueled Plenum 

(MELCOR CL) and 

Control Rods

(MELCOR NS)

119 219 319 419 519

CV320

Lower 

Plenum

CV371CV381

CV330

CV372CV382

CV373CV383

CV374CV384

FL370 FL380

FL334

FL383

FL112 FL122 FL132 FL374 FL384

CV345 CV355 CV375 CV385

FL391Bypass

FL434

Westinghouse 4-Loop PWR with

Ice Condenser Containment

CVH/FL Nodalization

FL433

FL432

FL431

CV310 Downcomer

FL392

FL382

FL381

FL372

FL373

FL360

CV365

FL340 FL350

FL390

FL344 FL354 FL364

FL304 FL314 FL324

FL303 FL313 FL323 FL333

FL302 FL312 FL322 FL332

FL301 FL311 FL321 FL331

CV341CV351CV361

FL343 FL353 FL363

FL342 FL352 FL362

FL341 FL351 FL361 FL371 CV342 CV352 CV362 CV343 CV353 CV363 CV344 CV354 CV364
3-7

The lower head is modeled as a hemispherical carbon steel structure. A one-dimensional model 

of the stress and strain distribution in the lower head was used to determine failure, or 

alternatively, the lower head can fail without stress when the melting temperature of steel is 

reached. When the failure criterion is met, a vessel breach of variable size opens between the 

reactor lower plenum volume and the reactor cavity below the RPV. 

Twenty control volumes are coupled to the fueled cells of the COR nodalization with four 

vertically stacked volumes associated with each of the five concentric rings. A parallel control 

volume represents the bypass region of the core. A single control volume represents the lower 

plenum and is coupled to the six lower axial levels of the COR nodalization. The remaining 

control volumes in the RPV represent the upper plenum as illustrated in Figure 3-2. At each 

axial level of the core, coolant cross-flow between channels and the bypass region (if indicated) 

are accommodated by horizontal flow paths. Structures making up the RPV not accounted for in 

the COR nodalization were modeled with a collection of heat structures representing the RPV 

wall, thermal shield, core barrel and upper plenum internals. 
3-8

Figure 3-2 Upper RPV CVH/FL nodalization 

CV344

CV390

Upper 

Plenum

Westinghouse 4-Loop PWR

with Ice Condenser Containment

Upper RPV CVH/FL Nodalization

CV310 Downcomer

FL394

FL344 CV347 CV345

CV387 CV386 FL386 FL388 CV385

CV349 Control Rods

CV389 Control Rods

FL393

FL385

FL389

CV384

FL384

FL348

FL345 FL349

CV374

FL374 CV377 CV375 CV379 Control Rods

FL378 FL375 FL379

CV364

FL364 CV367 CV365 CV369 Control Rods

FL368

FL365 FL369

CV354

FL354 CV357 CV355

CV359 Control Rods

FL358

FL355

FL359

FL306 FL316 FL326

FL336

FL337

FL308 FL318 FL328 FL338
3-9

The active core region is discretized into 60 nodes, which includes 5 axisymmetric rings and 

12 axial levels. Above the active core there is one (unfueled) level to represent the upper core 

structures and axial cladding. Below the core, 6 levels are used to represent the core support 

plate and lower plenum region. In total, 97 COR nodes are used to model the core and lower 

plenum of the reactor. The use of several core nodes permits spatially resolved calculations of 

heat transfer, oxidation reactions, and material relocation. 

The fuel assemblies are allocated over the 5 active core rings according to Table 3-2 and shown 

in Figure 3-3. This nodalization scheme captures the radial decay power distribution over the 

core, which can be important for the core degradation calculations performed by MELCOR. The 

outer two rings have relatively low power density compared to the inner three rings. 

Table 3-2 MELCOR rings and number of fuel assemblies. 

MELCOR Ring Number of 

Assemblies 

1 13 

2 44 

3 44 

4 48 

5 44 

Total 193 
3-10

Figure 3-3 MELCOR radial nodalization of Sequoyah core 

Consistent decay heat and fission product inventories inputs were created for the MELCOR and 

MACCS models of Sequoyah. SCALE6 calculations are performed to generate this information 

in conjunction with post-processing scripts (using Perl programming language) that directly 

create the MELCOR and MACCS input records in an automated fashion. Radionuclide class 

inventories, class decay powers, total core decay power information are derived for MELCOR, 

along with a corresponding nuclide-level inventory for MELMACCS input. The methodologies 

used to generate the MELCOR and MACCS inputs for Sequoyah are consistent with past bestestimate severe accident research for SOARCA uncertainty analysis efforts, and recent 

Fukushima research [22]. 

The SCALE6 calculations make use of proprietary plant operating data from cycles 12-14 at 

Sequoyah. The severe accident initiator and subsequent reactor shutdown is defined to occur 

during cycle 14. Reactor shutdown is assumed to occur near middle of cycle (MOC) for the 

MELCOR and MACCS analyses. The fuel assemblies in the Sequoyah core during cycle 14 

include fresh fuel and previously irradiated fuel that is largely from cycles 12 and 13. Thus the 

RPNML K J HGF E DCBA

15 5555555

14 55444444455

13 5444333334445

12 5433322233345

11 544332222233445

10 5433222 1 2223345

9 543222 111 222345

8 54322 11111 22345

7 543222 111 222345

6 5433222 1 2223345

5 544332222233445

4 5433322233345

3 5444333334445

2 55444444455

1 5555555
3-11

burnup calculations infer radionuclide inventories and decay heat information from known 

reactor operation for cycles 12 through 14. 

The ORIGEN-S/ARP cross section libraries used in this work are generated using the TRITON 

sequence in SCALE6, and the data libraries reflect the 17x17 fuel lattice used in Sequoyah. 

Since burnup calculations are to be executed for each assembly and for a variety of burnup 

conditions, it is desirable to perform standalone ORIGEN-S/ARP calculations in order to 

expedite CPU time and facilitate subsequent sensitivity studies. The decay heat and inventory 

inputs for MELCOR and MACCS are integral quantities (e.g. lumped radionuclide classes and 

whole-core nuclide inventories). Therefore, standalone depletion calculations with reasonably 

representative cross section libraries are sufficient for severe accident and consequence 

simulations, especially when taking into account the rather large uncertainties associated with 

severe accidents such as boundary condition and modeling uncertainties. Figure 3-4 provides 

the decay heat curve developed for the Sequoyah MOC for cycle 12-14 data. The radionuclide 

grouping and curie content used for MACCS are presented in Appendix B. 

Figure 3-4 Sequoyah middle of cycle decay heat curve 

The principal severe accident inputs are lumped radionuclide class inventories, decay power 

curves, and whole-core nuclide inventories. Within the domain of LWRs, these quantities are 

mainly functions of the integral fuel burnups and the most recent power history; integral burnup 

has the most influence on key nuclide inventories that are important for consequences (primarily 

137Cs), and the most recent operating power level largely determines the decay power soon 

after shutdown. The decay heat soon after shutdown is dominated by many short-lived nuclides 

that quickly reach equilibrium/saturation concentrations due to short half-life (and in some 

cases, significant neutron absorption cross section). For longer decay time (i.e. after 103 sec), 

the overall burnup has a significant effect on decay heat due to differing quantities of longerlived nuclides that tend to drive decay power hours after shutdown; the shorter-lived nuclide. 

Time (hr)

Decay power (MW)

0 10 20 30 40 50 60 70 80 90 100 0

5

10

15

20

25

30

35

40

45

50
3-12

3.1.2 Reactor Coolant System 

The reactor coolant loops, including the steam generators (SGs), pressurizer, hot- and cold-leg 

piping, and recirculation pumps, are modeled. The model of the SGs was based on the 

Westinghouse inverted U-tube design. When the secondary side of these SGs is water-filled, 

primary coolant system full-loop natural circulation can sustain primary-to-secondary system 

heat rejection following a loss of pumping power. Additionally, when the void fraction in the 

primary coolant system hot legs and SG becomes large, vapor-phase, hot-leg, countercurrent, 

natural circulation patterns can form, which have important heating effects on the hot legs, 

surge line, and SG tubes. These two types of circulation behavior are illustrated in Figure 3-5. 

The Sequoyah MELCOR model incorporates special modeling and hot-leg nodalization to 

manage hot-leg natural circulation. 

Figure 3-5 Natural circulation flow patterns in a PWR 

Natural Circulation Flow Patterns in a PWR 

One of the four circulation loops (loop 2 accommodating the pressurizer) in a Sequoyah unit is 

modeled separately and the remaining three loops are lumped together on the assumption that 

these loops would behave similarly. Each cold leg is represented using four control volumes 

with two volumes on either side of the recirculation pump. Heat structures are included to 

represent the walls of the hot and cold leg piping, as illustrated in Figure 3-6. 
3-13

The primary sides of the SGs are modeled using multiple volumes to represent the tube bundle 

and the inlet and outlet plena. The inlet plenum of each SG is represented using three volumes 

to simulate a range of mixing and countercurrent flow conditions. The outlet plenum of each SG 

is represented using a single volume. 

The pressurizer and surge line are each modeled as one CV in the Sequoyah MELCOR model 

as illustrated in Error! Reference source not found.. The surge line is connected to the singleloop hot leg. 
3-14

Figure 3-6 Sequoyah reactor coolant system hydrodynamic nodalization 
3-15

Figure 3-7 Sequoyah steam generator lumped Loop 2 

3.1.3 Secondary System 

The nodalization is configured with the minimum number of CVs and flow paths needed to 

accomplish reasonably accurate conditions in the model at rated power and to support hot leg 

countercurrent natural circulation (see Error! Reference source not found.). MSL SVs are 

epresented with the lowest set-point valve on each MSL accounted for individually, and all other 

valves accounted for jointly. Stochastic (over-cycling) failure of the lowest set-point valves to 
3-16

reseat is modeled with failure in the fully open position imposed on the 45th cycle which is the 

best-estimate cycles to failure based on the Surry UA [17]. Atmospheric relief valves are 

represented as are MSIVs. Turbine bypass is not represented (and is not needed for the subject 

calculations on account of being unavailable in a SBO). Leakage past the MSIVs was included 

only in the LTSBO calculations due to a logic error undiscovered until after the STSBO 

calculations had completed. The error is thought to be inconsequential primarily because 

investigating the contribution to risk of SGTRs was not a goal in the Sequoyah work12 and a 

detailed SGTR model (see Surry UA [17]) was not developed. 

3.1.4 Emergency Core Cooling System 

A working model of the emergency core cooling system (ECCS) is included in the Sequoyah 

model but, with the exception of accumulator actuation, was dormant in the SBO calculations. 

The accumulators are represented complete with nitrogen introduction to the cold legs upon 

their water inventory being exhausted. 

3.1.5 Natural Circulation Modeling 

The Sequoyah model is configured the same as the Surry model with respect to 

accommodating hot leg countercurrent natural circulation. See Appendix A of the Surry UA [17] 

for a detailed discussion on modeling natural circulation. 

3.1.6 Containment 

The containment in the Sequoyah model is made up of a collection of CVs representing the 

upper dome, lower compartments, annulus, reactor cavity, ice condenser, and the SG and 

pressurizer cubicles. Bypass flow areas between upper and lower containment are accounted 

for including the refueling canal drain. Figure 3-8 illustrates the containment nodalization 

showing the ice condenser CVs immediately below the upper plenum and upper dome. 

 

12 MSIV leakage would aggravate the stress in steam generator tubing by reducing secondary pressure. It would also 

provide a release pathway for fission products given an SGTR. The contribution to risk of SGTRs was studied in 

depth in the Surry UA [17]. 
3-17

Figure 3-8 Containment nodalization 

The ice condenser is an important accident mitigation feature limiting containment 

pressurization by condensing steam. It is important to consider potential ice condenser bypass 

flow paths, because any bypass flow will reduce the effectiveness of the ice condenser. The ice 

condenser is comprised of a lower plenum, ice chest, and upper plenum as seen in Figure 3-9. 

Lower plenum Inlet doors connect the lower compartment of containment to the lower plenum of 
3-18

the ice condenser. The lower plenum represents the volume directly beneath the ice baskets. 

The ice chest, representing the region from the base of the ice baskets to the intermediate deck 

doors, includes the heat structure representation of the ice stored within the ice baskets. The 

lower plenum and ice chest regions are divided azimuthally into four sections with an additional 

axial subdivision of the ice chest into three levels. Above the ice baskets, intermediate deck 

doors connect the ice chest to the upper plenum region, which is divided azimuthally into two 

control volumes. Finally, upper plenum doors connect the upper plenum control volumes to the 

upper containment. 

Figure 3-9 Sequoyah ice condenser nodalization 

The lower plenum, intermediate deck, and upper plenum ice condenser doors’ operations are 

modeled as presented in Reference [23] consistent with the early MELCOR modeling and 

calculations in support of GSI-189 [11]. Each door set functions differently, based on design 

and orientation; therefore, different modeling methods were used. Each door set, at a minimum, 

uses a flow path which operates from fully-closed to fully-open as a function of the forward 

differential pressure. If the forward differential decreases, the represented door area fraction is 

permitted to decrease and is referred to as “reversible.” The intermediate deck and upper 

plenum door models apply a second flow path to model doors which may not return to their 

closed position once opened. For example, the intermediate deck doors, which use a 

horizontal-fixed hinge, close under the force of gravity; therefore, these doors may open beyond 

ninety degrees and may not return to a closed position even though the forward differential 

pressure decreases. This operation is referred to as “irreversible” and is modeled using a 

second flow path which maintains its maximum open fraction. The forward differential pressure 

range of the “reversible” flow path terminates at the start of the opening range of the 

“irreversible” flow path. In Figure 3-9, flow paths 134, 136, 138, 139, 141, and 142 are 

reversible and flow paths 234, 236, 238, 239, 241, and 242 are irreversible. 
3-19

The lower plenum doors are modeled using only the reversible flow path; however, in the event 

the doors are fully-opened, the doors are modeled to remain fully-open to represent hinge 

deformation. An operation of the lower plenum doors to fully-open is taken as sufficient to 

deform the hinges given the relatively small amount of pressure necessary to operate the doors. 

Reversible and irreversible flow paths are used to represent the intermediate deck and upper 

plenum doors. Leakage through the doors is taken from “NUREG/CR-6427, “Assessment of the 

DCH Issue for Plants with Ice Condenser Containment,” [23] and is applied to the reversible 

door flow paths. The ice condenser is perfectly insulated in the MELCOR model. 

Within the ice chest, ice melting is modeled by activating the “degassing” ice model. The 

degassing model determines the associated mass generation in response to energy transfer to 

a heat structure. Concrete degassing provided the namesake of the model; however, a 

modification was implemented in an early MELCOR version to use the degassing model for ice 

melting in ice condenser containment designs. As energy is transferred to the ice heat 

structure, liquid water mass generation is determined and sourced to the control volume. Drain 

lines connecting the lower plenum to the lower compartment allow accumulated water, due to 

melting of ice, condensation, and film flow drainage, to be drained from the ice condenser. 

Selected ice condenser parameters are provided in Table 3-3. 

Table 3-3 Sequoyah ice condenser input parameters. 

Parameter Value Comments 

Ice mass 2.65x106 lbm 

(1.20x106 kg) Average ice mass from 12 measurements 

Ice baskets 486 baskets per bank

4 banks 1944 total baskets 

Initial ice 

temperature 

19°F 

(-7.2°C) 

The sensible heat from the initial temperature to the 

melting temperature is included in the heat of fusion

Effective 

density 584 kg/m3

Effective density of the ice in the baskets is 

calculated from the total mass divided by the ice 

basket volume 

Surface area 6200 m2 Plant data 

Additionally, the Sequoyah MELCOR model includes representations of containment spray and 

air-return fan operation, residual heat removal, and switchover to recirculation through the 

emergency sump. The SBO scenarios documented here, however, do not involve these 

systems or actions. 

3.1.7 Containment Leakage and Rupture 

Containment Description 

The Sequoyah containment is a free-standing steel containment consisting of a cylinder topped 

by a hemispherical dome as shown with the finite element model in Figure 3-10. The internal 

radius of the cylinder is 57.5 feet and its height to the intersection with the dome (springline) is 

115 feet. The thickness of the steel shell in the cylindrical portion varies along the height. This 

thickness is 1-3/8 inches at the bottom and decreases to 1⁄2-inch at the springline. The dome 
3-20

wall thickness also varies and ranges from 7/16-inch at the springline to 15/16-inch at the apex. 

The containment cylinder wall has a set of outside vertical stringers at a 4-degree 

circumferential spacing that extend partially into the dome. This wall also has a set of outside 

horizontal circumferential stiffeners and platforms. The steel for the containment structure is 

A516 Grade 60 steel. The design pressure for the containment is 10.8 psig. 

Figure 3-10 Three-dimensional finite element model of the Sequoyah 

containment 

Containment Rupture Pressure and Location 

Past NRC-sponsored research ([24][25][26][27][28]) extensively studied the capacity of the 

Sequoyah containment under internal pressurization associated with severe accident 

conditions. These studies used a variety of methods of analysis ranging from closed-form 

analytical approaches that relate the internal pressure capacity to the global membrane hoop 

strain in the containment shell to studies involving detailed three dimensional models of the 

containment shell including models that account for strain concentrations near abrupt increases 

in the thickness of the containment shell to accommodate large containment openings and other 

penetrations. Table 3-4 below shows best estimates (shown in parentheses) and ranges of 

internal pressure capacities calculated for the Sequoyah containment in studies completed 

since the later 1980s and the estimated location of containment rupture. The studies indicate 

that containment rupture would start where the containment shell thickness abruptly increases 

around the larger containment opening in the ice condenser region of the containment. 
3-21

Table 3-4 Best estimate of rupture pressures for the Sequoyah 

containment from various NRC-sponsored studies. 

Reference Date Rupture 

Pressure (psig) 

Method of Analysis and Rupture 

Locations 

NUREG/CR-5405 

([25]) 

1990 (63) Finite element analysis 

Rupture location: where the containment 

shell thickness abruptly increases around 

the larger containment openings in the ice 

condenser region of the containment. 

NUREG/CR-6706 

([27]) 

2001 (68) 

62 ------- 74 

Finite element analysis 

Rupture location: as above. 

NUREG/CR-6920 

([28]) 

2006 (67) 

54 ------- 82 

Finite element analysis 

Containment fragility provided lower and 

upper bounds corresponding to 0.05 and 

0.95 probabilities of failure 

Rupture location: as above. 

The rupture pressure capacities shown in Table 3-4 are for: 

• Normal ambient temperatures [25] show that the ultimate pressure capacity is relatively 

insensitive to temperature with a reduction in pressure in capacity of about 7 to 8 percent 

at about 300 °F) 

• Material properties based on certified material property test reports (CMTRs) for the 

Sequoyah containment 

• Strain-based rupture criteria associated with membrane strains in the containment shell 

that account for strain concentrations near abrupt increases in the thickness of the 

containment shell 

• Containment strains calculated using three dimensional finite element analysis of the 

containment building that model either a 90 degree segment of the containment [25] or a 

chosen segment subtending an angle of, approximately, 53 degrees. 

• Large penetrations are not included in the finite element models but increased plate 

thickness in the areas surrounding large openings is included in the models to account 

for strain concentrations near abrupt changes in plate thickness. 

The values shown in Table 3-4 compare well to each and also to the 65 psig mean of the 

aggregate distribution of failure pressures from an expert elicitation panel reported in 

NUREG/CR-4551 [26] and used for the NUREG-1150 study [29]. 

A single, confirmatory, three-dimensional finite element analysis of the Sequoyah containment 

was made using the three-dimensional finite element model shown in Figure 3-10. This model 

includes the major containment penetrations and the increase in the containment shell plate 

thickness around these penetrations. This is an approximate model because not all information, 

for example the full extent of the thicker plates around the major penetrations, was available. 

However, the model permits a point estimate confirmation of the internal pressure ranges shown 

in Table 3-4 and of the likely rupture location. This confirmatory assessment used a yield 
3-22

strength for the steel in the containment shell equal to the average value of the available CMTR 

data which is approximately equal to 47,800 psi. Analysis with this model shows that strains of 

the order of 3% are reached at an internal pressure of approximately 66 psig in the thinner 

containment shell plates where they join the upper part of the thicker steel plates that surround 

the equipment hatch. This is in the ice condenser region of the containment. Principal 

membrane strains in this region increase rapidly as the internal pressure increases above 66 

psig and strains of the order of 2% to 3% become widespread in the thinner steel plates in the 

ice condenser region. These results are consistent with the best estimate and range of rupture 

pressures shown in Table 3-4. 

The best estimates and ranges of containment rupture pressure shown in Table 3-4, together 

with the results of the three-dimensional finite element analysis permit recommending a best 

estimate and range for the rupture pressure for use in the Sequoyah accident progression 

analysis as follows: 

• Best estimate rupture pressure equal to 67 psig 

• Range of internal rupture pressure from 52 psig to 78 psig. This range is primarily based 

on the results reported in [28] and shown in the last line of Table 3-4. 

On the basis of these values, the probability density function chosen for uncertainty analysis is a 

simple triangular probability density function with a lower bound of 52 psig, an upper bound of 

78 psig and a most likely value of 67 psig. 

Results and observations of 1:8 scale and 1:32 scale tests of steel containments under internal 

pressurization have shown that containment leakage can be considered insignificant (nominal 

leakage) until the rupture pressure is reached at which time the leakage area increases rapidly 

[27]. Therefore, for this study, the leakage area is taken as negligible for internal pressures 

below the rupture pressure with an abrupt transition to a rupture leakage area (assumed 3 ft2 in 

this study) for pressures greater than the pressure capacity. 

In summary, the Sequoyah units have freestanding steel vessels serving as primary 

containments. The vessels have been determined as nominally capable of resisting an internal 

pressure of 67 psig (before rupturing). Upon overpressure rupture, a 3 square foot hole is 

envisioned to open at the spring line of the dome. The MELCOR modeling reflects those 

attributes and the UA varied the pressure required to rupture the containment between 52 and 

78 psig. 

Containment Nominal Leakage Estimates 

Nominal leakage from a Sequoyah primary containment is expected to be near or through 

penetrations into the annular space between the outside of the steel containment vessel and the 

inside of the surrounding concrete shield building. A flow path is included in the MELCOR model 

between lower containment and the annular space to facilitate this leakage. The path is defined 

as having a flow area of 2.518x10-6 m2 and a length of 0.029 m based on the Sequoyah Modular 

Accident Analysis Program input file. Leakage from the annular space through the shield 

building boundary to the environment is assumed to be via a flow area 100 times larger. 

Additional Containment Attributes for the MELCOR Modeling 

An additional attribute of containment reflected in the MELCOR modeling is the possible failure 
3-23

of the fabric seals that form part of the barrier between upper and lower containment. The seals 

are located along the bottom of the concrete floor under the ice condenser, between the ends of 

the ice condenser and the refueling canal concrete structure, and along the vertical sides of the 

refueling canal structure. The seals extend across the 4 inch gap between the inside surface of 

the steel containment vessel and the concrete structure within the vessel. The seals are 

modeled to fail variably in the UA given a differential pressure between upper and lower 

containment of between 12 and 57 psid. 

Surrounding the Sequoyah steel containment vessel is a concrete shield building. There is a 5 

foot distance between the outside of the steel vessel and the inside of the concrete building. 

The annular space is represented in the MELCOR model. There are two openings in the wall of 

the concrete shield building that are likely to become release pathways given an overpressure 

rupture of the steel containment vessel within – a 2’ x 7’-6” exhaust vent in the dome [FSAR, 

Amendment 20, p. 11.3-7 & "Concrete Dome Outline", Drawing 41N718-1, R5] and a 4'-0.5" x 

7'-4.75" doorway between the annular space and the auxiliary building [Watertight Doors 

Arrangement & Details Sheet 2, CCD No: 1,2-44N286, R1]. The exhaust vent is 130 feet above 

grade and a release through it would lead directly to the environment. The doorway is below 

grade. Both the vent and door opening are represented in the MELCOR model. Both openings 

are configured to open fully coincident with an overpressure rupture of the steel containment 

vessel (see flow paths 801 and 804 in Figure 3-8). The auxiliary building is not used in the 

present MELCOR model and all releases are directly to the environment. 

3.1.8 Hydrogen Combustion 

In a station blackout that proceeds to severe core damage at an ice condenser plant, ample 

hydrogen will have been generated to threaten the integrity of containment should much of it 

burn at one time. Hydrogen threat mitigation at Sequoyah is based on the use of thermal 

igniters to induce periodic burns that have moderate energy addition rates. The igniters, 

however, require AC electrical power to function and AC power would not be immediately 

available in an STSBO. One primary mitigative measure in a station blackout is to restore power 

to igniters. 

Hydrogen combustion has uncertainties in the ignition location, ignition timing, flammability 

limits, and combustion intensity. The previous SOARCA calculations included combustion at 

the default limit for spontaneous ignition (10%) [5]. There was no explicit recognition of an 

active ignition source (e.g., the hot jet at hot leg creep rupture, ex-vessel debris, or electrical 

sources). In the Surry SOARCA calculations [5], hydrogen burns occurred in the containment at 

the default spontaneous limit following hot leg failure and vessel failure. However, there were 

no late hydrogen burns after the burn at vessel failure. In contrast, the Surry UA [17] 

conservatively assumed an ignition source was always available. Combustion was assumed to 

occur at the lean limit for hydrogen as identified by Kumar [30]. The Sequoyah UA desired to 

expand the modeling of uncertainty to include an ignition source when there is a well-defined 

ignition source (e.g., hot gases above the hydrogen auto-ignition temperature exiting the RCS or 

hot debris in the reactor cavity) as well as provisions for a random ignition component (e.g., 

static electricity). 

Combustion requires an ignition source and a combustible concentration of gases. Similar to 

the Surry UA study [17], the determination of combustion gas concentrations is based on the 

work by Kumar [30] illustrated in Figure 3-11 and Figure 3-12. Kumar describes the 

experimental determination of the flammability limits of hydrogen/oxygen mixtures with various 

diluents with an active ignition source. The burn propagation direction has different thresholds 
3-24

for the lean limit on combustion [30]. The upward propagation limit for hydrogen is 4% (by 

volume %). The lean-downward limit is considerably higher because of the opposing effect of 

buoyancy on flame propagation. Downward propagation does not occur until the hydrogen 

concentration is 8.5%. Furthermore, the combustion efficiency is low at the lean thresholds for 

combustion. For example, the combustion efficiency is found near the lean limits of hydrogen, 

spanning from almost zero burn completeness at a 4% hydrogen concentration to 95% burn 

completeness at a 10% hydrogen concentration [30]. At the hydrogen rich condition, 

combustion is limited by the deficient reactant, which is oxygen. The experimental evidence 

shows at least 5% oxygen is required. Alternately, it can be expressed as, 

H2 (%) + Diluent (%) = 95% 

Figure 3-11 Flammability limits for hydrogen for Air-H2-H2O systems [30] 
3-25

Figure 3-12 Combustion of hydrogen and air mixtures near lean limits for 

upward propagation of the flame [30] 

However, the maximum hydrogen combustion with 5% oxygen is only 10% hydrogen due to the 

stoichiometric balance to create steam. 

2H2 + O2 → 2H2O 

The ice condenser is very effective at condensing steam and cooling the non-condensable 

gases. While the impact of steam as a diluent is noted above, the influence of the temperature 

is also incorporated using Kumar’s experimental findings and deduced theoretical observations. 

Over the temperature range of the data, the lower limits of hydrogen flammability decreases by 

0.5% hydrogen/100°C for upward propagation and 1% hydrogen/100°C for downward 

propagation. For rich limits, the oxygen concentration decreases by 0.5% oxygen/100°C. 

Hence, cooler temperatures require higher concentrations for combustion while higher 

temperatures can ignite at lower concentrations. 

In research experiments at the lower flammability limit, a strong ignition source (1 Joule) was 

used to ensure ignition [30]. There is uncertainty in the availability of an ignition source during a 

station blackout sequence without igniters or energized electrical equipment. Sources of 

ignition in severe accidents may include ex-vessel core debris, high temperature jets of 
3-26

combustible gases (e.g., above the auto-ignition temperature of hydrogen), static electrical 

discharges and, in the case of earthquake aftershocks, sparks from the falling debris. The 

minimum energy necessary for ignition in dry air has been characterized as << 1 Joule (see 

[31]). At the lower limit of flammability, the strength of the ignition source must be stronger. In 

previous research, [30], it is also noted the addition of a diluent, such as steam, will increase the 

required ignition energy, the amount is not quantified. 

NUREG/CR-2486 reports the final results of the hydrogen igniter experimental program [32]. 

The hydrogen igniters were typical of those deployed in the Sequoyah ice condenser 

containment. The experiment results show complete combustion between 8% and 9% with dry 

mixtures and H2 and no combustion for concentrations below 5%. Steam tests were done with 

hydrogen volume fractions of 8%, 10%, and 12%. Steam concentrations of up to 30% 

consistently resulted in ignition. Most of the 40% steam fraction tests resulted in combustion 

[32]. 

Identifiable Sources of Ignition 

A severe accident in a PWR leading to a breach in the RCS would present obvious sources of 

ignition for a hydrogen deflagration, such as hot gas flowing from a rupture in a hot leg nozzle, 

or core debris relocated from the RPV to the containment floor is another. Given either of these 

developments in a MELCOR calculation, all of lower containment was taken to have an active 

ignition source, and if a combustible mixture of gasses existed, a burn initiated. A temperature 

greater than the auto-ignition temperature of hydrogen (> 847 K) and a flow velocity exceeding 

0.1 m/s were conditions required of the gas flowing from a hot leg rupture. RPV lower head 

failure was taken to be an indication of core debris having relocated to the containment floor. 

Random Sources of Ignition 

Ignition of hydrogen by an unidentifiable source is a reasonable possibility. Static electrical 

discharges or sparks from falling debris during an earthquake aftershock are conceivable 

ignition sources. These sources could occur randomly in time and location. In one set of the 

MELCOR UA calculations, a source of ignition was momentarily introduced somewhere in 

containment every half hour. The location of the introduction, i.e., the control volume where the 

source was introduced, was randomly chosen. Actual combustion of hydrogen given an ignition 

source was still subject to flammability restrictions. 

Detonation and Burn Propagation 

As a practical consideration, MELCOR does not model detonation. However, a large 

deflagration has more than sufficient energy to over-pressurize the containment (e.g., a 

deflagration from a 14% hydrogen concentration provides up to a 4X increase in pressure [32]). 

Burn propagation to adjacent cells in the MELCOR UA calculations was governed by the 

standard MELCOR burn package model with default concentration limits. The default limits are 

nominally consistent with research findings [30]. 

3.1.9 Operator Actions 

The planned operator action at Sequoyah in the case of a LTSBO where the RCS will be rapidly 

cooled and depressurized at the rate of 100 °F/hr by venting two of the four SGs (Steam 

Generators 1 and 4) through the atmospheric relief valves (ARVs) to 160 psig, is represented in 
3-27

the MELCOR model although not identically. The lumped representation of Loops 1, 3, and 4 in 

the model does not accommodate specifically venting Steam Generators 1 and 4. 

Consequently, the operator action of rapidly depressurizing the RCS was accomplished in the 

MELCOR calculations by venting just the single Steam Generator 2. Note that a loss of 

essential control air resulting from a loss of all AC power will require that the ARVs be opened 

by hand with reach rod operators. Steam Generators 1 and 4 have ARVs that are readily 

accessible (via the 480 V Shutdown Board Room near the Main Control Room), while Steam 

Generators 2 and 3 have ARVs that can only be accessed by entry into a high heat stress area. 

The venting of the single SG in the MELCOR calculations to represent operators carrying out a 

maximum-rate depressurization is limited such that available full TDAFW delivery can maintain 

water level in the generator. This is consistent with what would be accomplished procedurally by 

the operators. The model was reviewed and it was confirmed that the TDAFW increases to 

maximum flow (i.e., limited by available SG pressure) upon DC power failure and that TDAFW 

fails when the SG overfills and flows water through the turbine. 

Maximum-rate depressurization begins in the MELCOR LTSBO calculations 1 hour after the 

onset of the blackout. 

In addition to the above, the following were accomplished to support the LTSBO scenario: 

• Imposed MSIV leak areas of 0.1 in2/MSIV. 

• Enabled TDAFW injection. Following DC battery failure, the TD-ADW runs at full flow 

until the SG over-fills and damages the TDAFW steam turbine. 

• Enabled DC batteries for 8 hours per Sequoyah post-Fukushima regulatory submittal 

crediting successful load shedding during the first 90 minutes after the onset of an 

LTSBO. 

• Enabled an operator action to control TDAFW at 15 minutes. Timing from Surry 

SOARCA LTSBO. 

• Enabled an operator action to perform controlled depressurization using one SG while 

maintaining TDAFW to one generator (based on ECA-0.0). 

• Confirmed a technical specification minimum of 230,000 gal water resource from CSTs 

being used for TDAFW injection. 

3.1.10 Mitigative Actions 

Consistent with SOARCA NUREG-1935 [3] limited mitigated scenarios are also evaluated. The 

only mitigative action modeled for the containment response is the activation of the igniters 

before significant hydrogen is generated and transported to the containment. Upon any 

recovery of electrical power, igniters will be enabled throughout the MELCOR model. For the 

LTSBO, sensitivity analyses were conducted to investigate specific actions and operations. In 

the mitigated scenarios, auxiliary feed water is available at the start of the accident. 

The Hydrogen Mitigation System (HMS), comprised of 68 individual igniters, is seismically 

supported and designed to mitigate sudden high pressure loads in the event of a hydrogen 

explosion [Ref. FSAR 6.2.5]. The thermal igniters induce periodic small burns and thus prevent 
3-28

large hydrogen accumulations causing large burns. Power is provided by Class 1E 480 V ACauxiliary and in the case of loss of offsite power, backed by emergency diesel generators. The 

HMS is energized manually from the main control room in accordance with emergency 

procedures following any accident which indicates inadequate core cooling. They remain 

inoperable during SBO without additional backup AC power supply. The 68 igniters are 

arranged with consideration for hydrogen release and transport throughout the containment. 

When local hydrogen ignition criteria at the active igniters exceed the lower flammability limit, 

the igniter may initiate a combustion event, typically a deflagration. By reducing the available 

hydrogen contributing to the explosion, the impulse pressure load challenging the containment 

barrier may be mitigated if the HMS is available before significant amount of hydrogen is 

released into the containment. 

The HMS system is modeled within the Sequoyah MELCOR input file by designating which 

control volumes contain igniters, the availability of support power, and the atmospheric 

conditions which can support an HMS initiated deflagration. In the Sequoyah MELCOR model 

igniters are designated within all containment control volumes with the exception of the ice 

condenser lower plenum and ice chest. Each designated igniter in the modeled volumes 

represents multiple igniter pairs physically installed in the containment compartments. Power 

availability and mitigation application of diesel generators are defined by the sequence. Finally, 

the following MELCOR default atmospheric conditions support igniter-initiated deflagrations: H2

> 7%, O2 > 5%, and diluents < 55%. 

The hydrogen igniters are powered by the 480 volt AC bus that is inoperable during a SBO. 

However, the igniters can be placed in operation by manual operator action if AC power is 

recovered. If the igniters are operable before significant hydrogen is released to the 

containment, then the likelihood of a large deflagration challenging the containment integrity can 

be minimized. According the FLEX instructions discussed in Section 2, the operator is expected 

to power 480 volt AC bus using a 225 kV generator, which is pre-staged on the roof of auxiliary 

building. 

3.1.11 Modeling Practices 

MELCOR includes capabilities to model the two-phase thermal-hydraulics, core degradation, 

fission product release, transport, and deposition, and the containment response. The 

SOARCA analyses include operator actions and equipment performance issues as prescribed 

by the sequence definition and mitigative actions. The MELCOR models are constructed using 

plant data and the operator actions were developed based on discussions with operators during 

site visits. 

 Pressure safety valves 

Pressurizer safety valves and power operated relief valves 

Each of the 3 SVs on a Sequoyah pressurizer is represented separately in the MELCOR model. 

Their opening pressures are staggered. The SV with the lowest opening pressure is configured 

to fail open according to an over-cycle criteria (for the valve type). The criteria is sampled as an 

uncertain parameter. It is judged that the valve will most likely fail in a partially open position, 

thus the valve open fraction at failure is also sampled. 

Each of the 2 power operated relief valves (PORVs) on a Sequoyah pressurizer is also 

represented separately, and their opening pressures are staggered. The PORV with the lowest 
3-29

opening pressure is configured to fail open according to standardized over-cycle criteria. The 

pressurizer PORVs were updated to fail at the best-estimate value of 45 cycles from the Surry 

UA study [17]. Additionally the valve is configured to fail open if a limited number of cycles at 

high temperature are exceeded. 

The SVs and PORVs vent to a separate control volume in the MELCOR model representing the 

Pressurizer Relief Tank (PRT). A flow path representing the PRT rupture disks is included 

between the PRT control volume and one of the lower containment control volumes. The flow 

path is and configured to open at a differential pressure of 100 psi. 

The PORVs are unavailable during a station blackout, and it is unlikely that the failure 

temperature of the SVs will be reached during these scenarios. However, due to the sampled 

number of cycles for over-cycle failure, it is expected that the lowest setpoint SV will fail open in 

the majority of uncertain realizations. 

Secondary system atmosphere relief valves and safety relief valves 

Each of the 4 steam lines serving a Sequoyah unit has 1 ARV and 5 SVs. The set points on the 

SVs are staggered. The representation of the Loop 2 main steam line in the MELCOR model 

has: 

• 1 flow path representing the ARV with over-cycle and over-temperature failure included; 

• 1 flow path representing the lowest-setpoint SV with over-cycle and over-temperature 

failure included; and 

• 1 flow path jointly representing the remaining 4 SVs. 

The lumped representation of the Loops 1, 3, and 4 main steam lines in the model has: 

• 1 flow path jointly representing 3 ARVs with over-cycle and over-temperature failure 

included; 

• 1 flow path jointly representing the 3 lowest-setpoint SVs with over-cycle and overtemperature failure included; and 

• 1 flow path jointly representing the remaining 12 SVs. 

The Sequoyah ARVs require control air (and hence AC power) to operate automatically or to be 

operated remotely. The ARVs serving Steam Generators 1 and 4 are readily accessible and can 

be manually opened (locally) with reach rod operators. This is not the case with the ARVs 

serving Steam Generators 2 and 3. Access to these ARVs requires entry into a high heat stress 

area. These attributes of the ARVs are reflected in the control logic included in the MELCOR 

model to manage the operation of the valves. 

 Pump seal leakage 

Under normal operating conditions, cooling water is supplied to the RCP seals at a higher 

pressure than the primary. As a result, the water keeps the RCP seals cool while preventing 

any primary coolant from flowing up the RCP shaft and contaminating areas outside the primary 

boundary. When a loss of AC power occurs, the RCP seal cooling system no longer supplies 
3-30

cooling water and as a result, primary coolant begins leaking along the RCP shaft through the 

mechanical seals at a rate of 21 gpm for pressures near operating pressure. RCP seal leakage 

was set at initially 21 gpm per pump for all scenarios. 

 Loop seal clearing and effects on the accident progression 

No SGTRs were included in this analysis because SGTRs were studied in detail with the Surry 

UA [17]. Thus, no variation of loop seal clearing is included. 

 Fuel degradation and relocation treatment 

MELCOR includes a model to characterize the structural integrity of the fuel rods under highly 

degraded conditions. The model acknowledges a thermal-mechanical weakening of the oxide 

shell as a function of time and temperature. As the local cladding oxide temperature increased 

from the Zircaloy melting temperature (i.e., represented as 2098 K in MELCOR) towards a 

default of 2500 K, a thermal lifetime function accrues increasing damage from 10 hours to 

1 hour until a local thermo-mechanical failure. 

 Lower plenum debris/coolant heat transfer 

Following the fuel-debris slump into the lower plenum, there may be fuel-coolant interactions. 

The lower plenum heat transfer settings were updated to reflect the approach adopted in Surry 

UA. 

 Core plate failure 

The timing of core plate failure affects the relocation of the degraded core materials from the 

core region into the lower plenum. The local thermal-mechanical failure of the lower core plate, 

the flow mixer plate, and the lower support forging are calculated within MELCOR using the 

Roark engineering stress formulae. The yield stress is calculated based on the loading and 

local temperature. 

 Fission product release, speciation, and volatility 

 The release model is consistent with the Surry UA [17]. Since MELCOR does not calculate 

chemical changes of fission products in the RCS, the speciation needs to be specified in the 

initial fuel inventory. Thus the base case for cesium (after combination with iodine) is 80% 

cesium molybdate and 20% cesium hydroxide using the best estimate for cesium partitioning in 

Surry UA [17]. The released iodine combines with the cesium, although a small amount remains 

gaseous, taken as 0.182% which is the best estimate value at middle of cycle from the Surry 

uncertainty analysis. 

Iodine is a volatile fission product and can be released as an elemental gas or bound together 

with cesium as CsI, an aerosol. Because of its volatility, a portion of iodine transports to the 

fuel-cladding gap, leading to early releases when gap failure occurs. Iodine has a relatively 

short half-life, particularly I-131 with a half-life of 8 days, compared to other fission products, and 

can dominate early health effects if releases occur before or during evacuation. Cesium is 

volatile but readily binds with other elements to form an aerosol. Specifically, it is known to bind 

with iodine as CsI, molybdenum as Cs2MoO4, and water as CsOH. Because of its volatility, a 

portion of cesium transports to the fuel-cladding gap, leading to early releases upon the 

occurrence of gap failure. Cesium has a relatively long half-life (i.e., ~30 years from the 
3-31

dominant isotope of Cs-137) compared to other high yield fission products and dominates late 

health effects. The in-vessel cesium and iodine release from fuel is near 100 percent of its total 

inventory following a severe fuel damage. 

The behavior of aerosol transport after release from fuel is very uncertain and is addressed by 

variation of the dynamic shape factor as described in Section 3.9. 

 Vessel lower head failure and debris ejection 

The base case approach of modeling the vessel lower head failure and debris ejection includes 

some special non-default modeling options in MELCOR. First, all the solid debris in the lower 

plenum is in contact with water, if present. Previously, a restrictive one-dimensional 

counter-current flooding limitation criterion prevented penetration of water into the debris bed. 

Second, the vessel lower head fails using a creep rupture model. A Larson-Miller failure 

criterion is calculated based on the one-dimensional conduction and stress profile through the 

lower head. The failure of a lower head penetration prior to gross head failure was judged 

unlikely based on observations from experimental studies at Sandia National Laboratories lower 

head failure (LHF) tests [33]. 

 Ex-vessel phenomena 

The ex-vessel debris surface heat flux to an overlying pool of water was enhanced to replicate 

the magnitude observed in the melt attack and coolability experiments (i.e., MACE tests) [34]. 

The default model did not include multi-dimensional effects of fissures, other surface 

non-uniformities, and side heat fluxes. The modeling approach is consistent with Surry UA. 

3.2 Selection of Uncertain MELCOR Parameters 

MELCOR uses thousands of parameters in the evaluation of the equations and algorithms 

embedded in its constituent models. Many parameters are basic well-known input, such as core 

inventory, material properties, sizes and lengths of piping, etc. There are many parameters for 

which the base values were established through comparison to experiments. Some parameters 

were established long ago by subject matter experts (SMEs) and are assumed reasonable for 

the present application. In development of the Peach Bottom and Surry UAs, a common set of 

phenomenological areas were investigated. The approach for Sequoyah began with the same 

set of areas and reduced the investigation to limit the scope of the analysis as directed in 

SECY-12-0092 [10]. Information from the Surry UA review of the MELCOR Reference Manual 

[21] and preliminary results influenced the final list of parameters ultimately selected for 

Sequoyah. The MELCOR uncertain parameters were selected to cover the following issues of 

severe accident phenomenological areas: 

• Sequence, 

• In-vessel accident progression, 

• Ex-vessel accident progression, 

• Containment behavior, and 

• Aerosol transport and deposition. 

The extensive investigation into parameters for the Peach Bottom and Surry UAs [9][17] was 

leveraged for the Sequoyah analysis. The Sequoyah MELCOR team included the same 

members that were involved in the Surry UA. The Surry UA parameter list was used as a 
3-32

starting point because it is also a PWR. The Sequoyah scope includes a limited UA together 

with the base case analyses. Thus, as a first step, those parameters that were not identified as 

having key importance in Surry were removed from consideration, after SMEs considered 

whether plant differences might raise their importance for Sequoyah. The main purpose of 

investigating Sequoyah was because it is an ice condenser design with a small containment. 

Therefore, parameters were added to research these aspects, notably the barrier seal and ice 

doors to represent the ice condenser design. Lastly, some parameters found to be important in 

Surry, such as the time at cycle, were not included in Sequoyah because the knowledge gained 

in Surry is judged to be sufficient to inform the analysis; therefore, these parameters were also 

removed to maintain the limited scope. 

Table 3-5 lists the distributions and bounds related to each MELCOR parameter sampled in this 

analysis. The sampling approach for many of the parameters is quite complex and is described 

in the following sections along with the rationale for each distribution, and justification for the 

upper and lower bounds. The basis for defining many distributions is strong where known 

physical or chemical phenomena are well understood. Correlation of the MELCOR parameters 

was considered, but ultimately no correlations were implemented. 

Table 3-5 MELCOR sampled parameters 

Uncertain 

Parameter 

Distribution 

type 

Distribution 

Parameters 

Lower 

Bound 

Upper

Bound 

Comment 

Sequence Related Parameters 

Primary SV 

stochastic FTC 

(priSVcycles) 

Beta1 α: 17.5 

β: 756.5

0 1 Distribution is used to 

sample on cycles to failure, 

full sampling is more 

complicated and described 

in reference [17] 

SV open area 

fraction 

(SV_frac) 

Uniform - 0.01 1 Sampled independently for 

each primary SV 

In-Vessel Accident Progression 

Effective 

temperature of the 

eutectic reaction for 

zircaloy oxide and 

uranium oxide 

(EU_melt_T) 

Normal Mean: 2479 K 

σ: 83 

- - 

Ex-Vessel Accident Progression 

Lower Flammability 

Limit (LFL) hydrogen 

ignition criteria with 

an obvious ignition 

source in the lower 

containment (H2

LFL). Set up for 

downward, upward, 

and horizontal. 

(burn_dir) 

Discrete Bin Prob. - - Included in all uncertain 

4% realizations. 

(down) 

0.33 

6% 

(Horizontal) 

0.33 

9% 

(Up) 

0.33 
3-33

Uncertain 

Parameter 

Distribution 

type 

Distribution 

Parameters 

Lower 

Bound 

Upper

Bound 

Comment 

Random CV 

sampling for ignition 

without an obvious 

ignition source 

Discrete 30 minutes - - This parameter described 

how often CVs will be 

checked for ignition 

conditions. Included in 

about half of uncertain 

realizations.

Containment Behavior 

Containment fragility 

curve (CFC) 

(rupture) 

Triangle Mode: 67 psi 52 

psi 

78 psi Pressure at which the 

containment ruptures, 

which results in a 3 ft2 hole 

Barrier Seal Open 

Area 

(Seal_Open_A) 

Discrete Bin Prob. - - Unit of m2

0.054 0.034 

0.068 0.005 

0.192 0.015 

0.304 0.024 

0.321 0.026 

0.325 0.026 

0.622 0.149 

0.676 0.054 

0.894 0.071 

1.012 0.081 

1.117 0.089 

1.732 0.276 

1.893 0.151 

Barrier Seal Failure 

Pressure 

(Seal_Fail_Dp) 

Bounded 

Beta 

α: 5.0

β: 1.8

0.0 57.14 Units are psid (differential 

pressure) 

Ice Condenser 

Doors Open Fraction 

Unitless 

(ajar) 

Uniform − 0.0 1.0 This parameter describes 

the fraction of the lower 

plenum doors that will 

remain open due to hinge 

deformation after they fully 

open 

Aerosol Transport and Deposition 

Dynamic Shape 

Factor 

Unitless 

(shape_factor) 

Scaled 

Beta2

α: 1

β: 5

1.0 5.0 

1 The sampling scheme is more complicated than a simple Beta. The probability of failure conditional on demand 

is described by the given beta, and then the stochastic aspect is also modeled to arrive at a sampled number of 

cycles to failure. 2 A Beta distribution by definition has bounds of 0 and 1. Any Beta with other bounds can be referred to as a 

Scaled Beta.

There are many alternative parameters that could represent the phenomenology of interest, and 

the process facilitated selection of a practical subset of parameters to capture important aspects 

of modeling without introducing unnecessary complications, such as the need to extensively 

specify correlations. 

An objective established for each sampled parameter was to clearly identify the point of 

departure from a known technical basis, at which use of professional judgment begins. Such 

judgment was sometimes required in the development of parameters and has been identified, 

where applicable. To facilitate development of parameter justification and rationale, a 
3-34

storyboard process, consistent with the one used in the Surry UA [17] was implemented. Error! 

eference source not found. illustrates the form that was used to identify the parameter, 

responsible owner, technical justification for uncertainty, type of distribution, and rationale for 

the distribution. The intent was to capture, in a concise format, specific information regarding 

each parameter from which the detailed technical bases could ultimately be developed. 

Parameter Name: Type of 

Distribution: 

Technical justification for the uncertainties: 

Rationale for type of distribution: 

Were similar or related parameters considered and rejected. 

Graphic: (plot of the distribution) 

Figure 3-13 Parameter storyboard used to capture key information for 

each parameter investigated 

The storyboards were reviewed internally where analysts explained and defended each 

parameter. The team challenged the technical leads to explain the basis and defend the 

appropriateness of supporting data. This approach often resulted in the need for additional 

research to support the rationale. 

The timing of SV failure was found to be the most influential uncertain parameter of all the 

uncertain parameters considered in the Peach Bottom UA [9]. With this insight from the Peach 

Bottom analysis, failure probabilities of SVs on the pressurizer and MSLs were included as 

uncertain in the Surry UA and are also included in this Sequoyah analysis. Failure to open 

(FTO) due to over-cycling and failure to close (FTC) due to over-cycling, passing water or 

overheating, were modeled and sampled probabilistically for the SVs on the pressurizer. FTC 

due to over-cycling was modeled deterministically for the lowest set-point SV serving the steam 

generator. FTC due to overheating of the MSL SVs was not considered because the valves are 

only exposed to design temperatures. FTO due to over-cycling of these valves was not 

considered because of the multiple SVs on each line and the small probability associated with 

FTO. If one of these valves were to fail to open, the function of pressure relief would simply 

move on to the next valve. As the integrated UAs are for the STSBO scenario, PORVs are not 

available on either the pressurizer or MSLs. 

3.3 Primary SV Stochastic Failure to Close (priSVcyles) 

The Sequoyah modeling was based on the Surry UA approach which used information from the 

Surry SOARCA analysis [5]. In the Surry SOARCA analysis [5], SV stochastic failure was 

modeled for the lowest-set-point SV and only for an FTC failure mode. The mean probability of 

failure per demand was taken and used as the number of SV demands at which failure 

occurred. See the probability distribution and cumulative distribution functions for the negative 

binomial distribution below: 

 ሿ ... 1,2,3ሾ~݊ 1, ≤ ݌ ≥ 0 ,ଵି)௡݌1−)݌ = (݌|݊=ܰ)ܲ = (݌|݊)݂

Eq. 3-1 

 ሿ ... 1,2,3ሾ~݊ 1, ≤ ݌ ≥ 0 ,ାଵ)௡݌1−) 1−= (݌|݊≥ܰ)ܲ = (݌|݊)ܨ
3-35

Eq. 3-2 

Where p is the probability of a failed state for the SV per demand, and n is the number of valve 

cycles the SV experiences. 

Once the distributions were defined, the Surry SOARCA analysis [5] used the mean of the 

number of SV demands at which failure occurred to define the number of cycles at which FTC 

occurs [5]. This approach did not include the FTO failure mode. The approach was justified by 

assuming that redundancy in the SVs would ensure an FTC state would be reached before an 

all valves FTO end state occurred. The Peach Bottom UA also assumed only FTC [9]. The 

sampling conducted to support the Peach Bottom UA was on the beta distribution for the FTC 

probability per demand and then taking the mean of the number of SV demands at which failure 

occurred instead of sampling directly from the distribution in Eq. 3-1 and Eq. 3-2. The Surry UA 

expanded upon the Peach Bottom UA approach by sampling from both the FTC and FTO 

uncertainty distributions. This application was used in the Sequoyah modeling. 

The Sequoyah pressurizer has three SVs (see Figure 1-4) with differing set-points. The setpoints serve automatic valve operation such that when pressure rises in the primary system to a 

set-point, the associated valve opens and steam is released to the PRT. Once the system 

reduces to 96 percent of the set-point, the valve reseats. This is considered one “cycle” of the 

SV. Every opening and closing of the SV has a failure probability associated with the state 

transition. 

Only valve responses to actual scram events were considered, i.e., data from valve testing 

reported in NUREG/CR-7037 was not considered. The testing data was not considered because 

the failure rates in the testing differed largely from the failure rates associated with actual scram 

events suggesting that something was not prototypic in the testing. The data is typically only for 

failures in a single SV cycle, so there may be some limitation in extrapolating to repeated cycles 

during a severe accident, but this is the best data available. Bayesian updating was conducted 

on the failure and testing counts for the SV FTO and FTC failure modes with a Jefferys 

uninformed beta distribution (α = 0.5, β = 0.5) as the ‘prior,’ which roughly corresponds to half a 

failure observed in a full trial. The Jefferys uninformed beta distribution is commonly used in 

Bayesian analysis with limited data because of invariance to transformation in the Fisher 

Information matrix, which describes the amount of information entropy within a distribution [35]. 

The failure information and the sampling distributions are presented in Table 3-6. 

The FTO and FTC distributions were obtained from data in Table 20, “Failure probabilities for 

PWR code safety valves (behavior after scrams),” of NUREG/CR-7037 [36]. In the Failure 

probabilities obtained from Table 20, “Failure probabilities for PWR code safety valves (behavior 

after scrams),” in NUREG/CR-7037 [36] informed the uncertainty characterizations of stochastic 

SV failure. Table 20 reports on SV operation subsequent to actual scram events. Information is 

included for both main steam system (MSS) and RCS valves. The assumption was made in the 

UA that MSS and RCS SVs are alike enough in construct and servicing that their failure data 

can be jointly considered. Subsequent demands were assumed to have the same failure 

probabilities as initial demands. Recovered valve function, e.g., a previously stuck-open valve 

closing when pressure reduces, was not taken to be successful valve operation. 

The “Main Steam System Code Safety Valves” section of Table 20, the value for all reports 0 

failures to open and 15 failures to close, including in 769 demands considering all failures 

(recovered and non-recovered is 15). The “Reactor Coolant System Code Safety Valves” 

section of the table, the value for all reports 0 failures to open and 2 failures to close, including 
3-36

recovered in 4 demands considering all failures. Combining the MSS and non-recovered is 2. 

RCS valve failures identify 0 failures to open and 17 failures to close in 773 demands. These 

failure rates served as the bases for uncertainty characterization of stochastic SV failure in the 

UA. Noteworthy with respect to these failure rates is that they are derived from actual events at 

US plants. NUREG/CR-7037 (Table 22) reports on failure rates in SV testing but the rates differ 

markedly from the rates evidenced by actual plant events suggesting (to the UA analysts) that 

aspects of the testing were inconsistent with actual conditions experienced by an installed valve. 

As such, the testing data was not considered applicable. The total FTC is therefore 17 as 

identified in Table 3-3 below. Table 20 identified no failures to open. 

Table 3-6 SV failure data and associated epistemic uncertainty 

distributions for probability of occurrence on demand for FTO 

and FTC. 

Mode # Failures # Tests Distribution* 

FTO 0 773 β(α = 0.5, β = 773.5) 

FTC 17 773 β(α = 17.5, β = 756.5) 

 *Development of indicated distributions is described below. 

The possible states of a 3-valve RCS primary pressure relief system are presented in Error! 

eference source not found. and the probability of being in a given state as a function of cycles 

can be seen in Figure 3-15. As FTOs occur, the system transitions from cycling on the lowest 

set-point valve (State 1), to cycling on the middle set-point valve (State 2), to cycling on the 

highest set-point valve (State 3). If an FTC does not occur at some point in the transition 

process such that a failed open state of the system develops (State 4), a failed closed state 

develops where pressure relief is no longer available (State 5). 
3-37

Figure 3-14 Possible transitions in the 3 SV pressurizer pressure relief 

system considering FTO and FTC valve conditions 

Figure 3-15 shows the mean SV FTC and FTO distributions associated with the transitions 

outlined in Error! Reference source not found.. The probability of one valve failing open 

ersus the probability of all 3 valves failing closed is illustrated in the curves. The plot is 

truncated at 1,000 cycles based on professional judgment that an SV would likely not cycle 

more than a few hundred times prior to failure. It is noted that a very low possibility all 3 valves 

would fail to open was identified, but was never sampled in the 1,200 created uncertain 

realizations. However, failure to open was observed for the lowest set-point SV. 

Figure 3-15 Mean CDF for number of cycles at which any SV of a 3-SV 

system fails open compared to the mean CDF for number of 

cycles at which all 3 valves fail 

3.4 Primary SV Open Area Fraction (SV_frac) 

The pressurizer SV thermal FTC open area fraction is represented by the MELCOR variable 

SVfrac. A uniform distribution is used to represent uncertainty with a lower bound of 0.01 and an 

upper bound of 1. 

The Sequoyah approach again followed that used in the Surry UA which was based on 

information from the Surry SOARCA analysis [5]. There were no SV thermal failures observed 

for the unmitigated STSBO scenario in the Surry SOARCA analysis where an open area fraction 

of 1.0 (fully open) was used [5]. The Peach Bottom UA [9] explained there is no data available 

to predict the uncertainty of the SV valve position at the time when the SV sticks open from 

thermal seizure. The Peach Bottom UA applied a triangular distribution with a lower limit of 0.0 

and mode and upper limit of 1.0 [9]. This was at least partially because Peach Bottom has a 
3-38

different type of SV which cycles more rapidly, and would indicate a bias towards larger open 

fractions at the time of failure. 

Rationale for Distribution 

For this Sequoyah analysis, all three damage modes (steel softening, thermal expansion, and 

spring degradation) are considered, meaning that all open fractions will be physically possible. 

Since it is not known which damage mode is most probable, a uniform distribution between 0. 

and 1.0 was selected to measure the potential effects of this event on accident progression. 

This distribution is shown in Figure 3-16. The black dot shown at 1:1 is provided for information 

only and shows the Surry SOARCA value [5]. 

Figure 3-16 CDF for the SV thermal FTC open area fraction (SVfrac) 

3.5 Effective Temperature of the Eutectic Reaction for Zircaloy 

Oxide and Uranium Oxide (EU_melt_T) 

This parameter represents the effective temperature at which a eutectic reaction13 between ZrO2

in the cladding and UO2 (and fission products) in the fuel causes the liquefaction and relocation 

of fuel rods. Within MELCOR, this is input as a eutectic sensitivity coefficient (SC1132) and a 

melting temperature. By including the temperature of the eutectic reaction between ZrO2 and 

UO2 as a sampled parameter, general fuel failure behavior of a complex non-equilibrium multicomponent system is represented by simplifying the process to one reaction which can cause 

failure. This parameter affects the overall in-vessel melt progression and the amount of 

 

13 It is noted that the pure UO2/ZrO2 system has only a melting point, not a eutectic point. High burnup pins have 

complex intermetallic effects which are referred to as eutectic reactions in the VERCORS [42] experiments and the 

MELCOR users guide. This nomenclature is maintained for this analysis. 
3-39

hydrogen generated that is released to the containment. The Sequoyah approach again 

followed that used in the Surry UA [17]. 

The binary phase diagram for ZrO2/UO2 suggests the melting point for equilibrium conditions is 

approximately 2800 K for a 50/50 molar mixture; hence this was the historical MELCOR default 

value. Any liquefaction below this temperature accounts for the effect of molten Zircaloy metal 

or alpha-Zr(O) ‘wetting’ the oxide surfaces. Observations of the Phebus ([37][38][39][40][41]) 

and VERCORS experiments [42] suggest that irradiated fuel and oxidized cladding exhibit 

eutectic liquefaction at significantly lower temperatures; thus the MELCOR default was 

subsequently modified to 2500 K. Following significant local cladding oxidation, the effective 

liquefaction of ZrO2 and UO2 mixtures results in local rod collapse as molten material rather than 

as rubble or debris. The parameter treatment for this work attempts to approximate the 

combined uncertainties associated with burnup, eutectic composition, material properties, and 

non-equilibrium effects on ZrO2/UO2 eutectic reactions. 

The fuel melt associated with this parameter is a localized effect. This means that MELCOR 

evaluates the temperature independently for each core cell and allows the fuel in that specific 

cell to melt when it reaches a failure temperature, such as the eutectic temperature (i.e., melting 

temperature of the eutectic formed between ZrO2 and UO2). Thus, the entire core will not fail at 

once when the hottest region reaches the sampled temperature. It is possible that melting of a 

core cell can cause the subsequent failure of fuel above in the same radial ring that was 

previously supported by the failed cell. However, this is a physical effect and still only affects a 

section of a single ring, and keeping it localized. 

In MELCOR, eutectic reactions are approximated by user-modification of melting and failure 

temperatures in model input. Thus, to represent the effective temperature of the eutectic 

reaction, the user must modify: 

1. The sensitivity coefficient SC1132(1), which defines the cladding temperature resulting 

in rod collapse without Zr-metal cladding remaining, and 

2. The melting temperatures of both UO2 and ZrO2 in the input deck (MP_PRC records). 

Rationale for Distribution 

Six experimental data points for eutectic induced core collapse are readily available from the 

VERCORS experiments to help inform the uncertainty distribution [42]. Table 3-7 shows the 

VERCORS test results along with the mean and standard deviation. 

Table 3-7 VERCORS test results for collapse temperature. 

Test Collapse Temperature (K) 

T1 2525 

HT1 2550 

HT2 2400 

HT3 2525 

V_6 2525 

RT6 2350 
3-40

Mean 2479 

Standard Deviation 83 

In selecting this experimental data, observed core slump is projected to a eutectic reaction. This 

was strongly indicated by the test results but cannot be definitively confirmed due to a lack of 

instrumentation. 

The selection of a specific distribution is complicated by the lack of detailed experimental data 

over ranges of severe accident conditions and reactor operation (e.g. high burnup). While the 

VERCOR testing matrix does not provide randomly distributed evidence of eutectic formation 

temperatures, these tests do provide a range of temperatures over which eutectics were 

significant enough to cause core collapse. A normal distribution fitted to the data in was used to 

assess uncertainty in the average core collapse temperature for higher burnup fuel. The simple 

parameter treatment is not intended to rigorously quantify eutectic effects on severe accidents 

and core degradation; such treatment is impossible when the MELCOR eutectic model is not 

activated. Rather, the proposed treatment roughly evaluates the influences that the physical 

state of the core debris has on core degradation kinetics and subsequent severe accident 

progression. A lower temperature for this parameter results in an increased generation of 

molten pools in the RPV during core disassembly. Alternatively, a higher temperature for this 

parameter results in a decreased generation of molten pool formation for ZrO2 and UO2 debris. 

The relocation and cooling of molten pools after formation are treated by several other models 

and parametric inputs in MELCOR; hence the eutectic parameter only affects the initial creation 

of molten pools for ZrO2 and UO2 immediately upon rod collapse. 

Even though the testing data was derived from independent, identical tests, a normal 

distribution was fitted to the VERCORS data in order to describe the general range of potential 

collapse temperatures from VERCORS given varying environmental conditions, and is seen in 

Figure 3-17. This normal distribution has a mean of 2479 K and a standard deviation of 83. The 

red star in Figure 3-17 is provided for information only and represents the 2800 K value used in 

SOARCA [5]. Even left unbounded, the probability of sampling beyond these bounds is 

effectively de minimis, thus the bounds should not affect the results of this work. 
3-41

Figure 3-17 CDF of effective temperature of the eutectic reaction 

temperature 

3.6 Hydrogen Ignition Criteria 

The hydrogen ignition criteria has two components, (a) ignition with a well-defined source and 

(b) random ignition. Ignition from a well-defined source is modeled based on hot gases exiting 

the RCS hot leg failure location above the hydrogen auto-ignition temperature or ex-vessel 

debris with vigorous concrete ablation. These two conditions are assumed to provide welldefined ignition sources throughout the lower containment. The random ignition, when 

activated, randomly provides one 1 second ignition source every 30 minutes at a specific 

location inside the containment. For combustion to occur when an ignition source is present, 

the local gas concentration must satisfy the combustibility limits determined by the experimental 

research [30]; however, the combustibility limits from this research were varied to represent 

uncertainty in the direction of propagation from the ignition source for upward, horizontal, and 

downward propagation. In particular, the flammability, or propagation, limits are greatly 

influenced by the initial direction of propagation from the ignition source. For example, the limits 

for hydrogen-air mixtures for upward, horizontal, and downward initial propagation of the flame 

are 4, 6, and 9 percent hydrogen, respectively, demonstrating the effect of buoyancy [30]. 

Sampling is performed on upward, horizontal, and downward propagation direction from the 

ignition source. Hence, combustion will occur in the lower containment when a well-defined 

source is present and the combustible gas concentration is above the criteria for the sampled 

direction. 

When random ignition sampling is activated, an ignition source is specified within a 

compartment. Each uniform time period (i.e., 30 min), a random ignition source will be present 

in a single control volume anywhere in the primary containment. A burn will be initiated in the 

specified control volume if the combustible gas concentration is above the hydrogen gas 

concentration criterion for the spontaneous ignition (i.e., using the MELCOR default value of 

10% [20]). 
3-42

Once a burn ignites from a well-defined or random source, the burn may propagate to adjacent 

control volumes based on well-established propagation limits (i.e., from research for upward, 

horizontal, and downward [30]). 

Rationale for Distribution 

Sources of ignition could be present throughout the compartmentalized containment such that 

the location(s) where hydrogen would ignite and the direction(s) the flame(s) would need to 

propagate would be uncertain and random. The experimental information [30] relied upon in 

defining the sampling distribution for hydrogen lower flammability limits is limited to flame 

propagation in the primary directions of upward, horizontal, or downward. This suggests a 

discrete distribution is applicable. No information is available to suggest that any one direction 

is more likely than another, thus a uniform weighting of the three discrete initial propagation 

directions (and corresponding lower flammability limits for hydrogen of 4%, 6%, and 9%) is 

appropriate. Figure 3-18 shows the discrete distribution, each equally likely, for each ignition 

source location and initial propagation direction. 

Figure 3-18 Uncertainty distribution for ignition propagation direction 

Ignition by an unspecified random source (IGN_RANDOM) is also considered in the MELCOR 

calculations recognizing, for example, that a static electrical discharge could start a burn. (Note 

that a spark on the order of only a millijoule can ignite hydrogen.) Static electrical discharges, if 

they occurred in an accident, would likely be occasional and momentary as well as variable in 

location. Other conceivable sources are sparks from ice condenser door openings/closings and 

hot RCS piping. (A temperature greater than 847 K exceeds the autoignition temperature of 

hydrogen.) Ignition by an unspecified source was modeled with consideration of the potential 

for static electrical discharges. 

3.7 Barrier Seal 

The upper and lower compartments of Sequoyah’s containment structure are separated by a 

divider barrier (see Figure 3-19). Under accident conditions, the divider barrier acts to limit the 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Upward Horizontal Downward

Probability 

Direction

H2-LFL Uncertainty Parameter Distribution 

(Propagation Direction)
3-43

amount of hot gases, steam and vapor that can bypass the ice condenser. The divider barrier is 

constructed of flexible, rubber coated fabric seals that extend across the gap between the inside 

surface of the outermost steel containment vessel and the concrete structure within each 

vessel. The seals consist of 12 vertical and 11 horizontal segments of flexible coated fabric that 

are bolted to the vessel and the concrete structure by clamps and seal bars. Each segment 

sealing surface is a minimum of 3.5 inches wide with lengths varying from 2.0 to 63.9 feet. In 

total, the barrier seal length is 463.7 feet with a total sealing area of 135.25 ft2 (12.56 m2). 

Figure 3-19 Barrier seal section 

The seals are intended to maintain their integrity during operating or design basis accident 

conditions for a minimum of 12 hours with total leakage of less than 0.4 percent (0.5 ft2). For 

comparison, it should be noted that the smallest segment (2.0 feet in length) seals have an area 

of 0.6 ft2. As such, failure of the fabric coated seal in one or more segments during an accident 

could significantly increase the effective bypass area of the divider. 

Postulated failure of the fabric seal under pressurized conditions would likely begin with a 

localized failure of the seal. This could occur due to a weakness in the fabric or could occur from 

shear stress during the seismic event. Possible weaknesses could be induced by clamping 

forces, clamping surface irregularities, localized fabric defects or strength reduction due to field 

splices. Once compromised, the failure could propagate along the length of the affected seal 

segment, thereby partially opening the associated segment seal area to bypass flow. The 

assumption that the entire seal area is opened for bypass upon seal segment failure is 

conservative because some fabric is likely to remain in place; however, no basis was identified 

to establish the amount of remaining fabric. 

The sealing areas (ft2) of the 23 segments are: 0.58 (8), 0.73, 2.07, 3.27, 3.45, 3.50, 6.70 (3), 

7.28, 9.62, 10.89, 12.02, 18.64 (2), and 20.38. Since larger seal areas have a greater chance of 

local weakness than smaller seal areas, one can consider that the probability of each segment 

failure is proportional to its segment area. For example, given that a local failure occurs, the 

probability that the failure will occur within the segment having a sealing surface of 2.07 ft2 is: 

Pfailure, 2.07 = 2.07 ft2/135.25 ft2, or 1.5%. 
3-44

This assumes that the probability of local failure is uniform along the entire length of the barrier 

seal. This creates the first distribution, (Figure 3-20) which samples on break area. 

The coated fabric seal material is intended to maintain a tensile strength of 100 lb/in and exhibit 

no leakage up to 30 psid (psi differential). Given the linear seal geometry, the tensile strength of 

100 lb/in corresponds to a pressure differential of 2 * 100 lb/in / 3.5 in = 57.14 psid. 

To determine the pressure conditions upon which seal failure will occur (Figure 3-21), a beta 

function spanning the range of 0 psid to 57.14 psid is used, assuming failure will never be above 

the specified tensile strength. The values of α and β are chosen to force two boundary 

conditions, based on engineering judgement to sample the physical range. The first condition 

specifies that the total probability of failure at pressures up to 30 psid is 0.1, or 1 in 10. The 

second condition specifies that the median of the CDF occurs at about the midpoint of testing 

criteria and the ultimate tensile strength, 43.57 (30 + (57.14-30)/2). The result is a beta function 

with α = 5.0 and β = 1.8. 

Rationale for type of distribution 

A discrete distribution for the local failure is appropriate since although the likelihood of localized 

seal failure is uniform over the length of the seal, with bolting and direction changes in the seal, 

it is less likely multiple segments would fail. The failure of even the smallest segment would be 

large enough to dissipate pressure differences, limiting the potential for another segment to fail 

due to over-pressurization. The probability for each segment is based on the area ratio, since 

larger seal areas have more material and more clamping, contributing to an increase in the 

occurrence of seal weaknesses. The distribution also takes into account that there are multiple 

segments with the same area, which is why 0.58 ft2 is more likely than 0.73 ft2, for example. 

There is some possibility that a large fire could destroy multiple sections of the seal 

simultaneous, but due to the location of the seal and wetted conditions, it is considered very 

unlikely the entire seal area would be opened. The barrier seal failure is represented by flow 

path 805 connecting the lower annular region (CV11) to the containment dome (CV24) in 

Figure 3-8. 

The beta distribution constraints were chosen so that the majority of failures occur between the 

design test pressure differential (30 psid) and the pressure difference corresponding to the 

ultimate tensile strength (57.14 psid). This tensile strength is also set as the upper bound of the 

distribution. Ten percent of samples are below design, to account for the possibility that even 

with testing, accident conditions could lead to failures at lower pressure differentials. The lower 

bound of the distribution is theoretically 0, but due to the Beta parameters chosen, it is 

effectively 15 psid. 
3-45

Figure 3-20 Seal break area probability 

Figure 3-21 CDF of failure pressure difference 
3-46

3.8 Ice Bed Lower Plenum Doors (AJAR) 

The ice bed lower plenum doors, if forced open to the point where they do not reclose, allow 

recirculation flows between the ice condenser and the lower containment and can effect 

conditions in the lower containment [18]. A uniform distribution with a range from 0 to 1 was 

used for the fraction of the lower plenum doors stuck open after they are fully opened (see also 

Section 3.1.6 and Figure 3-9). 

A discussion of various sensitivity calculations and uncertainty on the ice chest doors is 

provided NUREG/CR-5586 [18]. The base case calculations in NUREG/CR-5586 were based 

on the irreversible operation of the lower plenum doors, i.e., the doors were assumed to open 

and close until the maximum opening. The lower plenum doors characteristics are illustrated in 

Figure 3-22. 

Figure 3-22 Lower plenum doors characteristics (developed from 

NUREG/CR-5586) 

As explained in Table 2.4 of NUREG/CR-5586, once the pressure exceeds the value required 

for maximum opening, the lower plenum doors remained open [18]. This was considered to be 

more realistic (see Page 138 of NUREG/CR-5586 [18]). NUREG/CR-5586 acknowledges 

uncertainties in the behavior of the doors and two sensitivities were done (1) lower plenum 

doors are reversible, and (2) lower plenum doors are 50 percent reversible and 50 percent 

irreversible (as modeled in Table 2.2 of NUREG/CR-5586 [18]). 
3-47

For the present MELCOR calculations, it was decided to combine the above sensitivities by 

assuming a certain fraction of the doors remain open after they are fully opened. This fraction is 

sampled from a uniform distribution (see Figure 3-23) and applied to all the flow paths 

connecting the lower compartments (CV8, 9) to the lower plenum volumes (CV14, 15, 16, and 

17). The flow paths shown in Figure 3-9 are 49, 50, 54, 55, and 57. This is captured only in the 

initial full opening of the doors and for the remainder of the time, the doors are assumed 

reversible if they open again. For example, for a single realization, if 0.2 is sampled from the 

cumulative distribution in Figure 3-23, when FL49 (A=19.51 m2) is opened fully, it is assumed 

that a minimum flow area of 19.51 x 0.2 would remain for the remainder of the transient while 

80% of the doors continue to behave reversibly based on their differential pressure 

characteristics. 

Figure 3-23 Fraction of LP doors stuck open 

3.9 Dynamic Shape Factor (shape_factor) 

The dynamic shape factor is defined as “the ratio of the actual resistance force of the nonspherical particle to the resistance force of a sphere having the same volume and velocity” [46]. 

This unitless dynamic shape factor is used to account for the stringing out of aerosol 

agglomerates in a linear or complex manner as opposed to growing as a perfect sphere. The 

dynamic shape factor is sampled directly, and the uncertainty of it is represented with a scaled 

beta distribution with the parameters of α=1 and β=5 and bounds of 1.0 and 5.0 (consistent with 

the Surry UA [17]). A value of 1.0 is a perfect sphere and it is the lower limit for the dynamic 

shape factor. This value is the default value in MELCOR. 
3-48

Rationale for Distribution 

The MAEROS model in MELCOR is used to determine the mass concentration evolution of 

aerosols in a spatially homogenous volume, as well as deposition on surfaces and 

injection/removal from volumes. One of the critical assumptions of this model is that 

non-spherical particle effects are adequately parameterized with the dynamic shape factor, the 

collision shape factor, and the effective material density. The importance of these parameters is 

demonstrated in several uncertainty analyses of the MAEROS aerosol model performed in 

support of the development of the MELCOR program [43][44]. To determine the uncertainty of 

this model, the uncertainty of the dynamic shape factor is addressed. 

Kasper et al. [45] (Tables 9 and 10), experimentally measured values for the dynamic shape 

factor for linear chains with their long axis either parallel or perpendicular to the settling 

direction, as shown in Figure 3-24. As seen in the figure, the shape factor is reasonably linear 

with respect to the number of spheres. This data supports an upper bound of 2.8, with a 

weighting toward values less than 2.0. Compiled data from Hinds [46] (pg. 48) also supports a 

range of 1.0 to 2.04. However, Brockmann, J. E., et al [47], compiled data and models that yield 

a range of 1 to 10 for the dynamic shape factor. The same report includes a correlation for 

loosely-packed spheres which calculates dynamic shape factors to be in the range of 1 to 5 for 

void volumes ranging from 26 to 99 percent. Based on these sources, a range of 1 to 5 is 

reasonable and is the range used for this project. 

Although the bounds of the distribution were informed by the experiments described, these 

works cannot inform the shape of the distribution since they do not contain information on the 

normal shape of aerosols during a severe accident. Kissane [48] compiled information on 

aerosols from a number of nuclear accident experiments, including the Phebus fission product 

tests. The report concluded, “Concerning particle shape, relatively compact particles without 

branching chain-like structures appear to be typical in the RCS;” although a visual inspection of 

post-test deposition images did show slight evidence of aerosol chains. Since a compact, 

spherical particle has a shape factor of 1.0, the distribution is weighted more heavily toward the 

lower end of the range (1.0). Additionally, most references support weighting more heavily in 

the 1.0 to 2.0 range. Because of this weighting, a beta distribution is used (α=1; β=5), and the 

CDF of the distribution is shown in Figure 3-25. The α parameter yields a function that has a 

peak as close as possible to the lower bound of 1.0, while the β parameter gives the desired 

shape to the PDF and yields about 75 percent of samples between 1.0 and 2.0 while still 

allowing some samples at physically possible values up to 5.0. 

Additionally, there is a fundamental MELCOR limitation for aerosols in that the dynamic and 

collision shape factors and density are set globally at the beginning of the run and cannot be 

updated as the accident progresses. This means that the dynamic shape factor chosen from 

the uncertainty distribution cannot be changed during the accident progression. 
3-49

Figure 3-24 Dynamic shape factor compared to number of spheres within 

a chain 

Figure 3-25 CDF of the dynamic shape factor 

3.10 Containment Rupture Pressure (rupture) 

Section 3.1.7 provides details on the containment design and the rationale for the uncertainty 

distribution for the containment failure pressure. The uncertainty in the internal pressure that a 

Sequoyah free-standing steel containment vessel can withstand before rupturing is addressed 

through the parameter “rupture”. NRC SMEs suggested that 67 psig be considered in the UA as 
3-50

the best-estimate magnitude of the pressure a Sequoyah containment vessel can withstand and 

that a range between 52 psig and 78 psig be considered possible. Accordingly, a triangular 

distribution with a mode of 67 psig and lower and upper bounds of 52 psig and 78 psig, 

respectively, was prescribed in the random sampling of rupture. 
4-1

4. ACCIDENT PROGRESSION ANALYSIS 

This section describes the results of the severe accident progression. The MELCOR 2.1 code 

was used for the analysis of the scenarios specified in Chapter Error! Reference source not 

found. (i.e., the STSBO and LTSBO) using the MELCOR model described in Chapter 3. The 

objective of this study is to extend the analysis performed for the Surry SOARCA (a large dry 

containment plant) to consider the accident progression in an ice condenser plant. The severe 

accident progression in an ice condenser plant has some different characteristics from a large 

dry containment due to the relatively small freestanding steel containment with ice chests. The 

Sequoyah in-vessel accident progression was expected to be similar to Surry, since both are 

Westinghouse PWRs. The thermal power of the Westinghouse PWRs are approximately scaled 

based on the number of recirculation loops. The Sequoyah plant has four recirculation loops 

and steam generators versus only three for Surry. Consistent with the RCS loop ratio, the 

Sequoyah thermal power rating is ~35% larger. However, the number of assemblies, the mass 

of the fuel, and the size of the vessel are also larger in Sequoyah by about ~23% (i.e., 193 

assemblies in Sequoyah compared to 157 in Surry). Consequently, the in-vessel response is 

expected to scale somewhat similarly. 

Unlike the in-vessel accident response, the containment response of an ice condenser plant is 

expected to be substantially different than a large dry containment following the breach of the 

RCS. The ice condenser containment is expected to be more susceptible to failure from 

hydrogen burns following the RCS hot leg failure or RPV lower head failure.14 The analyses 

confirmed these expectations, which suggested two potential containment failure timings. An 

early containment failure was possible following the RCS breach due to a hydrogen deflagration 

that over-pressurizes the containment Alternatively, if hydrogen burns do not fail the 

containment early (in the initial 12 to 24 hours), ongoing steam production and noncondensable 

gas generation associated with core-concrete interaction eventually decrease oxygen 

concentration below combustible limits. However, the containment will fail later due to overpressurization from ongoing steam production, unabated fission product decay heating and 

noncondensable gas generation associated with core-concrete interaction. The details of the 

analysis and final results, along with UA, are presented in the following sections. 

4.1 Short Term Station Blackout (STSBO) Base Case 

This section presents the results of a base case unmitigated STSBO scenario in the Sequoyah 

plant. Although mitigative actions would be recommended and attempted throughout the 

accident, the unmitigated STSBO assumes no mitigative actions are successful. As described 

in Chapter 2, the STSBO is initiated by a seismic event that fails power supply to operate most 

of the safety systems, but does not fail the structural integrity of the RCS or containment. In 

particular, the TDAFW is unavailable to inject cooling water into the SGs. No other water 

injection system is available to maintain the water level in the SGs or RPV due to loss of AC 

power which renders the ECCS inoperable. 

Table 4-1 summarizes the timing of the key events in the base case unmitigated STSBO. There 

are two known sources of ignition included in the scenarios without random ignition as 

 

14 The RCS breach is used as a surrogate for an obvious hydrogen ignition source due to auto ignition from the hot jet 

from at the hot leg breach or the hot debris in the containment following RPV lower head failure. The scope of the 

results presented in this section also includes calculations with random ignition sources prior to RCS breach. 
4-2

discussed in Section 3.1.8. The two ignition sources are the creep rupture failure of hot leg and 

the failure of vessel bottom head. The ignition sources are imposed in the MELCOR model 

when conditions of the accident progression create an identifiable source for auto-ignition of 

hydrogen (i.e., a hot jet above the auto-ignition temperature from the hot leg or hot core debris 

in the containment). 

Table 4-1 Timing of key events for base case unmitigated STSBO. 

The response of the primary and secondary systems is shown in Figure 4-1. At the start of the 

accident sequence, the reactor protection system actuates to terminate the fission reaction in 

response to events from the loss of grid. The MSL, the main feedwater, and the containment 

Event Description Time 

(hh:mm) 

Initiating event 

 Station blackout – loss of all onsite and offsite AC power 

00:00 

MSIVs close 

Reactor trip 

TDAFW start but fails to inject 

RCP seals initially leak at 21 gpm/pump 

00:00 

First SG SV opening 00:00+ 

First SG SV fails stuck open (after 45 cycles) 00:30 

Steam generators dry out 00:42 

Pressurizer SV opens 01:39 

PRT rupture disk opens 01:56 

1% of the ice melted 01:57 

Water level below top of active fuel 02:13 

Pressurizer SV stuck open (45 cycles) 02:43 

First fission product gap releases 02:57 

Accumulators begin injecting 03:48 

25% of ice melted 03:50 

Creep rupture failure of the hot leg nozzle in combined loop 03:50 

1st hydrogen deflagration 03:50 

Half of ice condenser lower plenum doors stuck open 03:51 

Accumulator empty 03:52 

50% of the ice melted 04:24 

Vessel lower head failure by creep rupture 07:07 

Debris discharge to reactor cavity 07:07 

100% of the ice melted 10:22 

Containment fabric seal torn 52:23 

Containment failure due to slow over pressurization 52:23 

Selected MELCOR Results 

Debris mass ejected (1000 kg) 161 

In-vessel hydrogen generated (kg) 448 

Iodine release fraction at 72 hr 6.93E-2 

Cesium release fraction at 72 hr 6.31E-2 
4-3

isolation valves also automatically close. The RCPs coast down due to the loss of power. The 

primary system PORVs and the secondary system atmospheric relief valves are not available to 

control pressure and remain closed. Consequently, the secondary system pressurizes to the 

SV opening pressure. The SG safety valves open to prevent an over-pressurization and 

subsequently close when their closing pressure criterion is achieved. Steam flow out the SG 

SVs is the principle primary system heat removal mechanism in the first hour as shown in 

Figure 4-1, while the secondary pressure is kept a little above 1000 psia. There is also energy 

removal through the RCP seals leakage, but the leakage flow is small relative to the SG SV 

flow. 

After 0.57 hr, the SG SVs have cycled 45 times and are assumed to stick open at 50% of the 

nominal flow area. The SG SV failure starts a rapid depressurization of the SGs as shown in 

Figure 4-1. The SG depressurization greatly increases the cooldown and depressurization of 

the primary but also increases the loss of water inventory from the SGs. The water inventory in 

the steam generators decreases very rapidly and is completely gone in less than an hour as 

shown in Figure 4-2. 

The pressure in the primary system starts to increase due to the loss of cooling from SGs. A 

safety valve on the pressurizer begins cycling to remove excess energy and maintain the 

primary pressure below 2500 psia. The steam from the pressurizer SV is directed to sparger 

located below the water level in the PRT. The steam flow heats the PRT water to boiling, which 

pressurizes the tank. The PRT rupture disk opens at 1.93 hr. The pressurizer SV cycling also 

causes a decrease in the primary system coolant inventory. The fuel starts to uncover slightly 

after 2 hours as shown in Figure 4-3. As the RPV level decreases, the fuel at the top of the core 

starts to heat up as shown in Figure 4-4. The steam in the core starts to oxidize the fuel 

cladding at high temperature, which is an exothermic reaction that further accelerates the fuel 

heat up. The cladding pressure barrier fails at 900°C, which starts the fission product release 

from the fuel. The reactant gas from the Zircaloy-steam oxidation is hydrogen. The in-vessel 

hydrogen generation is shown in Figure 4-5. The hydrogen and high temperature steam exiting 

the core develop natural circulation patterns in the vessel, the hot leg, and in the SGs. The hot 

gases heat the structures in the natural circulation pathways. 

Similar to the SG SV response, the pressurizer SV is also assumed to stick open after 

45 cycles. The pressurizer SV sticks open at 50% of the nominal flow area at 2.7 hr, which 

depressurizes the primary system (Figure 4-1). The stuck open pressurizer SV draws hot gases 

from the core and out the SV. The new hot leg flow pattern heats the hot leg nozzle, which is 

calculated to fail the hot leg piping near RPV nozzle at 3.84 hours. Upon creep failure of the hot 

leg piping, the primary system rapidly blows down into the containment since the PRT had 

already failed. The RCS accumulators completely discharge their water inventory into the RCS, 

which floods the hot and degrading fuel in the core (i.e., see rapid water level recovery at 

3.84 hr in Figure 4-3). 

The decay heat in the fuel subsequently boils away the accumulator water which starts a 

second heat up of fuel. The fuel collapses and forms a hot debris bed that fails the supporting 

core plate structures. The hot debris relocates to lower head region of RPV, boils away the 

remaining water, and heats the vessel lower head. The lower head fails at 7.12 hr. Nearly all 

the fuel debris from the vessel relocates into the reactor cavity in the containment under the 

reactor vessel by 10 hr. The hot debris boils away the residual water in the reactor cavity and 

starts ablating the concrete. The ex-vessel core concrete interaction (CCI) continues for the 

remainder of the calculation, which generates non-condensable gases. The concentration of 

gases in the containment dome is shown in Figure 4-6. 
4-4

Initially the containment is heated and slightly pressurized by the loss of containment heat 

removal and the hot water leakage from the RCP seals. However, these small heat sources are 

inconsequential at melting any ice in the ice chests (see Figure 4-7). Following the PRT rupture 

disk opening at 1.93 hr, the ice begins melting. The rapid blowdown of the primary system 

following the hot leg failure at 3.84 hr swings the lower ice chest doors open and is assumed to 

deform the door hinges (see Section 3.8). The lower doors remain open at an assumed position 

of 50% for remainder of the accident. There is an increase in ice melting rate as hot gases from 

the degrading core flow out of the hot leg failure location and into the ice chests. The steam 

entering the ice compartments condenses and melts the ices. The ice melting rate slightly 

slows after the vessel dryout but increases following the start of CCI at 7.12 hr. All ice is melted 

before 10 hours. 

Figure 4-1 STSBO base case primary and secondary pressure history 
4-5

Figure 4-2 STSBO base case SG water level and SV cycling 

Figure 4-3 STSBO base case primary system water level 
4-6

Figure 4-4 STSBO base case fuel and debris temperature history 

Figure 4-5 STSBO base case in-vessel hydrogen production 
4-7

Figure 4-6 STSBO base case containment dome hydrogen, oxygen, and 

steam mole fractions 

The containment pressure response is shown in Figure 4-8. As shown in Figure 4-7, the 

pressure suppression capacity of the ice is gone by 10 hours. In addition to the steam released 

to the containment, there was a release of hydrogen to the containment after 1.93 hr and gases 

from CCI after 7.12 hr. Although larger hydrogen burns are evident at the hot leg failure 

(3.82 hr) and shortly after the start of CCI (~8 hr), the magnitude of the initial burn is limited to 

the amount of hydrogen discharged through the open PRT rupture disk.15 Immediately following 

hot leg failure, the lower containment is steam-inerted from the rapid steam generation in the 

core following the discharge of the accumulator water. However, the steam enters the ice 

chests and condenses. There are small burns thereafter as hydrogen exits the hot leg, which 

burns locally near the hot leg. Meanwhile, the gas generation from the CCI steadily pressurizes 

the containment. During the first 15 hours there is enough oxygen in the containment to allow 

deflagration of hydrogen. After 15 hours, the oxygen concentration in the lower containment 

decreases below the amount needed to support combustion (i.e., 5% molar concentration). The 

relative decrease in the oxygen concentration is due steam generation from evaporating water 

and CCI gas generation but also due to the oxygen consumed in the early burns. 

The response of the reactor cavity and the lower containment is shown in Figure 4-9. The figure 

shows the erosion of the concrete due to ablation was well as the ex-vessel debris temperature 

and the water levels. Melting of the ice led to accumulation of water in the lower compartments 

 

15 The accumulator discharge not only creates a large steam source that fills and inerts the lower containment from 

hydrogen combustion, the water and steam decreases the temperature of the hot jet exiting the failed hot leg below 

the hydrogen auto-ignition temperature. Consequently, no combustion is possible until the steam concentration 

decreases and an ignition reappears. 
4-8

and the failure of the containment resulted in the flashing of some of the water and water flowing 

into the cavity. The total concrete erosion in the reactor cavity is >1 m by 68 hr. The 

aggressive CCI erosion of the concrete generates a large amount of the gases (i.e., steam, 

carbon-dioxide, carbon-monoxide, and hydrogen). The CCI gas generation pressurizes the 

containment, which fails at 67 psig. The free-standing steel containment ruptures with a 

relatively large tear (i.e., ~3 ft2) near the equipment hatch area. Previous analysis [49], as well 

as the current uncertainty realizations indicate an early containment failure due to a large 

hydrogen burn pressure spike is also possible near the initial RCS breach. Consequently, the 

base case is only representative of one possible containment failure timing. 

Figure 4-7 STSBO base case fraction of ice melted 
4-9

Figure 4-8 STSBO base case containment dome pressure 

Figure 4-9 Base case containment ablation during STSBO with random 

ignition 
4-10

Figure 4-10 and Figure 4-11 show the fission product distribution of the Iodine and Cesium 

radionuclides that were released from the fuel, respectively. The fission products are released 

from the fuel starting at about 3 hours. A substantial portion of the released fission products 

flow through the stuck open pressurizer SV to the sparger located at the bottom of the pool of 

water in the PRT. The hot gas entering the PRT water and the decay heat from the captured 

fission products heats the PRT water to boiling, which pressurizes the PRT until the rupture disk 

fails at 1.93 hr. The sparger is initially very effective at retaining most of the released fission 

products in the water. However, the sparger becomes less effective when the pool becomes 

saturated. After the hot leg failure at 3.84 hr, the flow from the RPV to the PRT stops and the 

hot leg failure is the primary fission product pathway to the containment. 

Following the accumulator discharge at hot leg failure, the fuel continues heating and releases 

most of the remaining volatile fission products. The fission products that are released following 

hot leg failure are discharged into the containment. As shown in Figure 4-10 and Figure 4-11, 

the fission products are initially airborne but begin settling. Between 14 to 16 hours, the cesiumiodine and cesium hydroxide in the PRT revaporizes and flows into the containment. 16 Since 

most of the iodine is in the cesium-iodine compound, the impact of the cesium iodine 

revaporization from the PRT is more significant in the iodine distribution history (Figure 4-10). 

The aerosols slowly settle in the containment until the containment failure at 52.4 hours, which 

starts the radionuclide release to the environment. The subsequent containment 

depressurization also promotes revaporization of the cesium molybdate from the PRT, which 

greatly increases the cesium release to the environment after containment failure. 

Figure 4-12 shows the release fractions of radionuclide to the environment. At 72 hours 99.8% 

of the noble gases, 20% of Molybdenum, 19% Cadmium, 6.9% of Iodine, and 6.3% of Cesium is 

released to the environment. The large increase in the Molybdenum release to the environment 

after 62 hours corresponds to the complete oxidation of the common metals in the CCI (i.e., the 

steel and Zr), which leads to oxidation of the Molybdenum and the release of gaseous MgO. 

Sections 4.2.1 and 4.2.2 further describes the results of accident progression uncertainty 

analysis including the attributes that lead to early versus late containment failure and the 

magnitude of the radionuclide release to the environment. 

 

16 The PRT is tracked as part of the in-vessel total. Once fission products exit the PRT into the containment, they are 

tracked in the containment airborne and deposited in Figure 4-10 and Figure 4-11. 
4-11

Figure 4-10 STSBO base case iodine fission product distribution history 

Figure 4-11 STSBO base case cesium fission product distribution history 
4-12

Figure 4-12 STSBO base case environmental release fraction history 

4.1.1 Short-Term Station Blackout with Igniters 

An STSBO analysis was performed to demonstrate the benefits of the igniters. The MELCOR 

results confirm that an early containment failure can be avoided using the igniters, which allows 

the implementation of emergency plan including completing evacuations prior to a late 

containment failure. In this sensitivity calculation, it is assumed that the power for igniters is 

established prior to a significant accumulation of hydrogen in the containment. Figure 4-13 

shows containment pressure response for Realization 338, which resulted in earliest 

containment failure from the uncertainty assessment without random ignition. Realization 338 is 

discussed in more detail in Section 4.3. Realization 338 represents a sequence with earliest 

containment failure without power to the hydrogen igniters. The results of Realization 338 with 

and without operating igniters are shown in Figure 4-13. The igniters mitigate the large 

pressure spike at 3.79 hours and all subsequent other hydrogen burns. Without further 

mitigation, the accident progression proceeds to a late failure containment failure at 43 hr due 

the slow rise in pressure from to CCI. Nevertheless, the recovery of the igniters delays the large 

release of radionuclides for ~40 hours, which allows time to an evacuation. 
4-13

Figure 4-13 Containment Pressure for the STSBO with and without hydrogen igniters 

4.2 STSBO Source Term Parameter Uncertainty Analysis 

There were two sets of analyses conducted for the STSBO scenario, each of which included 

running 600 realizations. The two STSBO sets were identical except that the random ignition 

source was turned on in one set and random ignition was turned off in the other scenario. 

Section 4.2.1 describes results with non-random ignition and Section 4.2.2 discusses the results 

with random ignition. The non-random ignition cases included two active ignition sources; (1) 

auto-ignition from hot gases exiting the hot leg failure location and (2) the ex-vessel debris 

following the reactor pressure vessel melt-through. These active ignition sources would initiate 

a burn in the lower containment whenever the gas concentrations met combustible criteria. The 

random ignition cases included the active ignition sources and also a random ignition source at 

a specific location inside the containment every 30 min (see 3.6). 

A few general insights related to containment rupture with and without random ignition scenarios 

can be observed in Figure 4-14. First, the likelihood of an early containment rupture is greater 

in cases without random ignition. Second, the probability of containment rupture by 72 hours 

was essentially identical by the two methods. Finally, both sets of realizations showed a gap in 

the results around 24 hours between early containment failures and the later containment 

ruptures. Due to the potential for random ignition throughout the containment, the gap is less 

pronounced using a random ignition model. There are also fewer early containment ruptures 

with random ignition. If you ignore the 3 early random ignition realization outliers, then the 

random ignition CDF does not start increasing until 11 hours whereas the non-random 

realizations start accumulating at 4 hours. Although the random ignition scenario specified 
4-14

random ignition sources throughout the containment (i.e., including the potentially high 

hydrogen concentration ice chest region), the net effect was incremental combustion of 

hydrogen discharged from the pressurizer relief tank (PRT) in smaller quantities. Without 

random ignition, the hydrogen exiting the PRT following the rupture disk failure transported 

throughout the containment until hot leg failure (i.e., the earliest active ignition). The latter often 

provided conditions for a large burn that failed the containment at hot leg failure. 

For these uncertainty analyses, the ranking of the variables is conducted using the four 

regression techniques. A consequence of the use of multiple regressions is that the ranking of 

the inputs amongst themselves is not obvious when the different regressions disagree. A 

quantitative approach was implemented for the current analysis due to a strong belief that such 

a quantitative ranking is only an indicator and should be supported by expert opinion based on 

the physics of the problem. Two effects of the uncertainty in the input onto the output of 

consideration are estimated in the present study. The ‘Main Contribution’ represents the 

influence of the uncertain input by itself and is estimated with ܴ௖௢௡௧

ଶ in the stepwise regression 

and ܵ௜ for the other three regression techniques. Then the ‘Conjoint Contribution’ effect of the 

uncertain input from its interaction with other variables, which is ignored by the stepwise 

regression as it is an additive regression, is estimated with ܶ௜ − ܵ௜ for the other three 

regressions. Section 2.2 of Appendix A provides a detailed discussion of the ranking method 

employed. 

Figure 4-14 CDFs of containment rupture time with and without random 

ignition 
4-15

4.2.1 Uncertainty Evaluations with No Random Ignition 

Regression analyses were performed on the results of the Sequoyah MELCOR STSBO 

analyses. Regression tables in the following sections provide summaries of the analysis. In 

these tables, the parameters are listed first, followed by the four regression techniques used to 

create the results. The last two columns contain values of the main contribution of the 

parameter on the result metric and the conjoint influence17 of the parameter on the result metric. 

These are calculated as arithmetic averages of the overall contributions from the four regression 

techniques. Values of main contribution greater than 0.02 and conjoint contributions greater 

than 0.1 are considered significant and are highlighted. The parameters in the first column of 

the tables are ordered by the values in the column labeled Main Contribution, thus the 

parameters appear in rank order. 

It was not possible to include two parameters directly in the regression calculations because 

their sampled input value was due to the actual physical behavior of the reactor system. 

Because of this, these two parameters were post-calculated (see Table 4-2) for use in 

regressions, based on both the sampled input and actual behavior, and also to attempt 

complete coverage of the uncertainty space. 

Table 4-2 Post-calculated regression parameters. 

# Parameter Name Description Notes 

1 SV_Frac Open area fraction for the safety 

relief valve system 

Calculated as the sum of the open area 

fractions of the individual valves 

2 priSVcycles Cycles before failure 

experienced by the safety relief 

valve system 

Calculated as the sum of the cycles to failure 

experienced by each of the valves 

A high performance computing cluster was used to execute a Monte Carlo simulation with 600 

MELCOR runs, of which 467 were successful for the STSBO scenario with no random ignition. 

The reasons for the failed runs are listed in Table 4-3. MELCOR code failures occur when 

executing large analyses such as this. Throughout the project, these errors are investigated 

and fixed, which further advances the state of the code. However, some random code failures 

also occur in MELCOR often because uncertainty parameter permutations create 

computationally difficult conditions that have not been previously addressed. These failures are 

investigated, and those that are random are not subject to correction. For the Sequoyah 

analysis, there were two calculation sets that required further action (but are not related to code 

run time errors). First, the Sandia High Performance Cluster had an unexpected failure that 

affected 24 realizations. Second, the uncertainty algorithm that generated the pressurizer 

safety valve cycle failure unexpectedly included 31 realizations that were initialized with an 

immediate safety valve failure. The effected realizations were identified and removed from the 

sample set. The error was not identified until all calculations were completed.

An analysis was also completed to determine whether there is a correlation between failed runs 

and having the realization set in a particular area of the input space. Such a situation would 

indicate that a particular value for one input (or combination of values for several inputs) would 

 

17 Conjoint influence is the influence of two or more input parameters acting together, which may have synergistic 

effects that would not be uncovered by studying the influence of each parameter separately and individually. 
4-16

lead to failure, biasing the conclusion that could be drawn by only analyzing the successful runs. 

This analysis provided reasonable assurance that the failed runs should be randomly distributed 

in the input space, with one small exception. There was a small, but not significant, relationship 

between priSVcycles and realization completion. See Appendix A for further discussion. 

Table 4-3 Summary of the number of realizations that were 

unsuccessful in the without random ignition set. 

# Failed Reason 

Code run time 

3 Ex-vessel cavity surface heat loss error

28 Volume error in the COR Package

7 Bad debris temperature in the core lower head 

subroutine calculations

17

Executive detected abort signal (i.e., typically a 

failure to converge in the control volume 

hydrodynamics package) 

20 Convergence error in an ex-vessel cavity 

routine (CCMLTR)

3 Convergence error in an ex-vessel cavity 

routine (CCSVLR)

Other (hardware/input) 

24 Sandia High Performance Cluster Failure 

31 Failed pressurizer SV at time = 0 sec 

 General MELCOR results for the with no random ignition scenario 

The MELCOR results for the cases with no random ignition were configured to show the 

containment rupture timing as a function of the timing to the first RPV breach (i.e., see 

Figure 4-15 through Figure 4-19). If the realization had containment failure immediately 

following a RPV breach, then the result would appear on “coincident” line. The RPV breach line 

identifies the earliest possible timing for an active ignition source (i.e., either hot leg failure or 

RPV lower head failure). Since the containment rupture is the start of the release of 

radionuclides to the environment, the figures identify important characteristics for the source 

term timing. The following bullets summarize the general observations and insights from the 

realizations with no random ignition. Sections 4.2.1.2 through 4.2.1.5 present the statistical 

evaluation for the specific figures of merit. 

• The earliest containment failure occurs at 3.9 hours (Rlz 338). A pressurizer safety 

valve stuck almost fully open on the 2nd cycle. Almost all the hydrogen from the RPV 

was vented to the lower containment after the PRT rupture disk failed. The containment 

ruptured at hot leg failure (i.e., the first ignition source) at a relatively low sampled 

rupture pressure (i.e., the containment failure pressure). Rlz 338 is described in more 

detail in Section 4.3. 

• The latest containment failure occurred after 72 hr (i.e., Rlz 171). There were three 

realizations with containment failure expected to occur after 72 hours due to ex-vessel 

core-concrete interactions. In contrast to Rlz 338, the pressurizer SV did not fail in 

Rlz 171. In addition, the sampled value for the containment failure pressure was 
4-17

relatively high and not challenged by the hydrogen burns following the RPV breach. 

Rlz 171 is described in more detail in Section 4.3. 

• There is a distinct set of realizations showing early containment failure at approximately 

4 to 6 hours. These realizations lie on or near the RPV breach line (i.e., see the 

realizations in the lower left-hand side of Figure 4-15). The early containment failure in 

these realizations occurs following a hydrogen burn at RPV failure that fails the 

containment. 

• There is a second distinct grouping of realizations exhibiting later containment failure 

ranging approximately between 32 and 65 hr. The later failures occur following a 

continual pressurization resulting from ongoing steam production, unabated fission 

product decay heating and noncondensable gas generation associated with CCI 

(i.e., see the realizations in the upper-right of Figure 4-15). 

• The gap in containment failures between 20 and 32 hours differentiates between sudden 

containment over-pressurizations caused by deflagrations and gradual overpressurizations resulting from steady steam production, continuous fission product 

decay heating and ongoing CCI generation of noncondensable gasses (see 

Figure 4-15). 

• After RPV lower head failure, noncondensable gas produced by CCI combined with 

continued steam production and intermittent oxygen consumption by small deflagrations, 

lowers the relative concentration of oxygen in the containment atmosphere. The 

resultant oxygen concentration became insufficient to support hydrogen/carbon 

monoxide burns after about 15 hours.18 

• As shown by the dense cluster of markers in the upper left of Figure 4-15, most late 

containment ruptures are associated with an RPV breach occurring early, sometime 

between 3.5 and 4.5 hours. While an early hot leg failure sometimes resulted in an 

immediate hydrogen burn large enough to fail containment, more often such a burn did 

not occur and the containment atmosphere progressed to an inert condition as described 

in the previous bullet. 

• In contrast to late containment ruptures, early containment ruptures exhibit a slightly 

wider timing spread between 4 to 6 hours (see the grouping of realizations in the lower 

left of Figure 4-15). Noteworthy is that nearly all early containment ruptures are 

coincident with hot leg failure. 

• There are a few interesting cases with intermediate containment rupture times (i.e., the 

grouping of red markers off the coincident line on the left side of Figure 4-15). For 

example, an energetic deflagration failed containment at 14.8 hr in Rlz 133 but this was 

10.9 hr after hot leg rupture. This outcome resulted from a unique combination of an 

early pressurizer SV failure (3 cycles) and relatively high containment failure pressure 

 

18 The Sequoyah combustion model, which was developed from experimental data [30], shows a 5% oxygen 

concentration is required to support combustion. The number of moles of oxygen in the containment is reduced 

through the early hydrogen burns. The relative concentration is also reduced by the addition of gases from ex-vessel 

CCI. The combined effects caused the oxygen concentration to drop below 5% after about 15 hours. 
4-18

(72.8 psia). When a pressurizer SV failed early in the MELCOR calculations, the 

hydrogen produced by fuel cladding oxidation vented to containment prior to the hot leg 

rupture through the stuck open valve and the (broken) rupture disk on the PRT. Then 

when a hot leg ruptured, the vented hydrogen often ignited in a large burn that failed 

containment. A large burn at hot leg rupture was not always the case however, as in Rlz 

133 where the burn wasn’t large enough to fail the containment. A later burn did fail 

containment in this realization as described in Section 4.3. 

• Figure 4-16 shows a correlation to the number of pressurizer SV cycles to the 

containment rupture timing. If the pressurizer SVs operate without any failures (i.e., the 

black circles), then the containment only fails late.19 The progression of events in these 

sequences includes a hot leg failure from high reactor coolant system (RCS) pressure 

followed by a rapid discharge of the accumulators into the degraded core. The high 

RCS pressure retains much of the hydrogen in the vessel until the rapid discharge of 

RCS gases into the containment at hot leg failure. Consequently, there is less hydrogen 

in the lower containment at the moment of the RPV breach. The discharge of gas 

through the hot leg failure includes a concentration of steam, which is immediately 

followed by a large steam source from the accumulator water quenching the degraded 

core. The large and rapid accumulator water source into the core also eliminated the 

ignition source at the hot leg (i.e., the gases exiting the hot leg no longer satisfied the 

auto-ignition temperature criterion). The lower containment remained steam inerted 

and/or without an active ignition source and always progressed to an oxygen deficient 

state. 

• As described in the previous bullet, the realizations with 46 to 60 cycles (i.e., yellow 

triangles in Figure 4-16) and 61 to 74 combined cycles (i.e., red triangles on Figure 4-16) 

also primarily resulted in late containment failure. The SVs in these two groups 

functioned properly for second and third longest times. The other realizations with fewer 

SV cycles prior to a failure (i.e., 1 to 45 cycles) resulted in both early and late 

containment failures. There were more complicated factors that contributed to whether 

there was an early or late containment failure. However, the flow area of failed SV or 

SVs was an important factor (e.g., a small SV failure could stop further SV cycling but 

otherwise remain at relatively high RCS pressure, which is somewhat like a functioning 

SV realization). 

• Figure 4-17 shows the correlation between the number of pressurizer SV cycles (i.e., the 

priSVcycle parameter) versus the SV failure area (i.e., the SV_Frac parameter). As 

noted above, most of the realizations with >46 cycles progressed to late containment 

failure. An analogous correlation was observed with the resultant SV failure area. If the 

SV failure area was <0.3 (i.e., the non-dimensional SV failure flow area), then the 

realizations progressed to a late containment failure. Whereas the priSVcycles 

parameter accounted to the delayed in timing to the RCS blowdown, the SV_Frac 

parameter accounted for the resultant depressurization rate. Consequently, if the SV 

failed too late to depressurize the RCS or did not have adequate flow area to 

depressurize the RCS, then the amount of hydrogen discharged to the containment prior 

 

19 The timing of the initial RPV breach is also very consistent between the cases. With any variability in the timing of 

the RPV depressurization due to a failed SV, the hot leg failed roughly at the same timing (i.e., 3.7 h). The impact of 

the other uncertain parameters had almost no impact on the accident progression through hot leg failure. 
4-19

to the initial RCS breach was inadequate in its distribution and concentration to fail the 

containment by a hydrogen burn. 

• Figure 4-18 shows a surprising lack of correlation between overall in-vessel hydrogen 

production and either early or late containment failure. In-vessel hydrogen production 

ranges from a minimum of 303 kg in Rlz 469 to a maximum of 667 kg in Rlz 225. 

Inspections of these realizations, which is discussed in Section 4.3 show the contrasting 

substantial importance of the amount of hydrogen produced prior to hot leg rupture and 

vented to containment - especially to upper containment. Larger hydrogen production 

prior to hot leg rupture with efficient venting of the hydrogen to containment allows even 

the realizations with lowest overall in-vessel hydrogen production to experience a burn 

sufficient to rupture containment (see the red triangles grouped in Figure 4-18). 

• As identified above, containment rupture time exhibits substantial dependence on the 

amount of hydrogen produced prior to hot leg failure. Figure 4-19 further illustrates this 

dependence in that the majority of early containment failures had more than 400 kg of 

hydrogen production prior to hot leg failure. 

• An early failed pressurizer SV accelerates inventory loss, core uncovery, and hydrogen 

production. It also reduces the mechanical stress on the hot legs by reducing pressure. 

The consequence of these influences was more extensive core damage prior to hot leg 

failure. 
4-20

Figure 4-15 Containment rupture time versus RPV breach time colored by whether 

rupture was immediate to a deflagration – without random ignition 
4-21

Figure 4-16 Containment rupture time versus RPV breach time colored by number of 

pressurizer SV cycles – without random ignition 
4-22

Figure 4-17 Relationship of priSVcycles to SV_Frac colored by containment failure 

timing – without random ignition 
4-23

Figure 4-18 Containment rupture time versus in-vessel hydrogen production – 

without random ignition 
4-24

Figure 4-19 Containment rupture time versus in-vessel hydrogen production up to 

the RPV breach time – without random ignition 

 Iodine Release Fraction to Environment (with no random ignition) 

The magnitude and timing of the iodine release is an important radionuclide to characterize the 

potential severity of the early health effects. The timing of the iodine release can be very 

important if it occurs during the evacuation phase. Consequently, the following regression 

evaluations paid particular attention to factors that influenced large and early releases of iodine. 

4.2.1.2.1 Iodine Release History 

The fractional release of iodine to the environment by 72 hours is a primary figure of merit in the 

UA. Figure 4-20 shows the time dependence of the fractional iodine release in all the 

realizations, as well as the mean, median, 5th and 95th percentiles of the releases. There is a 

distinct set of realizations showing early initial release of iodine between roughly 4 and 6 hours 

followed by a second distinct set of realizations showing an initial release of iodine between 

roughly 35 and 55 hours. Some releases plateaued before 72 hours but most continue to 

increase through to the end of the calculation. Mean, median, 5th and 95th releases are all on an 

increasing trend at 72 hours. 

The split in the start of the iodine releases coincides with the containment rupture timing 

(e.g., Figure 4-15). The early iodine releases result from the early containment rupture following 

a hydrogen burn whereas the second grouping occurs from a late over-pressurization failure 
4-25

resulting from ongoing steam production, fission product decay heating, exothermic coreconcrete interactions and non-condensable gas generation from core-concrete interactions. As 

discussed previously in Section 4.2.1.1, there is a time gap following the containment becoming 

oxygen deficient (i.e., the end of combustion due to insufficient oxygen to support combustion) 

to the time required for ex-vessel steam production and non-condensable gas generation to 

over-pressurize the containment. The variation in the late over-pressure timings is influenced 

by the containment rupture pressure and the amount of hydrogen gas that was burned. The 

variation in the containment failure timing directly affects the start of the iodine release. 

However, the variation in the magnitude of the iodine release is a more complex function of 

other parameters that will be discussed in the following subsections. 

The bifurcation in containment failure timings led to a wide spread between the calculated 

median and the 95th percentile curves. The median realization is characteristic of a late 

containment failure while the mean curve has contributions from both the early and late 

containment failure results. Most realizations resulted in a late containment rupture. The 

median and 5th percentile curves show larger increasing trends following the late containment 

failure. Late increasing iodine releases were driven by a “chimney effect” where there 

developed a sustained buoyant flow of hot gases emanating from CCI that rose up into the RPV 

through the failed lower head exited the RPV through the rupture in the hot leg. The hot gases 

drove a revaporization of CsI previously deposited in the RPV. The late revaporization releases 

were also driven by revaporization of CsI from the PRT. 

The median curve approaches the mean curve by the end of the calculation, which shows the 

previous differences between the early and late containment failures have diminished. The 

mean, median, 5th and 95th traces all show continued releases at 72 hours. However, the 

slopes of the 5th percentile, median, and mean curves are higher than the 95th percentile. In 

contrast to the slower rise of the median result, the 95th percentile showed a large early release 

of iodine that exceeded 10% by 20 hr. The 95th percentile is characteristic of the trend of the 

iodine releases from the early containment failure results but not the magnitude (i.e., the 

magnitude is the 95th percentile of all cases). Since the airborne aerosol concentration in the 

containment is higher at the time of an early containment failure, the initial release upon the 

containment rupture is also higher. The trends of the horsetail curves show a broader range of 

results (i.e., 5th and 95th percentiles) for the late containment failure realizations but a lower 

median. 
4-26

Figure 4-20 Iodine release fractions over 72 hours with mean, median, 5th and 95th

percentiles 

4.2.1.2.2 Regression Tables and Scatterplots 

Table 4-4 shows the results for regression analysis of the iodine release fraction. A technical 

rationale explaining the contribution to uncertainty is provided for the top parameters. The SV 

open area (SV_Frac) fraction is the top parameter for iodine and is also the top parameter for 

cesium, in-vessel hydrogen production, and rupture regression analyses. 

In review of the regression results for all successful realizations, SV open fraction was identified 

as the most important parameter by all regression techniques, except MARS, which identified it 

as second most important behind priSVcycles. SV stochastic FTC (priSVcycles) was identified 

as the second most important parameter by all regression techniques, except MARS, which 

identified it as the most important. Rupture and eutectic melt (EU_melt_T) were identified as 

the third and fourth most important, respectively, for all regression techniques, except MARS 

which reversed the order for these two parameters. No other parameters had a significant 

contribution to the iodine release fraction uncertainty. 
4-27

Table 4-4 Regression analysis of iodine release fraction at 72 hours (with no 

random ignition). 

* highlighted if main contribution larger than 0.02 or conjoint contribution larger than 0.1 

Scatterplots were produced for the parameters that were ranked most important by the weighted 

average of the regression techniques (see Appendix A for a description of the calculation). Such 

plots are useful for verifying relationships between the parameter input values and iodine 

release fractions indicated by the regressions. The scatterplots of most interest are displayed in 

the respective parameter sections. 

SV_Frac 

The SV_Frac parameter indicates the open fraction of the primary SV system at 72 hours. All 

pressurizer SV cycling ceases following the RPV breach (i.e., <10 hours). Consequently, the 

final SV position represents the actual failure conditions, based on the post-calculated results. If 

there are no SV failures at 72 hours, then the open fraction is 0 (i.e., in the limit as the SV open 

fraction approaches zero, it approaches a normally operating SV). Finally, if one SV 

experienced cycling failure, then the sampled open fraction is used. If multiple SV experience 

cycling failure, then their open fractions are summed. Figure 4-21 shows the scatterplot of 

iodine release fraction versus SV_Frac. This scatter plot suggests a relationship between low 

fractional open areas and low fraction of iodine release (i.e., SV_Frac<0.28). Almost all of these 

realizations had an iodine release less than 0.1. In contrast, the fractional open area above 

0.28 corresponds with the entire range of iodine release. 

The response to the SV_Frac parameter has interdependence on the number of cycles. As 

discussed in Section 4.2.1.1, all the realizations where the pressurizer safety valves operated as 

designed without failures resulted in late containment failures. The progression of events in 

these sequences includes a hot leg failure from high RCS pressure followed by a rapid 

discharge of the accumulators into the degraded core. The early RPV failure occurred when ice 

was available to condense steam. Consequently, the hydrogen could burn in smaller amounts 

as it discharged from the hot leg. The hydrogen (and oxygen) was consumed without building 

to large concentrations that could threaten the containment. All these realizations progressed to 

a late containment failure (i.e., >32 hours, see Figure 4-16), which results in a delayed start of 

the iodine release. The late containment iodine releases were generally lower than the 

realizations with an early containment failure (see discussion in Section 4.2.1.2.1). 

Consequently, all the realizations with SV_Frac=0 include a late containment failure and 

correlate with smaller releases. 

Final R2

Input R2

 contr. SRRC Si Ti Si Ti Si Ti

SV_frac 0.20 0.29 0.34 0.58 0.27 0.57 0.33 0.32 0.182 0.117

priSVcycles 0.04 -0.27 0.14 0.34 0.13 0.42 0.49 0.49 0.110 0.110

rupture 0.03 -0.17 0.06 0.08 0.06 0.25 0.05 0.05 0.031 0.055

EU_melt_T 0.02 0.13 0.08 0.09 0.02 0.10 0.10 0.10 0.028 0.025

burn_dir --- --- 0.01 0.05 0.01 0.09 0.03 0.02 0.009 0.026

shape_fact 0.01 0.11 0.01 0.03 0.01 0.10 0.02 0.02 0.007 0.027

ajar 0.01 -0.08 0.02 0.05 0.02 0.15 0.00 0.00 0.007 0.039

Seal_Open_A --- --- 0.01 0.05 0.01 0.05 0.00 0.01 0.003 0.018

Seal_Fail_Dp --- --- 0.00 0.09 0.00 0.07 0.00 0.00 0.002 0.030Conjoint Contribution

0.31 0.44 0.82 0.47

Rank Regression Quadratic Recursive Partitioning MARS

Main 

Contribution
4-28

The SV_Frac realizations with a failure area less than 0.28 also had small source terms. The 

smaller leakage area behaved more closely to a normally operating pressurizer SV. 

Furthermore, if the leakage area on the first failed valve was too small to prevent further 

pressurizer SV cycling, then the behavior was also similar normally operating SVs and also 

resulted in late containment failure. 

The SV_Frac realizations with valve failure areas between ~0.28 to 1 resulted in both early and 

late containment failures. These cases spanned the full spectrum of iodine releases. As 

discussed in Section 4.2.1.1, Realizations 133 and 225 are representative of early and late 

containment failure, which generally correlates with larger and smaller source terms, 

respectively. The two realizations had very similar sampled parameters except the number of 

cycles until the pressurizer SV failed. In particular, both realizations had similar SV failure areas 

(i.e., ~0.6). However, the pressurizer SV stuck failed after 3 cycles in Rlz 133 versus 69 cycles 

in Rlz 225. An early and large pressurizer SV failure will depressurize the RCS and transfer 

more hydrogen into the lower containment prior to the RPV breach. This often resulted in more 

energetic burns that failed the containment at the RPV breach (i.e., the timing of the first active 

ignition source). The realizations within this SV failure area range filled the spectrum of the 

results but were also dependent on the number of SV cycles before failure. 

There is another factor influencing the iodine release for these cases. Prior to RPV breach, the 

iodine release pathway to the containment is through the pressurizer SV. The PRT can filter the 

releases from the SV sparger. The PRT rupture disk opens after ~25 SV cycles or shortly after 

a non-trivial SV failure (i.e., fails to close with SV_Frac > 0.28). An early SV failure with a large 

failure area can contribute to more iodine transport and capture in the PRT. Following hot leg 

failure, the hot leg becomes the dominant release pathway. While the PRT pool can be 

effective in the early retention of iodine, the decay heat from the captured radionuclides can 

evaporate the water. Following pool dryout, the radionuclides plate out on the PRT floor. The 

subsequent heating and release of radionuclides including iodine from the PRT dryout impacts 

the lower containment airborne concentration. With normally operating pressurizer safety 

valves, less iodine is transported to the PRT and the dryout timing is later. The SV_Frac 

realizations with valve failure areas between ~0.25 to 1 had variable effects from the PRT 

dryout due to timing of the valve failure. For example, Rlz 133 with an early SV failure (i.e., 3 

cycles) and 0.64 fractional failure area captured 183 kg of volatile radionuclide aerosols in the 

PRT pool (see Figure 4-22) whereas Rlz 255 with a late SV failure (i.e., 69 cycles) and 0.56 

fractional failure area only captured 31 kg of volatile radionuclide aerosols (see Figure 4-23). 

However, 83 kg and 7 kg revaporized by 72 hours, respectively, which included iodine 

revaporization releases. 

The SV_Frac realizations with total combined valve failure areas greater than 1 resulted in 

moderate iodine releases that were slightly above the median and the mean. There were six 

realizations with a SV_Frac greater than 1 that ranged in a total failure flow area from 1.05 to 

1.54. Since the SV_Frac is larger than 1, two or more pressurizer safety valves failed prior to a 

RPV breach. These realizations progressed to multiple valve failures because the initial valve 

failure did not depressurize the RCS. Closer examination of Rlz 139 (SV_Frac=1.54) shows the 

first valve failure occurred early with a flow area of 0.62 when the RCS pressurization rate was 

very high. The stuck-open SV contributed to a level swell in the pressurizer that limited the 

energy removal from through the open SV. The reduced energy flow from the RCS resulted in a 

second SV failure (flow area of 0.92), which coincidently had a failure after 1 cycle. 

Consequently, the total SV flow was equal to 1.54 (i.e., 0.62 + 0.92). The subsequent RCS 

depressurization promoted conditions that would lead to an early containment failure (i.e., an 

early complete release of the hydrogen from the RCS prior to the first ignition source). As 
4-29

expected, Rlz 139 had an early containment failure after the hot leg rupture. The source terms 

for 5 of these 6 realizations were above the mean and median release, which is consistent with 

an early containment failure.20 The remaining realization is consistent with a late containment 

failure magnitude iodine release where the second SV failure occurred late. 

Figure 4-21 Scatterplot of iodine release fraction versus SV_Frac 

 

20 The large SV failure flow area and dual pathways in Rlz 139 resulted in an unexpected reverse flow pattern from 

the PRT to the pressurizer. Consequently, the revaporized iodine was retained in the pressurizer. This limited the 

magnitude of the revaporization source term from the PRT the environment as previously described with Figure 4-22. 

Two of the three of the SV_Frac between 1.05 and 1.1 had larger iodine source terms >0.1, which is consistent with a 

large revaporization release from the PRT. 
4-30

Figure 4-22 Radionuclide mass in the pressurizer relief tank for Rlz 133 

Figure 4-23 Radionuclide mass in the pressurizer relief tank for Rlz 225 
4-31

priSVcycles 

The probability that a pressurizer SV fails to close is based on a per demand failure probability 

variable named priSVcycles, which was implemented in the MELCOR model as the number of 

cycles the SVs experienced. Figure 4-24 is the scatterplot of the iodine release fraction versus 

priSVcycles. The maximum number of SV cycles is 74 cycles before there was a RPV breach. 

The dense clustering of realizations between 71 to 74 cycles indicates normally operating SVs 

until RPV breach (i.e., hot leg failure). If the pressurizer SVs operate without any failures (e.g., 

the black circles in Figure 4-16), then the containment only fails late. A late containment failure 

generally correspond to lower releases, as shown in Figure 4-24. Most of the realizations with 

61 to 74 SV cycles resulted in a late containment failure (i.e., the red triangles in Figure 4-16). 

These realizations have lower iodine releases (i.e., <0.12, see Figure 4-24). Most of the 

realizations with 46 to 60 SV cycles also resulted in a late containment failure (i.e., the yellow 

triangles in Figure 4-16). All but two of the realizations from the 46 to 60 cycle group had iodine 

releases less than 0.13. 

As discussed in Section 4.2.1.1, the realizations with 1 to 45 SV cycles included both early and 

late containment failures. The results from Figure 4-24 show the highest iodine release occurs 

between 10 to 42 SV cycles. However, there were also many very low releases within this 

grouping. Consequently, the correlation of the priSVcycles is less definitive within this range. 

Other factors such as the SV failure area (SV_Frac variable) and containment failure pressure 

(Rupture variable) combine with the number of SV cycles to impact containment failure timing 

and the magnitude of the iodine release. 

Figure 4-24 Scatterplot of iodine release fraction versus priSVcycles 

Rupture 

The Rupture parameter represents the failure pressure of the containment. The containment 

failure is simulated as an abrupt opening of a 3 ft2 hole in the dome. Figure 4-25 is the 

scatterplot of the iodine release fraction versus rupture pressure. This plot shows a very slight 
4-32

downward sloping trend, which is supportive of the -0.17 regression coefficient on rupture in the 

rank regression. A higher containment failure pressure has the potential to withstand the 

pressurization from early hydrogen burns. A higher containment failure pressure will also delay 

a late over-pressurization from ex-vessel steam and CCI gas generation versus a lower 

containment pressure. A delay to the containment rupture allows more time for aerosol settling 

and less cumulative effect from the long-term iodine revaporization within 72 hours. 

Consequently, the trend of higher containment failure pressure lowering iodine release is 

evident on Figure 4-25. 

In contrast to the overall trend, some of the low containment failure pressure realizations include 

a small iodine release and some of the high containment failure pressure realizations include a 

large iodine release. Examples of a higher iodine release from a high containment failure 

pressure often results from a high airborne iodine concentration at containment failure (see 

Figure 4-20). A near vertical trend is most prevalent with an early containment failure but also 

occurs with late containment failures. As discussed in Section 4.2.1.1 (i.e., see Figure 4-15), all 

the early containment failures occurred following a hydrogen burn. In contrast, all the late 

containment failures occurred due to a gradual over-pressurization. Due to the variable nature 

of the hydrogen burn propagation, duration, and frequency, the influence of the containment 

failure pressure on the early containment failure and usually higher iodine releases was more 

uncertain. In contrast, the late containment failures always benefitted from a higher 

containment pressure. Since there were more late containment failures, the slight downward 

trend in iodine release as a function of containment failure pressure (and later containment 

failure) is reasonable. (The presentation in Figure 4-25 does not separate the early versus late 

containment failures.) 

Figure 4-25 Scatterplot of iodine release fraction versus rupture 
4-33

EU_melt_T 

This parameter represents the temperature at which the eutectic formed from UO2 and ZrO2

melts. This parameter is input into MELCOR as a eutectic sensitivity coefficient (SC1132) and a 

melting temperature. By including the temperature of the eutectic reaction between ZrO2 and 

UO2 as a sampled parameter, the fuel failure characteristics and debris behavior are affected. 

Figure 4-26 is the scatterplot of the iodine release fraction versus the eutectic melt temperature. 

The points are distributed with an upward sloping trend. This is also reflected in the regression 

table with the 0.13 regression coefficient on EU_melt_T in the rank regression. 

As discussed previously with the high ranked parameters affecting the iodine release, the timing 

of containment failure is an important factor. However, the amount of hydrogen production does 

not have a significant correlation on the timing of containment failure (see Section 4.2.1.1 and 

Figure 4-18). Figure 4-26 shows a weakly correlated upward slope in iodine release versus the 

eutectic melting temperature. However, all the high sampled eutectic temperatures (>2575 K) 

had iodine releases below 0.14. Consequently, the results do not suggest a meaningful 

correlation. 

A few of the lowest sampled eutectic melting temperatures fell below the Zircaloy breakout 

temperature (2350 K), which may impact the in-vessel hydrogen production and the in-vessel 

iodine release from fuel. All these cases had a fractional iodine release to the environment that 

was less than 0.1. Closer examination of the radionuclide release from selected realization 

results show that nearly all the iodine within the fuel matrix is released during the in-vessel 

accident progression (i.e., including before and after hot leg failure). Any iodine remaining in the 

fuel matrix will be released during the ex-vessel core-concrete interactions. Since the total 

hydrogen production did not impact early versus late containment failure (i.e., see 

Section 4.2.1.1 and Figure 4-18), the impact of a low eutectic melting temperature to promote 

late containment failure and a lower iodine release to the environment was ruled out. The other 

remaining possible impacts from a low eutectic melting temperature could be the timing of the 

iodine release relative to the containment failure and the magnitude of the revaporization, which 

is dependent on other factors (i.e., SV failure timing and flow area). Therefore, the magnitude of 

the iodine release to the environment was not judged to be significantly impacted by the eutectic 

temperature. 
4-34

Figure 4-26 Scatterplot of iodine release fraction versus eutectic melt 

temperature 

 Cesium Release Fraction to Environment (with no random ignition) 

The magnitude and timing of the cesium release is an important radionuclide to characterize the 

potential severity of the long-term health effects. The magnitude of the cesium release can be 

very important for latent effects. Consequently, the following regression evaluations paid 

particular attention to factors that influenced large releases of cesium. 

4.2.1.3.1 Cesium Release History 

The environmental release fraction of all chemical forms of cesium (percentage of initial cesium 

inventory) at 72 hours is a primary figure of merit for the analysis. Figure 4-27 shows the time 

dependent release fractions for the successful realizations, as well as the calculated mean, 

median, and 5th and 95th percentiles. Although the mean and median are reasonably well fit 

between the 5th and 95th percentiles, there is a significant difference in the release times for all 

curves. 

The split in the start of the cesium release to the environment coincides with the containment 

rupture timing (see Figure 4-15). The early iodine releases result from the early containment 

rupture following a hydrogen burn whereas the second grouping occurs from a late 

over-pressurization failure due to ex-vessel steam production and CCI gas generation. As 

discussed previously in Section 4.2.1.1, there is a time gap following the containment becoming 

oxygen deficient (i.e., the end of combustion due to a lack of oxygen) to the time required for 

steam generation from evaporating water and non-condensable gas generation to 

over-pressurize the containment. The variation in the late over-pressure timings is influenced 

by the containment rupture pressure and the amount of hydrogen gas that was burned. The 

variation in the containment failure timing directly affected the start of the cesium release. 
4-35

However, the variation in the magnitude of the cesium release is a more complex function of 

other parameters that will be discussed in the following subsections. 

The bifurcation in containment failure timings led to a wide spread between the calculated 

median and the 95th percentile curves. Most realizations resulted in a late containment rupture; 

however, the median realization is characteristic of a late containment failure while the mean 

curve has contributions from both the early and late containment failure results. All statistical 

measure curves show increasing trends from the following containment failure. The median 

trend has the flattest slope, which is characteristic of a late containment failure release. 

 

The median release of cesium is relatively low at 72 hours and reflective of the late containment 

failure response. The majority of the late containment failure realizations result in a relatively 

low cesium release (i.e., 0.025 or less). The mean release includes both early and late 

containment failure results. The mean result lies above the median and is rising slightly faster 

than the median result at 72 hours. This trend will be discussed further in the Section 4.2.1.3.2 

and has to do with late revaporization of cesium from the PRT. 

The overall magnitude of the cesium release to the environment (Figure 4-27) is smaller than 

the iodine release (Figure 4-20). The median cesium release at 72 hours is 0.025 (Figure 4-27) 

whereas the comparable iodine release is 0.075 (Figure 4-20). Similarly, the mean cesium 

release is 0.05 while the comparable iodine release is 0.08. The range of the releases is similar 

(i.e., 0 to 0.2 for both radionuclides except one cesium realization). The cesium release was 

expected to be lower than the iodine release due to the lower volatility of the characteristic 

compounds. The majority of the cesium is in the cesium molybdate compound (i.e., ~80% is 

Cs2MoO4), which has a lower vapor pressure than cesium iodide (i.e., the dominate iodine 

compound, see Figure 4-28). Consequently, the cesium released from the fuel forms aerosols 

more quickly than the iodine compounds, which promotes in-vessel deposition and is more 

difficult to revaporize. These attributes lead to a lower cesium release to the environment than 

iodine. 

Figure 4-29 shows an expanded view of the cesium release fractions over the first 20 hours of 

the simulation. The early containment failure releases start occurring at about 3.9 hours. Most 

early containment failures occur at the initial vessel breach (i.e., almost always hot leg failure) 

and end by approximately 9 hours (see Figure 4-15). However, some of the early containment 

failures following a hydrogen burn occur as late as 18.5 hours. The early releases tend to 

increase to a 1 percent release by 10 hours with a subsequent increase to about 2 to 3 percent 

thereafter. The initial release often includes the cesium in the gap and the initial 

thermally-driven releases until the RPV breach. The subsequent increase in the cesium release 

occurs after the second fuel heatup after the accumulator discharge following the RPV breach. 

A late phase release occurs in many realizations due to revaporization of cesium that was 

initially retained in the PRT (e.g., see Figure 4-22 and Figure 4-23). The magnitude of the 

revaporization from the PRT is a strong function timing of the failed pressurizer SV and the open 

area. If the pressurizer SV sticks open early, then more of the released radionuclides are 

transported to the PRT, which contributes to larger revaporization releases. A second source of 

revaporization releases is from hot gases from the ex-vessel debris flowing through the failed 

lower head and out the failed hot leg failure. The impact of the late-phase revaporization 

release is greatest when it occurs after the containment failure, which allows direct leakage to 

the environment without time to re-deposit. 
4-36

Finally, Figure 4-30 summarizes the cumulative probability of a cesium release to the 

environment as a function of time. Initially, there is a small variation in the release to the 

environment. However, there is a large variability of the release at 72 hours, which reflects the 

impact of early and late containment failure as well as late revaporization sources. 

Figure 4-27 Cesium release fractions over 72 hours with mean, median, 

5th and 95th percentiles 
4-37

Figure 4-28 Comparison of Iodine and Cesium Compound Vapor 

Pressures 

Figure 4-29 Expanded view of the cesium release fractions over the first 

20 hours of the simulation 
4-38

Figure 4-30 Cumulative probabilities for the fraction of cesium release for 

selected time intervals 

4.2.1.3.2 Regression Tables and Scatterplots 

Table 4-5 shows the results for regression analysis of the cesium release fraction. In review of 

the regression results for all successful realizations, the SV open fraction was identified as the 

most important parameter by all regression techniques. SV stochastic FTC (priSVcycles) was 

identified as the second most important parameter by all regression techniques, except 

Quadratic, which marginally identified it lower than shape_fact (shape factor). The parameters 

shape_fact and Ajar were identified as the third and fourth most important, respectively, for all 

regression techniques. No other parameters had a significant contribution to cesium release 

fraction uncertainty. 

 
4-39

Table 4-5 Regression analysis of cesium release fraction (with no 

random ignition). 

* highlighted if main contribution larger than 0.02 or conjoint contribution larger than 0.1

SV_Frac 

Figure 4-31 is the scatterplot of the cesium release fraction versus SV_Frac. There is a clearly 

strong positive relationship between SV_Frac and the fraction of cesium released. In particular, 

the limited spread of the points with both low combined fraction of open area and fraction of 

cesium release is indicated by the high SV_Frac contribution measures in each of separate 

regression techniques. The SV_Frac consistently accounts for more than 50 percent of the 

variance in each model. 

In comparison to the iodine release fraction to the environment (see Figure 4-21), the cesium 

release scatter plot shows a stronger correlation between the SV open areas and the total 

cesium release. The cesium release fraction generally increased as the SV_Frac increased. 

Similar to the low fraction of iodine release with SV_Frac<0.28, all of the realizations had a 

cesium release less than 0.03. With an SV_Frac>0.6, the scatter in the results increased. 

However, the overall trend was upward to a cesium release of ~0.12 as the SV_Frac 

approaches 1. As discussed in Section 4.2.1.2.2, the realizations with a SV_Frac>1 generally 

have different behavior because they include multiple SV failures. 

The response of the SV_Frac parameter has interdependence on the number of SV cycles. As 

discussed in Section 4.2.1.1, all the realizations where the pressurizer safety valves operated as 

designed without failures resulted in late containment failures. The progression of events in 

these sequences includes a hot leg failure from high RCS pressure followed by a rapid 

discharge of the accumulators into the degraded core. The early RPV failure occurred when ice 

was available to condense steam. Consequently, the hydrogen could burn in smaller amounts 

as it discharged from the hot leg. The hydrogen (and oxygen) was consumed without building 

to large concentrations that could threaten the containment. All these realizations progressed to 

a late containment failure (i.e., >32 hours, see Figure 4-16), which results in a delayed start of 

the cesium release. The late containment cesium releases were generally lower than the 

realizations with an early containment failure (see discussion in Section 4.2.1.2.1). 

Consequently, all the realization with SV_Frac=0 include a late containment failure and 

correlate with smaller releases. 

Final R2

Input R2

 contr. SRRC Si Ti Si Ti Si Ti

SV_frac 0.66 0.72 0.82 0.93 0.57 0.84 0.62 0.74 0.546 0.135

priSVcycles 0.02 -0.16 0.02 0.09 0.12 0.32 0.21 0.33 0.073 0.105

shape_fact 0.01 0.10 0.03 0.05 0.02 0.11 0.03 0.04 0.017 0.033

ajar 0.01 -0.10 0.01 0.03 0.00 0.06 0.01 0.02 0.006 0.022

EU_melt_T 0.01 0.09 --- --- 0.00 0.02 0.00 0.00 0.003 0.006

rupture 0.00 -0.04 0.00 0.03 0.00 0.01 0.00 0.00 0.001 0.010

Seal_Open_A --- --- 0.01 0.01 0.00 0.00 0.00 0.01 0.001 0.004

Seal_Fail_Dp --- --- --- --- 0.00 0.01 0.00 0.01 0.001 0.005

burn_dir --- --- --- --- --- --- 0.00 0.00 0.001 0.000Conjoint Contribution

0.71 0.68 0.88 0.75

Rank Regression Quadratic Recursive Partitioning MARS

Main 

Contribution
4-40

The SV_Frac realizations with a failure area less than 0.25 also had small source terms. The 

smaller leakage area behaved more closely to a normally operating pressurizer SV. 

Consequently, the SV_Frac<0.25 generally resulted in late containment failure and a smaller 

cesium release at 72 hours. 

The SV_Frac realizations with valve failure areas between ~0.25 to 1 resulted in both early and 

late containment failure. These cases spanned the full spectrum of cesium releases. As 

discussed in Sections 4.2.1.1 and 4.2.1.2.2, Realizations 133 and 225 are representative of 

early and late containment failure, which generally correlates with larger and smaller source 

terms, respectively. The two realizations had very similar sampled parameters except the 

number of cycles until the pressurizer SV failed. In particular, both realizations had similar SV 

failure areas (i.e., ~0.6). However, the pressurizer SV stuck failed after 3 cycles in Rlz 133 

versus 69 cycles in Rlz 225. An early and large pressurizer SV failure will depressurize the 

RCS and transfer more hydrogen into the lower containment prior to the RPV breach. This 

often resulted in more energetic burns that failed the containment at the RPV breach (i.e., the 

timing of the first active ignition source). The realizations within this SV failure area range filled 

the spectrum of the results but were also dependent on the number of SV cycles before failure. 

Perhaps the most significant factor influencing the cesium release for the realizations with a 

SV_Frac between ~0.25 to 1 is the impact of the revaporization from the PRT. Prior to a RPV 

breach, the cesium release pathway from the fuel to the containment is through the pressurizer 

SV. The PRT initially retains cesium radionuclide releases in the PRT water. However, the 

PRT rupture disk opens after ~25 SV cycles shortly after a non-trivial SV failure (i.e. fails to 

close with SV_Frac>0.25). An early SV failure with a large failure area can contribute to more 

cesium transport and capture in the PRT. While the PRT pool can be effective in the early 

retention of cesium, the decay heat from the captured radionuclides can evaporate the water. 

Following pool dryout, the radionuclides plate out on the PRT walls. The subsequent heat-up 

and revaporization of radionuclides including cesium from the PRT dryout impacts the 

environmental release if the containment is failed. The SV_Frac realizations with valve failure 

areas between ~0.25 to 1 had variable effects from the PRT dryout due timing of the valve 

failure. For example, Rlz 133 with an early SV failure (i.e., 3 cycles) and 0.64 fractional failure 

area captured 183 kg of volatile radionuclide aerosols in the PRT pool. The revaporization of 

radionuclides in the PRT started at ~12 hours (i.e., just prior to containment failure, see 

Figure 4-22).21 In contrast, Rlz 255 with a late SV failure (i.e., 69 cycles) and 0.56 fractional 

failure area only captured 31 kg of volatile radionuclide aerosols and did not start to revaporize 

cesium until ~60 hours (Figure 4-23). Consequently, both the timing of SV failure and the failure 

area are important. 

The realizations with a combined total SV_Frac greater than 1 resulted in moderate cesium 

releases that were above the median release. There were six realizations with a SV_Frac 

greater than 1 that ranged in total failure flow area from 1.05 to 1.54. Since the SV_Frac is 

larger than 1, two or more pressurizer safety valves failed prior to a RPV breach. These 

realizations progressed to multiple valve failures because the initial valve failure did not 

 

21 Note the various compounds of cesium are revaporized according to their vapor pressure as the PRT heats (see 

vapor pressures on cesium compounds in Figure 4-28). The cesium hydroxide (CsOH) and cesium iodide (CsI) 

compounds release first, which is followed by cesium molybdate (Cs2MoO4). Most of the cesium is in the cesium 

molybdate compound. Consequently, this is the last compound to revaporize and requires extremely high decay heat 

(i.e., a result of an early SV failure like Rlz 133). 
4-41

depressurize the RCS. Closer examination of Rlz 139 (SV_Frac=1.54) shows the first valve 

failure occurred early with a flow area of 0.62 when the RCS pressurization rate was very high. 

The stuck-open SV contributed to a level swell in the pressurizer that limited the energy removal 

through the open SV. The reduced energy flow from the RCS resulted in a second SV failure 

(flow area of 0.92), which coincidently had a failure after 1 cycle. Consequently, the total SV 

flow was equal to 1.54 (i.e., 0.62 + 0.92). The subsequent RCS depressurization promoted 

conditions that would lead to an early containment failure (i.e., a nearly complete release of the 

hydrogen from the RCS prior to the first ignition source). Rlz 139 had an early containment 

failure after the hot leg rupture. Three of the source terms for realizations were well above the 

median release, which is consistent with an early containment failure.22 

Finally, the impact of SV_Frac on the revaporization from the PRT for the cesium release is 

more important than for iodine. This is evident on the cesium SV_Frac scatterplot (Figure 4-31) 

versus the iodine SV_Frac scatterplot (Figure 4-21). The simulated chemical forms of iodine 

are cesium iodide and elemental iodine. Elemental iodine is always a gas. The predominant 

form of cesium is cesium molybdate. As shown in Figure 4-28, cesium molybdate has a much 

lower vapor pressure than cesium iodide. If SV_Frac was 0 or small, then there was (a) an 

early hot leg failure with a larger amount of the fission products released through the hot leg 

failure location and (b) late containment failure. There was inadequate decay heat from the 

fission products captured in the PRT to reach the relatively high cesium molybdate vaporization 

temperature. Consequently, any released cesium generally settled in the containment at low 

values of SV_Frac. As the magnitude of the SV_Frac increased, the potential for early 

containment failure and revaporization of cesium molybdate increased. This is evident by the 

increase in cesium release in Figure 4-31. Due to the higher volatility of cesium iodine, it can 

revaporize more easily at lower temperatures. This could happen from the cesium iodine 

captured in the vessel or PRT. Consequently, the revaporization of iodine compounds is less 

sensitive to having a very large amount of captured fission products in the PRT and occurred at 

a wide range of SV_Frac values (i.e., compare Figure 4-21 and Figure 4-31). Nevertheless, 

both cesium and iodine have considerable scatter for SV_Frac >0.3 due to the inability of 

SV_Frac to adequately characterize both the timing and flow area to the PRT (i.e., see the 

discussion of Rlz 133 and 255 above). 

 

22 The large SV failure flow area in Rlz 139 resulted in an unexpected reverse flow pattern from the PRT to 

pressurizer. This limited the magnitude of the revaporization source term from the PRT as previously described with 

Figure 4-22. Two of the three of the SV_Frac between 1.05 and 1.1 had larger iodine source terms >0.1, which is 

consistent with a large revaporization release from the PRT. 
4-42

Figure 4-31 Scatterplot of cesium release fraction versus SV_Frac 

Figure 4-32 Radionuclide mass in the pressurizer relief tank for Rlz 133 
4-43

Figure 4-33 Radionuclide mass in the pressurizer relief tank for Rlz 225 

priSVcycles 

Figure 4-34 is the scatterplot of the cesium release fraction versus priSVcycles. The plot has a 

mild negative slope with a high density of points with a low fraction of cesium released and high 

combined cycles to failure. This supports the negative regression coefficient for priSVcycles in 

the rank regression results. 

The maximum number of SV cycles is 74 cycles before there was a RPV breach. The dense 

clustering of realizations between 71 to 74 cycles indicates normally operating SVs until RPV 

breach (i.e., hot leg failure). If the pressurizer SVs operate without any failures (e.g., the black 

circles in Figure 4-16), then the containment only fails late. All the normally operating SV 

realizations had low cesium releases, as shown in Figure 4-34. The low environmental release 

with late containment failure is consistent with more time for aerosol settling and no significant 

revaporization releases prior to 72 hours. 

Similarly, all of the realizations with 61 to 74 SV cycles also resulted in early containment failure 

(i.e., the red triangles in Figure 4-16). Most of these realizations have lower cesium releases 

(i.e., all but 3 are <0.1, see Figure 4-34). 

As previously shown in Figure 4-16, most of the realizations with 46 to 60 SV cycles also 

resulted in a late containment failure (i.e., the yellow triangles). Most of the cesium releases 
4-44

were low but there was more scatter. All but three of the realizations from the 46 to 60 cycle 

group had cesium releases less than 0.13. 

As discussed in Section 4.2.1.1, the realizations with 1 to 45 SV cycles included both early and 

late containment failures. The results from Figure 4-34 show the many low cesium releases 

within this group (i.e., a trend of many cases with a cesium release <0.05). However, there 

were also many very high releases within this grouping (e.g., a group of >0.1 between 30 to 40 

cycles). Consequently, the correlation of the SV_Frac is less definitive within this range. Other 

factors such as the SV failure area and containment failure pressure combine with the number 

of SV cycles to impact containment failure timing and the magnitude of the cesium release. 

Figure 4-34 Scatterplot of cesium release fraction versus priSVcycles 

Dynamic Shape Factor 

The dynamic shape factor (shape_fac) parameter accounts for increased drag force for 

non-spherical aerosol particles. A perfect sphere has a dynamic shape factor of 1. The shape 

factor is identified by all regression techniques as the third most important parameter in 

explaining uncertainty in the cesium release, with minimal conjoint influence. Within MELCOR, 

the dynamic shape factor influences the three deposition processes: gravitational settling, 

Brownian diffusion, and thermophoresis. A higher sampling of the dynamic shape factor yields 

an increased aerodynamic drag, which allows aerosols to remain airborne longer and available 

for release. The various cesium compounds transport as aerosols in the containment (i.e., with 

the possible exception of initially revaporized cesium near high temperature regions by the 

ex-vessel debris and inside the PRT). Therefore, an increase in the shape factor was expected 

to increase the magnitude of the environmental releases. 

Figure 4-35 shows the scatterplot of the cesium release fraction versus the shape factor. A 

slightly positive regression coefficient was calculated. The correlation was judged relatively 

weak as evidenced by a significant number of large cesium releases (i.e., >0.1) for 
4-45

shape_fac<1.5 and small cesium releases (i.e., <0.03) for shape_fac>2.5. While the shape_fac 

affected the deposition rate within the containment, it had no effect on the timing of the 

containment failure. In particular, most early containment failures occurred at the time of the 

RPV breach. The containment fully depressurized following the rupture failure with low aerosol 

retention (i.e., little impact from variable settling time). 

Any increase in the shape factor also appears to have a minimal impact on the late containment 

failure releases. Small cesium releases (i.e., <0.05), which are consistent with late containment 

failure, occurred at all values of the shape factor. A portion of the late cesium release comes 

revaporization of cesium in the PRT. The revaporization of the cesium usually occurs after 

containment failure and has low retention before release through the rupture failure location. 

In summary, the aerodynamic shape factor had a small effect on the magnitude of the cesium 

release. Any cesium released through the RPV breach with an early containment failure did not 

benefit from long settling times before release to the environment. Similarly, late revaporization 

releases after a late containment failure also did not benefit from long settling times before 

release to the environment. In contrast to the large containment failure leakage response in 

Sequoyah following the containment rupture, the large concrete reinforced Surry containment 

had a relatively small leakage rate. The Surry containment slowly depressurized over 

many hours, which showed an important impact of the environmental release versus variations 

in the settling rates. Consequently, the Surry UA results [17] showed a greater impact by 

variations in aerosol shape factor whereas this Sequoyah analysis did not. 

Figure 4-35 Scatterplot of cesium release fraction versus shape factor 

Ajar 

The Ajar parameter represents the non-dimensional opening area of the lower ice condenser 

doors after they fully opened (i.e., exceeded the maximum differential pressure for opening). 

The ice condenser doors will stick at the AJAR fraction thereafter. The AJAR parameter 
4-46

addresses the uncertainty in the damage of the lower door hinges once they have opened the 

maximum range. 

Figure 4-36 is the scatterplot of the cesium release fraction versus Ajar. This plot does not 

indicate any particular relationship between the Ajar parameter and the cesium parameter. The 

density of points is uniform with respect to the horizontal axis, so no particular values of Ajar 

account for higher or lower fractions of the cesium release. It was hypothesized that the Ajar 

parameter would impact the lower compartment steam concentration by forming a natural 

circulation pattern between the lower compartment and the ice compartment. 

The typical results show the lower ice compartment doors operated normally until hot leg failure 

(e.g., see Figure 4-37). Events such as the PRT rupture disk opening did not fully open the 

lower doors. Consequently, there was no impact of the Ajar parameter prior to hot leg failure. 

The hot leg failure was the first RPV breach in nearly all of the calculations. If there was an 

early containment failure, it usually occurred immediately following the hot leg failure. 

Consequently, the value of Ajar would not affect the conditions of an early containment failure at 

hot leg failure. If there was no early containment failure, then the containment became oxygen 

deficient due to the steam generation from evaporating water and the non-condensable gas 

generation from the CCI as described in Section 4.2.1.1. There was adequate flow area for the 

slow pressurization via the ice compartment bypass routes even if Ajar was close to the 

minimum area. Consequently, the value of Ajar was not important. The low correlation of Ajar 

is evident by the uniform scatter at low cesium releases in Figure 4-36. 

The most important impact of the Ajar area would be the conditions following hot leg breach but 

prior to an oxygen deficient condition. There were only a few realizations that failed due to a 

hydrogen combustion that was not coincident with hot leg failure (see Figure 4-15). However, 

the majority of the realizations went through this time period to a late containment failure. 

Figure 4-36 does not show any correlation of Ajar with the low cesium releases associated with 

late containment failure. Consequently, it was concluded that Ajar did not impact the cesium 

release. 

Figure 4-36 Scatterplot of cesium release fraction versus Ajar 
4-47

Figure 4-37 Lower door percentage open for Rlz 133 

 Hydrogen Production (with no random ignition) 

The in-vessel hydrogen production in the Sequoyah model is primarily due to oxidation 

reactions of the Zircaloy fuel cladding, although there is also hydrogen produced by oxidation of 

stainless steel structures. The cladding oxidation will continue until unoxidized Zircaloy interior 

to the oxide shell melts, weakens the oxide layer, and relocates. The oxidation can further 

continue where the Zircaloy freezes after draining until the fuel itself reaches the melting 

temperature and collapses into a debris bed. The release of hydrogen is also an indicator for 

radionuclide releases as these occur during the fuel damage progression. The amount of 

hydrogen produced in-vessel along with the ex-vessel production of hydrogen and carbon 

monoxide during ex-vessel CCI are also the primary influences on whether deflagration and 

detonation conditions are reached in containment. A large burn can rupture the containment 

which starts the fission product release to environment. 

The 100% oxidation of the Zircaloy cladding around the fuel in the Sequoyah core generates 

1022 kg of hydrogen. In a rapidly progressing STSBO, the total hydrogen generation from 

Zircaloy and stainless steel oxidation is only 40% to 60% of this maximum value. In the STSBO 

realizations, approximately 85% of the total in-vessel hydrogen was from the Zircaloy oxidation 

while approximately 15% was from stainless steel oxidation. In addition to the in-vessel 

hydrogen generation, the ex-vessel CCI also generates hydrogen as well as other 

non-condensable gases (see Figure 4-38). The predominate gas generated from the ablation of 

the Sequoyah limestone concrete in the reactor cavity is carbon monoxide. The total carbon 

monoxide gas generation through 72 hours is ~30,000 kg. The CCI gas generation, along with 

evaporation of water from the hot CCI gases and radionuclide decay heat, will cause late 
4-48

containment failure (i.e., if not already failed). While not as reactive as hydrogen, carbon 

monoxide is also combustible. 

In addition to the large carbon monoxide gas production, other gases are also produced. For 

example, the ex-vessel hydrogen generation is about twice the magnitude of the in-vessel 

hydrogen production (e.g., 1173 kg in Figure 4-38). The carbon dioxide and steam gas 

generation are also comparable to the ex-vessel hydrogen generation over this time frame. 

Consequently, there is an ample source of hydrogen from in-vessel and ex-vessel processes. 

As discussed in Section 4.2.1.1, the most important factors for hydrogen combustion are (a) an 

adequate oxygen concentration (i.e., >5%), (b) limited steam (i.e., <55%), and (c) an ignition 

source (i.e., hot jet from the hot leg or ex-vessel debris). Once the ex-vessel gas generation is 

established, the partial pressure of the oxygen drops below the necessary value for combustion 

(i.e., ~18 hr). Between the timing of the hydrogen addition to the containment (i.e., the PRT 

rupture disk opening or hot leg failure) and oxygen-inerting, most realizations had significant 

time periods where combustion is not possible (i.e., no ignition source, inadequate hydrogen, or 

steam-inerted). See Section 4.3 for detailed description of the inert conditions following hot leg 

failure. 

In summary, the gas generation has three impacts on the accident progression. First, in-vessel 

and ex-vessel oxidation of Zircaloy and steel creates combustible gases that may lead to a burn 

that fails the containment. Second, the ex-vessel gas generation will eventually cause an 

over-pressurization failure if a combustion event does not fail the containment. The large 

ex-vessel gas production also helps vent airborne radionuclides following containment failure. 

Finally, the large ex-vessel gas production and steam generation from evaporating water will 

render the containment oxygen deficient as the oxygen partial pressure becomes small (i.e., 

<5%) compared to the total containment pressure. The uncertainty analysis only investigated 

the dependency of the uncertainty parameters on the total in-vessel hydrogen production. This 

is appropriate because nearly all the early containment failures occurred at hot leg failure, which 

was a direct result of only the in-vessel hydrogen production. The subsequent ex-vessel 

hydrogen gas sources were extremely large across all realizations. Since a focus of the 

Sequoyah UA is to investigate factors that lead to an early containment failure, the study did not 

investigate other uncertain parameters in the ex-vessel non-condensable gas production. 
4-49

Figure 4-38 Typical ex-vessel gas production (Rlz 133) 

4.2.1.4.1 Hydrogen Production History 

The total amount of hydrogen produced in the vessel through 72 hours is a figure of merit for the 

analysis. Figure 4-39 shows the time-dependent, in-vessel hydrogen production for the 

successful realizations, as well as the calculated mean, median, and 5th and 95th percentiles. As 

shown by the 5th and 95th percentiles, which exclude rare outliers, the total in-vessel hydrogen 

production is between about 400 and 600 kg. A few high and low outliers are analyzed in the 

single realizations section (i.e., see Section 4.3). The timing of the onset of hydrogen 

production is essentially the same for the mean, median, 95th, and 5th percentile curves. The 

hydrogen production begins around 2.5 hours and almost every realization has stopped 

hydrogen production by about 7 hours. Figure 4-40 illustrates the cumulative probabilities for 

the quantity of hydrogen produced for selected time intervals through 72 hours. Figure 4-41 

shows hydrogen CDFs by the hour for the first 20 hours of simulation and shows a more 

pronounced difference over time (converging to the CDFs in Figure 4-40). 
4-50

Figure 4-39 Total hydrogen production over 72 hours with mean, median, 

5th and 95th percentiles 

Figure 4-40 Cumulative probabilities for the quantity of hydrogen 

produced for selected time intervals to 72 hours 
4-51

Figure 4-41 Cumulative probabilities for the quantity of hydrogen 

produced for selected time intervals to 20 hours 

4.2.1.4.2 Regression Tables and Scatterplots 

The regression results are presented in Table 4-6. The SV open fraction (SV_Frac) is the most 

important parameter by all regression techniques, except Recursive Partitioning, which 

identified it as second most important behind eutectic melt temperature (EU_melt_T). The 

eutectic melt temperature was the second most important parameter with a main contribution 

slightly lower than SV_Frac. SV stochastic FTC (priSVcycles) was identified as the third most 

important parameter by all regression techniques. No other parameters had a significant 

contribution to the results. 

Table 4-6 Regression analysis of hydrogen production at 72 hours (with 

no random ignition). 

* highlighted if main contribution larger than 0.02 or conjoint contribution larger than 0.1 

Final R2

Input R2

 contr. SRRC Si Ti Si Ti Si Ti

SV_frac 0.27 -0.45 0.46 0.68 0.15 0.50 0.33 0.54 0.208 0.181

EU_melt_T 0.13 0.36 0.30 0.30 0.28 0.47 0.31 0.30 0.177 0.055

priSVcycles 0.01 0.12 0.01 0.24 0.14 0.38 0.15 0.37 0.056 0.151

Seal_Fail_Dp --- --- 0.01 0.01 0.01 0.07 0.00 0.00 0.006 0.018

Seal_Open_A --- --- --- --- 0.01 0.03 0.00 0.00 0.002 0.008

burn_dir --- --- --- --- 0.00 0.01 0.00 0.01 0.001 0.004

rupture --- --- --- --- 0.00 0.02 0.00 0.00 0.001 0.006

shape_fact --- --- --- --- 0.00 0.04 0.00 0.00 0.001 0.012

ajar --- --- --- --- 0.00 0.01 0.00 0.01 0.000 0.003Conjoint Contribution

0.41 0.51 0.86 0.60

Rank Regression Quadratic Recursive Partitioning MARS

Main 

Contribution
4-52

SV_Frac 

Figure 4-42 is the scatterplot of the in-vessel hydrogen production versus SV_Frac. The high 

density of points on either end of the scatter plot suggests a negative relationship between 

combined fraction of open area and hydrogen production, as reflected in the regression 

coefficient for the rank regression results. 

As discussed in Section 4.2.1.1, a SV_Frac value of zero corresponds to no pressurizer SV 

failures. Consequently, the RCS remained at high pressure in all the realizations where the 

pressurizer safety valves operated normally until hot leg failure. In fact, almost all the 

realizations with SV_Frac <0.284 resulted in late containment failure. These cases remained at 

a higher RCS pressure prior to hot leg failure. Initially, oxidation is more efficient at high 

pressure because more steam is available (i.e., high density at high pressure). However, the 

high RCS pressure also leads to an early hot leg failure and complete accumulator discharge 

which interrupts the in-vessel hydrogen production as shown in Figure 4-19.23 Since hot leg 

failure happens earlier prior to substantial core degradation, the intact core reheats and fills with 

steam at low pressure. The second hydrogen generation phase following hot leg failure 

increased the total hydrogen generation from the late containment failure values shown in 

Figure 4-19 (i.e., 225 to 450 kg) to the total hydrogen generation values in Figure 4-18 

(i.e., 450 kg to 625 kg). 

In contrast, the hydrogen production largely ceased following the RPV breach in realizations 

with an early containment failure (i.e., generally attributed to realizations with SV_Frac>0.424). If 

a large SV_Frac occurred early, then the initial RPV breach was delayed due to low RCS 

pressure, which resulted in more inventory loss, more core degradation, and possibly some 

accumulator discharge prior to the RPV breach. Following RPV breach, the core fully degraded 

with little additional hydrogen generation. 

In summary, the in-vessel hydrogen generation rate is generally higher at SV_Frac<0.4 and 

lower at SV>0.4. Almost all the realizations with a failure area less than 0.4 had hydrogen 

generation rates above the median. The larger valves of SV_Frac (i.e., >0.4) generally had 

hydrogen production rates below the median value. As SV_Frac increases from 0.4 to 1, there 

is a downward trend in hydrogen production. However, there is considerable scatter in the 

results in Figure 4-42. The variation in these realizations is due to timing of the pressurizer SV 

failure and eutectic melting temperature, which is discussed in the next section. In particular, 

the grouping of realizations with low hydrogen production at SV_Frac=0 had very low eutectic 

melting temperatures that limited in-vessel hydrogen production. 

Finally the realizations with a combined SV_Frac value greater than 1 had four realizations 

below the median and two above. There did not seem to be a strong pattern attributable to just 

the SV_Frac for these realizations. All these realizations had multiple valve failures that would 

depressurize the RCS prior to start of core damage. Consequently, the RCS was at low 

pressure, which is consistent with the low hydrogen production in 4 of the realizations. 

 

23 Almost all the cases with SV_Frac<0.4 resulted in a late containment (see Figure 4-45). Consequently, the 

realizations in Figure 4-19 with late containment failure and lower hydrogen generation prior to the RPV breach are 

being used as a surrogate for SV_Frac<0.4, which results in lower hydrogen generation prior to the RPV breach. 24 All the cases with an early containment failure had a SV_Frac>0.4 (see Figure 4-45). Consequently, the 

realizations in Figure 4-18 with early containment failure and a lower total in-vessel hydrogen generation are being 

used as a surrogate SV_Frac>0.4. 
4-53

Figure 4-42 Scatterplot of in-vessel hydrogen production versus SV 

72 hour post-calculated open fraction 

Effective Eutectic Melt Temperature 

Figure 4-43 shows the scatterplot of in-vessel hydrogen production versus the eutectic melt 

temperature. The positive sloping behavior in the scatter plot confirms the positive rank 

regression coefficient for EU_melt_T in the regression table. The variation in the EU_melt_T 

uncertainty parameter was expected to impact the in-vessel hydrogen. The fuel collapse criteria 

can occur by various mechanisms including the failure of the supporting structure, thinning or 

time-dependent thermal weakening of the oxide shell, and reaching the fuel melting 

temperature. A higher eutectic melting temperature failure generally results in higher peak fuel 

temperatures prior to collapse. The hydrogen generation rate is much higher from intact fuel 

rods due to the high surface areas and open hydraulic configuration. Consequently, if the fuel 

rods remain in an intact configuration longer due to a higher melting temperature, then there is 

potential for more hydrogen production. A higher eutectic melting temperature also increases 

the porosity of the debris, which enhances late phase oxidation. The scatter is attributed to the 

variation in the timing of the pressurizer SV failure and the SV failure area fraction. 

A few of the lowest sampled eutectic melting temperatures fell below the Zircaloy breakout 

temperature (2350 K), which impacted the in-vessel hydrogen production. In these realizations, 

the fuel collapsed before the molten Zircaloy behind the oxide shell is released. Consequently, 

the fuel slumps without oxidizing molten Zircaloy portion of the cladding. The resultant debris 

geometry blocks steam flow for oxidation. These cases had the lowest hydrogen generation 

rates and explain the low outlying realizations previously discussed with SV_Frac. 
4-54

Figure 4-43 Scatterplot of in-vessel hydrogen production versus eutectic 

melt temperature input values 

priSVcycles 

Figure 4-44 shows the scatterplot of in-vessel hydrogen production versus the priSVcycles. The 

high density of points with hydrogen production above 400 kg with more than 70 combined 

cycles to failure explains the small but positive regression coefficient for priSVcycles in the rank 

regression results. 

The maximum number of SV cycles is 74 cycles before there was a RPV breach. The dense 

clustering of realizations between 71 to 74 cycles indicates normally operating SVs until RPV 

breach (i.e., hot leg failure). The priSVcycles parameter complements the SV_Frac parameter, 

which showed normally operating valves when SV_Frac=0. The values of these parameters 

indicate the RCS is at high pressure during the initial core degradation, which enhances 

hydrogen production. Consequently, the in-vessel hydrogen generation rate is generally higher 

with more priSVcycles. Similar to the comments on the SV_Frac parameter, the variation in 

these realizations is due to eutectic melting temperature. In particular, the grouping of 

realizations with low hydrogen production at priSVcycles>71 had very low eutectic melting 

temperatures that limited in-vessel hydrogen production. 

As the priSVcycles decreases from 71 to 1, the in-vessel hydrogen production decreases from 

primarily above the median to primarily below the median. Below 50 cycles, there is clustering 

of realizations below the median. The variations are caused by the variations in the pressurizer 

SV failure area (i.e., SV_Frac) and the eutectic melting temperature. Although the priSVcycles 

had a lower correlation than the SV_Frac, the two parameters are highly interrelated. 
4-55

Figure 4-44 Scatterplot of in-vessel hydrogen production versus SV 

cycles to failure values 

In summary, the magnitude of the in-vessel hydrogen production is influenced by conditions that 

keep the RCS pressure high during fuel degradation and increase the fuel temperature before 

slumping. These attributes coincide with small values of pressurizer SV failure area, high 

values of eutectic melting temperature, and a large number of the SV cycles. The density of the 

steam is higher at high RCS pressure, which enhances hydrogen production. Furthermore, the 

rate of hydrogen production is greatest at high fuel temperatures, which is more likely with a 

high eutectic melting temperature. The scatter plots show trends of each of these factors. 

However, the impact of the in-vessel hydrogen production on timing of containment failure is not 

correlated (i.e., see discussion in Section 4.2.1.1 and Figure 4-18). In particular, there is ample 

hydrogen to fail the containment across the full range of hydrogen production results. 

Consequently, the total in-vessel hydrogen production had a surprisingly small impact on the 

containment failure timing and the source term. 

 Containment Rupture Timing (with no random ignition) 

Rupture timing is another figure of merit investigated. Rupture is defined as the point at which 

the specified containment pressure is exceeded and a 3 ft2 hole opens. To determine 

parameters important to rupture, a regression analysis was performed using the rupture time as 

a metric. 

4.2.1.5.1 Regression Tables and Scatterplots 

Regression analyses presented in Table 4-7 show the SV open area fraction (SV_Frac) is the 

top parameter for three of the four regression techniques. The SV_Frac parameter was 

identified as the largest main contributor to timing uncertainty for three of the four regression 

techniques. With the Rank Regression technique, SV_Frac was identified second to rupture. 

Rupture was the second most important parameter with three of the four techniques, with Rank 
4-56

Regression showing rupture as most important. The priSVcycles parameter was the third top 

parameter, with a considerably lower main contribution than the top two parameters. No other 

parameters had a significant contribution to rupture timing uncertainty, nor are there any other 

parameters that were expected to have a significant contribution. 

Table 4-7 Regression analysis of rupture time for all realizations (with 

no random ignition). 

* highlighted if main contribution larger than 0.02 or conjoint contribution larger than 0.1 

SV_Frac 

Figure 4-45 is the scatterplot of rupture time (in seconds) versus SV_Frac. The two clusters of 

high density points account for the strong negative sloping relationship between the fraction of 

open area and the combined fraction of open area. The early and late timing of the containment 

rupture is described in Section 4.2.1.1. However, this presentation clearly shows an early 

containment failure (i.e., the lower group of points) only occurs when the SV_Frac>0.32. 

Furthermore, a containment rupture following the hot leg failure (i.e., the lowest line of points) 

only occurs when the SV_Frac>0.44. In the early containment failures with larger SV failures 

areas, the RCS depressurized and vented hydrogen to the containment prior to the hot leg 

rupture. When the hot leg ruptured and created an ignition source, the hydrogen from the RCS 

was already distributed throughout the containment. The resulting burn propagated throughout 

the containment and into the dome, which caused a containment failure. 

In contrast to the early containment timings, the late containment failures generally occurred 

when more hydrogen was retained in the RCS prior to the hot leg failure. When the pressurizer 

SVs operated normally (i.e., SV_Frac=0), the burn at RPV breach was moderate and the 

containment ultimately failed by over-pressurization from ex-vessel gas generation (i.e., steam 

and CCI gases). Similarly, the containment always failed late if SV_Frac<0.3. A smaller SV 

failure area is less effective at depressurizing the RCS and transferring hydrogen throughout the 

containment. Between a SV_Frac of 0.3 and 1, there were both early and late containment 

failures. As discussed in Section 4.2.1.1 and with the priSVcycles parameter, the timing of the 

valve failure contributed to both results. An early valve failure would allow more hydrogen to be 

vented into the containment whereas a late valve failure vented less hydrogen. 

With respect to the strong dependence of containment rupture timing on the combined 

fractionally open position of the 3 pressurizer SVs (SV_Frac), it is important to note that an SV 

is modeled as fractionally open position only when it has failed to close in the model. Whether or 

not an SV fails to close is dictated by the number of successful cycles it must complete before 

failing to close which is a sampled number specified in each realization. If the sampled number 

Final R2

Input R2

 contr. SRRC Si Ti Si Ti Si Ti

SV_frac 0.09 -0.35 0.40 0.63 0.30 0.67 0.40 0.63 0.170 0.164

rupture 0.26 0.51 0.20 0.23 0.09 0.43 0.23 0.22 0.131 0.093

priSVcycles 0.00 -0.08 0.07 0.25 0.04 0.23 0.10 0.28 0.027 0.104

ajar 0.04 0.20 0.01 0.01 0.01 0.09 0.02 0.07 0.013 0.028

EU_melt_T 0.01 -0.12 0.02 0.04 0.02 0.21 0.02 0.02 0.011 0.054

Seal_Fail_Dp 0.01 -0.08 0.01 0.08 0.01 0.14 0.00 0.01 0.005 0.045

shape_fact --- --- --- --- 0.01 0.06 0.00 0.00 0.002 0.013

burn_dir --- --- 0.00 0.04 --- --- 0.00 0.00 0.000 0.005

Seal_Open_A --- --- --- --- 0.00 0.02 0.00 0.01 0.000 0.006Conjoint Contribution

0.40 0.44 0.79 0.45

Rank Regression Quadratic Recursive Partitioning MARS

Main 

Contribution
4-57

for a valve is large, the valve will not fail. The time at which an SV fractionally open position 

occurs is also dictated by the sampled number of successful cycles it must complete before 

failing to close. Therefore, the importance of SV_Frac to containment rupture timing is not 

separable from the combined number of cycles experienced by the pressurizer SVs 

(priSVcycles), i.e., with respect to containment rupture timing, the influence of SV_Frac cannot 

be considered independently of the influence of priSVcycles. 

Figure 4-45 Scatterplot of rupture time versus the combined open fraction 

of the pressurizer SV system 

Rupture 

Figure 4-46 is the scatterplot of rupture time (in seconds) versus rupture (i.e., the containment 

failure pressure). Although neither high rupture time nor low rupture time can be attributed to 

specific values of the rupture parameter, there is a distinct positive trend among the high rupture 

time cluster of points. There are two interesting results from the scatter plot. First, the early 

containment failure timing shows no significant dependency on the rupture pressure. The early 

containment results spanned the entire range of rupture pressures. In addition, there did not 

seem to be any dependency on the rupture pressure for the containment failures that occurred a 

few hours after the initial RPV breach. If the hydrogen was adequately distributed, then there 

was enough pressurization from the burn to fail the containment at any rupture pressure. 

The second key point evident on Figure 4-46 is the dependency of the rupture pressure on the 

late containment failure. Unlike the fast pressurizations from the combustion events that lead to 

an early containment failure, the late containment failure occurred due to a slow pressurization. 

The scatter plot shows an upward trend as the containment rupture occurs later with a higher 

rupture pressure. The parameters that led to the separation of the two groups of late failures 

(i.e., the lower trend line versus the main trend line) were not identified from this parameter or 

the other two high ranking parameters. 
4-58

Figure 4-46 Scatterplot of rupture time versus rupture 

priSVcycles 

Figure 4-47 is the scatterplot of rupture time (in seconds) versus priSVcycles. The vertical 

cluster of points around 70 combined cycles to failure and the horizontal cluster of points with 

rupture time below 0.5-E5 seconds accounts for the negative regression coefficient for 

priSVcycles in the rank regression results. The correlation of this parameter is better described 

in conjunction with containment rupture time versus RPV breach time as a function of the 

number of SV cycles, which was done in Section 4.2.1.1 (i.e., also see Table 4-4). 
4-59

Figure 4-47 Scatterplot of rupture time versus priSVcycles 

In summary, the rupture timing resulted in two groups (i.e., early and late containment failure). 

A large number of SV cycles primarily resulted in late containment failures. Similarly, a late 

containment failure could be correlated to a small or zero SV failure area. The SVs in these two 

groups functioned properly for second and third longest times. The other realizations with fewer 

SV cycles (1 to 45) or larger SV failure areas (>0.3) resulted in both early and late containment 

failures. There were more complicated factors that contributed to whether there was an early or 

late containment failure. However, an early containment failure only occurred if the SV failure 

area was >0.3. 

4.2.2 Uncertainty Evaluations with Random Ignition 

A second batch of MELCOR runs were completed for the ‘with random ignition’ feature turned 

on. The random ignition realizations introduce a "spark" of one second duration somewhere in 

containment every half hour. This modeling assumption recognizes the potential for random 

ignition sources. Sandia used a high performance computing cluster to execute a Monte Carlo 

simulation with 600 MELCOR runs, of which 432 were successful for the STSBO scenario with 

random ignition. The reasons for the failed runs are listed in Table 4-8. Some code failures are 

expected as the uncertainty parameter permutations create computationally difficult conditions 

that have not been previously addressed. Unfortunately, two conditions occurred that were not 

expected. First, the Sandia High Performance Cluster had an unexpected failure that affected 

56 realizations. Second, the uncertainty algorithm that generated the pressurizer safety valve 

cycle failure unexpectedly included 32 realizations that were initialized with an immediate safety 

valve failure. The effected realizations were identified and removed from the sample set. The 

error was not identified until all the calculations were completed. 
4-60

Table 4-8 Realizations that were unsuccessful with random ignition 

assumed to occur. 

# Failed Reason

Code run time 

1 Ex-vessel cavity surface heat loss error

30 Volume error in the COR Package

6 Bad debris temperature in the core lower head 

subroutine calculations

1 An error in the EDF file that specifies the 

uncertain parameters.

18

Executive detected abort signal (i.e., typically a 

failure to converge in the control volume 

hydrodynamics package)

23 Convergence error in an ex-vessel cavity 

routine (CCMLTR)

1 Convergence error in an ex-vessel cavity 

routine (CCSVLR)

Other (hardware/input) 

56 Sandia High Performance Cluster Failure 

32 Failed pressurizer SV at time = 0 sec 

 General MELCOR results for the with random ignition scenario 

Similar to the non-random ignition results presented in Section 4.2.1.1, the MELCOR results for 

the cases with random ignition were configured to show the containment rupture timing as a 

function of the timing to the first RPV breach (i.e., see Figure 4-48 through Figure 4-50). If the 

realization had containment failure immediately following a RPV breach, then the result would 

appear on “coincident” line. In the non-random realizations, the RPV breach line identifies the 

earliest possible timing for an active ignition source (i.e., either hot leg failure or RPV lower head 

failure). In contrast to the non-random realizations, the random cases include ignition sources 

prior to the initial RPV breach.25 Consequently, the initial RPV breach does not represent a 

threshold event for the start of ex-vessel combustions. Nevertheless, the timing of the 

containment rupture and the relative timing from the initial RPV breach identify important 

characteristics for the source term progression. It should be noted that no containment failures 

occurred prior to the initial RPV breach. 

The following bullets summarize the general observations and insights from the realizations with 

random ignition. Sections 4.2.2.2 through 4.2.2.5 will present the statistical evaluation for the 

specific figures of merit. 

 

25 The initial RPV breach was defined as either hot leg failure or vessel failure, whichever came first. In the 

realizations with random ignition, only 21 cases did not have a hot leg failure prior to the vessel breach. A hot leg 

failure usually occurs prior to vessel breach unless there were untypical conditions that include an early collapse of 

the core due to a low eutectic melting temperature of the fuel and an early failure of the pressurizer SV that greatly 

reduced the mechanical stress across the hot leg. 
4-61

• The earliest containment failure occurs at about 3.9 hours, which is approximately the 

same earliest containment failure timing as the non-random ignition scenario. Both the 

random and the non-random ignition containment failures occurred coincidental to the 

initial RPV breach. An early SV failure with a large failure flow area that depressurizes 

the RCS were previously identified as attributes that lead to an early containment failure 

(see Section 4.2.1.1). The earliest containment failure in the random ignition realizations 

occurred with a SV stuck open in the grouping of 16 to 30 SV cycles (see Figure 4-49) 

with a fractional failure area of 0.7. Both of these parameters (i.e., number of cycles to 

failure and the failure flow area) are somewhat different than the earliest non-random 

ignition containment failure (i.e., the SV failed on the 2nd cycle with a 0.98 open area in 

Rlz 338). However, Figure 4-16 and Figure 4-45 show a relatively broad range of cycles 

and flow areas that led to early containment failure in the non-random realizations, 

respectively. Consequently, this realization has consistent attributes for an early 

containment failure as the early containment failures in the non-random realizations. 

• Perhaps the most significant finding was that only 3 random ignition realizations resulted 

in an early containment failure at the time of the initial RPV breach (i.e., see only 3 

realizations on the initial RPV breach line in Figure 4-48). This was significantly different 

from the non-random ignition realizations (see Figure 4-15). The key attributes that 

contributed to early containment failure at the time of the initial RPV breach in the nonrandom ignition were an early pressurizer SV failure that substantially depressurized the 

RCS prior to the initial RPV breach. These attributes allowed hydrogen to be distributed 

throughout the containment prior to the first ignition source (i.e., typically hot leg failure). 

However, almost all random ignition realizations (i.e., all but 3) did not allow a significant 

build-up of hydrogen prior to the initial RPV breach. Consequently, the random ignitions 

prior to the first active ignition source diminished the amount of hydrogen available to 

burn at the initial RPV failure. 

• Unlike the non-random ignition realizations with an early containment failure, most 

realizations that failed the containment due to a hydrogen burn occurred hours after the 

initial RPV breach (see Figure 4-48). There is a less distinct separation between the set 

of realizations with direct failure due to combustion versus an over-pressurization. 

Similar to non-random conclusions, none of early containment failure cases had more 

than 60 SV cycles. The median SV cycle count and failure area for this grouping are 21 

and 0.73, respectively. These attributes are consistent with an early containment failure 

in the non-random set of realizations. The random ignition burns mitigated the build-up 

of a large hydrogen burn at the initial RPV breach. However, the subsequent 

combustion events resulted in some early containment failures but far fewer than the 

random ignition realizations (i.e., 15% versus 25%, see Figure 4-15 versus Figure 4-48). 

• There was one realization that resulted in a late containment failure by a hydrogen burn. 

Rlz 12 had a burn in the dome at 49 hr near the minimum oxygen concentration for 

combustion (i.e., just above 5%).26 This realization was an in-vessel production outlier 

where the eutectic melting temperature was less than the molten Zircaloy temperature 

(see discussion in Section 4.2.2.4.2, Figure 4-68). It had very low in-vessel hydrogen 

 

26 A hand calculation shows the maximum possible oxygen mole fraction at the time of the late combustion is only 

5.3% if no oxygen was consumed due to combustion. Most realizations were inert (i.e., oxygen mole fraction below 

5%) at >30 hours. 
4-62

generation, which resulted in very little oxygen consumption from early combustions. A 

random ignition in the dome at 49 hours caused a late containment failure by 

combustion. As evidenced by the graph, this was very unlikely. 

• Similar to the non-random ignition results, there is second distinct set of realizations 

showing later containment failure from 32 to 55 hours. The late containment failures 

occur following the containment over-pressurization due to ex-vessel water evaporation 

and gas generation from ex-vessel core-concrete interaction (CCI) (i.e., see the 

realizations in Figure 4-48 in the upper right-hand side). 

• The gap in containment failures between 22 and 33 hours is the transition from 

containment ruptures due to combustion to containment over-pressurization due to gas 

generation. This gap is smaller than non-random ignition realizations and contains two 

outliers27 whereas there were no outliers in the non-random realizations. The inclusion 

of random ignition burns diminished the importance of the ignition source at the initial 

RPV breach while providing more variability. Similar to the non-random ignition 

realizations, the steam evaporation and non-condensable gas production from the 

ex-vessel debris pressurized the containment. The resultant gas concentration from the 

early combustions and the gas production left the containment oxygen deficient after 

about 22 hours. This is slightly later than non-random ignition realizations (i.e., generally 

oxygen deficient after ~15 hr).28

• Four realizations progressed to 72 hours without containment failure versus only three in 

the non-random ignition results. Relative to the total number of completed realizations, 

the results showed that nearly all cases resulted in containment failure within the 

simulation duration (i.e., 72 hours). The attributes of these four realizations were not 

easily identifiable as late containment failure cases. In particular, the number of 

pressurizer SV cycles ranged from 12 to 38 and the SV failure area fractions ranged 

from 0.25 to 0.83. However, all four cases had high containment failure pressures 

(i.e., 66 to 73 psia). In these cases, the random burns occurred without failing the 

containment. 

• As discussed previously, most of the early containment failures in the random ignition 

realizations occurred several hours after the initial RPV breach versus at the timing of 

the initial RPV breach in the non-random ignition realizations. However, both scenarios 

had a tight timeframe of the initial RPV breach timing (~3.5 to 4.5 hours) for the early 

containment failure realizations that did not occur on the coincident line. 

• Figure 4-49 shows a similar correlation of the number pressurizer SV cycles to the 

containment rupture timing as the equivalent figure for the non-random ignition 

realizations (i.e., see discussion of Figure 4-16 in Section 4.2.1.1). If the pressurizer 

SVs operate without any failures (i.e., the black circles), then the containment only fails 

late. Similarly, all the realizations with 61 to 74 combined cycles (i.e., red triangles on 

Figure 4-49) resulted in a late containment failure. Most of the realizations with 46 to 60 

 

27 There are two outliers to this grouping with containment failure due to a burn at 30 hr and 49 hr. 28 This is an observation from the containment failure results, which showed an end of the containment failures due to 

combustion at 22 hours except for the two outliers noted in Footnote 25. The early containment failure due to 

combustion in the non-random ignition realizations ended at 15 hours. 
4-63

cycles (i.e., yellow triangles on Figure 4-49) resulted in late containment failure. The 

SVs in these two groups functioned properly for second and third longest times. The 

other realizations with fewer SV cycles (1 to 45) prior to a failure resulted in both early 

and late containment failures. There were more complicated factors that contributed to 

whether there was an early or late containment failure. However, the flow area of the 

failed SV or SVs was an important factor (e.g., a small SV failure area could stop further 

SV cycling but otherwise remain at relatively high RCS pressure, which is somewhat like 

a functioning SV realization). 

• Similar to the non-random ignition realizations, Figure 4-50 shows little correlation of the 

in-vessel hydrogen production to either early or late containment failure. The results 

may suggest slightly more in-vessel hydrogen production contributes to a late 

containment failure. This is similar to the findings in the non-random ignition 

realizations. 

Figure 4-48 Containment rupture time versus RPV breach time colored by whether 

the rupture was immediate to a deflagration – with random ignition 
4-64

Figure 4-49 Containment rupture time versus RPV breach time colored by number of 

pressurizer SV cycles – with random ignition 
4-65

Figure 4-50 Time of containment rupture versus in-vessel hydrogen production – 

with random ignition 

 Iodine Release Fraction to Environment (with random ignition) 

The magnitude and timing of the iodine release is an important radionuclide to characterize the 

potential severity of the early health effects. The timing of the iodine release can be very 

important if it occurs during the evacuation phase. Consequently, the following regression 

evaluations paid particular attention to factors that influenced large and early releases of iodine. 

4.2.2.2.1 Iodine Release History 

Figure 4-51 shows the time dependent release fractions for the successful realizations, together 

with the calculated mean, median, 95th and 5th percentiles of all realizations. The iodine release 

fraction is again separated into two distinct release patterns, very similar as those for the nonrandom ignition realizations. The first grouping contains the early containment failure 

realizations while the larger second grouping has the late containment failures. As discussed in 

Section 4.2.2.1, there were fewer early containment failures in the random ignition realizations 

and most early containment failure releases started later than the comparable non-random 

ignition realizations. 

The split in the start of the iodine releases between 15 and 30 hours coincides with the 

containment rupture timing (e.g., Figure 4-48). The early iodine releases result from the early 

containment rupture following a hydrogen burn whereas the second grouping occurs from a late 

over-pressurization failure due to ex-vessel steam and CCI gas generation. As discussed 
4-66

previously in Section 4.2.2.1, there is a time gap following the containment becoming oxygen 

deficient (i.e., the end of combustion due to a lack of oxygen) to the time required for ex-vessel 

gas generation to over-pressurize the containment. The variation in the late over-pressure 

timings is influenced by the containment rupture pressure and the amount of hydrogen gas that 

was burned. The variation in the containment failure timing directly affected the start of the 

iodine release. However, the variation in the magnitude of the iodine release is a more complex 

function of other parameters that will be discussed in the following subsections. 

The bifurcation in containment failure timings led to a wide spread between the calculated 

median and the 95th percentile curves. The median realization is characteristic of a late 

containment failure while the mean curve has contributions from both the early and late 

containment failure results. Most realizations resulted in a late containment rupture. The 

median and 5th percentile curves show larger increasing trends from the late containment 

failure. The increasing iodine release is often characteristic of a chimney effect from the long 

term flow of hot gases from the ex-vessel CCI through the failed RPV lower head and out failed 

hot leg location. The hot gases cause a revaporization of the previously deposited cesium 

iodine in the RPV. It is also characteristic of a revaporization release from the PRT, which is 

discussed in Section 4.2.1.2.2. 

In contrast to the non-random realization iodine horsetail results in Figure 4-20, there are fewer 

early containment failure realizations and a less coherent early iodine release to 0.10 by 20 

hours. As discussed in Section 4.2.2.1, most of the random ignition early containment failures 

occurred hours after the initial RPV breach. This is in contrast to the non-random ignition 

results where most early containment failure realizations occurred at the initial RPV beach. The 

initial iodine releases that occurred at the initial RPV breach in Figure 4-20 were less than 0.02 

but rapidly increased after 10 hours as more iodine was released from the fuel and flowed out 

the containment rupture. The early containment failure occurred after 11 hours in most of the 

random ignition realizations. Consequently, the early containment release trends more closely 

followed the non-random ignition rapid iodine release trends after 10 hours (i.e., a sharp 

increase in iodine release from the initial release of 0.02). 

In general, the late containment releases were similar between the random (Figure 4-48) and 

the non-random (Figure 4-20) realization. The median curve on these graphs is characteristic of 

the late containment failure response and are in close agreement. The random and the nonrandom ignition medians were ~0.06 and ~0.07 at 72 hours, respectively. The 5th and 95th

percentile and the mean values at 72 hours were also relatively similar. 
4-67

Figure 4-51 Iodine release fractions over 72 hours with mean, median, 5th and 95th

percentiles 

4.2.2.2.2 Regression Tables and Scatterplots 

Table 4-9 shows the results for regression analysis of the iodine release fraction. The 

regression results show the main contribution from rupture and priSVcycles is almost equal. 

However, the regressions presented in the table are pretty weak. The recursive partitioning will 

always be high. Thus, while the scatter plots support some of these results, care should be 

taken in drawing any conclusions. Rupture was identified as the most important parameter for 

iodine release fraction by all regression techniques, except MARS, which identified it as second 

most important behind priSVcycles. The priSVcycles parameter was identified as the second 

most important by two regression techniques, with MARS identifying it as the most important, 

and Rank Regression showing it equal to rupture. SV_Frac and Ajar were identified as the third 

and fourth most important, respectively, with Rank Regression and MARS reversing the order 

for these two parameters. The eutectic melt temperature also showed a significant main 

contribution and was the fifth most important parameter. No other parameters had a significant 

contribution to iodine release fraction. 
4-68

Table 4-9 Regression analysis of iodine release fraction at 72 hours 

(with random ignition). 

* highlighted if main contribution larger than 0.02 or conjoint contribution larger than 0.1 

Rupture 

Figure 4-52 shows the scatterplot of the iodine release fraction versus rupture (i.e., the 

containment failure pressure). The densest region of points has a negative sloping trend, which 

supports the negative regression coefficient on rupture for rank regression. The rank regression 

overall, however, is not strong. In contrast, the random ignition realizations had two parameters 

with significantly higher correlations (i.e., SV_Frac=0.182 and priSVcycles=0.110). The nonrandom ignition Rupture parameter had the third highest correlation of 0.031 versus being the 

highest for random ignition. 

The containment rupture pressure had an increased importance in the random ignition 

realizations due to fewer early containment failures. The likelihood of containment failure was 

not extremely sensitive to the magnitude of the Rupture failure pressure during the initial burn at 

the initial RPV breach. If a large burn occurred, then it often exceeded most Rupture failure 

pressure values. In contrast to an early sharp pressurization from a large hydrogen burn, the 

late is containment failure is due to a slow pressurization from ex-vessel gas generation. 

Consequently, the Rupture value has more importance. A higher Rupture value corresponds to 

a later containment failure with more time for aerosol settling and retention, which is generally 

reflected as a lower iodine release in the scatterplot. However, as discussed in Section 4.2.1.2 

(i.e., and also occurred in the non-random ignition realizations), late revaporization of iodine 

from the PRT can greatly impact the benefits of more settling time. The importance of the late 

iodine revaporization is greater in realizations with an early SV failure that captures more iodine 

in the PRT. 

Final R2

Input R2

 contr. SRRC Si Ti Si Ti Si Ti

rupture 0.07 -0.25 0.21 0.22 0.13 0.45 0.22 0.22 0.075 0.082

priSVcycles 0.07 -0.29 0.13 0.25 0.08 0.43 0.38 0.38 0.072 0.103

SV_frac --- --- 0.25 0.46 0.14 0.57 0.00 0.00 0.060 0.132

ajar 0.03 -0.18 0.05 0.11 0.02 0.31 0.22 0.21 0.031 0.081

EU_melt_T 0.01 0.07 0.06 0.08 0.01 0.09 0.18 0.19 0.021 0.025

shape_fact 0.01 0.11 0.03 0.03 0.02 0.11 0.00 0.00 0.009 0.023

Seal_Open_A 0.01 0.09 0.03 0.06 0.00 0.01 0.00 0.00 0.005 0.006

Seal_Fail_Dp --- --- 0.02 0.05 0.00 0.05 0.00 0.00 0.002 0.014

burn_dir --- --- --- --- 0.00 0.02 0.00 0.01 0.000 0.005Conjoint Contribution

0.19 0.28 0.78 0.32

Rank Regression Quadratic Recursive Partitioning MARS

Main 

Contribution
4-69

Figure 4-52 Scatterplot of iodine release fraction versus rupture 

priSVcycles 

Figure 4-53 shows the scatterplot of the iodine release fraction versus priSVcycles. The 

maximum number of SV cycles is 74 cycles before there was a RPV breach. The dense 

clustering of realizations between 71 to 74 cycles indicates normally operating SVs until RPV 

breach (i.e., hot leg failure). If the pressurizer SVs operate without any failures (e.g., the black 

circles in Figure 4-49), then the containment only fails late. A late containment failure generally 

corresponded to lower releases, as shown in Figure 4-53. All of the realizations with 61 to 74 

SV cycles resulted in a late containment failure (see Figure 4-54). The realizations also have 

lower iodine releases (i.e., all but three realizations are <0.10, see Figure 4-53). Most of the 

realizations with 46 to 60 SV cycles also resulted in a late containment failure (i.e., the yellow 

triangles in Figure 4-49). This led to lower iodine releases for most realizations from this group 

of SV cycles (i.e., most realizations are <0.08). 

The realizations with 1 to 45 SV cycles included both early and late containment failures (see 

Figure 4-47). However, as shown in Figure 4-14, the majority of the random ignition realizations 

progressed to a late containment failure. The results from Figure 4-53 show a wide range of 

iodine releases throughout this SV grouping. However, there were also many very low releases 

within this grouping. Consequently, the correlation of the SV_Frac is less definitive within this 

range. Other factors such as the SV failure area and containment failure pressure combine with 

the number of SV cycles to impact containment failure timing and the magnitude of the iodine 

release. 
4-70

Figure 4-53 Scatterplot of iodine release fraction versus priSVcycles 

Figure 4-54 Scatterplot of rupture time versus priSVcycles 

SV_Frac 

Figure 4-55 shows the scatterplot of the iodine release fraction versus SV_Frac. Similar to the 

non-random ignition discussion in Section 4.2.1.2.2, the SV_Frac and priSVcycles parameters 

have interdependent relationships on the fraction of iodine released. 
4-71

The SV_Frac parameter indicates the open fraction of the primary SV system at 72 hours. All 

pressurizer SV cycling ceases following the RPV breach (i.e., <10 hours). Consequently, the 

final SV position represents the actual failure conditions, based on the post-calculated results. If 

there are no SV failures at 72 hours, then the open fraction is 0 (i.e., in the limit as the SV open 

fraction approaches zero, it approaches a normally operating SV), which is also reflected with 

priSVcycles >70. Finally, if one SV experienced cycling failure, then the sampled open fraction 

is used. If multiple SV experience cycling failure, then their open fractions are summed. 

Figure 4-55 shows a relationship between low fractional open areas and low fraction of iodine 

release (i.e., SV_Frac<0.28). All but one of these realizations had an iodine release less than 

0.1. In contrast, the fractional open area above 0.28 corresponds with the entire range of iodine 

release. 

The response of the SV_Frac parameter has interdependence on the number of SV cycles. As 

discussed in Sections 4.2.1.1 and 4.2.2.1, all the realizations where the pressurizer safety 

valves operated as designed without failures resulted in late containment failures. The 

progression of events in these sequences includes a hot leg failure from high RCS pressure 

followed by a rapid discharge of the accumulators into the degraded core. The lower 

containment had long periods where it was steam-inerted or without an active ignition source 

and always progressed to an oxygen deficient condition without early containment failure. All 

these realizations progressed to a late containment failure (i.e., >32 hours, see Figure 4-49), 

which delayed the start of the iodine release. The late containment iodine releases were 

generally lower than the realizations with an early containment failure (see discussion in 

Section 4.2.1.2.1). Consequently, all the realizations with SV_Frac=0 include a late 

containment failure and correlate with smaller releases. 

The SV_Frac realizations with a failure area less than 0.25 also had small source terms. The 

smaller leakage area behaved more closely to a normally operating pressurizer SV. 

Consequently, the small SV_Frac behavior resulted. Furthermore, if the leakage area on the 

first failed valve was too small to prevent further pressurizer SV cycling, then the behavior was 

also similar normally operating SVs and also resulted in late containment failure. 

Similar to the results with priSVcycles between 1 and 45, the SV_Frac results >0.25 spanned 

the full spectrum of large and small releases. These realizations also included both early and 

late containment failures. As discussed in the non-random ignition realization results (see 

Section 4.2.1.2), the variability in iodine revaporization from the PRT increased the magnitude of 

the released iodine. 
4-72

Figure 4-55 Scatterplot of iodine release fraction versus SV_Frac 

Ajar 

The Ajar parameter represents the non-dimensional opening area of the lower ice condenser 

doors after they fully opened (i.e., exceeded the maximum differential pressure for opening). 

The ice condenser doors will stick at the AJAR fraction thereafter. The AJAR parameter 

addresses the uncertainty in the damage of the lower door hinges once they have opened the 

maximum range. It was hypothesized that the Ajar parameter would impact the lower 

compartment steam concentration by forming a natural circulation pattern between the lower 

compartment and the ice chests. It was not a significant parameter in the iodine release for the 

non-random ignition realizations. 

Figure 4-56 shows the scatterplot of the iodine release fraction versus Ajar. This plot does not 

indicate any particular relationship between the Ajar parameter and the iodine release fraction to 

the environment. The density of points is relatively uniform with respect to the horizontal axis, 

so no particular values of Ajar account for higher or lower fractions of iodine. 

The most important impact of the Ajar area would be the conditions following hot leg breach but 

prior to an oxygen deficient condition. If there was an early containment failure in the random 

ignition realizations, then it usually occurred during this time frame. However, the majority of the 

realizations went through this time period to a late containment failure. Figure 4-57 shows a 

slightly higher density of early containment failures with larger values of Ajar and a slightly 

higher density of late containment failures with smaller values of Ajar. Higher releases are 

expected with an early containment failure versus a late containment failure. However, the 

weak impact shown in Figure 4-57 was relatively insignificant on the iodine release in 

Figure 4-56. Consequently, it was concluded that Ajar did not significantly impact the iodine 

release. 
4-73

Figure 4-56 Scatterplot of iodine release fraction versus Ajar 

Figure 4-57 Scatterplot of rupture time versus Ajar 

EU_melt_T 

This parameter represents the temperature at which the eutectic formed from UO2 and ZrO2

melts. By including the temperature of the eutectic reaction between ZrO2 and UO2 as a 

sampled parameter, the fuel failure characteristics and debris behavior are affected. 
4-74

Figure 4-58 is the scatterplot of the iodine release fraction versus the eutectic melt temperature. 

The points are distributed with an upward sloping trend. This is also reflected in the regression 

table with the 0.07 regression coefficient on EU_melt_T in the rank regression. 

As discussed previously with the high ranked parameters affecting the iodine release, the timing 

of containment failure is an important factor. As will be discussed later, the amount of hydrogen 

production does not have a significant correlation on the timing of containment failure (see 

Section 4.2.2.1 and Figure 4-50). Figure 4-58 shows a weakly correlated upward slope in iodine 

release versus the eutectic melting temperature. However, all the high sampled eutectic 

temperatures (>2575 K) had iodine releases below 0.14. Consequently, the results do not 

suggest a meaningful correlation. 

A few of the lowest sampled eutectic melting temperatures fell below the Zircaloy breakout 

temperature (2350 K), which may impact the in-vessel hydrogen production and the in-vessel 

iodine release from fuel. All but one of these realizations had a fractional iodine release to the 

environment that was less than 0.1. Closer examination of the radionuclide release from 

selected realization results show nearly all the iodine within the fuel matrix is released during the 

in-vessel accident progression (i.e., including before and after hot leg failure). Any iodine 

remaining in the fuel matrix will be released during the ex-vessel core-concrete interactions. 

Since the total hydrogen production did not impact early versus late containment failure (i.e., 

see Section 4.2.1.1 and Figure 4-18), the impact of a low eutectic melting temperature to 

promote late containment failure and a lower iodine release to the environment was ruled out. 

The other remaining possible impacts from a low eutectic melting temperature could be the 

timing of the iodine release relative to the containment failure and the magnitude of the 

revaporization, which is dependent on other factors (i.e., SV failure timing and flow area). 

Therefore, the magnitude of the iodine release to the environment was not judged to be 

significantly impacted by the eutectic temperature. 

Figure 4-58 Scatterplot of iodine release fraction versus EU_melt_T 
4-75

 Cesium Release Fraction to Environment (with random ignition) 

The magnitude and timing of the cesium release is an important radionuclide to characterize the 

potential severity of the long-term health effects. The magnitude of the cesium release can be 

very important for latent effects. Consequently, the following regression evaluations paid 

particular attention to factors that influenced large releases of cesium. 

4.2.2.3.1 Cesium Release History 

Figure 4-59 shows the time dependent release fractions for the successful realizations, as well 

as the calculated mean, median, and 5th and 95th percentiles. The cesium release fraction is 

again separated into two distinct release patterns, very similar as those for the non-random 

ignition realizations. The early cesium releases result from the early containment rupture 

following a hydrogen burn whereas the second grouping occurs from a late over-pressurization 

failure due to ex-vessel gas generation. As shown in Figure 4-48, there were fewer early 

containment failures in the random ignition realizations and fewer early containment failures that 

were coincident with the initial RPV breach (i.e., compare to Figure 4-15). 

The bifurcation in containment failure timings led to a wide spread between the calculated 

median and the 95th percentile curves. The 95th percentile curve is characteristic of an early 

containment failure. The median realization is characteristic of a late containment failure while 

the mean curve has contributions from both the early and late containment failure results. Most 

realizations resulted in a late containment rupture. The median and 5th percentile curves show 

larger increasing trends from the late containment failure. The increasing cesium release is at 

72 hours is often characteristic of a chimney effect from the long term flow of hot gases from the 

ex-vessel core-concrete interactions through the failed RPV lower head and out failed hot leg 

location. The hot gases cause a revaporization of the previously deposited cesium iodine in the 

RPV. It is also characteristic of a revaporization release from the PRT, which is discussed in 

Section 4.2.1.2.2. 

In comparison to the non-random ignition iodine horsetail results in Figure 4-27, there are fewer 

early containment failure realizations and a less coherent start of early cesium release. As 

discussed in Section 4.2.2.1, most of the early containment failures occurred hours after the 

initial RPV breach. This is in contrast to the non-random ignition results where most early 

containment failure realizations occurred at the initial RPV beach. The initial cesium releases 

are shown on an expanded scale in Figure 4-60. The initial cesium releases rise to ~0.025 

shortly after containment failure (i.e., all but one realization). The cesium releases remain near 

this level until ~40 hours. Thereafter, some realizations begin to rise due to revaporization of 

cesium from the vessel or the PRT (i.e., see discussion in Section 4.2.2.3.2). 

 

Overall, the lower mobility of the cesium molybdate, which is the majority of the cesium, resulted 

in early releases were smaller than the iodine releases (see Section 4.2.2.2). Cesium 

molybdate generally transports as an aerosol and requires very high temperatures to 

revaporize. The long term cesium release to the environment spanned approximately the same 

range as the iodine (i.e., some realizations to >0.20). However, the mean cesium release is 

only 0.02 (Figure 4-59) versus 0.06 for iodine (Figure 4-51). 

In general, the late containment releases were similar between the random (Figure 4-59) and 

the non-random ignition realizations (Figure 4-27). The median curve on these graphs is 

characteristic of the late containment failure response and are in close agreement. Both the 

random and the non-random ignition medians were ~0.03 at 72 hours, respectively. The 5th 
4-76

percentile and the mean values at 72 hours were also relatively similar. The random ignition 

95th percentile (0.17) was slightly larger than the non-random value at 72 hours (i.e., 0.14). 

However these results were impacted by a small sample of higher release realizations. 

Consequently, although there were differences in the combustion assumptions, the overall 

impact on the cesium releases were minor. 

Figure 4-59 Cesium release fractions over 72 hours with mean, median, 

5th and 95th percentiles 
4-77

Figure 4-60 Expanded view of the cesium release fractions over the first 

20 hours of the simulation 

4.2.2.3.2 Regression Tables and Scatterplots 

SV_Frac is the top parameter identified in Table 4-10, well above the second listed parameter, 

priSVcycles. No other parameters had a significant contribution to the results. 

Table 4-10 Regression analysis of cesium release fraction (with random ignition). 

* highlighted if main contribution larger than 0.02 or conjoint contribution larger than 0.1 

SV_Frac 

Figure 4-61 shows the scatterplot for cesium release fraction versus SV_Frac. This plot is very 

similar to that of SV_Frac versus the fraction of cesium released without random ignition. It 

suggests a strong trend that is reflected in the highly positive regression coefficient on SV_Frac 

in the rank regression results and in the ܵ௜ values in the regression results from the other 

methods. Similar to the non-random ignition discussion in Section 4.2.1.3, the SV_Frac and 

priSVcycles parameters have interdependent relationships on the fraction of cesium released. 

The discussion in Section 4.2.1.3.2 concerning the dependency of the cesium release is also 

applicable for the random ignition results. In particular, a SV_Frac value between 0 and 0.25 

develops conditions for a late containment failure, which generally has low releases. Although 

most of the random ignition realizations had late containment failure, only the realizations with 

SV_Frac >0.25 had early containment failures (see Figure 4-62). A late containment failure 

generally had lower releases whereas an early containment failure generally had larger 

releases. Consequently, the scatter in the cesium release with SV_Frac >0.5 increases 

substantially. As also noted in Section 4.2.1.3.2, a larger SV_Frac can lead to more fission 

product transport to the PRT and therefore more susceptible to revaporization. However, 

cesium is less susceptible to revaporization versus iodine and only occurs at larger values of 

SV_Frac. Consequently, the cesium release resulted in a better correlation with SV_Frac than 

the iodine releases. As discussed in Sections 4.2.1.2 and 4.2.2.2, iodine is more volatile than 

cesium, which led to higher iodine median releases and revaporization under more situations 

(e.g., a wider range of SV_Frac). 

Final R2

Input R2

 contr. SRRC Si Ti Si Ti Si Ti

SV_frac 0.56 0.64 0.70 0.88 0.53 0.87 0.85 0.90 0.499 0.146

priSVcycles 0.02 -0.20 0.02 0.11 0.08 0.25 0.07 0.06 0.035 0.071

shape_fact 0.02 0.15 0.03 0.07 0.02 0.23 0.03 0.08 0.018 0.082

rupture 0.01 -0.06 0.00 0.06 0.01 0.03 0.00 0.01 0.004 0.018

EU_melt_T 0.00 0.05 0.02 0.02 0.00 0.04 0.00 0.00 0.004 0.013

ajar 0.01 -0.10 --- --- 0.00 0.05 0.00 0.00 0.003 0.015

burn_dir 0.00 0.07 0.01 0.05 --- --- 0.00 0.00 0.003 0.008

Seal_Fail_Dp --- --- 0.00 0.01 0.00 0.04 0.00 0.00 0.001 0.012

Seal_Open_A --- --- 0.00 0.02 0.00 0.02 0.00 0.00 0.001 0.010Conjoint Contribution

0.63 0.61 0.89 0.63

Rank Regression Quadratic Recursive Partitioning MARS

Main 

Contribution
4-78

Figure 4-61 Scatterplot of cesium release fraction versus SV_Frac 

 

Figure 4-62 Scatterplot of rupture time versus SV_Frac 

priSVcycles 

Figure 4-63 shows the scatterplot for cesium release fraction versus priSVcycles. The maximum 

number of SV cycles is 74 cycles before there was a RPV breach. The dense clustering of 

realizations around 71 to 74 cycles indicates normally operating SVs until RPV breach (i.e., hot 
4-79

leg failure). If the pressurizer SVs operate without any failures (e.g., the black circles in 

Figure 4-49), then the containment only fails late. A late containment failure generally 

corresponded to lower releases, as shown in Figure 4-63. All of the realizations with 61 to 74 

SV cycles resulted in late containment failure (see Figure 4-54). The 61 to 74 SV cycle 

realizations also have lower cesium releases (i.e., all but two realizations are <0.03, see 

Figure 4-53). Surprisingly, the largest cesium release also occurred in the priSVcycles 61 to 74 

grouping.29 

Most of the realizations with 46 to 60 SV cycles also resulted in a late containment failure 

(i.e., the yellow triangles in Figure 4-49). There is a significant number of realizations with lower 

cesium releases in this group of SV cycles (i.e., cesium release <0.04). However, there is an 

almost comparable number of realizations with higher cesium releases (i.e., >0.04 to >0.20). 

Similarly, there is a trend of gradually increasing cesium releases as priSVcycles decreases 

below 45 SV cycles. The realizations with 1 to 45 SV cycles included both early and late 

containment failures (see Figure 4-47). However, as shown in Figure 4-80, the majority of the 

random ignition realizations progressed to a late containment failure. The scatter of the cesium 

releases in this grouping is similar to the iodine results (Figure 4-53) but less defined than the 

cesium releases in the random ignition realizations (Figure 4-34). In general, the random 

ignition realizations showed a weaker correlation to priSVcycles versus the non-random 

realizations. 

 

29 No random realizations were analyzed in the individual realizations section (Section 4.3). However, the high 

cesium release outlier was particularly confusing because it did not have the key attributes of the Rlz 142, which was 

the maximum non-random ignition cesium release. Rlz 142 had the earliest SV failure (i.e., failed on the first 

opening), a large SV failure area (i.e., 0.97), and a high containment rupture pressure that delayed containment 

failure until the peak of the cesium revaporization from the PRT (74.9 psi). In contrast, the maximum random ignition 

cesium release realization had 68 SV cycles, which is not characteristic of a realization with large cesium 

revaporization potential from the PRT. 
4-80

Figure 4-63 Scatterplot of cesium release fraction versus priSVcycles 

 Hydrogen Production (with random ignition) 

As described in Section 4.2.1.4, the in-vessel hydrogen production in the Sequoyah model is 

primarily due to oxidation reactions of the Zircaloy fuel cladding, although there is also hydrogen 

produced by oxidation of stainless steel structures. The 100% oxidation of the Zircaloy in the 

fuel in the Sequoyah core generates 1022 kg of hydrogen. In both the random and non-random 

ignition realizations, the total hydrogen generation from Zircaloy and stainless steel oxidation is 

only 40% to 60% of this maximum value. The total in-vessel hydrogen generation was 

approximately 85% from the Zircaloy oxidation, while approximately 15% was from stainless 

steel oxidation. In addition to the in-vessel hydrogen generation, the ex-vessel debris and 

concrete interactions also generate hydrogen as well as other non-condensable gases. The 

predominate gas generated from the ablation of the Sequoyah limestone concrete in the reactor 

cavity is carbon monoxide. The total carbon monoxide gas generation through 72 hours is 

~30,000 kg along with evaporation of water from the hot CCI gases will eventually cause late 

containment failure (i.e., if not already failed). While not as reactive as hydrogen, carbon 

monoxide is also combustible. 

The amount of hydrogen produced in-vessel along with the ex-vessel production of hydrogen 

and other gases during CCI are also the primary influences on whether deflagration and 

detonation conditions are reached in containment, which cause sharp pressure increases 

leading to containment rupture and fission product release to environment. Finally, the large 

ex-vessel gas production (i.e., steam production and CCI gases) will render the containment 

oxygen deficient as the oxygen partial pressure becomes small (i.e., <5%) compared to the total 

containment pressure. 

The uncertainty analysis only investigated the dependency of the uncertainty parameters on the 

total in-vessel hydrogen production. Very few random ignition realizations progressed to an 

early containment failure and most did not occur until after RPV failure (i.e., RPV lower head 
4-81

failure occurred ~7 to 9 hours). Consequently, the random ignition realizations had early 

containment failure contributions from both in-vessel and ex-vessel combustible gas 

production.30 In contrast, most of the early containment failures from a hydrogen burn in the 

non-random ignition realizations occurred immediately after hot leg failure and were only 

dependent on the in-vessel hydrogen production. However, for consistency with the nonrandom realizations, the in-vessel hydrogen production was selected as the primary hydrogen 

generation figure of merit. 

4.2.2.4.1 Hydrogen Production History 

Figure 4-64 shows the time dependent hydrogen production for the successful realizations. As 

shown by the 5th and 95th percentile curves, the total production of hydrogen is between 400 kg 

and 600 kg. The in-vessel hydrogen production begins around 2.5 hours and most realizations 

have stopped producing hydrogen by about 7 hours. The hydrogen production for the sequence 

is very similar as the sequence where random ignition is not implemented (i.e., see 

Section 4.2.1.4.1). The mean and median results in Figure 4-64 are relatively close at about 

490 kg and 470 kg, respectively. Figure 4-65 illustrates the cumulative probabilities for the 

quantity of hydrogen produced for selected time intervals through 72 hours. Figure 4-66 shows 

hydrogen CDFs by the hour for the first 20 hours of simulation and shows a more pronounced 

difference over time (converging to the CDFs in Figure 4-65). 

 

30 For example, non-random ignition Rlz 133 reached 200 kg of ex-vessel hydrogen production in the first 3 hours 

after RPV lower head failure and exceeded the in-vessel hydrogen production by 14 hours after RPV failure (i.e., 

442 kg of ex-vessel hydrogen at 22 hours). The total ex-vessel hydrogen generation was 1180 kg at 72 hours. 
4-82

Figure 4-64 Total hydrogen production over 72 hours with mean, median, 

5th and 95th percentiles 
4-83

Figure 4-65 Cumulative probabilities for the quantity of hydrogen 

produced for selected time intervals through 72 hours 
4-84

Figure 4-66 Cumulative probabilities for the quantity of hydrogen 

produced for selected time intervals to 20 hours 

4.2.2.4.2 Regression Tables and Scatterplots 

The review of the regression results for all successful realizations presented in Table 4-11 

shows SV_Frac and eutectic melt as the most important parameters. The SV open area 

fraction was the top parameter with the Rank Regression technique, while eutectic melt 

temperature was the top parameter for all other techniques. The method for calculating the 

main contribution results in these two parameters having an equal main contribution. The third 

most important parameter is priSVcycles. The rank order of the top three parameters is 

identical to the regression rank order in the without random ignition scenario, and the main 

contributions are reasonably similar. No other parameters had a significant contribution to the 

results. 
4-85

Table 4-11 Regression analysis of hydrogen production at 72 hours (with random 

ignition). 

* highlighted if main contribution larger than 0.02 or conjoint contribution larger than 0.1 

SV_Frac 

The SV_Frac parameter indicates the open fraction of the primary SV system at 72 hours. 

Figure 4-67 shows the scatterplot for in-vessel hydrogen production versus SV_Frac. As 

reflected in the rank regression coefficient on SV_Frac, there is a moderate negative trend 

between the clusters of points in the plot. The hydrogen production trends with SV_Frac 

discussed below are very similar to the non-random ignition realizations, which were discussed 

in Section 4.2.1.4.2. 

As discussed in Section 4.2.1.4.2, the in-vessel hydrogen generation rate is generally higher at 

SV_Frac<0.4 and lower at SV>0.4. Almost all the realizations with a failure area less than 0.4 

had hydrogen generation rates above the median. The larger valves of SV_Frac (i.e., >0.4) 

generally had hydrogen production rates below the median value. As SV_Frac increases from 

0.4 to 1, there is a downward trend in hydrogen production. However, there is considerable 

scatter in the results in Figure 4-67. The variation in these realizations is due to timing of the 

pressurizer SV failure and eutectic melting temperature, which are discussed in the next 

sections. In particular, the grouping of realizations with low hydrogen production at SV_Frac=0 

had very low eutectic melting temperatures that limited in-vessel hydrogen production. 

The larger valves of SV_Frac (i.e., >0.4) generally had hydrogen production rates below the 

median value. As SV_Frac increases from 0.4 to 1, there is a downward trend in hydrogen 

production. The depressurization of the RCS occurs faster as the SV failure area gets larger. 

Similar to the previous grouping of cases, the timing of the SV failure will also impact the system 

pressure during the accelerated oxidation phase. 

Final R2

Input R2

 contr. SRRC Si Ti Si Ti Si Ti

SV_frac 0.24 -0.37 0.32 0.56 0.17 0.53 0.27 0.55 0.180 0.201

EU_melt_T 0.15 0.37 0.34 0.35 0.25 0.44 0.28 0.31 0.180 0.061

priSVcycles 0.02 0.19 0.06 0.31 0.11 0.39 0.15 0.46 0.059 0.186

rupture 0.00 -0.06 0.01 0.04 0.01 0.05 0.00 0.00 0.005 0.017

Seal_Fail_Dp --- --- 0.00 0.01 0.01 0.03 0.01 0.00 0.004 0.008

Seal_Open_A --- --- --- --- 0.01 0.03 0.00 0.00 0.002 0.006

burn_dir 0.01 0.09 --- --- 0.00 0.01 0.00 0.00 0.002 0.001

ajar --- --- 0.00 0.02 0.00 0.07 0.00 0.00 0.000 0.024

shape_fact --- --- --- --- 0.00 0.06 0.00 0.00 0.000 0.016Conjoint Contribution

0.42 0.54 0.86 0.61

Rank Regression Quadratic Recursive Partitioning MARS

Main 

Contribution
4-86

Figure 4-67 Scatterplot of in-vessel hydrogen production versus SV 

72 hour post-calculated open fraction 

Eutectic Melt Temperature 

Figure 4-68 shows the scatterplot for in-vessel hydrogen production versus eutectic melt 

temperature. The positive sloping trend in this plot, and particularly the positive sloping trend of 

the densest cluster of points, supports the highly positive regression coefficient for EU_melt_T 

in the rank regression results. The variation in the EU_melt_T uncertainty parameter was 

expected to impact the in-vessel hydrogen. The fuel collapse criteria has many components 

including the failure of the supporting structure, thinning or time-dependent thermal weakening 

of the oxide shell, and reaching the fuel melting temperature, which is a surrogate for the 

eutectic formation. However, a higher eutectic melting temperature failure generally results in 

higher peak fuel temperatures prior to collapse. The hydrogen generation rate is much higher 

from intact fuel rods due to the high surface areas and an open hydraulic configuration. 

Consequently, if the fuel rods remain in an intact configuration longer, then there is potential for 

more hydrogen production. A higher eutectic melting temperature also increases the porosity of 

the debris, which enhances late phase oxidation. The scatter is attributed to the variation in the 

timing of the pressurizer SV failure and the SV failure area fraction. 

A few of the lowest sampled eutectic melting temperatures fell below the Zircaloy breakout 

temperature (2350 K), which impacted the in-vessel hydrogen production. In these realizations, 

the fuel collapsed before the molten Zircaloy behind the oxide shell is released. Consequently, 

the fuel slumps without oxidizing molten Zircaloy portion of the cladding. The resultant debris 

geometry blocks steam flow for late phase oxidation. These cases had the lowest hydrogen 

generation rates and explain the low outlying realizations previously discussed with SV_Frac. 
4-87

Figure 4-68 Scatterplot of in-vessel hydrogen production versus eutectic 

melt temperature input values 

priSVcycles 

Figure 4-69 shows the scatterplot for in-vessel hydrogen production versus priSVcycles. The 

maximum number of SV cycles is 74 cycles before there was a RPV breach. The dense 

clustering of realizations between 71 to 74 cycles indicates normally operating SVs until RPV 

breach (i.e., hot leg failure). The priSVcycles parameter complements the SV_Frac parameter, 

which showed normally operating valves when SV_Frac=0. The values of these parameters 

indicate the RCS is at high pressure during the initial core degradation, which enhances 

hydrogen production. Consequently, the in-vessel hydrogen generation rates are generally 

higher with more priSVcycles. In particular, the grouping of realizations with low hydrogen 

production at priSVcycles>71 had very low eutectic melting temperatures that limited in-vessel 

hydrogen production. 

As the priSVcycles decreases from 71 to 1, the in-vessel hydrogen production decreases from 

primarily above the median to primarily below the median. Below 20 cycles, there are clustering 

of realizations below the median. The variations are caused by the failure area and the eutectic 

temperature. Although the priSVcycles had a lower correlation than the SV_Frac, the two 

parameters are highly interrelated. 
4-88

Figure 4-69 Scatterplot of in-vessel hydrogen production versus 

priSVcycles 

 Rupture Timing (with random ignition) 

Rupture timing, defined as the point at which the specified pressure is exceeded and a 3 ft2 hole 

opens is another figure of merit investigated. The rupture timing is important to the source term 

timing and often the source term magnitude. 

4.2.2.5.1 Regression Tables and Scatterplots 

The regression analysis in Table 4-12 shows rupture (i.e., the containment failure pressure) as 

the top parameter for all regression techniques. The main contribution from rupture was 

considerably higher than SV_Frac, which was the second most important parameter. Burn 

direction and Ajar were the third and fourth parameters, respectively, but had an equal main 

contribution. No other parameters had a significant contribution to rupture time uncertainty, nor 

were there any other parameters that were expected to have a significant contribution that 

didn’t. 
4-89

Table 4-12 Regression analysis of rupture time for all realizations (with 

random ignition). 

* highlighted if main contribution larger than 0.02 or conjoint contribution larger than 0.1 

Rupture 

Figure 4-70 shows the scatterplot of rupture time (in seconds) versus rupture. Although neither 

a late rupture time nor an early rupture time can be attributed to specific values of the rupture 

parameter, there is a distinct positive trend among the high rupture time cluster of points. There 

are two interesting results from the scatter plot. First, the early containment failure timing shows 

no significant dependency on the rupture pressure. The early containment results spanned the 

entire range of rupture pressures. In addition, there did not seem to be any dependency on the 

rupture pressure for the failures that occurred a few hours after the initial RPV breach. If the 

hydrogen was adequately distributed, there was enough pressurization from the burn to fail the 

containment at any rupture pressure. As noted previously in the hydrogen production section 

(i.e., see Footnote 30), many of the random ignition realizations included a significant ex-vessel 

hydrogen source for the early containment failures that occurred after the RPV failure. 

The second key point evident on Figure 4-70 is the dependency of the rupture pressure on the 

late containment failure. Unlike the fast pressurizations from the combustion events that lead to 

an early containment failure, the late containment failure occurred due to a slow pressurization. 

The scatter plot shows an upward trend as the containment rupture occurs later with a higher 

rupture pressure. The parameters that led to the separation of the two groups of late failures 

(i.e., the lower trend line versus the main trend line) were not identified from this parameter or 

the other two high ranking parameters. 

Final R2

Input R2

 contr. SRRC Si Ti Si Ti Si Ti

rupture 0.29 0.54 0.44 0.47 0.24 0.64 0.67 0.76 0.207 0.112

SV_frac --- --- 0.11 0.34 0.12 0.55 0.16 0.25 0.058 0.145

burn_dir 0.02 0.14 0.05 0.10 0.02 0.12 0.06 0.07 0.016 0.034

ajar 0.04 0.22 0.01 0.08 0.02 0.19 0.00 0.00 0.016 0.050

EU_melt_T 0.01 -0.10 0.02 0.12 0.02 0.22 0.00 0.00 0.007 0.063

priSVcycles --- --- 0.01 0.19 0.01 0.02 0.00 0.00 0.002 0.027

Seal_Fail_Dp --- --- --- --- 0.01 0.13 0.00 0.00 0.002 0.029

Seal_Open_A --- --- 0.01 0.07 0.00 0.01 0.00 0.00 0.002 0.011

shape_fact --- --- --- --- 0.00 0.10 0.00 0.00 0.000 0.026Conjoint Contribution

0.36 0.38 0.74 0.29

Rank Regression Quadratic Recursive Partitioning MARS

Main 

Contribution
4-90

Figure 4-70 Scatterplot of rupture time versus rupture 

SV_Frac 

Figure 4-71 shows the scatterplot of rupture time versus SV_Frac. The two clusters of high 

density points account for the strong negative sloping relationship between the containment 

rupture timing and the SV_Frac. The early and late timing of the containment rupture is 

described in Section 4.2.2.1, However, this presentation clearly shows an early containment 

failure (i.e., the lower group of points) only occurs when the SV_Frac>0.32. In the early 

containment failures with larger SV failures areas, the RCS depressurized and vented hydrogen 

to the containment prior to the hot leg rupture. When the hot leg ruptured and created an 

ignition source, the hydrogen from the RCS was already distributed throughout the containment. 

The resulting burn propagated throughout the containment and into the dome, which caused a 

rupture. However, these characteristics did not lead to as many early containment failures in 

the random ignition realizations due to random burns that limited the hydrogen build-up 

throughout the containment (i.e., see Section 4.2.1.5 for the impact on the non-random 

realization rupture timing). 

In contrast to the early containment rupture timings, the late containment failures generally 

occurred when more hydrogen was retained in the RCS prior to the hot leg failure. When the 

pressurizer SVs operated normally (i.e., SV_Frac=0), the burn at RPV breach was moderate 

and the containment ultimately failed by over-pressurization from ex-vessel gas generation. 

Similarly, the containment always failed late if SV_Frac<0.3. A smaller SV failure area is less 

effective at depressurizing the RCS and transferring hydrogen throughout the containment. 

Between a SV_Frac of 0.3 to 1, there were both early and late containment failures. As 

discussed in Section 4.2.2.1 and with the priSVcycles parameter, the timing of the valve failure 

contributed to both results. An early valve failure would allow more hydrogen to be vented into 

the containment whereas a late valve failure vented less. 
4-91

Figure 4-71 Scatterplot of rupture time versus SV_Frac 

Burn Direction 

Figure 4-72 shows the scatterplot of rupture time versus burn direction. The burn direction 

uncertainty parameter is interpreted as an integer. The parameter prescribes the values of 1, 2, 

and 3 to upward, horizontal, and downward ignition. The nominal hydrogen thresholds for those 

directions are 4%, 6.5%, and 9%, respectively. However, the precise hydrogen concentration 

for the parameters is also affected by the local temperature and the local steam concentration. 

The realizations with an upward burn direction allow ignition to occur at lower hydrogen 

concentrations. This can result in less energetic burns. However, it can also result in more 

burns that increases the potential for a burn propagation to other regions. The propagation 

criteria after a burn has started is identical for all values of burn direction. The results are 

relatively uniform and not suggestive of a significant correlation to rupture timing. 31

 

31 The random ignition criteria of 10% hydrogen concentration was used, which is the MELCOR default concentration 

for spontaneous ignition. In addition, all active ignition values used this value. Consequently, this parameter had no 

impact in the uncertainty sampling. 
4-92

Figure 4-72 Scatterplot of rupture time versus burn direction 

Ajar 

The Ajar parameter represents the non-dimensional opening area of the lower ice condenser 

doors after they fully opened (i.e., exceeded the maximum differential pressure for opening). 

The ice condenser doors will stick at the AJAR fraction thereafter. The AJAR parameter 

addresses the uncertainty in the damage of the lower door hinges once they have opened the 

maximum range. 

Figure 4-73 shows the scatterplot of rupture time versus Ajar. The dense cluster of points with 

low values of Ajar and high rupture time supports the positive regression coefficient on Ajar in 

the rank regression results. Additionally, the same region of the input space has the least dense 

collection of low rupture time data points. Thus, for low values of Ajar, the rank regression 

model can attribute variance in rupture time to variation in Ajar, whereas this relationship is not 

apparent for higher values of Ajar. 

It was hypothesized the Ajar parameter would impact the lower compartment steam 

concentration by forming a natural circulation pattern between the lower compartment and the 

ice chests. The results show some trends for the impact of Ajar on the containment failure 

timing. Figure 4-73 shows a trend between a large number of late containment realizations at 

low values of Ajar versus more early containment failures with larger values of Ajar. In addition, 

the timing of the late containment failure is relatively uniform at small values of Ajar while values 

>0.2 show more scatter. These trends are consistent with the expected impact of Ajar. 

 
4-93

Figure 4-73 Scatterplot of rupture time versus Ajar 

4.3 Analysis of Select MELCOR Individual Realizations 

Additional insights related to accident progression can be obtained through investigation of 

single realizations. A set of single realizations listed in Table 4-13 was chosen for investigation 

in greater detail to identify phenomena driving the Cs and I releases to the environment and 

in-vessel hydrogen production. The set was selected from those realizations where early or late 

release was observed or low or high release magnitudes were observed. Note that numbers 2 

and 6, 4 and 10, and 5 and 7 represent the same realization; thus, a total of 10 single 

realizations were investigated. 
4-94

Table 4-13 Single realizations for detailed analysis. 

I.D. Selection criterion Rlz number (Rlz)*

1 The base STSBO UA case Base calculation 

2 The case with earliest containment rupture 338 

3 A case with containment remaining intact at 72 hr 171 

4 The case with the earliest FTC of a pressurizer SV 142 

5 A case without a FTC of a pressurizer SV 469 

6 A case with coincident RPV breach and containment rupture 338 

7 The case with the least in-vessel hydrogen production 469 

8 The case with the most in-vessel hydrogen production 225 

9 The case with the smallest Cs release to the environment 174 

10 The case with the largest Cs release to the environment 142 

11 The case with the earliest RPV breach 551 

12 The case with the latest RPV breach 148 

13 A case without hot leg rupture 77 

14 A case with early RPV breach and early containment rupture not coincident 133 

*Note, all realizations are without random sources of ignition 

Figure 4-74 illustrates a comparison of the select realizations with respect to when the reactor 

vessel is first breached and when the steel containment vessel ruptures. The reactor vessel 

could breach first from a hot leg rupture or from lower head failure. The containment rupture 

could be sudden in response to a large deflagration or eventual as the containment continually 

pressurized from heat produced by fission product decay and from gasses produced through 

core-concrete interactions. Containment failure coincident with hot leg rupture and a large 

deflagration occurred in realizations 338, 77 and 148. In realization 171, containment rupture 

did not occur within the 72 hours modeled in the MELCOR calculation. 
4-95

Figure 4-74 Time of containment rupture versus time of RPV breach for 

selected realizations without random ignition 

The sampled values of the uncertain parameters in the select realizations are presented in 

Table 4-14. Table 4-15 lists the timing of key events, important occurrences and attributes, the 

magnitudes of Cs and I releases, and the amounts of hydrogen produced in-vessel in the 

realizations. 

Table 4-14 Select realizations sampled parameters. 

*

 Direction of propagation from the ignition location: 1 = upward, 2 = lateral, 3 = downward 
4-96

Table 4-15 Selected realization results. 

 * A sudden rise to rupture immediate to a deflagration or a continual increase without rupture immediate to a 

deflagration 

To support an understanding of the differences between selected realizations, comparisons of 

pairs of realizations were made. 

Comparisons between the realization with earliest containment rupture (Rlz 338) and a 

realization with containment remaining intact at 72 hr (Rlz 171) 

Rlz 338 experienced the earliest rupture of the steel containment vessel while Rlz 171 did not 

experience a rupture of the vessel within the 72-hr duration of the calculation. Figure 4-75 and 

Figure 4-76 show the pressure responses of containment in Rlz 338 and Rlz 171, respectively. 

Also shown in these figures is the integral energy produced by deflagrations in the whole of 

containment and in the containment dome. Evidenced is that containment ruptured in Rlz 338 

on an energetic deflagration where over half of the energy in the burn was produced in the 

containment dome. The deflagration was coincident with the rupture of an RCS hot leg. There 

were no deflagrations in Rlz 171 energetic enough to rupture containment and there was no 

involvement of the containment dome in the deflagration that occurred coincident with the hot 

leg rupture in Rlz 171. 
4-97

Figure 4-75 Burn energy and containment pressure for Rlz 338 w/o 

random ignition 

Figure 4-76 Burn energy and containment pressure for Rlz 171 w/o 

random ignition 
4-98

In the calculation set performed without random ignition, it is important to note that ignition was 

disallowed until a hot leg ruptured, the pressurizer surge line ruptured, or core debris relocated 

to the containment floor. In the MELCOR calculations, typically a hot leg rupture occurred first. 

Consequently, the first opportunity for hydrogen to burn was at hot leg rupture. Substantial 

hydrogen may have vented to containment through the pressurizer SVs and a broken PRT burst 

disk before hot leg rupture, but it would not have been allowed to burn until a hot leg ruptured. 

As shown in Figure 4-77, roughly twice the hydrogen was produced by the time of hot leg failure 

in Rlz 338 than in Rlz 171 clearly providing potential for a substantially larger burn in Rlz 338 at 

hot leg rupture. 

The difference in the amount of hydrogen produced up to the time of hot leg failure in Rlzs 338 

and 171 is attributable to a difference in pressurizer SV function. In Rlz 338, the lowest set-point 

valve failed to close in a fully open position (as opposed to failing to close in a partially open 

position) after only 2 successful cycles of operation. In Rlz 171, the lowest set-point valve did 

not experience a failure to close. The venting of the RCS by the stuck-open SV in Rlz 338 was 

conducive of greater hydrogen production leading up to hot leg rupture. Virtually all hydrogen 

produced up to the time of hot leg rupture in Rlz 338 was vented through the stuck-open SV and 

a broken PRT burst disk to the containment as can be seen by comparing Figure 4-77 and 

Figure 4-78. Much of this hydrogen passed through the ice chests to the containment dome. 

Comparatively little hydrogen was vented through the functioning SV in Rlz 171.

Figure 4-77 In-vessel hydrogen production for Rlzs 338 and 171 w/o 

random ignition 
4-99

Figure 4-78 Integral safety valve hydrogen flow for Rlzs 338 and 171 w/o 

random ignition 

For Rlz 338 leading up to hot leg rupture, Figure 4-79, Figure 4-80 and Figure 4-81 show a 

containment primed with hydrogen ready to burn given an ignition source. Figure 4-79 shows 

hydrogen concentration throughout containment being supportive of a burn while Figure 4-80 

shows containment not overly laden with steam so as to prohibit a burn. Figure 4-81 shows the 

conditions in the containment dome in detail relative to supporting a burn. The information in 

Figure 4-81 is relative to a burn being propagated to the dome as opposed to originating in the 

dome, because burns were only allowed in lower containment in the calculations without 

random ignition. The only way a burn could occur in the dome was if a burn propagated there 

from lower containment. The conditions in Figure 4-81 are supportive of a burn when lines are 

solid and not supportive of a burn where the lines are dashed. Inerting gas and fuel lines in the 

figure are reflective of each other, e.g., too much inerting gas for the amount of fuel present is 

indicative in the same way as too little fuel for the amount of inerting gas. 
4-100

Figure 4-79 Hydrogen mole fraction and burn energy for Rlz 338 w/o 

random ignition 

Figure 4-80 Steam mole fraction and burn energy for Rlz 338 w/o random 

ignition 
4-101

Figure 4-81 Conditions in containment dome with respect to burn 

propagation for Rlz 338 w/o random ignition 

For Rlz 171 Figure 4-82 shows, leading up to hot leg rupture, a containment with considerably 

less hydrogen than in Rlz 338. Similar to Figure 4-80 for Rlz 338, Figure 4-83 shows 

containment not overly laden with steam so as to prohibit a burn. Figure 4-84 shows the 

conditions in the containment dome in detail relative to supporting a burn being propagated to 

the dome. A burn occurred in Rlz 171 at hot leg rupture but of considerably less magnitude than 

in Rlz 338 and the burn was insufficient to rupture the steel containment vessel. 
4-102

Figure 4-82 Hydrogen mole fraction and burn energy for Rlz 171 w/o 

random ignition 

Figure 4-83 Steam mole fraction and burn energy for Rlz 171 w/o random 

ignition 
4-103

Figure 4-84 Conditions in containment dome with respect to burn 

propagation for Rlz 171 w/o random ignition 

Following hot leg rupture in Rlz 171 through to the end of the calculation, there is little 

opportunity for large deflagrations to occur. This is because of either a lack of sufficient 

hydrogen or an excess of inerting gasses. A couple of energetic burns occur, as evidenced in 

Figure 4-76, but the burns don’t threaten containment. Figure 4-85 shows how conditions in 

lower containment are almost continually unsupportive of a burn originating in lower 

containment. The existence of an ignition source, i.e., hot gas emanating from a hot leg breach 

or core debris relocated to the containment floor, is included in this Figure 4-85. A large number 

of inconsequential burns occur in the reactor cavity as combustible gasses generated by 

core-concrete interactions are burned off locally. These burns don’t propagate elsewhere 

because of conditions in lower containment being unsupportive. 
4-104

Figure 4-85 Lower containment conditions for Rlz 171 

Comparisons between the case with the earliest FTC of a pressurizer SV (Rlz 142) and a case 

without a FTC of a pressurizer SV (Rlz 469) 

The results of Rlzs 338 and 142 differ markedly in that Rlz 338 ruptured containment at 3.79 hr 

coincident with hot leg rupture and a large deflagration; whereas, Rlz 142 didn’t fail containment 

until 54.5 hr after a continual pressurization. The sampled parameter of the pressure necessary 

to rupture the steel containment vessel was responsible for this difference. The two realizations 

show nearly identical trends through hot leg rupture but the higher containment rupture pressure 

defined in Rlz 142 isn’t reached. This can be seen by comparing Figure 4-86 associated with 

Rlz 142 to Figure 4-75 associated with Rlz 338. 
4-105

Figure 4-86 Burn energy and containment pressure for Rlz 142 w/o 

random ignition 

Comparisons between a case with coincident RPV breach and containment rupture (Rlz 338) 

and a case with early RPV breach and early containment rupture not coincident (Rlz 133) 

Rlz 338, characterized above, suffered a very energetic burn at the time of hot leg rupture that 

ruptured the steel containment vessel. Rlz 133 suffered a very similar burn but, as evidenced in 

Figure 4-87, a higher sampled value of the pressure necessary to rupture the containment 

vessel allowed the vessel to survive the sharp pressure rise resulting from the burn. The strong 

burn at the hot leg breach in Rlz 133 is attributable to a pressurizer SV failing to close after very 

few cycles, the same as described above for Rlz 338. While containment survived the strong 

burn in Rlz 133 at hot leg rupture, a later burn drove the containment pressure over sampled 

rupture pressure and the containment vessel ruptured. Review of Figure 4-88 through 

Figure 4-91 identifies that the later burn originated in the lower annulus (CV 11) and propagated 

to most of lower containment inside the crane wall (CV 9) and then to the containment dome. 

The energy released from burning in the dome contributed most to the overall energy released 

in the burn. Not all of containment participated in the burn, e.g., CV 8 and the reactor cavity, did 

not participate. 
4-106

Figure 4-87 Burn energy and containment pressure for Rlz 133 w/o 

random ignition 

Figure 4-88 Reactor cavity conditions for Rlz 133 w/o random ignition 
4-107

Figure 4-89 Lower containment conditions (CV 8) for Rlz 133 w/o random 

ignition 

Figure 4-90 Lower containment (CV 9) for Rlz 133 w/o random ignition 
4-108

Figure 4-91 Lower annulus (CV 11) for Rlz 133 w/o random ignition 

Comparisons between the case with the least in-vessel hydrogen production (Rlz 469) and the 

case with the most in-vessel hydrogen production (Rlz 225) 

Table 4-14 and Table 4-15 identify general dependencies in the MELCOR calculations of 

in-vessel hydrogen production on the sampled values of the melting temperature of the eutectic 

formed between UO2 and ZrO2 and the combined number of cycles completed by the system of 

3 parallel SVs atop the pressurizer. Higher melting temperatures and larger numbers of cycles 

generally led to greater hydrogen production with melting temperature being the dominant 

influence. Fuel rod collapse was configured to occur in the MELCOR calculations when oxidized 

cladding reached the sampled value of the melting temperature UO2-ZrO2 eutectic. Robust 

oxidation of a fuel rod ends when the rod collapses and lower melting temperatures of the 

eutectic lead to earlier rod collapse. Rlz 469 had a considerably lower eutectic melting 

temperature than Rlz 225. Consequently, fuel collapsed earlier, less oxidation took place, and 

less hydrogen was produced in Rlz 469 relative to Rlz 225. Figure 4-92 illustrates these 

differences. The vertical violet color in the reactor core insets in Figure 4-92 indicate intact fuel 

rods. At 5 hours little fuel remains intact in Rlz 469 and hydrogen production has abated. In 

Rlz 225, contrarily, substantial fuel remains intact at 5 hr and hydrogen production remains 

strong. The Rlz 469 sampled eutectic melting temperatures fell below the Zircaloy breakout 

temperature (2350 K). As shown in Figure 4-43, all the realizations with a sampled eutectic 

melting temperatures below the Zircaloy breakout temperature resulted in the very low in-vessel 

hydrogen generation. Since the fuel collapsed before the molten Zircaloy behind the oxide shell 

is released, molten Zircaloy does not oxidize. The resultant debris geometry is not supportive of 

steam flow for oxidation as shown Figure 4-92. 
4-109

Figure 4-92 In-vessel hydrogen production for RLZs 225 and 469 w/o 

random ignition 

Comparisons between the case with the smallest Cs release to the environment (Rlz 174) and 

the case with the largest Cs release to the environment (Rlz 142) 

Figure 4-93 and Figure 4-94 show the distribution of cesium between the RCS, containment, 

and environment for Rlz 174 and Rlz 142, respectively. These figures differ dramatically for 

reasons associated with successful functioning of the lowest set-point pressurizer SV in Rlz 174 

and the very early failure (first cycle) of the valve to close in Rlz 142. The early stuck-open 

condition of the valve in Rlz 142 resulted in it being open at the onset of core damage. 

Consequently, much of the core inventory of fission products, including Cs as CsOH, CsI, and 

Cs2MoO4, carried to the PRT as the RCS vented through the stuck-open valve. The fission 

products deposited in the water volume of the tank and on the wall of the tank once the water 

boiled away. The heat generation from decay of the deposited fission products was 

considerable, and the PRT heated as a consequence. The heating is evidenced in Figure 4-95. 

The cesium compounds in the PRT vaporized continually in response to the heating, migrated 

from the tank, and aerosolized in the containment atmosphere. When the containment ruptured 

at 54.5 hr, as can be seen in Figure 4-96, the resulting drop in containment pressure aggravated 

the vaporization and the cesium released from the PRT escaped to the environment carried by 

flows seeking the breach in the dome of the containment vessel. The cesium escaping 

containment was considerable (26.3% of the initial core inventory). The fact that PRT wall 

temperature in Figure 4-95 exceeds the melting temperature of steel is a complication not 

accounted for in the MELCOR modeling. The excessive temperature suggests that the integrity 

of the tank would be lost. This temperature would probably reduce somewhat if the outside of 

the PRT were not modeled as adiabatic, i.e., facilitating heat rejection via convection and 

radiation might realistically lower the temperature to a value tolerable by steel. If the tank were 

to lose its integrity, some aerosolization of the fission products that had deposited in it seems 

likely as does the relocation of some fission products to the floor of containment. Neither of 

these redistributions of material are addressed in MELCOR. It is likely, however, that both would 
4-110

reduce the concentration of fission products that caused the failure of the PRT, leading to less 

revaporization and less release to the environment than suggested by Rlz 142. 

Figure 4-93 Cesium distribution for Rlz 174 w/o random ignition 

Figure 4-94 Cesium distribution for Rlz 142 w/o random ignition 
4-111

Figure 4-95 Containment temperature for Rlz 142 w/o random ignition 

Figure 4-96 Burn energy and containment pressure for Rlz 174 w/o 

random ignition 

Figure 4-96 and Figure 4-97 show for Rlz 174, information analogous to the information shown 

in Figure 4-86 and Figure 4-95 and for Rlz 142. Without a stuck-open SV, Rlz 174 exhibits little 

fission product deposition in the PRT, little heating of the PRT, and no release of cesium from 

the PRT at containment rupture. Cesium released from the fuel in-vessel and ex-vessel simply 

deposits and resides in a stable condition in the RCS and containment. 
4-112

Figure 4-97 Containment temperature for Rlz 174 w/o random ignition 

Comparisons between the case with the earliest RPV breach, Rlz 551 and the case with the 

latest RPV breach, Rlz 148 

Realizations 551 and 148 were selected to compare the earliest versus the latest initial RPV 

breach. The specified uncertainty parameters in the two realizations uniquely accelerated and 

delayed the timing to the initial RPV breach, respectively. The key in-vessel uncertainty 

parameter differences and results included the following; 

• Realization 551 

o SV #1 fails after 2 cycles with a fraction area of 0.1579 

o SV #2 cycled normally for 14 times, which was much less than the maximum 

number of cycles until failure (i.e., failure at 137 cycles) 

o The total number of SV cycles for the two SVs was 16 

o The eutectic melting temperature was 2475.5 K 

o The accumulator did not discharge prior to the initial RPV breach 

o The hot leg failed at 3.464 hr 

• Realization 148 

o SV #1 fails after 11 cycles with a fraction area of 0.9867 

o SV #2 did not open 

o The total number of SV cycles was 11 
4-113

o The eutectic melting temperature was 2566.8 K 

o There was no hot leg failure 

o The accumulator fully discharged prior to the initial RPV breach 

o The RPV lower head failed at 9.109 hr 

As noted above, SV #1 in Rlz 551 failed after two cycles with a small flow area. The SV #1 

failure area was not sufficient to immediately depressurize the RPV as noted in Figure 4-98. 

Consequently, SV #2 cycled 14 times before the RPV began to depressurize due to the leakage 

through SV #1. The inventory loss out SV #1 led to the core uncovery but with a minimal RCS 

depressurization (see Figure 4-98 and Figure 4-99). The small SV #1 leakage area created 

optimum conditions for an early hot leg creep rupture. In particular, the small SV leakage area 

promoted high temperature gas flow from the degrading core towards the pressurizer while 

maintaining a relatively high RCS pressure. The hot leg creep index rose rapidly to 1 at 

3.464 hr (see Figure 4-101), which indicates a hot leg failure. Following the rapid 

depressurization from the initial RPV breach of the hot leg, the full inventory of the accumulators 

discharged into the RCS (see Figure 4-100). 

In contrast to the progression of events in Rlz 551, SV #1 cycled 11 times in Rlz 148 and then 

stuck almost fully open (i.e., 0.9867). The RCS depressurized below the accumulator set 

pressure (4.4 MPa) during the initial core uncovery, which started the accumulator water 

injection. The accumulator water flooded the core and cooled the hot leg nozzles (i.e., see the 

level response in Figure 4-99 and the first plateau in the hot leg creep rupture index in 

Figure 4-101). There was a rapid increase in steam production as the hot fuel boiled the 

accumulator water. The resultant steam production increased the RCS pressure, which 

stopped the accumulator injection (see the sharp pressurization at 3.5 hr in Figure 4-98). The 

RCS subsequently depressurized through the failed SV. The subsequent loss of inventory 

through the open SV uncovered the fuel (Figure 4-99) and heated the hot leg nozzle (i.e., see 

the second increase in the hot leg creep index at 4.8 hr in Figure 4-101). The RCS pressure fell 

below the accumulator pressure, which allowed a second accumulator injection. The cycle of 

an accumulator injection, an increase in core cooling, and an increase steam generation 

repeated three more times until the accumulator was empty. The accumulator water injection 

was very effective at interrupting the increase in the creep rupture index, which reached a 

maximum of 0.67 at 6.3 hr. The accumulator water was also effective at slowing the fuel core 

degradation. However, an uncoolable debris plenum formed in the lower bed following the final 

accumulator injection at 7.2 hours. The debris on the RPV lower head slowly heated the RPV 

lower head inner surface after 7.9 hours to above 1500 K (see the black line for the maximum 

lower head temperature in Figure 4-101). The RPV lower head yielded at 9.109 hours with the 

hot leg creep index below the failure threshold. 

In summary, Rlz 551 included conditions that maximized an inventory loss while preventing a 

significant RCS depressurization. The early core uncovery at high pressure led to the earliest 

hot leg creep rupture of all the realizations. Rlz 148 included conditions that allowed a full RCS 

depressurization below the accumulator injection pressure. The resultant intermittent 

accumulator injections cooled the fuel and the hot leg, which prevented a hot leg failure and 

delayed the RPV lower head failure. The differences in the initial RPV breach timing from these 

two realization was the SV failure area as both had a relative small number of SV cycles. 
4-114

Figure 4-98 Pressurizer pressure responses for Rlz 551 and 148 

Figure 4-99 Core swollen water level responses for Rlz 551 and 148 
4-115

 Figure 4-100 Total accumulator water injection responses for Rlz 551 and 148 

 Figure 4-101 Creep rupture index and lower head temperature responses for 

Rlz 551 and 148 

Rlz 469 

Analysis of Rlz 469 is presented in Figure 4-102 and Figure 4-103 and illustrates what is often 

happening in the calculations to keep deflagrations from occurring. Any time all the lines in 

these figures are solid, conditions are ripe for a burn, i.e., enough fuel and oxidizer (e.g., 
4-116

hydrogen and oxygen) are present to facilitate a burn and insufficient inerting gasses (e.g., 

steam) reside to suppress a burn. Figure 4-102 pertains to the ignition of a burn within a control 

volume (burning downwards in this instance) while Figure 4-103 pertains to the propagation of a 

burn to a control volume upwards from another volume. In Figure 4-102, excessive inerting gas 

and insufficient combustible gas are reflective, i.e., there could be too little combustible gas for a 

burn given the amount of inerting gas or likewise too much inerting gas for a burn given the 

amount of combustible gas. In this realization, containment ruptures at about 50 hours, as 

observed in the figure. 

 

Figure 4-102 Rlz 469 no random ignition, lower containment CV-8 conditions with 

respect to a burn starting within the CV, as opposed to propagating to 

the CV 
4-117

Figure 4-103 Rlz 469, no random ignition, conditions in the containment dome with 

respect to a burn being propagated to the dome 

4.4 LTSBO Accident Progression Analysis 

The LTSBO is assumed to be initiated by a seismic event. Section 4.4.1 presents the results of 

an unmitigated scenario with initially successful operator actions to depressurize the RCS and 

maintain TDAFW flow. However, once the DC station batteries fail at 8 hours, no more operator 

actions are successful. The analysis in Section 4.4.3 assumes that power to the igniters is 

restored prior to core damage and onset of hydrogen generation. Section 4.4.2 presents the 

results of a series of sensitivity calculations. 

4.4.1 Unmitigated Long-Term Station Blackout Base Case 

The accident scenario initiates with a complete loss of all onsite and offsite AC power but the 

DC station batteries are available. The reactor successfully scrams and the containment 

isolates but all powered safety systems are unavailable except the TDAFW. The MELCOR 

parameters for the base case are the mean values sampled for the short-term uncertainty 

distributions. Table 4-16 summarizes the timing of the key events in the unmitigated LTSBO 

base case. 
4-118

Table 4-16 Timing of key events for unmitigated LTSBO base case. 

The heat removal through the steam generator depressurizes the primary system to 11.2 MPa 

by 60 min. At 60 min, the operator starts a controlled (100oF/hr) cooldown of the primary 

Event Description Time 

(hh:mm) 

Initiating event 

 Station blackout – loss of all onsite and offsite AC power 

00:00 

MSIVs close 

Reactor trip 

 RCP seals initially leak at 21 gpm/pump 

00:00 

TDAFW auto initiates at full flow 00:00+ 

First SG SRV opening 00:00+ 

Operators control TDAFW to maintain level 00:15 

Operators initiate controlled RCS cooldown of secondary at ~100oF/hr 01:00 

Accumulators begin injecting 03:08 

SG cooldown stopped at 300 psig to maintain TDAFW flow 03:42 

DC Batteries Exhausted 08:00 

SG ARVs reclose 08:00 

1% of the ice melted 08:16 

SG1 fills up and floods the AFW steam turbine (AFW shuts down) 10:14 

SG3 SV stuck open (after 45 cycles) 14:36 

SG1 SV stuck open (after 5 cycles with water flow) 17:06 

Pressurizer SV opens 20:06 

PRT failure (4% ice melted) 20:30 

Water level below top of active fuel 20:48 

Pressurizer SV stuck open (45 cycles) 22:20 

First fission product gap releases 22:48 

25% of ice melted 24:42 

Creep rupture failure of the hot leg nozzle in combined loop 24:42 

1st hydrogen deflagration 24:42 

Half of ice condenser lower plenum doors stuck open 24:42 

Containment fabric seal torn 24:42 

Accumulator empty 24:44 

50% of the ice melted 24:14 

Containment failure due to hydrogen combustion 24:42 

Vessel lower head failure by creep rupture 28:36 

Debris discharge to reactor cavity 28:36 

100% of the ice melted 34:38 

Selected MELCOR Results 

Debris mass ejected (1000 kg) 169 

In-vessel hydrogen generated (kg) 510 

Iodine release fraction at 72 hr 9.23E-2 

Cesium release fraction at 72 hr 2.27E-2 
4-119

system by opening an atmospheric relief valve (ARV) in a single steam generator32. As the 

secondary pressure in the single steam generator (SG1) decreases, the saturation temperature 

of the water in the boiler section of the steam generator also decreases, which cools the primary 

system fluid and also the fluid in the other steam generators (SG3). At about 3.7 hr, steam 

generator SG1 reached 2.17 MPa (300 psig), where the secondary system pressure was 

stabilized. Figure 4-105 shows that the TDAFW adequately maintained the steam generator 

level until 8 hr when DC power fails, and by 10.2 hr, the SG1 overfills and fails the AFW steam 

turbine. By this time, the CST water has decreased to 37% of the original inventory. 

Figure 4-104 Unmitigated LTSBO base case primary and secondary 

pressure history 

The primary system depressurization via the secondary system cooldown has some short-term 

benefits, i.e., a reduction in the leakage through the RCP seals, and the actuation of the 

accumulators. In the base case calculation, it is assumed that the RCP seals do not fail 

(leakage corresponds to 21 gpm at full system pressure). Even if the RCP seals fail the 

resulting leakage flow would be much lower than if the primary system pressure was not actively 

controlled to low pressure. The accumulators begin injecting at 4.4 MPa at about 3 hours until 

7.3 hours discharging about 9% of their water inventory. The accumulators are a source of cold 

water to replace the losses due to RCP seal leakage and the volume shrinkage during the 

cooldown. The accumulators discharge again at 14.6 hours in response to the second primary 

system depressurization for a total of 31% of their initial inventory. 

 

32 The single SG is identified as SG1, and the combined other three SGs are identified as SG3. 
4-120

Figure 4-105 Unmitigated LTSBO base case secondary system water level and valve 

history 

The station batteries were estimated to fail at 8 hours. At the same time, the steam generator 

atmospheric relief valve closed in the single steam generator (SG1) and was no longer actively 

controlled. In response to the steam generator valve closure, both the primary and secondary 

systems rapidly pressurized to the secondary safety valve opening pressure at 12.3 hours and 

remained around this pressure until 14.6 hours when the lowest opening pressure safety valves 

in the lumped steam generators (SG3) were stuck open at 45 cycles (Figure 4-105). This led to 

a rapid depressurization of SG3 and the primary system until the water loss resulted in the 

dryout of SG3 at 15.5 hours as shown in Figure 4-105. The primary system and the single 

steam generator (SG1) pressures were then equilibrated and once again reached the 

secondary safety valve opening pressure at about 17 hours. Note that the leakage through the 

main steam isolation valves was not sufficient to avoid secondary side safety valve cycling. 

Because of relatively high water level in the single steam generator, there were only a few 

cycles before water began passing through SG1 safety valves. The valves were assumed to be 

stuck open after 5 cycles with water flow. Shortly after SG1 depressurization through the stuck 

open safety valve and the associated water loss, SG1 dried out at 18.2 hours. With no more 

cooling available, the primary system pressurized to the pressurizer safety valve opening set 

point at 20.1 hours and began to relieve steam and water. The pressurizer safety relief valves 

began cycling until they were stuck open after 45 cycles at 22.4 hours. The loss of coolant 

during the valve cycling resulted in a rapid drop in the vessel water (see Figure 4-106) and the 

top of the fuel was uncovered by 20.8 hr and the core heatup began (see Figure 4-107). 
4-121

Figure 4-106 Unmitigated LTSBO base case primary system water level history 

Following the core heatup, an in-vessel natural circulation flow develops between the hot fuel in 

the core and the cooler structures in the upper plenum. Hot gases rise out of the center of the 

core to the upper plenum and return down the cooler peripheral sections of the core. 

Simultaneously, a natural circulation circuit develops between the vessel and the steam 

generator33. The flow of the hot gases through the hot leg causes the hot leg nozzle to fail by 

creep rupture at 24.7 hours. The failure of the hot leg results in rapid depressurization of the 

vessel and injection of the remaining 69% of the accumulator water inventory. Figure 4-106 

shows the temporary surge in the vessel water level all the way up to the top of active fuel 

and the cooldown of the debris (see Figure 4-107). 

 

33 Failure of the steam generator tube is not modeled in the present calculations. 
4-122

Figure 4-107 Unmitigated LTSBO base case peak fuel and debris history 

During the heatup and degradation of the fuel, the outer surface Zircaloy cladding reacts with 

the steam to form an oxide shell. The oxidation reaction is an exothermic process that greatly 

increases the fuel heatup rate and releases hydrogen. During the rapid fuel heatup, the oxide 

shell thickness increases and the unoxidized Zircaloy clad material in the interior of rod melts. 

The molten Zircaloy cladding weakens the oxide shell and drains to lower regions of the core, 

leaving a fragile ZrO2 shell around over-heated fuel. This mechanically weak material 

subsequently fragments into particulate debris, which relocates toward the lower regions as 

rubble. The changes in core geometry during this time frame, which are caused by the 

formation and downward relocation of molten and particulate debris, are illustrated in 

Figure 4-108. 

A large debris bed formed in the center of the core resting on the core plate at 23.9 hour. By 

24.1 hr, most of the core fuel assemblies (i.e., the central three of five radial rings in the 

MELCOR model) have collapsed. Highly oxidized, but vertically intact assemblies remain 

standing in the outer rings of the core. The outer core assemblies also fail a short time later 

(24.2 hr) and accumulate on top of the lower support plate. The hot debris fails the lower 

support plate at 24.4 hr. Following the lower support plate failure, the debris relocated onto the 

lower head. The hot debris starts to boil away the lower head water, but the failure of the hot 

leg and accumulator injection recover the water level in the core at 24.7 hr. The decay heat 

from the debris takes an additional 1.2 hours to boil away the remaining water (see 

Figure 4-106). 

Following the lower plenum dryout, the hot debris started heating the lower head, which 

eventually failed at 28.6 hr due to the creep rupture failure criterion. Because reactor vessel 
4-123

pressure is relatively low during the heat up of debris in the lower plenum, the failure of the 

lower head is more strongly influenced by thermal rather than mechanical stresses. The failure 

of the lower head results in the ejection of core debris onto the floor of the reactor cavity. 

Figure 4-108 Unmitigated LTSBO base case core degradation and relocation history 

The containment pressure remains relatively low until the failure of the PRT at 20.5 hours (see 

Figure 4-109). The containment pressure gradually increases as the coolant (and later 

hydrogen) is discharged from the vessel. The containment pressurization following PRT failure 

leads to the flow of the steam through the ice bed. Significant in-vessel hydrogen generation 

began at 22.8 hours and the hydrogen was released to the containment. By the time the hot leg 

failed, there was a substantial amount of hydrogen in the lower containment and with the steam 

mole fraction below the threshold of 55%, a hydrogen deflagration started in the lower 

compartments that propagates to the upper dome. The source of ignition is the hot gases 

exiting the vessel. The containment was still below the design pressure at the time of hot leg 

failure. The hydrogen burn resulted in the failure of the fabric seal dividing the lower and upper 

parts of the containment, and more importantly, the gross failure of the containment. It is 

important to note that containment failed before lower head failure. 

The response of the ice is shown in Figure 4-110. In the absence of containment cooling, the 

flow of gases through the lower plenum doors results in some melting of the ice (~4%) by the 

time the PRT rupture disk fails. The discharge of the coolant during the cycling of the 

pressurizer safety valve and the flow of steam through the ice bed results in substantial melting 

of the ice until the hot leg fails. The melting of the ice continues after containment failure as the 

gases circulate through the ice chest and by the time of lower head failure more than 60% of the 

ice has melted. The flow of hot gases from the cavity and the lower compartments melted the 

remaining ice by 34.6 hr. The hot debris in the reactor cavity started to ablate the concrete 

soon after lower head failure as shown in Figure 4-111, and the ex-vessel core-concrete 

interactions (CCI) continued for the remainder of the calculation, which generated noncondensable gases. The CCI gases promote flow from the containment to the environment. 
4-124

Consequently, there is low retention of fission products that are released or revaporized after 

containment failure.

Even though the cavity is dry at the time of the lower head failure, continued melting of the ice 

and accumulation in the lower compartments eventually led to flow of water to the cavity at 

about 33 hr. Soon after lower head failure and discharge of the debris to the cavity, the debris 

temperature started to gradually decrease as a result of heat transfer to the concrete basemat 

and from the top of the debris bed. The flow of water to the top of debris at 33 hr resulted in 

increased heat transfer from the top debris crust and lowered the average debris temperature 

significantly. However, as long the interface temperature between the core debris and the 

concrete basemat remained above the ablation temperature, concrete ablation continued. By 

the end of the calculation about 80 cm of concrete was eroded. 

Figure 4-109 Unmitigated LTSBO base case containment pressure history 
4-125

Figure 4-110 Unmitigated LTSBO base case ice melting history 

Figure 4-111 Unmitigated LTSBO base case concrete ablation history 
4-126

 Radionuclide Release 

Figure 4-112 and Figure 4-113 show the fission product distributions of the iodine and cesium 

radionuclides that were released from the fuel, respectively. Approximately 98% of the iodine 

and 99% of cesium were released from the fuel in-vessel while the remaining amount was 

released ex-vessel. 

The fission product releases from the fuel started following the first failures of the fuel cladding 

at 22.8 hr, or about 2 hours after the uncovery of the top of the fuel rods. The in-vessel fission 

product release phase continued until 32.7 hr or about 4 hours after lower head failure when the 

remaining debris from the vessel was discharged to the cavity34. Initially, the fission product 

releases from the fuel circulated through the primary system as well as being released to the 

PRT and the containment through the pressurizer safety valves. The start of the fission product 

release occurred more than 2 hours after failure of the RPT rupture disk and 28 minutes after 

the pressurizer safety valve was stuck open. The water inside the PRT remained slightly 

subcooled after fission product release and nearly 30% of the iodine and cesium initial 

inventories were retained in the tank. Between the start of the fission product release and the 

failure of hot leg (nearly 2 hours), a significant amount of volatile radionuclides released from 

the fuel remained in-vessel (PRT is considered part of the RCS in the MELCOR model). The 

resultant blowdown of the vessel after hot leg failure immediately discharged some of 

radionuclides to the containment. The radionuclides settled on the containment surfaces after 

being released from the RCS (about 10% of the cesium and 15% of the iodine). The retention 

of the radionuclides inside the RCS, the PRT, and the containment was significant since 

containment failure occurs shortly after the hot leg failure, but there was little airborne mass 

released to the environment. 

The chemical form of the released iodine was cesium iodide, which was more volatile than the 

predominant form of the released cesium, which was cesium-molybdate (Cs2MoO4) and CsOH. 

As shown in the iodine distribution figure (see Figure 4-112), the in-vessel iodine mass was 

slightly decreasing until the PRT dried out and started to heatup. The decrease of mass 

represents a revaporization process of previously deposited radionuclides. The large increase 

in iodine resident in the containment was coincident with a decrease in the RCS due to the 

release of much of inventory of iodine in the PRT to the containment atmosphere. The late invessel revaporization continued after this time and had a contribution to the environmental 

release. In contrast to the iodine behavior, the deposited cesium-molybdate was less volatile 

and mostly remained deposited in the RCS and the PRT. Only about 6% of the cesium was 

released from the PRT (compared to nearly complete release of iodine). 

Figure 4-114 summaries the releases of the radionuclides to the environment. At 3 days,

99.7% of the noble gases, 8.7% of the tellurium, 9.23% of the iodine, 2.27% of the cesium, and 

0.88% of the barium had been released to the environment. The release of the less volatile 

fission products does not start until after the lower head failure and start of the core concrete 

interaction.

 

34 Only a very small amount was released during this time. 
4-127

Figure 4-112 Unmitigated LTSBO base case Iodine fission product 

distribution history 

Figure 4-113 Unmitigated LTSBO base case Cesium fission product 

distribution history 
4-128

Figure 4-114 Unmitigated LTSBO base case environmental release fraction 

history 

4.4.2 Unmitigated Long-Term Station Blackout Sensitivity Calculations 

This section presents the results of a series of sensitivity calculations to better understand the 

response of the reactor and the containment for the LTSBO scenario. These limited sensitivity 

calculations are informed by the uncertainty analysis for the STSBO scenario and focus on the 

assumptions regarding valve cycling and the availability of station batteries. A description of the 

cases is given in Table 4-17. The results of the calculations are presented in Figure 4-115 

through Figure 4-119. 

Table 4-17 Description of unmitigated LTSBO sensitivity cases. 

Case Description 

S1 SG1 SRV is assumed to fail at 1st cycle with water flow (see Figure 4-105) 

S2 Secondary side SRVs do not fail 

S3 Same as S1 but the flow area for SG3 SRV is reduced to correspond to a 

single SG 

S4 Station batteries run out at 4 hours 

S5 Pressurizer SRV is assume to stick open on 1st cycle 

In sensitivity case S1, it was assumed that the secondary side safety valve fails on the 1st

opening with water flow (see Figure 4-105, which shows the base case SG SV sticks open after 

5 cycles). However, the depressurization of the single steam generator (SG1) also resulted in 

the delay of stuck open safety valve on the lumped steam generators (SG3). The loss of 

secondary side cooling was a result of steam generator dryout, which occurred slightly sooner in 
4-129

S1 as compared to the base case. However, the heatup of the primary system and the 

pressure increase was not significantly different between the two cases as shown in 

Figure 4-115. Therefore, the timing of hot leg failure and the containment failure were only 

different by about half an hour. 

For sensitivity case S2, it was assumed that the safety valves on the steam generators did not 

stick open but both the single and lumped steam generators could gradually depressurize after 

dryout due to the leakage from the main steam isolation valves. The behavior is not very 

different from the base case but includes a delay in the hot leg and containment failure that is 

similar to case S1. 

Since three steam generators were lumped together in the MELCOR model, sensitivity (S3) 

calculation was done to examine the effect of secondary side depressurization assuming that 

the area for the valve sticking open corresponds to a single steam generator. This caused a 

slower depressurization but only delayed the dryout by about half an hour. 

Although the availability of the station batteries (4 hours in case S4) had a significant effect on 

the timing of the accident progression, the overall behavior and trends were very similar to the 

base case. A difference of only 4 hours of station batteries in the base case resulted in the 

delays of about 5.5, 6.0, and 6.5 hours in the timing of the steam generator dryout, pressurizer 

safety valve stuck open, and hot leg creep rupture, respectively. 

In sensitivity case S5 with the pressurizer safety valve stuck open at the first cycle, the core 

damage started sooner due to higher coolant loss from the vessel compared to the base case. 

In-vessel hydrogen generation started about 1.5 hour earlier as compared to the base case with 

somewhat similar differences in the timing of the hot leg and containment failure. 

The amount of in-vessel hydrogen generation between the various sensitivity cases and the 

base case was also similar. This parameter is most sensitive to the eutectic melt temperature 

(see the discussion on STSBO) but was not varied for the LTSBO sensitivity calculations. The 

mean settings on the uncertain parameters, including the eutectic melt temperature produced 

sufficient hydrogen to fail the containment at the time of hot leg failure. 

Figure 4-118 and Figure 4-119 show the environmental release fractions for iodine and cesium, 

respectively. The horsetail plots from the STSBO uncertainty analysis (without random ignition) 

are also superimposed on the figures. The behavior of release for the LTSBO is similar to the 

STSBO scenarios with early containment failure since all the sensitivity cases discussed here 

resulted in containment failure following the hot leg failure. With 8 hours of station batteries, the 

release timing can be significantly different (by as much as 20 hours) between the short-term 

and LTSBO scenarios. The differences in the total releases shown in Figure 4-119 are 

attributed to variations in the late revaporization from the RCS and PRT (see Figure 4-112 and 

Figure 4-113 for the base case). 
4-130

Figure 4-115 Unmitigated LTSBO reactor pressure sensitivity cases 

Figure 4-116 Unmitigated LTSBO containment pressure sensitivity cases 
4-131

Figure 4-117 Unmitigated LTSBO in-vessel hydrogen sensitivity cases 

Figure 4-118 Unmitigated LTSBO iodine environmental release fraction 

sensitivity cases 
4-132

Figure 4-119 Unmitigated LTSBO cesium environmental release fraction 

sensitivity cases 

4.4.3 Long-Term Station Blackout with Igniters 

The effect of igniters on the containment performance is presented in this section. The igniters 

are assumed to be available in all control volumes inside the containment except the ice 

condenser lower plenum and the ice chest. However, a burn in any control volume is allowed to 

propagate to other regions including the ice chest when it satisfies the propagation criteria. 

Table 4-18 Description of mitigated LTSBO sensitivity cases with igniters. 

Case Description 

S6 Ignition occurs once hydrogen mole fraction reaches 7% 

S7 Ignition occurs once hydrogen mole fraction reaches 4% 

S8 Same as S6 with 4 hours of station batteries 

Figure 4-120 compares the response of the containment pressure to the burning of the 

hydrogen with igniters versus the base case response without igniters. The most revealing 

impact of the igniters was that they prevented early containment failure. In the unmitigated base 

case, hydrogen accumulated to high levels so that at the time of hot leg failure when an ignition 

source became available, the first deflagration failed the containment. In case S6, ignition is not 

allowed until the hydrogen mole fraction reaches 7% (i.e., the MELCOR default for control 

volumes with igniters). In sensitivity case S7, the criterion for ignition was decreased to the 

lower flammability limit. The pressure spikes for the lower ignition criterion were smaller 
4-133

because of more gradual burning of the hydrogen. The slow containment pressurization was a 

result of the non-condensable gas generation (including CO and H2) from core concrete 

interaction. The containment pressure; however, remained below the lower bound of 

containment fragility at 3 days. 

Figure 4-121 through Figure 4-123 show the time history of the hydrogen, oxygen, and steam in 

the upper containment (CV24). The steam mole fraction in the containment remains well below 

55% threshold for inerting. In the base case without the active igniters, the hydrogen mole 

fractions exceeded 10% and with enough oxygen present (Figure 4-122) resulted in the 

combustion induced failure of the containment. Activation of the igniters controls the hydrogen 

concentration through multiple burns and prevents containment failure. In all cases, however, 

the igniters were not effective after about 35 hours once the oxygen concentration fell below 5% 

(see Figure 4-123). 

Figure 4-120 LTSBO containment pressure history with igniters 
4-134

Figure 4-121 LTSBO containment hydrogen mole fraction history with 

igniters 

Figure 4-122 LTSBO containment oxygen mole fraction history with 

igniters 
4-135

Figure 4-123 LTSBO containment steam mole fraction history with igniters 
5-1

5. SEQUOYAH MACCS MODEL 

5.1 Introduction 

The Sequoyah Nuclear Plant, Unit 1 SOARCA offsite consequence analysis uses MACCS to 

calculate potential doses and risk metrics for the population in the vicinity of Sequoyah. The 

MACCS consequence model (Version 3.10), along with the graphical user interface (GUI) 

WinMACCS, was used in this analysis to calculate offsite doses and their effect on members of 

the public. MACCS and WinMACCS are developed at SNL under the direction of the NRC. 

The codes have been used extensively in commercial nuclear reactor PRAs and other 

regulatory applications to simulate the impacts of accidental atmospheric releases of 

radiological materials on humans and on the surrounding environment. 

The principal phenomena considered in MACCS are atmospheric transport using a straight-line 

Gaussian plume segment model, short-term and long-term dose accumulation through several 

exposure pathways including cloudshine, groundshine, inhalation, deposition onto the skin, and 

food and water ingestion. The ingestion pathway was not treated in the analyses reported here 

because uncontaminated food and water supplies are abundant within the United States and it 

is unlikely that the public would consume radioactively contaminated food or water. This section 

discusses various site-specific MACCS model parameters. A complete list of the MACCS 

model inputs are found in Appendix B. In this report, calculations and results are reported 

based on 2015 data, except for weather data which is based on 2012. 

Site-specific MACCS input parameters for Sequoyah and its surrounding area include: 

• demographic information, 

• weather or meteorological data, 

• land cover information to inform atmospheric transport and dispersion, and 

• protective actions and emergency response timelines. 

As a brief overview of the major elements of a MACCS offsite consequence analysis, a source 

term describes the quantities and types of radionuclides released into the atmosphere from a 

nuclear power plant along with the timing and related characteristics of the release. The release 

information and onsite meteorology data are used to calculate the atmospheric transport, 

dispersion, and deposition of the radionuclides. The emergency response and protective 

actions are designed to minimize exposures or avoid them if possible. Doses can result from 

exposure to radionuclides via various exposure pathways. 

The risk metrics reported for this analysis are individual latent cancer fatalities (LCFs) and 

individual early fatality (EF) risks to residents within selected radial distance intervals centered 

on the reactor site. The individual early fatalities are those expected to occur within a relatively 

short period from the time of exposure and the latent cancer fatalities are those expected to 

occur later in life of exposed individual as a result of early or chronic exposure to radioactive. 

As the risk metrics LCF and early fatality affect the entire population in each region, the 

individual risk metrics represent the average over each population modeled. That is, the risk 

values represent the predicted number of fatalities divided by the population. 

Two types of results can be presented in a consequence assessment: scenario frequencyweighted risk or conditional risk. The scenario frequency-weighted risk is the product of the 

core damage frequency for the accident scenario and the conditional risk for that scenario. The 
5-2

scenario frequency-weighted risk is the likelihood of a latent cancer fatality or early fatality for an 

individual living within specified radii of the plant per year of plant operation (i.e., LCF risk per 

reactor year (pry) or early fatality risk pry). Conditional risks, alternatively, are the risks 

predicated on the accident occurring. That is, the core damage frequency is assumed to be 

unity. To simplify presentation of results, all consequence results presented in Section 6 of this 

analysis are conditional risks. 

The consequence analysis has three phases that are user-defined time periods: 

• early phase of seven days, 

• intermediate phase of one year, and 

• chronic (long-term) phase of 50 years. 

Throughout this section certain input parameters have a single deterministic value specified 

while other parameters (or parameter groups) are varied across a specified distribution of 

values. These varied parameter distributions enable investigation of the sensitivity of the result 

measures to uncertainty in input parameters. This process of incorporating uncertainty in input 

parameters is referred to as the UA and is applied to the STSBO analysis with and without 

random ignition sources. The remainder of this section describes the basis for selecting both 

the deterministic value parameters and the UA parameters. 

5.2 Site Location and Surrounding Area 

Sequoyah is near the center of Hamilton County, Tennessee, 18 miles northeast of the 

Chattanooga, Tennessee, and 14 miles west-northwest of Cleveland, Tennessee. Sequoyah is 

a two-unit nuclear power plant site where reactors Unit 1 and Unit 2 are located adjacent to 

each other sharing a common turbine building. This analysis considers results and release 

pathways from Sequoyah Unit 1. Therefore, the radial distance intervals considered in the 

MACCS analysis originate from the center of the Unit 1 reactor containment building with 

latitude and longitude coordinates of 35° 13' 35.65" N and 85° 05' 28.17" W (UTM Coordinates: 

Zone 16, N 3,899,640.62 and E 673,718.24). The spatial grid is divided into 64 radial sectors 

with sector one oriented North and the sector index increasing in a clockwise direction. Grid 

radii extend from the Unit 1 reactor building to various distances of interest. 

Figure 5-1, Figure 5-2, and Figure 5-3 show the site in relation to the surrounding area in 

increasingly finer detail [50]. Figure 5-1 shows that Tennessee, Alabama, Georgia, and North 

Carolina are within a 50 mile radius of Sequoyah. To facilitate a preplanned strategy for 

protective actions during a nuclear power plant emergency, there are two emergency planning 

zones (EPZs) around each nuclear power plant. The first is the plume exposure pathway EPZ 

which has a radius of about 10 miles from the reactor site. The second is the ingestion 

exposure pathway EPZ which has a radius of about 50 miles from the reactor site. For this 

analysis, further references to the “EPZ” are referring to the 10 mile plume exposure pathway 

EPZ, although results out to 50 miles are presented. Figure 5-2 is a six mile radius map of the 

plant site with the town of Soddy-Daisy approximately five miles west of the plant. An aerial 

photograph with site boundary and location descriptions is presented in Figure 5-3. For the 

offsite consequence analysis, some of the key distances include the 0.345 mile exclusion area 

boundary (EAB), the 10 mile EPZ, and the 50 mile ingestion exposure pathway zone. As 

mentioned previously, the ingestion pathway was not included in analysis results at 50 miles. 
5-3

Figure 5-1 Sequoyah Nuclear Power Plant site with 50-mile radius [50] 
5-4

Figure 5-2 Sequoyah 2 mile, 5 mile and 10 mile radial distances and EPZ 

sectors [50] 
5-5

Figure 5-3 Sequoyah site boundary and aerial layout [50] 

5.3 Site Demographics and Economic Data 

SecPop version 4.3 was used to calculate estimated population, economic, and land use data in 

the vicinity of Sequoyah. SecPop accesses population, land use, and economic value 

databases to map pertinent data to each grid element for any continental United States (U.S.) 

location [51]. 
5-6

 

SecPop uses population data from the 2010 Census and therefore needs to be adjusted to the 

project’s target year, 2015. To calculate the adjustment factor, county level population data for 

all counties within 50 miles for 2010-2014 were used to calculate an average annual growth rate 

[52]. This growth rate (0.41 percent) was then applied from 2014 forward to 2015 which 

resulted in a 50 mile region population multiplier of 1.0208. By this method, the estimated 2015 

population within 10 miles and 50 miles of the plant is 97,731 and 1,106,196, respectively. 

SecPop uses economic data from a variety of databases for 2012 farmland wealth, farmland 

annual sales, and non-farmland wealth. In order to adjust the county level economic data 

available in SecPop for years beyond 2012, a SecPop MACCS economic multiplier parameter 

can be derived. Using the same methodology as in Surry and Peach Bottom SOARCA 

analyses [4][5], the economic multiplier is based on the U.S. Consumer Price Index (CPI). For 

this analysis, data was used from the U.S. Bureau of Labor Statistics Consumer Price Index for 

urban consumers (CPI-U-RS). The CPI-U-RS is a modified value of the CPI-U that excludes 

the more volatile pricing components such as food and energy [53]. To adjust the 2012 data to 

the project’s target year, 2015, a ratio of the most recently available data of the CPI-U-RS for 

2014 (342.4) to the 2012 value (330.7) was used to compute a resulting adjustment factor of 

1.0535. This economic multiplier is then used to adjust 2012 farmland wealth, farmland annual 

sales, and non-farmland wealth values to 2015 values. 

5.4 Site Topography and Land Cover 

Sequoyah is located on two peninsulas totaling 630 acres on the western shore of the 

Chickamauga Lake at Tennessee River mile marker 484.5. The larger peninsula includes the 

power block and support facilities surrounded primarily by grass fields. The smaller peninsula 

includes the training center surrounded by evergreen and deciduous forests. The major 

structures onsite include two reactor buildings, turbine building, auxiliary building, control 

building, service and office building, diesel generator building, intake pumping station, ERCW 

pumping station, and two natural draft cooling towers. The largest structures onsite are the two 

cooling towers at approximately 459 feet (140 m) high. The Tennessee River bounds the site to 

the East and South. Private properties bound the northern and western portions of the site. 

Several housing developments are located adjacent to the site EAB. 

The Sequoyah site lies in the Tennessee River valley which is oriented in a north-northeasterly 

to south-southwesterly direction. The Sequoyah river valley orientation exhibits significant 

influence on the wind direction near the plant, as discussed in the meteorology section below. 

Local topography is characterized by small ridges and valleys with a generally similar 

orientation. The local terrain varies from flat to rolling hills with the ground cover primarily 

consisting of forested land. Recent GPS land use data obtained from the U.S. Department of 

Agriculture (USDA) CropScape indicates a 30 mile radius around the plant is 60 percent 

forested, 20 percent grass/pasture, 14 percent developed, 4 percent cropland, and 2.5 percent 

is covered by water [53]. On a larger scale, the Tennessee River Valley is adjacent to the 

Cumberland Plateau to the west and the Appalachian Mountains to the east. 

5.5 Meteorological Data 

 

Weather bin sampling was chosen as the meteorological sampling option for this analysis; 

therefore, the MACCS model requires one year of complete weather data for Sequoyah. Data 

was obtained from three sources for analysis and selection of site-specific representative 
5-7

weather data: the TVA meteorological tower located onsite, the Environmental Protection 

Agency (EPA) SCRAM website35 database of seasonal mixing layer height data, and the 

National Weather Service (NWS). Sequoyah onsite meteorological tower hourly weather data 

was obtained from TVA for years 2008 through 2012. The onsite data were compared to the 

NWS data collected from a nearby weather station to better understand how a particular year 

compared to long-term regional trends. The MACCS Sequoyah meteorological data file 

contains one year of onsite weather data and the long-term EPA mixing height data. 

TVA maintains a meteorological facility at Sequoyah consisting of a 91 meter (300 feet) tower 

instrumented at 10 meters, 46 meters, and 91 meters. Data collected at those heights include 

wind direction (degrees) and speed (miles per hour), averaged wind speed, and air temperature 

(degrees Fahrenheit). Dew point was collected at 10 meters and solar radiation 

(Langley/minute) and rainfall (inches) data were collected at 1 meter. These data are compiled 

in hourly increments that represent averaged values of hour-long intervals for each hour of a 

365 day year (a total of 8760 observations). Wind direction and speed are measured every 5 

seconds and averaged over the one-hour intervals. 

The meteorological data required for the MACCS model include: 

• the time as sequential day and hour, 

• the direction the wind is moving toward, 

• the average hourly wind speed (m/sec), 

• the atmospheric stability class, 

• the precipitation rate (1/100th inch/hr), and 

• winter, spring, summer and autumn mixing heights for neutral (morning) and unstable 

(afternoon) conditions. 

Table 5-1 presents the average diurnal mixing heights for each season. The seasonal mixing 

heights for neutral (morning) and unstable (afternoon) conditions are calculated by averaging 

90-day blocks of data from the EPA SCRAM website. The Sequoyah mixing height data is 

based on EPA data from Memphis, Tennessee (the only station in Tennessee). 

Table 5-1 Seasonal diurnal mixing heights for Tennessee (m). 

Time of Day Winter Spring Summer Fall 

Morning 580 520 420 490 

Afternoon 1110 1790 1820 1000 

Other than seasonal diurnal mixing height data, the weather sampling algorithm invoked for this 

analysis requires meteorology data for each hour of the year. EPA has published acceptable 

methods for synthesizing data to fill gaps. Missing data are calculated as follows: 

1. To synthesize a single hour of missing data, averaging the hour prior with the hour 

subsequent to the missing datum was used. 

 

35 http:\\www.epa.gov/scram001/mixingheightdata.htm. For data analysis using EPA SCRAM data, winter is 

considered to start on January 1st, not December 21st. 
5-8

2. For two hour periods of missing data, the average of the two hours of data prior to and 

subsequent to the missing data was used for each of the hours. 

3. In relatively very few instances, data from a station located at an adjacent height is 

utilized. 

4. During the few extended time periods (more than a few hours) in which data was 

unavailable, substitution of a similar day from the adjacent time period is performed to 

maintain similar daylight and seasonal weather variables. 

In accordance with NRC Regulatory Guide 1.23 [54], the meteorological data recovery rate 

should be above 90 percent (i.e., all parameters were measured in a given hour for at least 90 

percent of the hours of the year). Sequoyah 2008 to 2012 weather data completeness is well 

above the 90 percent threshold and is computed as two percentages shown in Table 5-2. 

Complete hours refers to the percentage of hourly meteorology data points that exist within a 

year’s worth of data. For instance, in 2008, 187 hourly data points were not logged out of 8760 

hours in a year, thus the complete hours are 8573 out of 8760, or 97.8 percent. Within that 

8573 hours of recorded data, some of the hours may be missing one or more data points for 

wind speed, temperature, etc. In 2008, 111 hours of the 8573 recorded hours were missing at 

least one data type. Thus, the complete data percentage is 8462 out of 8573 recorded hours, or 

98.7 percent. 

Table 5-2 Meteorology data completion rates. 

Year Complete Hours Complete Data 

2008 97.8% 98.7% 

2009 98.2% 98.9% 

2010 97.4% 99.0% 

2011 99.4% 99.7% 

2012 98.9% 99.4% 

5.5.1 Selection of Representative Weather Data 

In general, the annual site meteorological data are remarkably consistent with respect to wind 

speed, wind direction, and stability class. Table 5-3 lists the total precipitation measured onsite 

for each year. Figure 5-4 presents the Sequoyah site precipitation data as monthly precipitation 

for each year. The year 2012 is representative of the site weather because the annual 

precipitation was consistent with the five year average precipitation, and it has the second 

highest data completion of years 2008 to 2012. 

 
5-9

Table 5-3 Summary of precipitation by year (inches). 

Year Total 

2008 39.7 

2009 47.9 

2010 25.5 

2011 52.9 

2012 39.3 

Average 41.1 

Std Dev 10.4 

Figure 5-4 Monthly precipitation by year (inches) 

5.5.2 Sequoyah Site Meteorological Features 

Table 5-4 summarizes the 2012 stability class, precipitation, and wind speed average derived 

from the hourly data. The site has very stable weather conditions. Measurable precipitation 

was recorded for 528 hours out of 8760 hours per year which is 6 percent of the year. The 

Sequoyah site stability class, wind direction, and wind speed characteristics are described 

below. 

 

0

2

4

6

8

10

12

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Monthly Precipitation by Year (inches)

2008 2009 2010 2011 2012 AVG
5-10

Table 5-4 Summary of 2012 meteorological statistics. 

Category Metric Frequency 

Stability Class 

Unstable (%) 6.0% 

Neutral (%) 42.0% 

Stable (%) 52.0% 

Precipitation 

Total (in) 39.3 

Frequency (hr) 528 

Frequency (%) 6.0% 

Wind Speed Average (m/s) 1.66 

Atmospheric stability can be categorized using the Pasquill-Gifford scale where stability classes 

in MACCS are numbered 1 through 7 corresponding to classes A through G in the PasquillGifford scale [55]. The hourly stability class occurrences are shown as unstable (classes 1, 2, 

3)36, neutral (class 4)37, and stable (classes 5, 6, 7)38 in Figure 5-5. Generally, nighttime hours 

are the most stable. The mornings and evenings transition from stable to neutral stability. Midday hours have the highest proportion of unstable classes, although neutral conditions occur 

two to three times more frequently than unstable conditions. Figure 5-5 presents the overall 

stability class frequency for 2012 and is recorded by the hour of the day. The dominant classes, 

4 and 5, occurred more than 75% of the time. 

 

36 This corresponds to Pasquill-Gifford stability classes A, B, and C. 

37 This corresponds to Pasquill-Gifford stability classes D. 

38 This corresponds to Pasquill-Gifford stability classes E, F, and G. 
5-11

Figure 5-5 2012 stability class frequency annually and by time of day 

Figure 5-6 presents 16 sector windrose data. The near-surface wind pattern is oriented NorthNortheast and South-Southwest. At left, the representative weather year used in this analysis, 

2012, is shown alone; at right, the windrose frequencies are shown for years 2008-2012. These 

consistent wind fields indicate a strong river valley wind pattern, which means the onsite wind is 

preferentially aligned with the Tennessee River valley at Lake Chickamauga. The wind direction 

data recorded at 46 meters and 91 meters exhibit the same pattern. Figure 5-7 shows the 2012 

wind speed data arranged by various bins. Wind speeds between 1-5 meters per second were 

most frequently observed. 
5-12

Figure 5-6 Sequoyah combined Windrose plots for 2012 and 2008 – 2012 

Figure 5-7 Wind speed bins for 2012 

5.5.3 Weather Sampling 

The timing of a future hypothetical release of radioactive materials from a severe nuclear 

accident is by nature impossible to predict. Future weather conditions cannot be known in 

advance, thus the use of historical data to quantify the consequences of a future event assumes 

future weather data will be statistically similar to historical data. 

MACCS has several options for treatment of meteorological data, which include the ability to 

directly input weather data, or the ability to sample weather data from an external input file. 

Sampling options include a strategy that uses meteorological bins defined by the user (weather 

0%

5%

10%

15%

20%

25%

0.50 1.00 1.50 2.00 3.00 5.00 10.00 20.00 >

CALM CALM 0.50 1.00 1.50 2.00 3.00 5.00 10.00 20.00

Frequency

Wind Speed Bins (m/s)
5-13

binning), or one or more random samples from each day of the weather file. Weather binning is 

a type of importance sampling used to categorize similar weather data based on wind speed, 

stability class, and precipitation. With regard to wind direction, the assumption is that sampling 

within each weather bin is sufficient to represent the wind rose for that bin, i.e., the likelihood of 

the wind blowing in each compass direction. Because each plume segment travels in its own 

direction, as explained below, this assumption should be satisfied. 

The weather sampling strategy adopted for Sequoyah SOARCA uses the nonuniform weatherbinning approach in MACCS [8]. The weather binning structure is the same as in the Surry and 

Peach Bottom SOARCA analyses [4][5], which consists of 16 predefined bins for combinations 

of stability class and wind speed and 20 user-defined bins for rain occurring before the plume 

travels 32 kilometers (20 miles) [8]. The number of weather trials selected from each bin was 

the maximum of 12 trials and 10 percent of the number in the bin. Some bins contain fewer 

than 12 trials. In those cases, all trials in the bin are used. This strategy results in roughly 

1,000 weather trials to represent the 8,760 hours of data in a 365-day year. A sensitivity study 

conducted for the Peach Bottom UA showed that this sampling strategy matched the mean 

results that would have been obtained by choosing every hour in the weather file (8,760 

samples) within 3 percent for health risks evaluated with the linear, no-threshold (LNT) doseresponse assumption and within 12 percent for health risks evaluated with the non-LNT doseresponse assumptions [9]. This comparison of the current weather binning approach (1,000 

weather trials) with the UA analysis (8,760 weather trials) shows that the weather binning 

approach, while reducing the required number of simulations, produces results that are 

acceptably close to the mean results of using all hours of weather data. 

5.6 Atmospheric Transport and Dispersion 

The transport and dispersion of radioactive materials is calculated in MACCS using a Gaussian 

plume segment model and the sampled site weather data. A plume segment is an integrated 

one hour source term characteristic of the release. MACCS allows for as many as 500 plume 

segments to be created through the MELMACCS pre-processor. MELMACCS transforms the 

environmental release output information generated from MELCOR into a MACCS source term 

input file. 

As the release evolves in time, the wind direction, wind speed, and stability class change in 

hourly increments. However, once released, each individual plume segment is transported 

throughout the domain with their initial wind speed, direction, and dispersion conditions constant 

until the radioactive materials in the plume deposit on the ground and other surfaces or exit the 

domain. 

The model calculations consider the release of plume segments, building wake effects, plume 

rise resulting from sensible heat content (i.e., buoyancy), dispersion, dry and wet deposition 

(precipitation), and the decay and ingrowths of up to 150 radionuclides for a maximum of six 

generations. Plume meander is calculated in accordance with NRC Regulatory Guide 1.145 

[56]. More detailed descriptions are provided in the sections below. The model does not 

consider temporal variations in wind direction (within a single hour), spatial variations in the wind 

field, or terrain differences at increasing radial distances. 

 
5-14

5.6.1 Radiological Release Information 

The MELMACCS analysis process captures the characteristics of environmental releases (i.e., 

the accidental release from Sequoyah plant containment systems) generated from MELCOR. 

The release features include: 

• timing, magnitude, and location(s) of releases, 

• physical data such as mass flow rate, mass density, heat contents, as well as the 

building height and plume release height, 

• plume segment radionuclide release fraction, and 

• total number of plume segments and the plume segment estimated to be risk dominant. 

MELMACCS calculates radionuclide specific information for use in MACCS. The Sequoyah 

Unit 1 middle-of-cycle core inventory was estimated using ORIGEN and is listed in Appendix B. 

MELCOR tracks all radionuclides as nine groups which each behave similarly within the reactor 

systems. It also accounts for the deposition and internal holdup of radionuclide groups within 

the reactor systems (i.e., the fractions of radionuclides which are released from fuel but not 

released into the environment). MELMACCS calculates release fractions and particle size 

information for each group, then sub-divides each group into constituent radionuclides based on 

the reactor inventory and individual radionuclide decay. 

The plume segments are divided into approximately one hour increments to align with the hourly 

weather data. In the Sequoyah SOARCA study, quantitatively minor segments containing either 

flow path fractions or mass fractions of less than 0.001 of the total amount released were 

excluded. 

5.6.2 Surface Roughness 

Surface roughness affects the atmospheric transport and dispersion of radioactive material 

because increased surface roughness increases plume vertical mixing and increases 

disposition velocities for all aerosol sizes. The methodology to determine an appropriate siteaveraged surface roughness measure accounting for site topography and land cover for 

Sequoyah is discussed in this section. 

The USDA CropScape database provides data for EPA land use categories for any area in the 

continental U.S. Table 5-5 summarizes the surface roughness data for Sequoyah. The surface 

roughness calculated for use in MACCS characterizes the degree of interaction that a plume 

would have with ground cover, based on the height of the ground cover. Data for different 

ground covers, such as deciduous and coniferous forests or corn and soybeans, are combined 

and designated by major land use type. The surface roughness value is a weighted-averaged 

of aggregated land use data from within a 30 mile radius of Sequoyah. This distance is 

considered sufficient to characterize the area before the plume can be considered well-mixed 

between the ground and mixing layer height. The resulting weighted-average surface 

roughness is 39 centimeters. Eq. 5-1 describes the calculation of MACCS parameter ZSCALE 

using the surface roughness length value for Sequoyah. 
5-15

Table 5-5 Surface roughness. 

Type of Land Use Acreage Area 

Fraction 

Surface 

Roughness (cm) 

Open Water 44,553 2.5% 0.03 

Barren 2,025 0.11% 1 

Grass/Pasture 333,206 18% 3 

Developed/Open Space and 

Developed/Low Intensity 148,153 12% 5 

Shrubland 53,498 3% 5 

Wetlands 5,478 0.31% 5 

Crops, vegetables, fruit 11,088 4.1% 10 

Forest 797,351 58% 60 

Developed/Medium Intensity 22,222 1.2% 70 

Developed/High Intensity 11,103 0.61% 350 

Sum 1,806,113 100.00% 

Weighted-Average Surface Roughness 39 

The effect of surface roughness of vertical dispersion is calculated as the MACCS parameter 

ZSCALE. 

൬ = ܧܮܣܥܼܵ

39	ܿ݉

3	ܿ݉ ൰

଴.ଶ

= 	1.67

Eq. 5-1 

5.6.3 Atmospheric Dispersion 

Within MACCS, plume dispersion during downwind transport is modeled using a segmented 

Gaussian plume model. As the plume segment moves away from the source, dispersion 

generally increases the “spread” or volumetric distribution of the contamination which 

simultaneously decreases the concentration of the radionuclides in a particular volume of air. 

The plume is vertically bounded (Z-direction) between the ground surface and the mixing layer 

height. 

The Gaussian plume model uses two spatially dependent dispersion parameters, sigma-y (ߪ௬) 

and sigma-z (ߪ௭), to estimate the atmospheric dispersion. These parameters can be specified 

in two ways in MACCS: as power-law functions or with lookup tables. The dispersion 

parameters are specified for each stability class. Sequoyah SOARCA used the power law 

function option as a function of time beyond 800 meters. The crosswind (horizontal 

perpendicular to the plume transport direction) and vertical dispersion coefficients, ߪ௬ and ߪ௭, 

respectively are discussed in more detail in the UA section. 

5.6.4 Dry and Wet Deposition 

Deposition is the transfer of aerosol particles containing radionuclides from the atmospheric 

plume onto ground-level surfaces (e.g., crops, lakes, etc.). As described in Section 5.5, 

precipitation occurs during six percent of the hours at Sequoyah. Therefore, dry deposition is 

the predominant mechanism for plume depletion. However, wet deposition is also important 

because when precipitation occurs it is very effective in removing aerosols. 

 
5-16

Dry deposition is calculated dependent on the particle size, atmospheric turbulence, and other 

factors. Wet deposition is primarily a function of the precipitation rate within a particular vertical 

region of the atmosphere. For example, rainout or removal of radionuclides during rain can 

create localized relatively high concentrations of contamination called hotspots. Both dry and 

wet deposition are strongly influential parameters which are discussed extensively in the UA 

section. 

5.7 Protective Action Parameters 

Protective actions can include evacuation, sheltering in place, relocation, and potassium iodide 

(KI) administration. For Sequoyah, the Tennessee Department of Health has a KI program, but 

does not pre-distribute KI to EPZ residents. KI is available for pickup by residents that live 

within 5 miles of the plant, and KI is planned to be available at mass-care shelters [50]. The 

population within 5 miles is approximately 10 percent of the total EPZ population, and it is 

assumed that many residents will not have KI on hand at the time of an accident. There is also 

a reduction in efficacy if KI is not ingested within the appropriate time. Furthermore, the impact 

from dose to the thyroid is a risk injury, which is not reported in the SOARCA results; therefore, 

no KI administration was modeled. 

This Sequoyah SOARCA report differs from earlier Peach Bottom and Surry SOARCA 

documents [4][5] with regard to the state of infrastructure assumed in the analyses. In those 

prior analyses, the the impacts to the evacuation road networks and infrastructure were 

considered in sensitivity analyses. The Sequoyah MACCS model has been developed using 

the assumption that the large seismic initiating event not only disrupts Sequoyah plant systems, 

but also affects the evacuation conditions. Roadway access and capacity are affected due to 

the assumed loss of all bridges in the EPZ. In order to account for this scenario, a limited-scope 

roadway capacity analysis was conducted of the EPZ surrounding Sequoyah and is described in 

Appendix C. Evacuation routes are dispersed throughout the EPZ such that addressing the 

infrastructure issues by quadrant provides a reasonable approach for developing evacuation 

time estimates (ETEs) that account for damage to bridges. All bridges along these roadways 

were assumed to fail due to the earthquake. The roadway network was reviewed using aerial 

mapping to determine alternate routes to exit the EPZ. Accessible routes out of the EPZ were 

identified, and the capacities of these routes were calculated. Topography of the area was 

reviewed using United States Geological Service mapping to identify low lying areas where the 

potential for roadway flooding exists, should the upstream Watts Bar dam fail. From the ETE 

study, the number of vehicles exiting different areas of the EPZ was identified [57]. An estimate 

of the evacuation time was then developed using the number of vehicles, available evacatuion 

routes, and roadway capacity at the available exit points of the EPZ. A description of the 

capacity analysis methodology, bridge locations, alternate routes, vehicles and results is 

discussed in Appendix C. The earthquake-impacted roadways increase the times required for 

evacuees to exit the EPZ. 

5.7.1 Cohort Definitions 

Modeling of emergency response includes the timing of response actions related to protecting 

the public health and safety. MACCS treats protective action response activities at the cohort 

levels. A cohort is defined as a segment of the population with unique response characteristics. 

The cohort groupings used for Sequoyah are shown in Table 5-6. 
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Table 5-6 Sequoyah evacuation cohorts. 

Cohort Distance Cohort Description 

1 0 to 10 miles Schools 

2 0 to 10 miles Special facilities (e.g., hospitals) 

3 0 to 10 miles Transit dependent evacuees 

4 0 to 10 miles Early public evacuees 

5 0 to 10 miles General public evacuees 

6 0 to 10 miles General public tail evacuees 

7 10 to 15 miles Shadow evacuees 

8 0 to 10 miles Non-evacuating public 

Cohorts were established to represent members of the public who may evacuate early, 

evacuate late, those who refuse to evacuate, and those who evacuate from areas not under an 

evacuation order (e.g., shadow evacuees). SOARCA [3] was the first application of the newly 

increased number of allowable cohorts in MACCS. At the time, there was uncertainty regarding 

increased computer runtime with increasing cohorts, and it was decided that a maximum of 6 

cohorts would be used for Surry and Peach Bottom SOARCA analyses [4][5]. The run time 

issues never materialized, and MACCS analyses have since included many cohorts with no 

computer run time issues. Thus, when structuring the populations in this Sequoyah analysis, 

the use of 8 cohorts was determined to best represent the response. Further description of 

each cohort group is provided below: 

Cohort 1: 0 to 10 mile schools 

This cohort included elementary, middle, and high school student populations within the 

EPZ. Schools within the Sequoyah EPZ receive notification upon declaration of a Site 

Area Emergency (SAE). Approximately 19 percent of residents within the EPZ are 

school-aged children, based on data provided in the site ETE. 

Cohort 2: 0 to 10 mile special facilities 

The special facilities population includes residents of medical facilities, such as hospitals 

and nursing homes. In an emergency, special facilities would be evacuated individually 

over a period of time based upon available specialized transportation (e.g., ambulances) 

and the number of return trips needed to evacuate a facility. In this regard, the special 

facilities cohort behaves similarly to the transit dependent cohort in that they shelter for 

an extended period of time prior to evacuating. Special facilities were modeled as a 

single cohort because the percentage of the total Sequoyah population is less than one 

percent. 

Cohort 3: 0 to 10 mile transit dependent evacuees 

This cohort includes EPZ permanent residents who do not have access to a vehicle or 

are dependent upon help from outside the home to evacuate. This group depends upon 

public transportation to evacuate, which can take additional time to implement. 

Approximately one percent of residents within the EPZ are identified in the ETE as 

transit-dependent [50]. 

Cohort 4: 0 to 10 mile early public evacuees 

This cohort includes one of three general public categories. It represents a portion of the 

general public that begins to evacuate prior to other cohorts which allows their potential 

evacuation speed to be higher. Under the postulated earthquake and assumed damage 
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to infrastructure within the EPZ (e.g., bridge failures, power outage, etc.), it was 

assumed this cohort responds following the first sirens, which are sounded at SAE for 

this site. Sirens are expected to activate because they have backup batteries. The 

extensive infrastructure damage, assumed in this scenario, would be evident to EPZ 

residents, and there is the possibility that a power outage could limit ability of residents 

to receive instructions via the Emergency Alert System (EAS) messages. Thus, it was 

assumed this cohort evacuates in response to the first sirens heard. This cohort enters 

the roadway network prior to congestion. Evacuation speeds are estimated to be slow 

due to the extent of infrastructure damage. Approximately 40 percent of residents within 

the EPZ were modeled as early public evacuees. This is based on the ETE mobilization 

curves, but applied after the SAE sirens in this scenario. 

Cohort 5: 0 to 10 mile general public evacuees 

This cohort includes EPZ permanent residents who leave after the sirens are sounded 

for GE, and the evacuation order is broadcast via EAS messages. Even with a power 

outage, many residents and facilities have tone alert radios, battery powered radios, and 

car radios, where EAS messaging is transmitted. Thus, this group is assumed to receive 

the official evacuation order prior to departing. Approximately 30 percent of residents 

within the EPZ are considered general public. 

Cohort 6: 0 to 10 mile general public tail evacuees 

This cohort includes the last remaining members of the general public to evacuate from 

the 10 mile EPZ. The evacuation tail takes longer to evacuate for valid reasons, such as 

shutting down farming or manufacturing operations, performing other time consuming 

actions prior to evacuating, or they may have missed the initial notification. 

Approximately 10 percent of residents within the EPZ are tail evacuees (i.e., the last 

fraction of the general population to evacuate). 

Cohort 7: 10 to 15 mile shadow evacuees 

This cohort includes evacuees from beyond the EPZ who leave after the advisory to 

evacuate. The size of the shadow evacuation can be influenced by communication 

during the emergency and may be influenced due to external events such as the 

earthquake. Shadow evacuations typically begin shortly after a general evacuation 

begins. It is assumed 20 percent of the population from the 10 to 15 mile area beyond 

the EPZ shadow evacuate [58]. 

Cohort 8: 0 to 10 mile non-evacuating public 

This cohort includes evacuees who may refuse to evacuate and are modeled as though 

they are performing normal activities. Approximately 0.5 percent of the population is 

considered to be non-evacuating. This percentage of the population that does not 

evacuate is consistent with research on large scale evacuations that has shown a small 

percentage of the public refuses to evacuate [88]. 

This Sequoyah SOARCA analysis differs from earlier Peach Bottom and Surry SOARCA 

analyses [4][5] because it implements a keyhole evacuation model representative of the 

expected protective action recommendation (PAR) and protective action decision (PAD). The 

PAR is a recommendation that is considered by offsite authorities when they make their PAD. 

This keyhole evacuation modeling feature was not available when the previous SOARCA 

analyses were performed. It is intended to reflect site-specific protective actions where the full 

EPZ is not evacuated, but instead a smaller region around the plant is evacuated together with 

downwind sectors. The keyhole model accounts for changing wind direction over the course of 
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the release and preferentially evacuates the population in the downwind sectors as wind shifts 

occur. If the wind shifts, based on hourly weather data, the evacuation is expanded to include 

additional sectors as illustrated in Figure 5-8. The model requires that an inner radial distance 

be defined, along with the number of compass sectors on either side of the wind direction. 

Figure 5-8 Keyhole evacuation area with a wind shift 

MACCS implements the keyhole model using radial distances and sectors as discussed above; 

however, offsite protective actions are implemented using emergency response planning areas 

(called “Sectors” at Sequoyah). These Sectors are established by local authorities and align 

with geographical features or political boundaries. Thus, to accommodate the modeling, the 

expected response was reviewed and the most appropriate radial distances, available in the 

MACCS model, were used to define the keyhole dimensions. 

To determine the keyhole distances to be modeled (radius and downwind distance) the licensee 

protective action recommendation (PAR) procedures and the 2013 FEMA After Action Report 

(AAR) for a full scale exercise were reviewed [58], [95]. The licensee PAR procedures include 

multiple keyhole descriptions, suggesting a keyhole is a likely PAR. The FEMA AAR provides 

information on a PAD that was determined in a 2013 full scale exercise [95]. The initial 

evacuation in the 2013 exercise included Sectors A-1, B-1, C-1 and D-1, three of which extend 

beyond the 2 mile radial distance. The initial evacuation in the PAD also included Sectors B and 

C, which extend to the 10 mile EPZ boundary as shown in Figure 5-2. Reviewing the potential 

keyhole options, all of which would include Sectors A-1, B-1, C-1 and D-1 plus downwind 

sectors, it was determined that modeling a 5 mile radius, together with downwind sectors to 10 

miles was most appropriate. The shadow evacuation is modeled following the keyhole 

approach to a distance of 5 miles beyond the limit of the area for which an evacuation was 

ordered. 
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5.7.2 Emergency Response Timelines 

Response timing, travel speeds, and durations of elements of the response are developed to 

represent emergency response actions directed by offsite response organizations (OROs) and 

implemented by the public. The modelling considers that notification of the public occurs first, 

followed by sheltering of the public, and then evacuation. MACCS employs three evacuation 

phases (early, middle, and late) where travel speeds and durations are defined for each 

evacuating cohort. In order to ensure all individuals complete their evacuation, the late phase of 

the evacuation is considered to last as long as necessary for all evacuating individuals to fully 

evacuate. 

Figure 5-9 illustrates the modeling implemented for the Sequoyah emergency response timeline 

and travel speeds. The MACCS input parameters related to mobilization and evacuation route 

information were developed primarily from the ETE for Sequoyah [57]. The postulated initiating 

event for the accident is an earthquake of such magnitude that damage to offsite infrastructure 

would be expected. The figure shows the normal activity, sheltering, and the early and middle 

phases of the evacuation by cohort relative to the timing of the SAE, SAE siren, General 

Emergency (GE), and GE sirens (0.25, 0.50, 2.0 and 2.75 hours, respectively). In the 

evacuation phases, it also lists the estimated average EPZ travel speeds in miles per hour. 

The cohort travel speeds are consistent at a particular time of the evacuation with the exception 

of cohort 7 (shadow evacuees located outside of the EPZ and travelling on the roads outside of 

the EPZ). For the STSBO and LTSBO scenarios modeled in this analysis, it is assumed 

Sequoyah would declare a SAE and notify the OROs within 15 minutes. Comparing Sequoyah 

emergency action levels (EALs) with results from a full participation plume exposure pathway 

EPZ exercise reported in the FEMA AAR [95], it was determined that a GE would likely be 

declared two hours after accident initiation for both the STSBO and LTSBO. OROs then make 

a PAD, activate sirens, and broadcast EAS messages. From the FEMA AAR for Sequoyah [95], 

the time from ORO receipt of the GE declaration to sounding of the GE sirens was 36 minutes 

which was rounded to the next 15 minute increment (45 minutes). A 15 minute increment in 

modeling response actions is generally applied. 

An assumption is that the earthquake is of such magnitude that the early general population 

cohort (#4 on Figure 5-9) responds to the SAE siren, rather than the GE siren. This cohort is 

assumed to prepare to evacuate based on having felt the earthquake, heard the SAE sirens, 

become aware of school evacuations, and other media broadcasts. Although this is an early 

response, the timing is such that their departure occurs 1.25 hours after the earthquake, and 

they exit the EPZ in 4.5 hours; whereas the tail, which is the latest population to mobilize, exits 

the EPZ in 11.75 hours. 

There is some uncertainty surrounding the timing, delays, durations, and travel speeds; 

therefore, the input parameters for these are discussed extensively in the UA section. In 

addition, the specific behaviors of each cohort are also discussed in that section particularly as 

they apply to the UA and ranges of sampled parameters. 
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Figure 5-9 Sequoyah emergency response timeline and travel speeds 

(mph) 

MACCS allows implementation of a delay to shelter (DLTSHL) in which shielding values are 

applied based on normal activity. This period is followed by delay to evacuation (DLTEVA) 

which represents the time residents are in their homes preparing to evacuate. One value is 

provided for each cohort, thus it is an average value applied to that entire cohort population. 

• DLTSHL represents a delay from the time of the start of the accident until a cohort 

begins to shelter. Activities during this period typically include the licensee providing a 

PAR to the offsite authorities, OROs notification of the public to evacuate, and the time 

needed to begin sheltering. Sheltering is typically assumed to be a place of residence. 

• DLTEVA defines the length of the sheltering period from the time a cohort enters the 

shelter until the cohort begins to evacuate. Delay to evacuate represents the shelter 

period. The duration may reflect a delay in response to the evacuation order, a need to 

wait for the return of commuters, a need to wait for public transportation, a need to shut 

down operations prior to leaving work, etc. 

The phase durations are defined by DURBEG (duration of beginning phase), DURMID (duration 

of middle phase) and the late phase defining whatever additional time is needed for a cohort to 

complete the evacuation. Durations are assigned uniquely for each cohort and are adjusted 

based on the cohort specific attributes. The times are typically assigned such that DURBEG 

plus DURMID provides sufficient time for evacuees to exit the EPZ. The late phase begins at 

the end of DURMID and ends when the last cohort exits the analysis area. MACCS allows one 
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speed value for each cohort for each phase duration described as ESPEED1, ESPEED2, and 

ESPEED3. 

The weather can affect emergency response by reducing the travel speeds. An evacuation 

speed multiplier (ESPMUL) reduces travel speed when precipitation is occurring, as indicated 

from the meteorological data file. The speed multiplier was set at 0.8, which reduces the 

ESPEED values to 80 percent of the fair weather travel speed whenever precipitation is 

identified in the weather data. The meteorology data does not distinguish the type of 

precipitation, however the climate data indicates historically snow and ice are rare (less than 10 

percent of the precipitation) as compared to rain. 

5.7.3 Shielding From Dose 

The shielding or protection factor is a unitless quantity used to reduce the radiation dose to 

account for protection provided by a given protective action or mitigating environmental 

condition. Shielding is specified by the user for the cloudshine, inhalation, groundshine, and skin 

dose exposure pathways, and for three activity types (normal activity, sheltering, and 

evacuation). A shielding factor of unity represents the limiting case of a person receiving the full 

dose (i.e., standing outdoors and completely unprotected from exposure), and a shielding factor 

of zero represents the limiting case of complete shielding from the exposure. 

Normal activity refers to a combination of activities that are averaged over a week and over the 

population, including being indoors at home or work, commuting, and being outdoors. The 

normal activity values assume that the average person spends 19 percent of the day outdoors 

and 81 percent of the day indoors [96]. MACCS shielding parameters (CSFACT, GSHFAC, 

PROTIN, and SKPFAC) were researched in the development of NUREG/CR-4551, Vol. 2, 

Part 7 [80]. For other SOARCA analyses, the normal activity shielding values for CSFACT and 

GSHFAC started with the site-specific NUREG/CR-4551 recommended values and adjusted 

these to represent the percent time indoors and outdoors. The values used for Sequoyah were 

similarly adjusted using site-specific shielding factor recommendations. For example, in 

NUREG/CR-4551, the recommended Sequoyah values for evacuation CSFACT and sheltering 

CSFACT were 1.0 and 0.65, respectively [80]. The normal activity, regular facilities CSFACT 

value presented in Table 5-7 is developed by multiplying 81% of the time indoors by the 0.65 

sheltering value and adding this to the 1.0 evacuation value (because evacuation represents 

predominately unshielded time outdoors) multiplied by 0.19 for a time-weighted result of 0.72. 

As show in Table 5-7, regular facilities are normal residences and special facilities are larger, 

more robust buildings with different shielding values, such as hospitals and schools. The 

breathing rate, skin protection, and inhalation protection are unchanged from Peach Bottom and 

Surry SOARCA [4][5]. Normal activity alone is assumed for nonevacuees. While some near the 

EPZ may shelter, most of the exposure to the plume is not in the near vicinity of the EPZ since 

results show the plume typically depletes slowly with distance. 

Table 5-7 Shielding and Protection Factors for Evacuating Cohorts. 

Population 

Facility 

Type 

Cloudshine Shielding 

(CSFACT) 

Inhalation and Skin 

Protection (PROTIN and 

SKPFAC) 

Groundshine Shielding 

(GSHFAC) 

Normal 

Activity Sheltering Evacuating Normal Activity Sheltering Evacuating Normal Activity Sheltering Evacuating 
5-23

Regular 

Facilities 0.72 0.65 1.00 0.46 0.33 0.98 0.26 0.20 0.50 

Special 

Facilities 0.31 0.31 1.00 0.33 0.33 0.98 0.05 0.05 0.50 

Two specific shielding factors important for the UA, GSHFAC and PROTIN, are described in 

detail in Section 5.9.3. 

5.7.4 Hotspot Relocation Time (TIMHOT) 

The hotspot relocation time (TIMHOT) is the estimated time needed to relocate residents from 

areas that exceed the hotspot dose threshold (DOSHOT). This user specified time (36 hours) is 

implemented in MACCS after plume arrival. Hotspot and normal relocation are determined in 

MACCS based on the total effective dose commitment projected to be received by an individual 

who remained in place for the entire emergency phase period while engaging in normal activity. 

For Sequoyah base case analyses, this is 5 rem over a 36 hour exposure period. The pathways 

used for calculating the total effective dose commitment are cloudshine, groundshine, direct 

inhalation, and resuspension inhalation. The reference time for the relocation dose criteria is 

plume arrival. Relocated individuals are removed from the problem for the duration of the 

emergency phase and receive no additional dose during this phase. 

The EPA PAGs for evacuation are typically used in MACCS as the dose thresholds at which the 

public would be relocated. In practice, when these thresholds are projected to be exceeded, 

OROs would be expected to relocate individuals from the affected areas. This application is 

typically considered for residents beyond the EPZ, but also applies to residents within the EPZ 

who may have refused to follow the initial evacuation orders. It is assumed these individuals will 

relocate when they understand a release has occurred, and they are informed they are located 

in elevated dose areas. 

The hotspot and normal relocation time, TIMHOT and TIMNRM, should include all temporal 

elements that contribute to the relocation activities. This includes time for OROs to define the 

affected areas, develop messaging and initiate the EAS to notify the public. This also includes 

time for the public to prepare to leave and travel out of the affected area. Each of these 

elements can be influenced by factors such as size of the affected area, number of affected 

residents, location of residents when the warning is received, available resources to coordinate 

the relocation, clarity of data, weather, etc. Notification may be augmented by route alerting 

(which is identified in the offsite emergency plan), Reverse 911®, or other communication 

methods. 

MACCS implements this parameter by removing the entire affected population from the dose 

equation a point in time, the time after plume arrival specified by TIMHOT. Because relocation 

takes time to implement, this user specified value was developed as an average time for 

relocation. In the UA section, a distribution has been developed to account for a range of 

source terms, population densities, seismic impact, and other characteristics that influence 

relocation. 

5.7.5 Normal Relocation Time (TIMNRM) 

TIMNRM is the time to relocate residents from areas that exceed the normal dose threshold 

(DOSNRM). This user specified time is relative to plume arrival. For Sequoyah base case 

analyses, this is 1 rem over a 24 hour exposure period. The time includes the elements 
5-24

described for TIMHOT. In addition, time may be needed to allow clearance of residents 

relocated due to the hotspot criteria. MACCS implements this parameter by removing the entire 

affected population from the dose equation at a point in time after plume arrival specified by 

TIMNRM. As with TIMHOT, an average time is developed by considering the influencing 

factors. Both TIMHOT and TIMNRM time periods are varied linearly and discussed in the UA 

section. 

5.7.6 Intermediate and Long-Term Phase 

The previous sections have focused on protective actions that occur during the early phase. 

The early phase, also referred to as the emergency phase, focuses on the time scale of the 

release, emergency response, and prompt dose effects associated with exposure to radiation 

from the atmospheric plume via direct inhalation, cloudshine, groundshine, and skin deposition. 

Although the early phase lasts for days, the cumulative health effects that could result from early 

phase exposure are calculated over a period of one year [55]. Unlike previous Surry and Peach 

Bottom SOARCA analyses [4][5], Sequoyah SOARCA models an intermediate phase, 

 Intermediate Phase 

The intermediate phase is modeled as one year, beginning at the end of the early phase. It 

spans the time period required for authorities to determine whether contaminated areas are 

suitable for habitation (MACCS habitability criterion) and prepare to decontaminate where 

needed. It is, therefore dependent on the magnitude of a release and the size of contaminated 

areas. The dose criteria for the intermediate phase was selected as 2 rem in the first year, 

which is consistent with the EPA protective action guide (PAG) [94]. The chronic phase spans 

50 years to account for doses accumulated from groundshine (mainly longer lived gamma 

emitting radionuclides) and inhalation from re-suspension of contaminated soils. The dose 

criteria for the long-term phase was selected as 500 millirem per year after the intermediate 

phase, which is also consistent with the EPA PAGs [94]. 

 Long-term Phase 

This analysis also uses updated long-term protective action parameters to reflect changes in 

cost parameters. Decontamination and cost parameters were based on values from updated 

guidance on MACCS cost parameters related to protective measures and decontamination [97]. 

However, these parameters are not used to calculate economic consequences in this analysis, 

as was similarly done in Surry and Peach Bottom SOARCA analyses. Instead, cost decisions 

were only used to support the habitability decisions in the model to evaluate long-term doses. 

These parameters affect decisions on whether contaminated areas can be restored to 

habitability and therefore affect predicted doses and health effect risks. Unlike previous 

analyses, Sequoyah SOARCA evaluated three levels of decontamination represented by dose 

reduction factors (DRF) of 3, 5 and 15. Using three DRFs provides a level of conservatism 

because for any area where a DRF of 3 is insufficient, the model applies progressively higher 

DRFs, up to 15, which costs more to implement than a DRF of 3. For example, if the model 

calculates the need for a DRF of greater than 5 but less than 15, such as a DRF of 10, the full 

cost for a DRF of 15 is applied. All long-term protective action values for the dose projection 

period, dose limit, population relocation cost, along with all associated decontamination 

parameters can be found in Appendix B. 

While SecPop enables grid-element-specific values of farmland wealth and non-farmland 

wealth, MACCS requires a single value of each for the entire region to determine whether 
5-25

decontamination would be cost-effective if needed to restore habitability. The non-farmland 

wealth parameter, VALWNF, escalated to 2015 was calculated to be $342,714 per person 

based on a population-weighted average of all grid elements within 50 miles. This value 

includes public and private property, not associated with farming that would be unusable if a 

portion of the land was temporarily or permanently interdicted due to contamination. A similar 

process, using farmland-area-weighted averages was used to identify the single value of 

farmland wealth parameter, VALWF, for the region as $11,287 per hectare. VALWF represents 

farmland property such as publicly and privately owned grazing lands, farm buildings, and 

machinery. 

5.8 Dosimetry 

The doses computed by MACCS use dose conversion factors. The radiological dose to an 

individual in a spatial grid element is the sum product of (1) the integrated air concentration or 

total ground deposition of a radionuclide (in that element), (2) the exposure duration for an 

exposure pathway, (3) the shielding factor for an exposure pathway, (4) the dose conversion 

factor for a radionuclide and pathway, and (5) the usage factor for an exposure pathway. The 

total dose to an organ or the whole body used for modeling of health effects or protective action 

decision-making is then summed across all radionuclides and applicable exposure pathways. 

Several key dose and risk components are examined in the UA section of this report. 

For the early phase, two kinds of doses are calculated: (1) acute doses used for calculating 

early fatalities and injuries and (2) lifetime dose commitment used for calculating cancers 

resulting from the early exposure. For the long-term phase, only lifetime dose commitments are 

calculated. 

The quantities used in the dose equations depend on the source term from the accident and the 

exposure pathway and are either user inputs or are computed by MACCS. The radionuclide 

ground level concentrations are calculated by MACCS along the plume centerline. In order to 

calculate doses at different locations within a grid element, a correction factor (discussed in 

Section 3.1.1 and 3.2.1 of NUREG/CR-4691, Volume 2 [108]) is derived to reduce concentration 

moving away from the plume centerline. Shielding factors for the long-term phase use the early 

phase normal activity value for the entire population. 

The dose conversion factors used in this analysis are identical to those used in the Surry UA 

[17]. They are based on a methodology which considers the updated dosimetry and health 

effects models from Federal Guidance Report No. 13 (FGR-13), as well as the instantaneous 

dose rate values provided in the supplemental files provided with FGR-13 [96]. This allows both 

a consideration of the acute effects due to short-term exposure, as well as the ability to consider 

annual doses as well as committed doses. The dose conversion factor file set used in this 

analysis, “FGR13GyEquivDCF.INP”, together with its annual dose files, contains dose 

conversion factors based on FGR-13 for 825 radionuclides, 26 tissues, organs, and the whole 

body effective dose, and four exposure pathways. MACCS contains a more limited set of organ 

dose quantities than are available in the DCF file based on FGR-13. MACCS considers nine 

organs (including whole body) for stochastic effects from chronic exposures and six organs for 

deterministic effects from acute exposures. Due to a current use of eight cancer sites (organs), 

MACCS calculates the dose to seven specific cancer sites and one residual cancer site. To 

estimate residual cancers, the dose coefficients for the pancreas are used as a surrogate for 

dose to soft tissue, following recommendations of the Oak Ridge National Laboratory letter 

report [82]. 
5-26

The dosimetry calculation considers a usage factor for the inhalation and ingestion exposure 

pathways. For cloudshine and groundshine, the value in the calculation is one. For inhalation, 

the usage factor is a volumetric breathing rate. Consistent with past studies, this parameter is 

2.66E-4 m3/s for all populations and time periods. This value was derived in NUREG/CR-4551, 

Vol. 2, Rev. 1, Part 7 for an adult man who sleeps 8 hours per day and engages in light activity 

when awake [80]. 

The effective dose (ICRP60ED) is used internally by MACCS for simulating protective action 

decisions based on dosimetric quantities computed under a system of radiation protection. The 

tissue weighting factors used in the computation of the dose conversion factor for the 

ICRP60ED effective dose are taken from ICRP-60 and are identical to those used in the Surry 

UA [17]. 

5.8.1 Exposure and Commitment Periods 

The exposure period for internal pathways, inhalation and ingestion, is the period of time when 

the inhalation or ingestion occurs; however, doses can continue over a person’s entire lifetime 

following the exposure. The period of time over which doses are received from an internal 

pathway is accounted for in the construction of dose coefficients by integrating the doses over a 

finite period, representing a person’s lifetime, called a dose commitment period, which is usually 

taken to be 50 years when calculating internal pathway dose coefficients for adults. The 

assumption is that the average adult lives for an additional 50 years following the exposure. 

Most of the exposures during the long-term phase are from groundshine; a smaller fraction is 

from ingestion inhalation of resuspended aerosols. Since groundshine is an external pathway, 

doses received are concurrent with the exposure, whereas, exposures from inhalation and 

ingestion during each year of the long-term phase contribute to doses received over the 

subsequent 50-year commitment period. 

The total dose received in the first year thus corresponds to: 

• all dose from external exposure during the emergency phase, 

• most of the dose from internal exposure during the emergency phase, 

• all of the dose from external exposure during the first year of the long-term phase, and 

• most of the dose from internal exposure during the first year of the long-term phase. 

The annual doses received in subsequent years correspond to: 

• a fraction of the dose from internal exposure during all previous years 

• most of the dose from internal exposure during that year, and 

• all of the dose from external exposure during that year. 

The maximum exposure time (EXPTIM) is 50 years. This was chosen to be the same as the 

commitment period. 

5.8.2 Dose-Response Models 

Analysis of different dose threshold levels, including those that limit health effects from exposure 

to doses above the threshold values stated, are investigated to understand how the risk of 
5-27

cancer changes with radiation dose. Table 5-8 compares five dose threshold levels considered 

in this analysis. The types of models are LNT, annual threshold, and combined annual and 

lifetime thresholds. Three predominate dose-response models were considered in previous 

Surry and Peach Bottom SOARCA analyses [4][5]. The dose-response relationships can be 

described as follows: 

1. The NRC accepts the LNT hypothesis as a model for estimating radiation risk and thus 

is reported here consistent with prior Surry and Peach Bottom SOARCA analyses. 

2. Annual threshold models truncate dose-response below an average background 

radiation to a US citizen of 620 millirem including the averaged medical exposure or 310 

millirem solely from US averaged natural background radiation. These thresholds are 

based on background radiation estimates from the National Council on Radiation 

Protection and Measurements (NCRP) [96]. 

3. The highest truncation level used in this analysis is consistent with the Health Physics 

Society (HPS) position statement which states that a quantified risk should not be 

assigned to doses below 5 rem annually with a lifetime limit of 10 rem [100]. 

The 10 millirem annual dose truncation level was investigated in this analysis, but as 

determined for the other SOARCA analyses produced results that were only slightly lower than 

the LNT assumption, and thus were not included in offsite consequence results [8]. 

Table 5-8 Dose responses modeled. 

Model type LNT Annual Annual Annual HPS 

Annual 

threshold n/a 10 mrem/year 

310 

mrem/year 

620 

mrem/year 5 rem/year 

Lifetime 

threshold n/a n/a n/a n/a 10 rem 

The probability of a LCF is calculated separately for each cancer syndrome related to each 

target organ and is based on the technical approach described in the National Academy of 

Sciences / National Research Council Biological Effects of Ionizing Radiation (BEIR) V report 

[101]. In 2009, the National Research Council released the BEIR VII report, which is the most 

up to date study of cancer induction from exposure to ionizing radiation [102]. However, 

because the dose coefficients used in the SOARCA studies are from FGR-13 which was 

developed using risk factors from BEIR V, it was decided to use risk factors from BEIR V rather 

than BEIR VII. 

A dose and dose rate effectiveness factor (DDREF) was applied to all doses in the late phase of 

the offsite consequence calculation and to those doses in the early phase that were less than 

20 rem (0.2 Sv) to the whole body. This factor, which reduces the health impact of dose, 

accounts for the fact that protracted low doses are believed to be less effective in causing 

cancers than acute doses. DDREF for all cancers, except the breast, was 2.0, and for the 

breast, it was 1.0, as recommended in the BEIR V report [101]. The cancer dose-response 

linear and quadratic factors originate from BEIR V, which recommends a linear dose-response 

model for all cancer types. A complete list of cancer risk factors is provided in Appendix B. 
5-28

5.9 Uncertain MACCS Input Parameters and Distributions 

There are potentially thousands of parameter values that could be varied in MACCS. The 

phenomenology of the submodels (meteorology, dose response, atmospheric transport, etc.) is 

well studied; therefore the UA focuses on a smaller set of parameters. The figures of merit for 

MACCS are individual LCF risk and individual early fatality risk at specified distances. The Surry 

UA [17] included a complete review of MACCS parameters identified in Appendix D, “Glossary 

of Input File Variables,” of NUREG/CR-6613, “Code Manual for MACCS2,” [55]. The results of 

that review influenced the selection of the Sequoyah parameters. A review of the MACCS 

parameters in the Peach Bottom and Surry UAs was conducted, and it was determined that all 

parameters previously considered would be considered in this analysis. The following 

parameter sets were included in the analysis. 

• deposition, 

• dispersion, 

• shielding, 

• early health effects, 

• latent health effects, and 

• emergency response. 

Both epistemic and aleatory uncertainty exists for many MACCS parameters, and the combined 

effect adds complexity when developing the distributions. Furthermore, for some MACCS 

parameters, compromise values were necessary when a large number of different situations 

(e.g., weather trials and time of day) exist, but only one input value may be assigned. The 

specified values selected were deemed the most appropriate to use for these situations. As a 

result, the epistemic uncertainty distributions for these parameters attempt to characterize the 

uncertainty of the most appropriate values. 

Aleatory uncertainty in weather is treated in MACCS. Weather conditions that would apply in 

the case of a potential accident at some time in the future cannot be known in advance. 

MACCS accounts for weather variability by analyzing a statistically significant set of weather 

trials, and the modeled results are ensemble averages of weather that represent the full 

spectrum of meteorological conditions. This sampling strategy was chosen to represent the 

statistical variations of the weather and is consistent with MACCS best practices [8]. 

Emergency response to a potential accident cannot be predicted in advance either, because it is 

dependent on source term magnitude, time of day, weather, and site specific 

characteristics. The MACCS parameters varied in the analysis are listed in Table 5-9. 

Table 5-9 Sequoyah MACCS model uncertain parameters. 

Epistemic Uncertainty 

Deposition 

Wet Deposition Coefficient (CWASH1) 

Dry Deposition Velocities (VDEPOS) 

Shielding Factors 

Groundshine Shielding Factors (GSHFAC) 

Inhalation Protection Factors (PROTIN) 

Early Health Effects 

Early Health Effects LD50 Parameter (EFFACA) 
5-29

Epistemic Uncertainty 

Early Health Effects Exponential Parameter (EFFACB) 

Early Health Effects Threshold Dose (EFFTHR) 

Latent Health Effects 

Dose and Dose Rate Effectiveness Factor (DDREFA) 

Lifetime Cancer Fatality Risk Factors (CFRISK) 

Long-Term Inhalation Dose Coefficients 

Dispersion 

Crosswind Dispersion Linear Coefficient (CYSIGA) 

Vertical Dispersion Linear Coefficient (CZSIGA) 

Time-Based Crosswind Dispersion Coefficient (CYCOEF) 

Emergency Response 

Evacuation Delay (DLTEVA) 

Evacuation Speed (ESPEED) 

Hotspot Relocation Time (TIMHOT) 

Normal Relocation Time (TIMNRM) 

Hotspot Relocation Dose (DOSHOT) 

Normal Relocation Dose (DOSNRM) 

Aleatory Uncertainty 

Weather Trials 

The storyboard process described in Section 3.2 was also implemented for MACCS 

parameters. Many of the uncertain MACCS parameters have both epistemic and aleatory 

contributions, and the combined effect adds complexity when developing the distributions. The 

focus of this analysis is on epistemic uncertainty; however, some of the distributions developed 

for the uncertain inputs include aleatory uncertainty. For example, some of the evacuation 

parameters also include the effects of weather, which is considered aleatory uncertainty. While 

the effects of adverse weather on evacuation speed are partially accounted for through use of a 

single speed reduction factor, this may not address all weather conditions, and this leads to 

some uncertainty in the input parameters resulting from weather uncertainty. No effort was 

taken to separate aleatory and epistemic uncertainties when they both contribute to individual 

parameters; instead, a simple approach is taken where weather is considered to be aleatory in 

nature, and all other uncertainties are considered to be epistemic in nature. A total of 622 

parameters were sampled for the MACCS portion of the UA. Table 5-10 lists the distributions 

and bounds related to each parameter. For many of these parameters, the sampling approach 

is quite complex and is described in detail in this section. 

Several of the parameter distributions selected for this analysis are based on expert elicitation 

data captured in NUREG/CR-7161, “Synthesis of Distributions Representing Important NonSite-Specific Parameters in Off Site Consequence Analysis” [79]. The United States and the 

Commission of European Communities conducted a series of expert elicitations to obtain 

distributions for uncertain variables used in health consequence analyses related to accidental 

release of nuclear material. The distributions reflect degrees of belief for non-site specific 

parameters that are uncertain and are likely to have significant or moderate influence on the 

results. The methodology in NUREG/CR-7161 [79] used a resampling of the expert values and 

was based on the assumption of equal weights of the expert opinions. 
5-30

Table 5-10 Uncertain MACCS parameters applied in the analysis. 

Uncertain Parameter Distribution 

type 

Mode 

α, β

Lower 

Bound 

Upper 

Bound 

Wet Deposition (CWASH1) Log uniform 10-6 10-4 

Dry Deposition Velocities (VDEPOS, 

m/s) Triangular

1 5.04E-04 1.60E-04 1.60E-03 

2 4.63E-04 1.46E-04 1.46E-03 

3 6.06E-04 1.92E-04 1.92E-03 

4 1.02E-03 3.23E-04 3.23E-03 

5 2.00E-03 6.32E-04 6.32E-03 

6 4.09E-03 1.29E-03 1.29E-02 

7 7.89E-03 2.50E-03 2.50E-02 

8 

9 

10 

1.29E-02 

1.60E-02 

5.15E-02 

4.09E-03 

5.07E-03 

1.63E-02 

4.09E-02 

5.07E-02 

1.63E-01 

Groundshine Shielding Factors 

(GSHFAC)* 

Continuous 

Linear 

Inhalation Protection Factors 

(PROTIN)* Uniform 

Early Health 

Effects LD50

Parameter 

(EFFACA)* 

Hematopoietic 

Continuous 

Linear Pulmonary 

Gastrointestinal 

Early Health 

Effects* 

Exponential 

Parameter 

(EFFACB) 

Hematopoietic 

Continuous 

Linear Pulmonary 

Gastrointestinal 

Early Health 

Effects Threshold 

Dose (EFFTHR)* 

Hematopoietic 

Continuous 

Linear Pulmonary 

Gastrointestinal 

Dose and Dose 

Rate 

Effectiveness 

Factor 

(DDREFA)* 

 

 Continuous 

Linear 

 

Lifetime Cancer 

Fatality Risk 

Factors 

(CFRISK) 

Multiple organs Truncated Log 

normal 

Varies by 

organ 

Varies by 

organ 

Varies by 

organ 

Long-Term 

Inhalation Dose 

Coefficients 

Multiple organs 

and radionuclides Log normal Varies Varies Varies 

Crosswind 

Dispersion Linear 

Coefficient 

(CYSIGA, m) 

A/B 

Continuous Log 

triangular 

0.7507 0.3002 1.8768 

C 0.4063 0.1625 1.0158 

D 0.2779 0.1112 0.6948 

E/F 0.2158 0.0863 0.5395 

Vertical 

Dispersion Linear 

A/B Continuous Log 

triangular 

0.0361 0.0144 0.0903 

C 0.2036 0.0814 0.509 
5-31

Uncertain Parameter Distribution 

type 

Mode 

α, β

Lower 

Bound 

Upper 

Bound 

Coefficient 

(CZSIGA, m) 

D 0.2636 0.1054 0.659 

E/F 0.2463 0.0985 0.6158 

Evacuation Delay 

(DLTEVA, hr) 

 

 

 

Cohort 1 

Triangular 

5 1 8 

Cohort 2 3 2 6 

Cohort 3 4.5 3 9 

Cohort 4 0.75 0.25 3 

Cohort 5 1.5 0.5 3 

Cohort 6 3 1 5 

Cohort 7 1.5 1 3 

Cohort 8 N/A N/A N/A 

Evacuation 

Speed 

(ESPEED, mph) 

 

 

Cohort 1 

Triangular 

2 0.5 5 

Cohort 2 3 0.5 10 

Cohort 3 2 0.5 5 

Cohort 4 3 0.5 10 

Cohort 5 2 0.5 5 

Cohort 6 3 0.5 10.0 

Cohort 7 20 10 30 

Cohort 8 N/A N/A N/A 

Hotspot Relocation Time (TIMHOT, hr) Uniform 24 48 

Normal Relocation Time (TIMNRM, hr) Uniform 36 72 

Hotspot Relocation Dose (DOSHOT, 

rem) Triangular 5 1 7.5 

Normal Relocation Dose (DOSNRM, 

rem) Triangular 1 0.5 2 

Weather Trials - Aleatory Uncertainty 

*Multiple values, refer to specific parameter section for discussion 

The MACCS parameters described below represent some of those that were considered but not 

included in the analysis. 

Risk of Early Injury 

The figures of merit for all of the SOARCA activities have not included risk of early injury. For 

consistency, this metric was also not included in the Sequoyah analysis. 

Ingestion Pathway 

The ingestion pathway was not treated in the SOARCA analyses because uncontaminated food 

and water supplies are abundant within the United States, and it is unlikely that the public would 

eat radioactively contaminated food. 

Habitability 

The habitability criteria is considered to be an important uncertain parameter, but was not 

included because a detailed sensitivity analysis was performed with the Peach Bottom UA. The 

Peach Bottom analysis showed, as would be expected, that when the dose truncation models 
5-32

were used, the LCF risks within the EPZ were orders of magnitude lower when the habitability 

criteria was below the dose truncation level. Beyond the EPZ, the habitability criteria showed a 

smaller effect on the overall LCF risk when a dose truncation model was applied. 

Non-Evacuees 

Evacuation research has documented that some residents refuse to evacuate. As a contributor 

to the risk in previous analyses [4][5], investigating uncertainty in the number of non-evacuees 

would be of interest. However, MACCS does not have a capability to reapportion population 

fractions in an uncertain application. 

Wet Deposition 

The wet deposition model parameters include CWASH1 and CWASH2. CWASH1 was selected 

to vary because there is considerable research and data available to support development of 

the distribution. The same is not true for CWASH2. Although this would be a reasonable 

parameter to evaluate, it was not considered because there are insufficient data to support 

development of a defensible distribution. Furthermore, CWASH1 captures the effect of wet 

deposition sufficiently, such that it was not necessary to evaluate CWASH2 as an exploratory 

parameter. 

Cloudshine Shielding Factor 

The cloudshine shielding protection factor (CSFACT) was included in the Peach Bottom 

uncertainty analysis [9]. However, it was subsequently determined there was an error in the 

reference from which the cloudshine distribution was taken. There was no other source found 

to inform an accurate distribution for this parameter. This was not identified as an important 

parameter in the Peach Bottom UA and the team lacked a basis for specifying a distribution for 

this parameter; therefore, a single point estimate was used. 

5.9.1 Wet Deposition Model (CWASH1) 

CWASH1 is a linear factor in the expression for the wet deposition rate for aerosols. A log 

uniform distribution was used to represent uncertainty with a lower bound of 10-6 and an upper 

bound of 10-4. Under rainy conditions, wet deposition is very effective and rapidly depletes the 

plume. This process can produce concentrated deposits on the ground and create what is often 

referred to as a hotspot (i.e., an area of higher radioactivity than the surrounding areas) [87]. 

The epistemic uncertainty associated with CWASH1 is potentially important in reactor accident 

consequence calculations because increasing values for CWASH1 result in increased local 

concentrations of radionuclides deposited by rainfall events. In turn, such increased local 

radionuclide concentrations can have two opposing effects on radiation exposure and resulting 

health effects. First, high local radionuclide concentrations can increase early individual 

exposure and resulting early health effects. Second, high local radionuclide concentrations can 

increase the total fraction of a radionuclide release that is interdicted, and thus decrease longterm population exposure and resulting latent health effects. This happens because the 

localized, high-concentration area created by the rain is likely to be interdicted, reducing the 

exposures to the residents in the long term. Populations at longer distances receive less 

exposure because most of the plume has been washed out by the rain. 

Rationale for Distribution 

The model used in MACCS for wet deposition of aerosols over a unit area for a time interval [ti, 

ti+1] is formally given by Eq. 5-2 
5-33

2

1 d d ( ) / ( ) ( ), c Ct t Ct cR Ct =− =− λ

Eq. 5-2 

where t = time (units: seconds), C(t) = vertically integrated aerosol concentration in the plume at 

time t (units: Bq/m2), 

2

1

c λ = c R = fractional removal rate for aerosols (units: 1/second), R = 

rainfall rate (units: mm/hr), and 1 c (units: 

2 1 s(mm/hr) / c

) and 2 c (units: dimensionless) are 

constants used in the definition of λ. In turn, Eq. 5-3 

[ ]

1 1

1

1

(, ) () ( )

( ) ( )exp ( )

( )()

ii i i

i i ii

i ii

Dt t Ct Ct

Ct Ct t t

t t Ct

λ

λ

+ +

+

+

= −

=− −−

≅ −

Eq. 5-3 

is the estimated aerosol deposition (units: kg/m2) that takes place over the time interval [ti, ti+1]. 

In the computational implementation of aerosol deposition in MACCS, the rainfall rate R

changes hourly for each rainfall event; however, the same values for 1 c and 2 c are used for all 

rainfall events throughout a year. Although rainfall rate is taken into account in the definition of 

λ, it is well known that many additional properties of a rainfall event affect aerosol deposition, 

including (i) distribution of aerosol size, (ii) chemical and physical properties of aerosols, (iii) 

distribution of rain drop size, (iv) different aerosol removal rates within clouds (i.e., rainout) and 

below clouds (i.e., washout), and (v) short-term temporal variations in rainfall rate that are lost in 

reported hourly rainfall rates [70][71][72][73]. 

Given that MACCS uses fixed values for 1 c and 2 c for all rainfall events over the course of one 

year, the indicated range of properties that can affect aerosol deposition presents a major 

challenge in defining uncertainty distributions for 1 c and 2 c . Specifically, the values used for 1 c

and 2 c in MACCS compromise values that are used for a large number of different rainfall 

situations, while being the most appropriate values to use for only a few of these situations. As a 

result, the epistemic uncertainty distributions for 1 c and 2 c should characterize the uncertainty 

in the locations of the most appropriate values to use for 1 c and 2 c , given that a single value for 

1 c and a single value for 2 c are used for all rainfall events during a year. Given the manner in 

which 1 c and 2 c are used in MACCS, care should be taken to avoid using distributions for 1 c

and 2 c that are intended to characterize the variability in 1 c and 2 c over the range of individual 

rainfall events, as the use of such distributions may produce unrealistic uncertainty analysis 

results. For example, the results of the expert elicitations for the wet deposition of aerosols in 

Appendix A of [74] appear to be for aleatory uncertainty over individual rainfall events. 

The strategy adopted here to quantify the epistemic uncertainty associated with values for 1 c

and 2 c chosen for universal use (i.e., for all rainfall conditions in a year of weather data) was 
5-34

first to look at the results of a number of studies that obtained values for 1 c and 2 c from data for 

real rainfall events or from computational models that incorporate effects that are difficult to 

quantify for observational data (e.g., [70] Tables 10 and 11; [71] Tables 7-11; [72] Table 3; [73] 

Equations (16) and (21); [75] Table 1; [76] Equations (1) and (2); [77] Figures 2 and 3; [78] 

Table 9). Next, choose a range of possible values for 1 c and 2 c that is representative of these 

values, while avoiding extreme values. The outcome of this effort was a range of [10−6, 10−4] for 

1 c and a fixed value of 0.7 for 2 c . A fixed value for 2 c was selected because, given a potential 

range of perhaps [0.6, 0.8] for 2 c , the effects of the much larger range for 1 c should dominate 

the effects of the uncertainty associated with 2 c . Given the absence of a reason to assign a 

particular distribution to the possible values for 1 c , a log-uniform distribution is specified for the 

indicated range of [10−6, 10−4] so that each order of magnitude is assigned the same probability 

(i.e., 0.5). The CDF is presented in Figure 5-10. 1 c and 2 c in the above discussion correspond 

to CWASH1 and CWASH2 in the MACCS framework, respectively. 

Dr. Hanna, one of the experts involved in the expert elicitation documented in NUREG/CR-6244 

[74], was asked to review the wet deposition parameter distribution. He offered a number of 

constructive comments and indicated that this distribution is appropriate for this application. 

Most of Dr. Hanna’s comments have been addressed in the final version of this document. 

Figure 5-10 CDF of the linear coefficient in the MACCS wet deposition 

model, CWASH1 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.0E-06 1.0E-05 1.0E-04

Cumulative Probability

CWASH1 ( 1/s (mm/hr)C2 )
5-35

5.9.2 Dry Deposition Velocities (VDEPOS) 

Dry deposition velocities are established for each aerosol bin to represent the dependence of 

deposition velocity on particle size. Because MELCOR predicts less than one percent of the 

iodine release group to be in vapor form and subject to deposition, vapors are not explicitly 

considered in this section. 

A triangular distribution is used to represent uncertainty in each of nine aerosol particle sizes. 

Dry deposition is the only mechanism for deposition onto the ground for about 94 percent of the 

hours of the year at Sequoyah. Since long-term exposures usually contribute more than 50 

percent of the overall exposure, deposition is important because deposited material is the only 

source of exposure during the long term. Furthermore, the Peach Bottom UA [9] indicated that 

dry deposition velocity is the most important parameter of all those considered for individual 

latent cancer risk. 

Dry deposition involves a variety of mechanisms that cause aerosols to deposit, including 

gravitational settling, impaction onto surface irregularities, including buildings and other 

manmade structures, and Brownian diffusion. Dry deposition is a much slower process than 

wet deposition but occurs continuously; whereas, wet deposition occurs intermittently. Larger 

values of dry deposition velocity result in larger long-term doses at shorter distances and 

smaller doses at longer distances. The converse is also true that smaller values of dry 

deposition velocity result in smaller long-term doses at shorter distances and larger doses at 

longer distances. 

Rationale for Distributions 

As a starting point, the distributions for dry deposition velocity use the expert elicitation data in 

NUREG/CR-6244 [74]. These expert data are evaluated in Revision 1 of Synthesis of 

Distributions Representing Important Non-Site-Specific Parameters in Off-Site Consequence 

Analysis [13], Section 3. The expert data show uncertainty ranges of nearly five orders of 

magnitude for small particles (0.1 μm) and about three orders of magnitude for large particles 

(10 μm). These ranges seem larger than should be expected for representative values that are 

to be applied to an entire year of weather trials. In reviewing the expert elicitation documents, it 

is likely that the experts interpreted the uncertainty to represent instance by instance 

uncertainties rather than representative values for a large set of weather trials to represent one 

year of weather data. 

In NUREG/CR-4551, Vol. 2, Rev. 1, Part 7, (pp. 2-18 to 2-20) [80], the authors describe the 

uncertainty in deposition velocity for NUREG-1150 [12]. In that study, a single deposition 

velocity was used to characterize the entire range of aerosol sizes that would be released into 

the atmosphere during a reactor accident. The best estimate, based on empirical equations and 

physical models, was that deposition velocity is 0.3 cm/s and that the reasonable range of 

uncertainty is 0.03 to 3 cm/s, a range of a factor of 100. This range accounts for uncertainties in 

aerosol size, wind speed, surface roughness, and aerosol density. Of these uncertainties, 

aerosol size is explicitly accounted for in the current study, but uncertainty in the other three 

parameters should have an effect on the overall uncertainty in deposition velocity. This 

reasoning should lead to an uncertainty range for a specific aerosol size that is less than two 

orders of magnitude, which is clearly narrower than the range provided in the expert elicitation 

study. Another parameter not discussed in the NUREG-1150 documentation is aerosol shape 

factor, but this should have a relatively minor contribution to uncertainty in deposition velocity. 
5-36

Estimation of the importance of wind speed, surface roughness, and aerosol density can be 

made from Eq. 5-4, which is a modified version of Equation 3.1 in NUREG/CR-7161 [79]. The 

coefficients a through f are updated 50th percentile values based on Table 3-1 from NUREG/CR7161 [79]. For establishing a range of deposition velocities considering uncertainties in surface 

roughness and wind speed, the important parameters in the equation are e and f, which are 

respectively 1.061 and 0.169 in the table. By examining the meteorological data file for 

Sequoyah, a representative range for wind speed is 1 to 10 m/s, with 1.66 m/s being the annual 

mean wind speed. 

From Eq. 5-4, this range in wind speed leads to about a factor of 5 in deposition velocity. 

Aerosol density has a significant effect for larger particles and a negligible effect for small 

particles. For larger particles, a range of 4 in particle density corresponds to a range of about 2 

in deposition velocity. For small aerosols, particle density has almost no effect on deposition 

velocity and for that reason it is not included here. If a reasonable range of surface roughness is 

10 cm to 60 cm (0.1 m to 0.6 m), the deposition velocities vary by about a factor of 1.5. From 

this discussion, assuming that the influences of wind speed and surface roughness are 

independent, which is reasonable, then the uncertainty range in deposition velocity to account 

for these effects should be about a factor of 8, as explained below. 

݈݊(ݒௗ) = ܽ + ܾ൫ln ݀௣൯ + ܿ൫ln ݀௣൯

ଶ + ݀൫ln ݀௣൯ଷ

ܸ∙+݂ ଴ݖ∙+݁

Eq. 5-4 

 Where 

(݉ߤ) ,ݎ݁ݐ݀݅ܽ݉݁	݈݅ܿ݁ݐݎܽ݌	݊ܽ݉݅ܿݕ݀݋ݎ݀ݕh௣ = ݀

݉ ,ݏݏ݊݁h݃ݑ݋ݎ	݂ܽܿ݁ݎݑݏ = ଴ݖ

ݏܿ݉/ ,ݕݐܿ݅݋݈݁ݒ	݊݋݅ݐ݅ݏ݋݌݀݁ = ௗݒ

ݏ݉/ ݁݁݀,݌ݏ	݅݊݀ݓ = 	ܸ

ܽ, ܾ, ܿ, ݀, ݁, ݂ = correlation	coefficients

The above discussion corresponds to the following equation, which is simply the difference in 

Eq. 5-4 evaluated at an upper bound and a lower bound set of values: 

݈݊(ݒௗ)௎஻ − ݈݊(ݒௗ)௅஻ = ln ቆ(ݒௗ)௎஻

(ݒௗ)௅஻ ቇ=݁∙ ሾ(ݖ ஻௎(଴−) ݖ஻௅(଴ሿ +݂∙ ሾ(ܸ)௎஻ − (ܸ)௅஻ሿ

= 1.061 ∙ (0.6	 − 0.1) + 0.169 ∙ (10 − 1)

Eq. 5-5 

 Where 

UB indicates an upper bound value 

LB indicates a lower bound value 

Solving Eq. 5-5 for the ratio of the upper bound to the lower bound value of deposition velocity 

produces a factor of about 8. This value is rounded up to a factor of 10 to account for variations 

in particle size and density within a bin. 

To construct distributions to be used in this study for each aerosol size, the 50th percentile 

values from the expert elicitation are used as the modes of a set of triangular distributions. 

These values were chosen because, although the distributions from the expert elicitation 

process are considered too wide, the median values from these distributions should represent 

best estimates from the group of experts. The lower and upper bounds of triangular distributions 
5-37

are chosen to be the mode divided and multiplied by the square root of 10, respectively. The 

50th percentile values from the expert elicitation (modes of the triangular distributions) are 

evaluated from Eq. 5-4 with choices for surface roughness to be 0.39 m and wind speed to be 

the mean wind speed, 1.66 m/s. The distribution parameters are tabulated in Table 5-11 and 

shown in Figure 5-11. Dr. Hanna, one of the experts involved in the expert elicitation 

documented in NUREG/CR-6244 [74], was asked to review the dry deposition distributions. He 

offered a number of constructive comments but did not indicate that the bounds should be 

modified or that the distribution is inappropriate for this application. 

Table 5-11 Dry deposition velocities. 

Triangular 

Distributions Deposition Velocity (m/s) 

Representative 

Aerosol Diameter (μm) 

Lower 

Bound Mode Upper Bound 

0.15 1.60E-04 5.04E-04 1.60E-03 

0.29 1.46E-04 4.63E-04 1.46E-03 

0.53 1.92E-04 6.06E-04 1.92E-03 

0.99 3.23E-04 1.02E-03 3.23E-03 

1.8 6.32E-04 2.00E-03 6.32E-03 

3.4 1.29E-03 4.09E-03 1.29E-02 

6.4 2.50E-03 7.89E-03 2.50E-02 

12 4.09E-03 1.29E-02 4.09E-02 

22 5.07E-03 1.60E-02 5.07E-02 

41 1.63E-02 5.15E-02 1.63E-01 
5-38

Figure 5-11 CDF of dry deposition velocities for mass median diameters 

representing MACCS aerosol bins 

VDEPOS is assumed to be perfectly rank-order correlated across the aerosol sizes. This 

prevents small aerosols from depositing faster than large aerosols, which would contradict our 

understanding of aerosol physics. Rank-order correlation is the most commonly used method of 

computing a correlation coefficient using the rank order of the sampled values between two 

variables. 

5.9.3 Shielding Factors (GSHFAC and PROTIN) 

As part of the overall SOARCA project, an updated technical basis and methodology for 

radiation dose and health risk estimation was established [82]. This technical basis, updated 

shielding factors as described below and other risk factors discussed within this study. 

During the investigation of the cloudshine shielding factor, CSFACT, issues were identified 

regarding the source data, which was an unpublished internal Sandia document 

"Recommendations for MACCS2 Parameter Uncertainty Distributions," prepared by Heames, et 

al. in 2003. The internal Sandia report was an attempt to use data from expert elicitation to 

create distributions for MACCS parameters, and was based on an earlier Sandia letter report 

written by Gregory [81]. However, while the distributions in Gregory for inhalation and 

groundshine are identical to Heames, there is no distribution in Gregory [81] for cloudshine and 

no supporting discussion. Physically, cloudshine shielding and inhalation protection are 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.0E-04 1.0E-03 1.0E-02 1.0E-01 1.0E+00

Cumulative Probability

Dry Deposition Velocity (m/s)

0.15

0.29

0.53

0.99

1.8

3.4

6.4

12

22

41
5-39

completely different. Protection from cloudshine represents the fraction of radiation from a 

plume external to a structure that is able to penetrate the structure; inhalation protection is the 

fraction of the external concentration that leaks into a structure and is able to be inhaled. 

Because physically the two distributions should not be identical and because there is no 

discussion in either source regarding the basis for a distribution for cloudshine shielding factor, 

a point value was used in the analysis. Since the cloudshine pathway is typically a small 

contributor to overall dose in MACCS analyses, using a point value for the shielding factor 

should have a minor effect on the estimated uncertainties. 

Groundshine Shielding Factors (GSHFAC) 

The values of GSHFAC are important because the doses received from groundshine are 

directly proportional to these factors and groundshine is usually the most important of the longterm dose pathways. Uncertainty exists in factors that affect GSHFAC, such as indoor 

residence time, household shielding value, and departures from the infinite flat plane 

assumptions. There are additional contributions to uncertainty in the energy deposited within a 

human organ for a specified incident radiation, which is represented by the groundshine 

deposited energy (GSDE) scaling factor. GSDE is a dimensionless scaling factor used to 

account for the amount of ionizing radiation energy deposited within various human organs from 

external radiation emanating from the ground. These uncertainties of the deposition in 

individual organs stem from age, height, and weight variations of the exposed population and 

are incorporated into GSDE uncertainty; but GSDE is not a specific MACCS input. So the 

uncertainty in GSDE is incorporated into GSHFAC uncertainty distribution as explained below. 

The two mechanisms, shielding from structures and energy deposition into organs, are 

independent and so are treated as being uncorrelated. 

Rationale for Distributions 

Piecewise uniform distributions (normal activity, sheltering, and evacuation) for the combined 

GSHFAC and GSDE uncertainty were implemented in MACCS as an overall uncertainty for 

GSHFAC. Construction of this distribution function is described in the following paragraphs. 

The piecewise uniform values used in GSHFAC represent a combination of uncertainty derived 

from NUREG/CR-6526 [83] and uncertainty in GSDE. Gregory et al. [81] evaluated the expert 

data to derive distributions for groundshine from NUREG/CR-6526. In Gregory et al. [81], three 

types of activity, normal, sheltering, and evacuation, are evaluated for the groundshine dose 

pathway, resulting in three sets of shielding factors, shown on Figure 5-12. 

Regarding GSDE, to simplify the implementation of uncertainty in the energy deposited within a 

human organ for a specified incident radiation, Eckerman [82] recommends that a single 

triangular distribution be applied as a multiplicative factor for all radionuclides and for all organs. 

Eckerman [82] recommends a triangular distribution with a minimum of 0.5, a peak (mode) of 

0.8, and a maximum of 1.5 that incorporates variations in ionizing radiation energy and human 

body variations from age, sex, height, and weight. The triangular distribution used to represent 

uncertainty in the dose coefficients for GSDE is shown in Figure 5-13. 

Furthermore, Eckerman [82] suggests that the uncertainties in organ-specific groundshine dose 

conversion factors are highly correlated. As a result, this UA combines the uncertainty in 

GSHFAC and the uncertainty in the dose coefficients (i.e., through the GSDE uncertainty 

distribution) into a single uncertainty factor, which can be implemented as an overall uncertainty 

in the GSHFAC input for MACCS. The uncertainties in the GSHFAC and the groundshine dose 
5-40

coefficients are treated as uncorrelated, since they have no relation (i.e., the shielding from a 

building (GSHFAC) does not bear upon variations in organ geometry or variations in ionizing 

radiation energy). 

Figure 5-14 shows an example of the combination of uncertainty in GSHFAC and GSDE for 

evacuation. For long-term groundshine exposure, the distribution is assumed to be the same as 

for normal activity. GSHFAC is specified to be the same for each of the six cohorts in this UA, 

with one exception. Cohort 2, which is the population in special facilities, uses the same 

shielding factors for normal activity as for sheltering because this cohort is normally indoors and 

resides in fairly stout concrete structures that provide better shielding than typical housing. 

The distributions for normal activity and evacuation are uncorrelated, i.e., a rank correlation 

coefficient of 0.0. The distributions for normal activity and sheltering are correlated with a rank 

correlation coefficient of 0.8 because both represent fractions of time spent indoors. The 

distributions for sheltering and evacuation are taken to be uncorrelated. The distributions for 

normal activity and long-term groundshine shielding factors are considered perfectly rank 

correlated. No correlation is assumed between GSHFAC and inhalation protection factors 

(PROTIN). Table 5-12 lists the groundshine shielding factor distribution parameters. A value of 

unity represents full groundshine for a person standing on an infinite flat plane with uniform 

concentration; a value of zero represents complete shielding. 

Figure 5-12 shows the final piecewise uniform distributions used for GSHFAC, which represent 

both types of uncertainty described above. The long-term shielding factor is taken to be 

identical to the value for normal activity during the emergency phase. 

Figure 5-12 Cumulative distribution functions of GSHFAC for normal activity, 

sheltering, and evacuation based on expert elicitation data 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Cumulative Probability

Groundshine Shielding Factor (GSHFAC)

Sheltering

Evacuation

Normal & LongTerm

Normal (Cohort 2)
5-41

Figure 5-13 Cumulative distribution function of GSDE 

Figure 5-14 CDF of GSHFAC for evacuation accounting for uncertainty in 

GSDE 

0

0.2

0.4

0.6

0.8

1

0.4 0.6 0.8 1 1.2 1.4 1.6

Cumulative Probability

Groundshine Deposited Energy Scaling Factor (GSDE)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Cumulative Probability

GSHFAC for Evacuation

Combined GSHFAC &

GSDE
5-42

Table 5-12 Simplified groundshine shielding factor (GSHFAC) for 

Sequoyah. 

Special Facilities 

Cohorts 1 and 2 

Quantile Evacuation 

Normal 

Activity Sheltering 

0 0.042 0.008 0.008 

0.5 0.500 0.050 0.050 

1 1.000 0.497 0.497 

Point Values 0.500 0.050 0.050 

General Population 

Cohorts 3-8

Quantile Evacuation 

Normal 

Activity Sheltering 

0 0.042 0.027 0.008 

0.5 0.500 0.260 0.200 

1 1.00 0.822 0.497 

Point Values 0.500 0.260 0.200 

Inhalation Protection Factors (PROTIN) 

The inhalation dose protection factor is represented by PROTIN. Population dose is reduced by 

the PROTIN inhalation protection factor based on the activity the population is engaged in, such 

as sheltering, evacuating, or performing normal activities. The inhalation protection factors used 

in the MACCS calculation are important because the doses received from inhalation are directly 

proportional to these factors and inhalation is the dominant dose pathway during the emergency 

phase. The distributions used in this analysis are derived from NUREG/CR-6526 [83] which 

collected data from an expert elicitation panel on deposited material and external doses. The 

expert data indicates that there is a large range of uncertainty in current expert best estimates 

on the inhalation protection factor. 

Rationale for Distributions 

Gregory et al. [81] evaluated the expert data in NUREG/CR-6526 [83] to derive distributions for 

the inhalation protection factor. Three distributions were derived, one each for normal activity, 

sheltering, and evacuation. Only data from the U.S. expert were used to determine the time 

spent in various structures (outdoors, vehicles, and low-, medium-, and high-shielded buildings). 

These data provided the needed information for normal activity. The times were shifted towards 

outdoors in vehicles for evacuation and shifted towards highly shielded buildings for sheltering. 

To determine the final distributions from all the experts, the air concentration ratios 

(indoors/outdoors) were considered for both normally ventilated buildings and buildings with all 

windows closed. 
5-43

In reviewing these distributions, it appears that the ranges of the distributions are too large, 

especially for sheltering and evacuation, with the protection factor for sheltering skewed toward 

too little protection and evacuation skewed toward too much protection. The experts seemed to 

be answering the question of what are the maximum and minimum possible protection factors 

for any individual person in the zone, whereas for this work, a representative value for all people 

in the zone is needed. 

To verify that these judgments are correct, the Gregory distributions were compared to 

distributions for inhalation protection factor given in NUREG/CR-4551 [80], which were prepared 

for NUREG-1150 [12]. This source recommended a uniform distribution from 0.15 to 1.0 for 

normal activity, a uniform distribution from 0.1 to 0.4 for sheltering, and a constant value of 1.0 

(no protection) for evacuation. 

The recommended distribution for normal activity is similar to the distribution in [81]; whereas, 

the distribution for sheltering has a significantly smaller range because it only allows for 

relatively high amounts of protection, indicating sheltering is in stouter building structures. In 

practice sheltering may be enforced wherever people currently are (shelter in place), so the 

higher values in [81] are plausible for some individuals but not for an entire cohort. 

Thus, the decision was made to use the distributions for sheltering and normal activities from 

[81], except with a truncation at the 0.2 and 0.8 quantiles of each CDF to be representative of 

cohorts instead of a single member of a cohort. New CDFs were drawn, using the .02 and 0.8 

quantile values as the upper and lower bound while maintaining the 0.5 quantile value and 

shape of the distributions. The distributions are defined to be piecewise-uniform to reflect 

information from the expert elicitation. Figure 5-15 also shows the SOARCA values as black 

triangles, which were obtained from the NUREG-1150 values used for Sequoyah. 

The single inhalation protection factor of 1 for evacuation [80] is significantly different than the 

values from the distribution in [81], which includes low values for PROTIN that are difficult to 

defend. Additionally, the SOARCA default for evacuation of 0.98, which came from NUREG/CR6953, Volume 1 [84], is still considered the best estimate. Therefore, the best judgment was to 

create a uniform distribution from 0.9 to 1.0, which allows for a limited uncertainty investigation 

in PROTIN for evacuation but doesn’t include high amounts of protection as indicated in 

Gregory et al. that do not seem physically possible for an entire cohort. 

The distribution for the long-term inhalation protection factor is assumed to be the same as the 

distribution for normal activity during the emergency phase. PROTIN must be specified for each 

cohort (population group) in a MACCS analysis. The protection factors for Cohorts 1 and 3 

through 8 were chosen to be identical. Because normal activity for Cohort 2 would be 

approximately the same as sheltering due to the robust nature of the special facility buildings, 

the distribution for normal activity was chosen to be the same as for sheltering for Cohort 2. 

These distributions are shown in Figure 5-15 as dashed lines overlaying the base distributions. 

The distributions for normal activity and sheltering are correlated with a rank correlation 

coefficient of 0.75, based on the assumption that the majority of normal activity is indoors. 

Therefore, if sheltering is sampled with a lower value, normal activity should also be a lower 

value. The distributions for normal activity and long-term inhalation are perfectly rank 

correlated. No correlations are assumed between PROTIN and GSHFAC for any of the three 

activity types, evacuation, normal activity, and sheltering. Table 5-13 lists the inhalation 

protection factors (PROTIN) for Sequoyah. 
5-44

Figure 5-15 CDFs of PROTIN for normal activity, sheltering, and 

evacuation 

Table 5-13 Inhalation protection factor (PROTIN) for Sequoyah. 

Special Facilities 

Cohorts 1 and 2 

Quantile Evacuation Normal 

Activity Sheltering 

0 0.900 0.098 0.098 

0.5 0.330 0.330 

1 1.000 0.699 0.699 

Point Values 0.98 0.330 0.330 

General Population 

Cohorts 3-8

Quantile Evacuation Normal 

Activity Sheltering 

0 0.900 0.336 0.098 

0.5 0.950 0.460 0.330 

1 1.000 0.896 0.699 

Point Values 0.98 0.460 0.330 
5-45

5.9.4 Early Health Effects (EFFACA, EFFACB, EFFTHR) 

When radioactive material is inhaled and retained in the respiratory system, an individual may 

continue to receive a radiation dose for long periods of time after the material was inhaled. 

Depending on particle size and chemical form, clearance mechanisms may remove the material 

from the body or transport it from the respiratory system to other organs of the body. The 

MACCS early health effects model accounts for dose protraction from radioactive material 

inhaled and retained within the body and calculates an effective acute dose, i.e., a one-day 

dose that would induce the same effects as the protracted dose [55]. External radiation is only 

received during the exposure period, so dose protraction is not an issue. 

The individual risk of an early fatality is modeled in MACCS using a three-parameter hazard 

function, expressed in Eq. 5-6 for specified target organs (i.e., red bone marrow, lungs, and 

stomach) with an acute dose threshold. The early health effects parameters represented in the 

equation include: 

• LD50 parameter (EFFACA), 

• Exponential parameter (EFFACB) also called shape factor, and 

• Threshold dose (EFFTHR). 

The Weibull function contains the EFFACA parameter for the lethal dose to 50 percent of the 

population (LD50), EFFACB as an exponential parameter that defines the steepness of the doseresponse function, and a threshold dose defined by EFFTHR, below which no health effects are 

estimated to occur. 

ܧܱܵܦ ൬	 0.693 = ܪ

൰ܣܥܣܨܨܧ

ாிி஺஼஻

Eq. 5-6 

Where: 

H = hazard function for individual risk of an early fatality (unitless) 

DOSE = effective acute dose (Gy) to a target organ. DOSE is set to zero when it is 

below the threshold, EFFTHR 

EFFACA = LD50 (Gy) for a specific health effect 

EFFACB = the exponential parameter in the hazard function (unitless) 

All three of the variables used to determine the individual risk of early fatality in Eq. 5-6 are 

treated as uncertain. Furthermore, there are three values for each of the parameters for the 

three organs listed above. A dose to each of the organs is associated with a specific type of 

early fatality, as follows: (1) an acute dose to the red bone marrow is used to assess 

occurrences of the hematopoietic syndrome, (2) an acute dose to the lungs is used to assess 

occurrences of the pulmonary syndrome, and (3) an acute dose to the stomach is used to 

assess occurrences of the gastro-intestinal syndrome. 

When estimating early health effects, it is appropriate to use gray (Gy) as the unit of measure 

for doses. Doses measured in gray and Sieverts (Sv) are the same for low linear energy transfer 

(LET) radiation (gamma and beta), but different for high LET radiation (alpha). Doses in Sieverts 

are based on a standard radiation weighting factor for high LET radiation of 20; doses from high 

LET radiation in units of gray are based on either a value of unity or a nonstandard value for 

radiation weighting factor. In the case of latent health effects, a Sievert captures the biological 
5-46

effects of radiation in terms of the risk of cancer induction. In the case of early health effects, a 

nonstandard radiation weighting factor of 10 is used for acute doses. This difference is because 

high LET radiation has a different biological impact for early health effects than it does for latent 

health effects. 

Early Health Effects LD50 Parameter (EFFACA) 

As explained above, the EFFACA parameter represents LD50, the lethal dose to 50 percent of 

the population, in the hazard function for a target organ [55]. The EFFACA distribution is based 

on expert elicitation data for four types of early health effects, hematopoietic syndrome, 

gastrointestinal syndrome, pulmonary syndrome, and pneumonitis [79]. The first three of these 

early health effects are potentially fatal and are included in the documented results in this report. 

Pneumonitis is nonfatal and is not reported. 

Rationale for Distributions 

The EFFACA distribution for the three target organs considered are piecewise uniform and are 

based on linear interpolation between data points taken from Table 6-1 of Bixler et al. [79]. The 

basis for the EFFACA distribution are uncertain characteristics associated with estimation of 

four types of early health effects, and are taken directly from Bixler et al. [79] and are derived 

expert elicitation values provided in NUREG/CR-6545. The parameter distributions and 

SOARCA values associated with the three fatal, early health effects are shown on Figure 5-16. 

The upper and lower bounds of each of the distributions incorporate the entire range of the 

expert elicitation data [79]. 

Bixler et al. [79] recommended that EFFACA be correlated with the threshold parameter 

(EFFTHR) for the same health effect using a 1.00 rank correlation coefficient. 
5-47

Figure 5-16 CDFs of EFFACA for specified health effects 

Early Health Effects Exponential Parameter (EFFACB) 

The EFFACB parameter represents the exponential or shape factor in the hazard function for 

the target organ [55]. The same target organs identified with EFFACA are considered here. 

Rationale for Distributions 

The EFFACB distribution for all three target organs considered are piecewise uniform, and are 

based on linear interpolation between data points taken from Table 6-1 of Bixler et al. [79]. The 

basis for the EFFACB distributions is derived from NUREG/CR-6545 and evaluated by Bixler et 

al. [79]. The parameter distributions and SOARCA values associated with these three potentially 

fatal early health effects for EFFACB are shown on Figure 5-17. The upper and lower bounds of 

each of the three distributions incorporate the entire range of the expert elicitation data [79]. 

0

0.2

0.4

0.6

0.8

1

0 8 16 24 32 40 48 56 64 72 80

Cumulative Probability

EFFACA - Early Health Effects LD50 Parameter (Gy) 

Hematopoietic System

Pulmonary System

Gastrointestinal System
5-48

Figure 5-17 CDFs of EFFACB for specified health effects 

Early Health Effects Threshold Dose (EFFTHR) 

The EFFTHR parameter represents the threshold dose associated with the target organ [55]. 

The same target organs identified with EFFACA and EFFACB are considered here. 

Rationale for Distributions 

The EFFTHR distribution for all three target organs considered are piecewise uniform, and are 

based on linear interpolation between data points taken from Table 6-1 of Bixler et al. [79]. The 

EFFTHR distributions are derived from NUREG/CR-6545 and evaluated by Bixler et al. [79]. 

The parameter distributions and SOARCA values associated with these three potentially fatal 

early health effects for EFFTHR are shown on Figure 5-18. The upper and lower bounds of 

each of the three distributions incorporate the entire range of the expert elicitation data [79]. 

For each health effect, EFFTHR is correlated with the LD50 parameter (EFFACA) for the same 

health effect using a rank correlation coefficient of 1.00 [79]. 

0

0.2

0.4

0.6

0.8

1

2 4 6 8 10 12 14 16 18 20 22 24

Cumulative Probability

EFFACB - Early Health Effects Exponential Parameter (-) 

Hematopoietic System

Pulmonary System

Gastrointestinal System
5-49

Figure 5-18 CDFs of EFFTHR for specified health effects 

5.9.5 Latent Health Effects (DDREFA, CFRISK, Long-Term Inhalation Dose 

Coefficients) 

Dose and Dose-Rate Effectiveness Factor (DDREFA) 

The dose and dose-rate effectiveness factor (DDREFA) is a dimensionless dose- and organdependent reduction factor. Epidemiological studies used in the development of radiationexposure risk models involved subjects who experienced high radiation doses delivered in a 

relatively short time period [82]. There is evidence that indicates that the biological response 

per unit dose at low doses may be overestimated if one extrapolates from observations made 

with high doses and dose rates [82]. The degree of overestimation is commonly expressed in 

terms of a dose and dose-rate effectiveness factor. DDREFA is used in MACCS to modify the 

organ-specific lifetime cancer fatality risk factors (CFRISKs) in order to distinguish between low 

and high dose exposures incurred during the early phase. There is significant uncertainty in 

DDREFA because there is a large amount of scatter in dose-response data at low doses. 

Low dose is generally defined as 0.2 Gy (20 rad) or less and low dose rate is defined as 0.1 

mGy/min (600 mrad/hr) or less 16]. MACCS only considers dose and not dose rate in the 

implementation of DDREFA. In MACCS, doses received during the emergency phase are 

divided by DDREFA when the committed dose is less than 0.2 Sv (MACCS does not estimate 

doses in units of Gy for the purpose of calculating latent health effects, so the dose threshold is 

0

0.2

0.4

0.6

0.8

1

0 4 8 12 16 20 24 28 32 36 40

Cumulative Probability

EFFTHR - Early Health Effects Threshold Parameter (Gy) 

Hematopoietic System

Pulmonary System

Gastrointestinal System
5-50

implemented as dose measured in Sv to the target organ). Doses received during the long-term 

phase are assumed to be controlled by the habitability criterion to be well below 0.2 Gy, so the 

risk factors are always divided by DDREFA in the calculation of latent health effects during this 

phase. 

Rationale for Distributions 

Age- and gender-averaged cancer mortality risk uncertainties were developed for SOARCA 

[82]. Additionally, a PDF specific for breast cancer and one for all other types of cancers was 

developed [82]. However, the PDFs provided in [82] did not integrate to unity; therefore, the 

PDF equations were normalized to the curves shown in Figure 5-19. An upper limit of DDREFA 

is 3.0 for breast and 8.0 for other cancers [82]. 

Eckerman recommended that high LET radiation be assigned a DDREFA of unity (1.0) with no 

uncertainty [82]; however, the distinction between low and high LET radiation to an organ 

cannot be distinguished within the MACCS framework; therefore, the PDFs suggested for low 

LET radiation are applied to all types of radiation. To implement the uncertainty in DDREFA, 

the CDF was segmented into equally spaced quantiles to construct a piecewise uniform 

distribution as shown in Figure 5-20. 

The value of DDREFA for each organ is independent of the other organs and so they are not 

correlated [82]. The DDREFA for each organ has a corresponding CFRISK parameter, and 

these parameters are also uncorrelated. 

Figure 5-19 Normalized PDFs for DDREFA for breast and other cancers 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

12345678

Probability Density Function

DDREFA (unitless)

Breast

Other
5-51

Figure 5-20 CDFs of DDREFA for breast and other cancer types 

Lifetime Cancer Fatality Risk Factors (CFRISK) 

The lifetime CFRISKs are based on a 50-year lifetime dose commitment to specified target 

organs (risk/Sv). The probability of a LCF is calculated separately for each cancer syndrome 

related to each target organ and is based on the technical approach described in the National 

Academy of Sciences / National Research Council Biological Effects of Ionizing Radiation 

(BEIR) V report [101]. The BEIR V risk models are used in FGR-13. 

Lifetime doses can occur during all phases of an accident, early, intermediate, and long-term. 

Projected doses are the basis for decisions to relocate individuals during each of these phases. 

When allowed to return, doses are used to estimate potential health effects. Return of the 

population falls within the scope of radiation protection and uses the standard dosimetric 

quantities of radiation protection, i.e., effective doses. Estimating health effects is based on 

doses to specific organs and organ-specific risk factors to estimate the number of excess 

cancer fatalities in a population. Both the dose coefficients and risk factors are uncertain. This 

section evaluates uncertainties in the cancer fatality risk factors and a subsequent section 

evaluates uncertainties in the dose coefficients. 

Rationale for Distributions 

Truncated log-normal distributions were selected for the cancer fatality risk factors based on 

guidance from Keith Eckerman [82]. Estimates of the gender and age averaged uncertainty in 

CFRISK for each of the organs used for latent health effects were obtained from Eckerman [82]. 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

12345678

Cumulative Probability

DDREFA (unitless)

Breast

Other
5-52

The uncertainty distributions provided by Eckerman [82] indicate that cancer risk for leukemia 

has a very small uncertainty; lung, breast, colon, and residual (cancer sites not explicitly 

identified – esophagus, stomach, skin, ovaries, bladder, kidney, etc.) are moderately uncertain; 

and thyroid, liver, and bone have large uncertainties. Eckerman [82] concludes this based on 

the premise that inconsistency in expert judgment is a valid measure of uncertainty in CFRISK. 

To implement the uncertainty in CFRISK, a CDF was developed for each of the organs as 

shown in Figure 5-21. Each log normal distribution is truncated at ±3 sigma for each distribution. 

This represents a truncation at quantile 0.001 and 0.999. CFRISK for each organ is considered 

independent of the other organs and therefore the risk factors are not correlated [82]. 

Figure 5-21 CDF for CFRISK for each of the included organs 

Long-Term Inhalation Dose Coefficients 

The long-term inhalation dose coefficients are used to calculate the committed organ-specific 

equivalent dose and the effective dose (all in Sv) from the inhalation of 1 Bq of a radionuclide 

integrated over a 50-year commitment period. Uncertainties in inhalation dose coefficients were 

examined by Pawel et al. [85] to evaluate the uncertainties in the cancer risk coefficients of 

FGR-13. Based on the Eckerman report for SOARCA [82], the assignment of uncertainty 

distributions to long-term inhalation dose coefficients for individual radionuclides is difficult 

because these values are the end products of complex calculations. These calculations involve 

a collection of uncertain biokinetic and dosimetric coefficients and assumptions, which Pawel et 

al. [85] determined to be the largest uncertainty for most risk coefficients. 

The LCF risk factors in MACCS are based on the technical approach described in BEIR V in 

which long-term inhalation dose coefficients are part of that approach. The assessment of the 

uncertainty in dose is evaluated on the basis of sensitivity analyses in which various 
5-53

combinations of plausible biokinetic and dosimetric models are used to generate alternative 

dose coefficients, such as those discussed in Pawel et al. [85], and Eckerman report [82]. 

Rationale for Distributions 

For the long-term dose coefficients related to the inhalation pathway, Eckerman [82] 

recommends that the uncertainty distributions for the coefficients be treated as truncated log 

normal distributions with the geometric means and standard deviations for the 58 radionuclides 

listed in Table 3. The upper and lower values used to create the distributions are assumed to 

represent the 90 percent confidence interval for each dose coefficient. 

For an individual radionuclide, the long-term inhalation dose coefficients are treated as 

correlated with a rank correlation coefficient of 1.0 for all of the organs except the lung. The 

lung is correlated with a rank correlation coefficient of -1.0 with the dose coefficients of all of the 

other organs (red bone marrow, bone, breast, thyroid, liver, colon, and ‘residual’). This, in 

effect, means that only one input is sampled independently (e.g., long-term inhalation dose 

coefficient for the red bone marrow) to represent all organ-specific long-term inhalation dose 

coefficient uncertainty. The logic behind this is that the inhaled radionuclides may spend more 

or less time (residence time) in the lungs, depending on the chemical form of the radionuclide 

and its solubility, and subsequently after departing from the lung, the radionuclide is carried 

through the blood stream to other systemic tissues. Thus, the longer the time spent in the 

lungs, the greater the dose in the lungs and the less the dose to the other systemic tissues. The 

shorter the time spent in the lungs, the smaller the dose in the lungs and the greater the dose to 

the other systemic tissues. 

There are 69 radionuclides considered in the analysis. Of those, only 58 radionuclides have 

nonzero dose coefficients for inhalation. In this UA, these 58 radionuclides, listed in Table 5-14, 

are assigned uncertain long-term inhalation dose coefficients for each of the eight organs used 

in this analysis, which are lung, red bone marrow, bone surface, breast, thyroid, liver, colon, and 

residual. The residual dose coefficient represents the collection of organs that are not 

specifically represented and that are important for cancer induction. 

Figure 5-22 and Figure 5-23 provide examples of the truncated log normal distributions for two 

radionuclides, Cs-137 and Pu-241, for the eight different organs included in this analysis. 

Uncertainties in the acute inhalation dose coefficients (used to estimate the early health effects 

discussed earlier) were considered to be less important and were not included as uncertain 

parameters in this analysis. 

Peach Bottom and Surry SOARCA [4][5] did not include the ingestion dose pathway because 

uncontaminated food and water supplies are abundant within the United States, and it is unlikely 

that the public would eat radioactively contaminated food. Therefore, Sequoyah SOARCA also 

does not consider the ingestion pathway in the analysis. Uncertainties in groundshine dose 

coefficients are treated through uncertainties in the groundshine shielding factors, as explained 

above. Cloudshine uncertainty is not treated because it is a relatively unimportant dose pathway 

compared with groundshine and inhalation. 

Table 5-14 Radionuclides treated as having uncertain inhalation dose coefficients. 

Radionuclides 

Co-58 Y-90 Nb-97 Te-127 I-132 Ba-140 Pr-144 Cm-242 

Co-60 Y-91 Mo-99 Te-127m I-133 La-140 Nd-147 Cm-244 
5-54

Radionuclides 

Rb-86 Y-91m Tc-99m Te-129 I-134 La-141 Np-239 

Rb-88 Y-92 Ru-103 Te-129m I-135 La-142 Pu-238 

Sr-89 Y-93 Ru-105 Te-131 Cs-134 Ce-141 Pu-239 

Sr-90 Zr-95 Ru-106 Te-131m Cs-136 Ce-143 Pu-240 

Sr-91 Zr-97 Rh-103m Te-132 Cs-137 Ce-144 Pu-241 

Sr-92 Nb-95 Rh-105 I-131 Ba-139 Pr-143 Am-241 

Figure 5-22 Cs-137 lifetime inhalation dose coefficient distributions for 

organs included in the consequence analysis 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.0E-11 1.0E-10 1.0E-09 1.0E-08 1.0E-07 1.0E-06

Cumulative Probability

Cs-137 Long-Term Inhalation Committed Absorbed Dose Coefficient (Gy/Bq)

Red Bone Marrow

Bone

Breast

Lung

Thyroid

Liver

Colon

Residual
5-55

Figure 5-23 Pu-241 long-term inhalation dose coefficient distributions for 

organs included in the consequence analysis 

5.9.6 Dispersion (CYSIGA, CZSIGA) 

Dispersion of a radioactive plume following a severe accident directly affects doses to members 

of the population and resulting health effects. Thus, the dispersion parameters used to estimate 

atmospheric dispersion are important to the outcome of the calculation. The coefficients used to 

calculate dispersion were chosen to be the median values from an expert elicitation for 

SOARCA, but there is significant uncertainty in these values [79]. 

In terms of predicted health effects, these parameters tend to have a non-linear effect when 

using the LNT hypothesis for estimating latent health effects because dispersion influences the 

amount of land that is interdicted. Dispersion has an even greater influence on estimated health 

effects when truncation is used in the dose-response model because a smaller dose to an 

individual can reduce a nonzero risk to zero and, conversely, a larger dose can raise a zero risk 

to be nonzero. 

Within MACCS, plume dispersion is calculated using the Gaussian plume model, shown in 

Eq. 5-7 for the simple case of an unbounded plume. 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.E-13 1.E-12 1.E-11 1.E-10 1.E-09 1.E-08 1.E-07 1.E-06 1.E-05 1.E-04 1.E-03 1.E-02

Cumulative Probability

Pu-241 Long-Term Inhalation Committed Absorbed Dose Coefficient 

(Gy/Bq)

Red Bone Marrow

Bone

Breast

Lung

Thyroid

Liver

Colon

Residual
5-56

Eq. 5-7 

where χ(x,y,z) is the time-integrated air concentration at a downwind location (x,y,z), Q is the 

emitted activity, ū is the mean wind speed, h is the release height, and ߪ௬ and ߪ௭ are the 

standard deviations of the crosswind and vertical normal concentration distributions, 

respectively. Q and h are determined by the accident sequence, while ū, ߪ௬ , and ߪ௭ are 

functions of weather. Uncertainty in ߪ௬ is used to capture the uncertainty in crosswind 

dispersion. The function used to define ߪ௬ is given in Eq. 5-8. 

஻ீௌூ஼௒ݔ ∙ 	ܣܩܫܻܵܥ = ௬ߪ

Eq. 5-8 

where x is the downwind distance and CYSIGA and CYSIGB are empirical constants chosen to 

fit observed results. A value for both of the constants must be entered for each of the six 

Pasquill-Gifford stability classes (A-F). 

In this work, it was beneficial to only sample one of the two constants to simplify the 

specification of uncertainty. Bixler et al. [79] characterized the uncertainty as an uncertainty in 

CYSIGA and this characterization is used here. 

Crosswind Dispersion Linear Coefficient (CYSIGA) 

The crosswind dispersion linear coefficient is represented by CYSIGA. The SOARCA best 

estimate values for CYSIGA were chosen to be the median of the results of an expert elicitation 

[79]. Distributions were fit to the expert data and these were roughly lognormal. The median of 

the distributions is considered to be a reasonable best estimate value for this parameter; 

however, the distributions created from the elicitation are not used in this analysis. The expert 

data indicate about one order-of-magnitude uncertainty within the 90 percent confidence interval 

and about two orders of magnitude at the 100 percent confidence interval. 

In retrospect, it appears that the experts considered the question: what is the uncertainty in the 

dispersion at a specific point in time, i.e., for a specific weather instance. The appropriate 

question for this UA is rather: what is the uncertainty in dispersion that is representative of a 

year of weather data? The second question leads to a narrower distribution because a single 

set of dispersion parameters is selected to represent a whole year of weather variability in 

MACCS. This distinction is important when the primary results are means over weather 

variability. If an extreme value of a parameter could exist at a point in time but is highly unlikely 

to persist over a large collection of randomly chosen weather variations, then it should not be 

used in this study to characterize the mean over a year of weather variations. Distributions that 

express the values that a parameter could take at a specific weather instance should be used in 

conjunction with a single weather trial or possibly just a single hour from a weather trial, not with 

a set of weather trials that characterize a whole year of weather data. The sampling method 

used in this study does not draw a set of epistemic parameters for each weather trial but rather 

for a whole year of weather trials, so this is an important consideration for a correct treatment of 

uncertainty. 
5-57

There are a number of other sources for CYSIGA, three of which are given as examples in the 

MACCS User’s Guide. These values were compared to the best estimate values from expert 

elicitation. It was found that 2/3rds of the values were within a factor of 3 of the best estimate 

value and were about evenly distributed between larger and smaller. These historic values (from 

1977, 1979, and 1989) were used by multiple experts in constructing their distributions, showing 

they continue to be regarded as reasonable bases to estimate dispersion. 

Additionally, NUREG/CR-6853 [86] compared results from MACCS (a Gaussian plume code) 

with 3 other codes for transport and dispersion and found that the results from the 4 codes were 

within a factor of 2 for compass averaged concentrations and a factor of 3 for sector specific 

concentrations. This factor can be considered to be an approximation of the uncertainty in 

transport and dispersion. As a first-order approximation at short distances, the uncertainty in 

plume concentration is inversely proportional to the uncertainty in σy multiplied by the 

uncertainty in σz. Accounting for an uncertainty in concentration of a factor of three above and 

below the median corresponds to a range of uncertainty in CYSIGA from 1/1.73 to 1.73 times 

the median assuming that both cross-wind and vertical dispersion have the same ranges of 

uncertainty. If this range is considered to be the uncertainty at the 90 percent confidence level, 

the entire range of uncertainty is reasonably chosen to be bounded by factors of 1/2.5 and 2.5 

on the median value for CYSIGA. 

Rationale for Distributions 

The 50th percentile of the distribution from the expert elicitation [79] is a reasonable and 

defensible choice for the best estimate value to use for CYSIGA. Expert elicitation results are 

based on a combined category for stability classes A and B and a combined category for 

stability classes E and F, yielding 4 distributions for A/B, C, D, and E/F. Based on the previous 

discussion, the bounds of the distribution are set to be a factor of 2.5 higher and lower than the 

best estimate value for each weather class. All calculated values are found in Table 5-15. 

In the Peach Bottom UA, a piecewise log-uniform distribution was used for CYSIGA because 

the expert elicitation data were approximately lognormal [17]. Since the range of uncertainty 

was chosen to be different for this analysis, a simpler log-triangular distribution was chosen. 

This choice is consistent with a most likely value at the center of the distribution and a likelihood 

that decreases as the bounds are approached. A log-triangular distribution was used instead of 

a triangular one because it gives equal sampling weight on either side of the mode when using 

a multiplicative factor for the lower and upper bounds. 

Table 5-15 Log-triangular values for dispersion parameter CYSIGA for 

each stability class. 

Class Lower Bound Mode Upper Bound 

A/B .3002 .7507 1.8768 

C .1625 .4063 1.0158 

D .1112 .2779 0.6948 

E/F .0863 .2158 0.5395 

Values of CYSIGA are perfectly rank correlated across the stability classes. These values are 

also perfectly rank correlated with the values of CZSIGA, the vertical dispersion parameters 
5-58

discussed below. This indicates that uncertainty in CYSIGA is partially caused by changing 

weather conditions that would result in an increase or decrease in both the crosswind and 

vertical dispersion together. It also ensures that the order between the stability classes is 

preserved. The CDFs are presented in Figure 5-24. 

Figure 5-24 CDFs of CYSIGA for individual stability classes 

Vertical Dispersion Linear Coefficient (CZSIGA) 

The Surry and Peach Bottom SOARCA best estimate values for CZSIGA were also chosen to 

be the median results of an expert elicitation [79]. Distributions were fit to the expert data, which 

were again roughly lognormal. These median values are appropriate to be used as median 

value in the UA for Sequoyah. However, the distributions for CZSIGA have the same issues as 

the distributions for CYSIGA described above, so a narrower distribution is developed and used 

in this work. The same approach described in the previous subsection leads to distribution 

bounds on CZSIGA that are also factors of 2.5 above and below the median value for each 

stability class. 

Rationale for Distributions 

The 50th percentile of the distribution from the expert elicitation [79] is a reasonable and 

defensible choice for the best estimate value for CZSIGA. Based on the previous discussion, 

the bounds are set at a factor of 2.5 higher and lower than the best estimate value for each 

stability class. The distribution parameters are provided in Table 5-16. 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.01 0.1 1 10

Cumulative Probability

CYSIGA (m)

Class

A/B

Class C
5-59

CZSIGA distributions are perfectly rank correlated with each other and with values of CYSIGA. 

This indicates that uncertainty in CZSIGA is partially caused by weather conditions that would 

result in an increase or decrease in both the crosswind and vertical dispersion together. It also 

ensures that the order between the stability classes is preserved. Figure 5-25 shows the CDFs 

of a log-triangular distribution for each of the four SOARCA stability class groupings. 

Table 5-16 Log-triangular values for dispersion parameter CZSIGA for 

each stability class. 

Class Lower Bound Mode Upper Bound 

A/B .0144 .0361 .0903 

C .0814 .2036 .509 

D .1054 .2636 .659 

E/F .0985 .2463 .6158 

Figure 5-25 CDFs of CZSIGA for individual stability classes 

5.9.7 Weather Forecast (KEYFORCST) 

During an emergency response, protective action decisions for a keyhole evacuation require 

consideration of wind direction. Because wind tends to shift over time, MACCS allow the 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.01 0.1 1

Cumulative Probability

CZSIGA (m)

Class A/B

Class C

Class D

Class E/F
5-60

keyhole to be expanded to account for such wind shifts. It also allows the expansion to occur in 

advance of an actual wind shift to account for weather forecasting. A parameter in MACCS, 

KEYFORCST, defines the number of hours of weather forecasting to use in the model. A 

uniform distribution was used with a lower bound of 1 hour and an upper bound of 6 hours as 

shown in Figure 5-26. MACCS assumes perfect foreknowledge of wind direction for the number 

of hours defined by KEYFORCST. 

Figure 5-26 CDF of KEYFORCST 

5.9.8 Time-Based Crosswind Dispersion Coefficient (CYCOEF) 

CYCOEF is the linear coefficient for the time-based, crosswind dispersion model. Hanna [109] 

recommends that plume dispersion beyond 30 kilometers (19 miles) be based on time, not on 

distance. The basis for the recommendation of 30 kilometers is that most measurements upon 

which dispersion tables are based have a limit of about 30 kilometers. In the MACCS 

implementation of the time-based crosswind dispersion model, this downwind distance, along 

with the linear coefficient, are used to define the switching point from distance-based to timebased dispersion. The recommended default value for the linear coefficient is 0.5 m/s [109]. 

Sequoyah is the first in the SOARCA series of analyses to use this parameter, so no distribution 

had been developed for Peach Bottom or Surry [9][17]. Switching to a time-based dispersion 

model at longer downrange distances is a commonly-used practice in atmospheric transport 

modeling, and this MACCS model option was selected for the Sequoyah UA study. This 

approach homogenizes variations due to stability class, so it is expected to give somewhat 

different, less variable, results than in the previous SOARCA studies. However, any differences 

in results are not expected to change the relative importance of dispersion for the overall 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

123456

Cumulative Probability

KEYFORCST (hours)
5-61

results. As implemented in this study and discussed above for the switch from distance-based to 

time-based dispersion, CYCOEF only affects results beyond 30 kilometers (19 miles), so it 

affects 50 mile results but has no influence within 10 miles and little influence within 20 miles. 

To be consistent with the short-range uncertainty modeling of atmospheric dispersion, the linear 

parameter in the long-range model was made uncertain and assigned a similar distribution to 

those used for the short-range parameters. All of the dispersion parameters were fully 

correlated to ensure smooth transitions with changes in stability class and a smooth transition 

between short-range and long-range dispersion modeling. 

The approach used to develop a distribution for this parameter is the Sequoyah uncertainty 

analysis is the same as that used to develop distributions for CYSIGA and CZSIGA, as 

described above. Since 0.5 m/s is the recommended value, it is used as the median of a logtriangular distribution. The lower bound is chosen to be 0.5 / 2.5 = 0.2 and the upper bound of 

the distribution is chosen to be 0.5 · 2.5 = 1.25. 

Figure 5-27 CDF of CYCOEF 

5.9.9 Emergency Response (Evacuation and Relocation) 

The phase durations DURBEG, DURMID, and the late phase and corresponding speed values 

ESPEED1, ESPEED2, and ESPEED3 were defined above. For the uncertainty analysis, it was 

not necessary to make each of these parameters uncertain. In determining which parameters to 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3

Cumulative Probability

Linear coefficient for time-based crosswind dispersion model (m/s)
5-62

select, it was decided that one delay parameter (DLTEVA) and one speed parameter would 

adequately represent the uncertainty of the set. ESPEED2 was selected to be uncertain 

because this occurs during the middle phase when congestion within the EPZ is at its greatest. 

In addition to speed and delay, relocation parameters are sampled to reflect uncertainty in post 

evacuation activities. Following a release of radioactive material, levels of deposition can vary 

resulting in areas with elevated dose rates called hotspots [87] that may warrant the relocation 

of residents from affected areas. MACCS implements relocation using the hotspot and normal 

relocation parameters to represent these ORO actions, which would be determined based on 

dose projections using state, utility, and federal agency computer models and field 

measurements. In practice, this protective measure could be implemented as a post-accident 

relocation or as an expanded evacuation. 

 Evacuation Delay (DLTEVA) 

The evacuation delay (DLTEVA) for each cohort is unique. Although there is high confidence in 

the alert and notification system used to warn the public, time varying evacuation response 

rates in relation to the issuance of evacuation orders have been observed. Wolshon et al. [91] 

found that delay is not uniform with most of the evacuees experiencing a smaller delay (e.g., 90 

percent of the public evacuates in about 60 percent of the response time). However, there are 

reasons for expecting delays in response to nuclear power plant emergencies to be limited. 

Research has found that EPZ residents understand actions that may be expected during an 

emergency and are well prepared, with 20 percent of EPZ residents having go-bags and are 

ready to leave promptly [92]. The variation in public response makes this a good candidate for 

an uncertain parameter. Information used to develop the DLTEVA distributions was obtained 

from the ETE report. 

Rationale for Distribution 

A triangular distribution is used to represent DLTEVA uncertainty because there is data that 

supports a peak in the response, representing some people leaving early, some late, and most 

during the middle portion of the response. Delays can increase due to a slow response to the 

evacuation order, a need to wait for the return of commuters, a need to wait for public 

transportation, a need to shut down operations prior to leaving work, etc. Likewise, those 

members of the public with go-bags may respond promptly. MACCS moves cohorts as a group 

based on population fraction; therefore, compromise (average) values are used to represent the 

entire cohort response. 

Cohort 1 represents the schools within the EPZ. The ETE report [50] identifies school bus 

mobilization time under normal conditions taking 150 minutes for most schools and identifies 

two waves of evacuation. This time begins after schools are notified directly by OROs, which is 

0.5 hours after the initiating event (15 minutes for licensee to notify OROs of the SAE and 15 

minutes for OROs to notify schools). 

In this seismic scenario, conditions are not assumed to be normal; thus, some additional 

assumptions are added for schools including: 

• One wave of evacuation would be conducted. 

• High school students with vehicles would not wait for bus arrival and would also 

transport friends. 
5-63

• The number of teachers necessary to support loading buses would stay and evacuate 

with children on the buses. Any remaining teachers would evacuate in their personal 

vehicles taking as many children as possible. 

• The EPZ is close to Chattanooga, thus it is reasonable to assume additional buses could 

be acquired such that a second wave would not be necessary. 

The earthquake is assumed to damage bridges and local communications may also be affected; 

thus, the time for bus drivers to be notified and travel to the bus depot to pick up their bus is 

assumed longer than under normal conditions. Driving from the depot to the schools may also 

be expected to take longer than normal conditions. An additional 2.5 hours was added to the 

school bus mobilization to account for these activities. 

A mode of 5 hours was selected which includes the initial 2.5 hours mobilization time identified 

in the ETE study, and the additional 2.5 hours for mobilization described above. A lower bound 

of 1 hour was judged reasonable for this cohort for conditions where the evacuation occurs in 

the morning at the beginning of school or in the afternoon at the end of school when buses are 

already mobilized and are at or near the school when the event occurs. Furthermore, it is 

possible the earthquake would not significantly damage roadways. The Loma Prieta 

earthquake caused extensive building damage; however, except for the collapse of a single link 

span of the double deck section of the San Francisco-Oakland Bay Bridge, most bridges in the 

area of the San Francisco Bay survived the earthquake with relatively minor damage [93]. An 

upper bound of 8 hours was established to account for a broader range of delays in school 

notification, communication with drivers, driver mobilization time to pick up the buses, roadway 

impediments on the way to the schools, etc. 

Cohort 2 represents the special facilities. There are 756 special facility residents identified in 

the 2013 ETE [50], and these facilities would evacuate as resources are available. It is not 

necessary to model each facility individually; therefore, special facilities are modeled as a group. 

The departure time represents an average of when the facilities may depart. The 2013 ETE 

report identifies about 1.5 hours to mobilize the first wave of vehicles under normal conditions. 

The 1.5 hours begins after OROs have notified the facilities, which is 0.5 hours after the 

initiating event. The ETE study identifies an insufficient number of vehicles to complete the 

evacuation in a single wave, and shows a second wave beginning 4 hours after notification. 

Unlike the schools, it is not assumed that nurses or other staff would load residents into 

personal vehicles. However, as close as the EPZ is to Chattanooga, it is assumed that vehicles 

could be acquired such that a second wave would not be necessary. A mode of 3 hours was 

selected for Cohort 2. This includes the 1.5 hours to mobilize as described in the ETE study 

and an additional 1.5 hours to acquire additional resources and account for additional travel 

time to the facilities due to damaged infrastructure. A lower bound of 2 hours was selected to 

represent that there are some facilities with onsite transportation resources and 3 of the 5 

facilities (80% of the special facility population) are within 2 miles of the EPZ boundary. A factor 

of 2 was applied to the mode providing a value of 6 hours for the upper bound. The factor of 2 

was based on analyst judgment which considered the proximity to Chattanooga provides access 

to additional resources; however, additional time may be needed to acquire these resources 

due to competing priorities. 

Cohort 3 represents the 0-10 mile transit dependent population. The ETE study identifies 5.25 

hours to evacuate the transit dependent public. This group is identified in the ETE study as 

evacuating in two waves due to the limited resources, which are first used for special facilities. 

Because this population is made up of individuals who require specialized transportation, there 
5-64

are no assumptions on additional resources provided to expedite their evacuation. This cohort 

represents the homebound residents who are considered sheltered until they begin to evacuate. 

A mode of 4.5 hours was selected which includes 0.5 hours for the licensee to notify OROs and 

OROs to initiate contacts with response agencies to provide transportation and 4 hours to 

mobilize vehicles. Evacuating individual residents from random locations throughout the EPZ 

can be time consuming. A lower bound of 3 hours was selected to represent conditions where 

there are potentially fewer transit dependent evacuees due to support from family and friends 

who assist with evacuation. An upper bound of 9 hours (double the mode) was selected to 

represent the second wave of vehicles needing to travel through areas with damaged 

infrastructure. 

Cohort 4 represents the first group of the 0-10 mile public to evacuate and includes about 50 

percent of the EPZ population. It is expected that this group of public responds early and 

spontaneously, beginning to mobilize and evacuate prior to the official evacuation order. This 

assumption is based on the large seismic event which would be felt by everyone within the EPZ. 

Research has found that EPZ residents understand actions that may be expected during an 

emergency and are well prepared, with 20 percent of EPZ residents having go-bags and are 

ready to leave promptly. The focus groups conducted with this research identified a small 

number of EPZ residents had pre-planned their actions in case of an emergency and were 

ready to leave promptly [92]. Because this is a large earthquake, it is assumed this cohort 

leaves after the SAE sirens. A DLTEVA mode of 0.75 hours was selected for the average of the 

mobilization time of the early evacuees. This was selected considering sirens for SAE will be 

activiatedand residents take some time to prepare and pack prior to leaving. A DLTEVA mode 

of 0.75 hours, together with 0.5 hours for DLTSHL, equates to the early evacuees being 

modeled departing at 1.25 hours after the initiating event. A lower bound of 0 hours would 

indicate there is no delay after the public has been notified, which it is not realistic. A lower 

bound of 0.25 hours was selected because of an implied urgency from the earthquake and SAE 

sirens are sounded for this EPZ. An upper bound of 3 hours was used for this early evacuating 

cohort because impediments, such as inability to open garage doors, need to shut off gas and 

electricity, etc., could delay residents as they are preparing to leave. 

Cohort 5 represents the 0-10 mile general public. The 2013 ETE report shows that 90 percent of 

the general public is mobilized in about 1.5 hours after the siren. This value was used as the 

mode. Although there may be greater urgency due to the earthquake, there may be 

impediments that slow the preparation activities. Therefore, a lower bound of 0.5 hours was 

selected to represent the urgency due to the earthquake, and potentially fewer impediments that 

might slow preparations. An upper bound of 3 hours was selected to represent additional 

potential impediments, such as inability to open garage doors, need to shut off gas and 

electricity, etc., could delay residents as they are preparing to leave. 

Cohort 6 represents the evacuation tail for the 0 to 10 mile public. The 2013 ETE report [57] 

shows that 90 percent of the general public complete evacuation in about 5 hours under the 

roadway impact scenario, which is the time the tail would begin. A mode of 3 hours was 

selected representing the tail takes longer to mobilize than the general public [91]. A lower 

bound of 1 hour was selected to reflect a reduction in delay because residents sense of urgency 

having felt the earthquake. An upper bound of 5 hours was selected to reflect a range of 

potential delays that contribute to the tail, such as waiting for commuters, shutting down 

equipment, and the potential delays identified with cohort 4. 

Cohort 7 represents the shadow evacuation in the 10 to 15 mile area. The size of the shadow 

evacuation can be influenced by communication during the emergency and may be influenced 
5-65

due to the earthquake. The timing and content of messaging can cause the shadow to increase 

or decrease and can cause residents to leave earlier or later in the event depending on their 

perceived risk. Shadow evacuations typically begin shortly after a general evacuation begins. A 

mode of 1.5 hours was selected because early evacuees have begun evacuating and schools 

have begun to mobilize within the EPZ. A lower bound of 1 hour was selected to reflect a 

reduction in delay due to resident’s sense of urgency having felt the earthquake. An upper 

bound of 3 hours was selected to reflect an effective communication strategy that delays 

shadow evacuees allowing EPZ evacuees to travel through the area first. 

Cohort 8 does not have an associated uncertainty distribution. An uncertainty distribution is not 

applicable to Cohort 8 because they are deterministically considered to be non-evacuating 

during the accident. Therefore, there is no variability considered in their evacuation delay or 

speed. 

Table 5-17 provides a listing of the cohorts and the values used in the triangular distribution. 

The cohort sampling approach is shown in Figure 5-28. Evacuation delays are sampled 

independently for each cohort. Cohorts 4, 5 and 6 were perfectly correlated to ensure for 

example, that the tail does not evacuate prior to the early public. For each cohort the timing of 

evacuation is specified independently at each radial distance in the grid (a ring). These values 

are rank correlated so that the entire cohort begins to evacuate at approximately the same time. 

Table 5-17 MACCS uncertain parameters – evacuation delay. 

Cohort Mode 

(hours) 

Bounds 

(hours) 

Cohort 1: 0-10 mile schools 5.0 LB = 1.0 

UB = 8.0 

Cohort 2: 0-10 mile special facilities 3.0 LB = 2.0 

UB = 6.0 

Cohort 3: 0-10 mile transit dependent population 4.5 LB = 3 

UB = 9 

Cohort 4: 0-10 mile early public evacuation 0.75 LB = 0.25 

UB = 3 

Cohort 5: 0-10 mile general public evacuation 1.5 LB = 0.5 

UB = 3.0 

Cohort 6: 0-10 mile general public tail of the 

evacuation 3.0 LB = 1.0 UB = 5.0 

Cohort 7: 10-15 mile shadow evacuation 1.5 LB = 1.0 

UB = 3.0 
5-66

Figure 5-28 CDF of DLTEVA for each cohort 

 Evacuation Speed (ESPEED) 

The ESPEED2 parameter represents the speed for each of the evacuating cohorts for the 

duration of the middle phase. Speeds are developed for the cohorts as a group. As cohorts 

enter the roadway network, congestion builds and speeds slow down. The approach to 

developing speeds begins with the ETE study which provides the time to evacuate 90% and 

100% of the public and time to evacuate schools, special facilities, and the transit dependent 

public. Thus, the time to evacuate 90% and 100% of the public is known. The time to depart is 

also known from the ETE study. The difference in depart and exiting the EPZ is the travel time. 

With knowledge of the evacuation distances, the speeds are developed for each cohort. Similar 

to the delay times, the speeds are also adjusted to account for the earthquake. 

Following the NRC guidance on developing ETE studies [58], the Sequoyah ETE report [50] 

includes a roadway impact scenario, in which SR 58 was removed from the evacuation network 

and evacuees were rerouted to exit points out of the EPZ. SR 58 is the primary evacuation 

route on the eastern side of the EPZ [50]. The ETE study provides an evacuation time of about 

5 hours and 8 hours for the 90 and 100% ETEs respectively. Because the earthquake affects 

the entire EPZ, not just SR 58, a separate capacity analysis, provided as Attachment A, was 

completed to determine the evacuation speeds. 

Rationale for Distribution 

A triangular distribution was used to represent uncertainty because there is confidence that the 

mode developed from the capacity analysis is a most likely value. These speeds can vary by 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.25 1.25 2.25 3.25 4.25 5.25 6.25 7.25 8.25 9.25

Cumulative Probability

Evacuation Delay (hr)

Cohort 1

Cohort 2

Cohort 3

Cohort 4

Cohort 5

Cohort 6

Cohort 7
5-67

cohort depending on the evacuation routes selected by each cohort, the location of the cohort 

within the EPZ (e.g., schools may be near the edge of the EPZ), and the time the cohort enters 

the roadway. After speeds were established for the roadways, adjustment factors were applied 

using the ESPGRD parameter to account for congestion. 

For Cohort 1 (schools), a speed of 2 mph was used for the mode because the long mobilization 

time places the school buses on the roadway during the peak of the evacuation when 

congestion is greatest. A lower bound of 0.5 mph was judged reasonable to account for 

additional congestion and other impediments to the evacuation. An upper bound of 5 mph was 

judged reasonable based on the potential for less if the evacuation were to occur in the morning 

or afternoon while buses are already mobilized. This would place them on the roadway network 

before significant congestion occurs. 

Cohort 2 represents the special facilities within the EPZ. A mode of 3 mph was developed and 

reflects facilities that evacuate independently, but on average, are likely to enter the roadway 

network while some congestion exists. A lower bound of 0.5 mph was selected to represent the 

potential for greater congestion and the need to travel further to find accessible exits from the 

EPZ. An upper bound of 10 mph was judged reasonable to represent evacuating later in the 

event when roadways are less congested or very early for those facilities with transportation 

resources onsite. 

Cohort 3 represents the 0-10 mile transit dependent population. The mobilization to evacuate 

this group takes time putting the evacuees on the roadway while other cohorts are also on the 

roadway. The mode is 2 mph which reflects the transit dependent population evacuating under 

congested conditions while the schools, general public, and tail are evacuating. A lower bound 

of 0.5 mph was selected to reflect the potential for greater congestion and the need to travel 

further to find accessible exits from the EPZ. An upper bound of 5 mph was selected to reflect 

the possibility that the evacuation is less impeded. 

Cohort 4 (the first group of the 0-10 mile public to evacuate) enters the roadway network prior to 

congestion. Infrastructure damage is quickly encountered, as reflected by the slow speeds. A 

mode of 3 mph was estimated based on the potential extent of infrastructure damage which will 

cause evacuees to alter their routes and find less obstructed pathways out of the EPZ. A lower 

bound of 0.5 mph was selected to reflect the potential for greater congestion and the need to 

travel further to find accessible exits from the EPZ. An upper bound of 10 mph was selected to 

reflect the possibility that evacuation is less impeded. This cohort is on the roadway prior to 

other cohorts which allows the potential for their speeds to be higher than other cohorts. 

Cohort 5 represents the EPZ general public evacuees who begin to evacuate after OROs 

declare an evacuation is needed. At this time in the event, schools, medical facilities, and the 

early evacuees have mobilized and congestion is already assumed to have occurred. A mode of 

2 mph was estimated based on the evacuation times calculated in the capacity analysis 

(Attachment A). A lower bound of 0.5 mph was selected to reflect severe congestion. An upper 

bound of 5 mph was selected to reflect the possibility that the evacuation is less impeded. 

Cohort 6 represents the evacuation tail for the EPZ public, which follows the general public 

evacuation. The initial speed of the tail is the same as the general public because they enter 

the roadway immediately behind the general public. A mode of 3 mph was estimated because 

this cohort is on the last group on the roadway and speeds increase slightly as congestion 

diminishes. The lower bound was set at 0.5 mph (the same as the lower bound for Cohort 5) 

because the tail enters the roadway network at the end of the peak congestion. The upper 
5-68

bound of 10 mph represents roadways have less congestion once the majority of vehicles have 

left the area. At this time in the event, it is likely local news would broadcast the locations of 

bridge failures, reducing the time needed for the tail to find their alternative routes. 

Cohort 7 represents the 10 to 15 mile shadow evacuation which is travelling on different 

roadways than the EPZ population, thus the speeds are not directly related to the EPZ 

evacuees. Although shadow evacuations occur in response to emergencies, the effect of the 

shadow evacuation on the evacuees from the hazard area is seldom significant [88]. A mode of 

20 mph was judged reasonable to reflect the shadow evacuees are beyond the congested EPZ 

when they begin their travel. An upper bound of 30 mph was judged reasonable to account for 

limited traffic congestion and increased roadway capacity beyond the EPZ. Furthermore, the 

infrastructure beyond the EPZ is not assumed damaged in this scenario, thus the roadways are 

assumed accessible. A lower bound of 10 mph was selected to reflect the potential that power 

outages exist in the Chattanooga area causing traffic delays. An upper bound of 30 mph was 

selected to represent an unimpeded evacuation of shadow evacuees due to the available 

roadway capacity. 

Cohort 8 does not have an associated uncertainty distribution. An uncertainty distribution is not 

applicable to Cohort 8 because they are deterministically considered to be non-evacuating 

during the accident. Therefore, there is no variability considered in their evacuation delay or 

speed. 

Table 5-18 provides a listing of the cohorts and the values used in the triangular distribution. 

Cohorts are sampled as shown on Figure 5-29. Evacuation speeds are perfectly rank 

correlated between cohorts. 

Table 5-18 MACCS uncertain parameters – evacuation speeds. 

Cohort 

Speed 

Mode 

(mph) 

Speed 

Bounds 

(mph) 

Cohort 1 (0-10 Schools) 2.0 LB = 0.5 

UB = 5.0 

Cohort 2 (Special Facilities) 3.0 LB = 0.5 

UB = 10.0 

Cohort 3 (Transit Dependent) 2.0 LB = 0.5 

UB = 5.0 

Cohort 4 (0-10 Early) 3.0 LB = 0.5 

UB = 10.0 

Cohort 5 (General Public) 2.0 LB = 0.5 

UB = 5.0 

Cohort 6 (Evacuation Tail) 3.0 LB = 0.5 

UB = 10.0 

Cohort 7 (10-15 Shadow) 20.0 LB = 10 

UB = 30 
5-69

Figure 5-29 CDFs of ESPEED for each cohort 

 Hotspot Relocation Time (TIMHOT) 

A hotspot relocation time distribution has been developed to account for a range of source 

terms, population densities, seismic impact, and other characteristics that influence relocation. 

Rationale for Distribution 

A uniform distribution was used to represent uncertainty, because the potential influences in 

activity duration during the emergency make any value within the range described below 

possible. Few of the time contributing activities can be estimated with confidence prior to an 

event, such as the affected area, affected population, available transportation infrastructure, 

need for traffic control, and plume travel speed. Furthermore, data related to prompt relocation 

of residents was identified in research, thus values were developed from large scale 

evacuations that have aspects that resemble relocation type activities. 

It is assumed relocation is implemented after evacuation is complete to limit extra traffic in areas 

through which the evacuees are travelling. This approach allows emergency responders who 

were supporting the evacuation transition to relocating residents. The lower bound was 

developed based on an optimal relocation implemented promptly after plume arrival. 

Capabilities exist to project hotspot areas, and OROs could request the public make 

preparations to leave, prior to the release. Relocation could then begin promptly once it is 

determined the plume has passed. Assuming the public is prepared to leave, the final time 

element is travel out of the affected area. A review of the SOARCA results from NUREG/CR0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.5 5 50

Cumulative Probability

Evacuation Speed (mph)

Cohort 1, 3 & 5

Cohort 2, 4 & 6

Cohort 7
5-70

7110 Volume 2 was conducted to understand the extent to which contamination was projected 

beyond the EPZ for that sequence and plant. The review identified that the release fraction of 

cesium for the STSBO scenario might only exceed the hotspot dose threshold over a small 

portion of the 5 mile area beyond the EPZ. The review of a different plant with different source 

terms was only used to inform the process. Sequoyah has a much different containment, the 

seismic event is larger than previous analyses, and the source terms identified in early runs are 

large. The review would suggest that relocation would affect a larger population than the 

previous analysis. Therefore, for this analysis, it is assumed that hotspot relocation will be 

implemented for the entire population within the 5 mile ring beyond the EPZ as shown in 

Figure 5-30. This is about 390 square miles. 

From SecPop, the 2010 population within the radial area 5 miles beyond the EPZ (i.e., 10 to 15 

miles from the plant) is about 184,000. The emergency response scenario includes an 

assumption that 20 percent of the population in this area evacuates as a shadow evacuation, 

which reduces the number of people potentially affected by hotspots. It is assumed the 

remaining population of 147,000 residents (80% of 184,000) will be relocated under the hotspot 

criteria. It is also assumed that OROs have competing priorities due to the seismic event such 

as damaged infrastructure, injured residents, power outages, and inaccessible roadways and 

are unable to implement relocation promptly. 

A time of 36 hours was used in the base case analysis. Considering the number of people 

potentially affected, the need for authorities to notify these people and support the relocation, a 

lower bound value of 24 hours after plume arrival was judged reasonable. The 24 hour value 

represents an average time for relocation of the affected area. This value was compared to 

evacuation information in NUREG/CR 6981, “Assessment of Emergency Response Planning 

and Implementation for Large Scale Evacuations,” [104] which includes case studies of 11 

evacuations, seven of which involved populations between 100,000 and 3 million people. Very 

large evacuations almost always occur in a staged manner, and for hurricanes, where there is 

ample time to prepare, these resemble prompt relocation activities. The large evacuations that 

were reviewed were generally completed in 24 hours or less. 

The upper bound represents conditions in which there is a delay in relocation, which could occur 

for many reasons. For example, OROs may decide to wait until the morning, rather than trying 

to mobilize families at night. Dow and Cutter [105] identified that the majority of hurricane 

evacuation trips begin during normal waking hours. Fu and Wilmot [106] profiled a hurricane 

evacuation time of day pattern which showed people are least likely to evacuate at night, with 

an increasing likelihood during the morning and even greater during the afternoon. Loss of 

communication, infrastructure damage, or OROs focused on activities within the EPZ or having 

other conflicting priorities that could contribute to such a delay. Because relocation is based on 

projected dose over the emergency phase, it may not have the same priority as other urgent 

requirements. It is also possible that response officials may choose to shelter in place for a 

period of time. An upper bound of 48 hours after plume arrival was judged reasonable for the 

affected area. This represents an average time for relocation of the affected area. The CDF is 

presented in Figure 5-31. 
5-71

Figure 5-30 Assumed hotspot and normal relocation areas 

Figure 5-31 CDF of TIMHOT 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

24 26 28 30 32 34 36 38 40 42 44 46 48

Cumulative Probability

Hotspot Relocation Time (hr)
5-72

 Normal Relocation Time (TIMNRM) 

The normal relocation time (TIMNRM) is the time to relocate residents from areas that exceed 

the normal dose threshold (DOSNRM). This user specified time is relative to plume arrival. The 

time includes the elements described for TIMHOT. In addition, time may be needed to allow 

clearance of residents relocated due to the hotspot criteria. MACCS implements this parameter 

by removing the entire affected population from the dose equation at a point in time after plume 

arrival, the specified TIMNRM. As with TIMHOT, an average time is developed by considering 

the influencing factors. 

Rationale for Distribution 

A uniform distribution is used to represent uncertainty in TIMNRM because the potential 

influences in activity duration during the emergency make any value within the range described 

below possible for the same reasons described with TIMHOT. However, normal relocation has 

a lower dose threshold and may not be implemented with the same urgency as hotspot 

relocation. As described above, in some aspects, relocation resembles hurricane evacuation. 

This lack of directly applicable data also supports use of a uniform distribution. 

Similar to TIMHOT, the lower bound is developed based on an optimal response where OROs 

relocate residents promptly after plume arrival. As described above, it is assumed that the area 

from 10 to 15 miles, also shown on Figure 5-30, is relocated due to exceeding the hotspot 

criteria. For normal relocation, it is assumed that the entire area from 15 to 20 miles (about 550 

square miles) would be relocated due to exceeding normal relocation criteria. From SecPop, the 

2010 population of the 15 to 20 mile area is about 195,000. Notifications can be accomplished 

via EAS messaging, but route alerting would be necessary to notify the affected public and is 

estimated to take about 8 hours to complete. For this size of population, traffic control would 

likely be established to support a relocation effort. A time of 48 hours was used in the base case 

analysis. A lower bound of 36 hours after plume arrival was judged reasonable to represent an 

average time for relocation of the affected area. 

The upper bound represents conditions where OROs may be focused on activities within the 

EPZ or have other conflicting priorities which delay relocation activities. Furthermore, OROs 

may delay relocation until daylight hours. Because normal relocation is due to a 1 rem dose 

projected over the emergency phase, it may not have the same priority as other urgent 

requirements. An upper bound of 72 hours after plume arrival was judged reasonable. The CDF 

is presented in Figure 5-32. 

TIMNRM is perfectly rank correlated with TIMHOT because MACCS requires TIMNRM be less 

than TIMHOT. 
5-73

Figure 5-32 CDF of TIMNRM 

 Hotspot Relocation Dose (DOSHOT) 

The hotspot relocation dose (DOSHOT) is a projected dose used to initiate hotspot relocation. 

If the total dose to individuals exceeds DOSHOT, those people are relocated (i.e., removed from 

the analysis) at a user specified hotspot relocation time (TIMHOT) in the early phase. DOSHOT 

is often represented as the upper bound of the projected dose range provided in the EPA PAG 

Manual (Table 1-1. Planning Guidance and Protective Action Guides for Radiological Incidents) 

[94]. This should not be confused with the EPA PAG Manual criteria for relocation, which is 

specified for the intermediate phase. 

Rationale for Distribution 

A triangular distribution is used to represent the uncertainty in DOSHOT because the mode is 

considered to be the most likely value (5 rem) based on the EPA PAGs. The range was 

developed recognizing that ORO decisions are influenced by many factors that could result in a 

higher or lower value. 

The EPA PAG Manual provides a range of 1 to 5 rem for implementing protective actions and 

explains that under normal conditions, evacuation should be implemented if residents are 

expected to receive 1 rem over a 4 day emergency phase. The upper bound for this distribution 

was increased 50 percent above the EPA PAG to 7.5 rem, based on analyst judgment. The 

lower bound was set at 1 rem, which is the mode of the normal relocation criteria (DOSNRM). 

The CDF is presented in Figure 5-33. 

DOSHOT is perfectly rank correlated (i.e., coefficient of 1.0) with the normal relocation dose 

(DOSNRM) because MACCS requires DOSNRM to be less than DOSHOT. 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

36 40 44 48 52 56 60 64 68 72

Cumulative Probability

Normal Relocation Time (hr)
5-74

Figure 5-33 CDF of DOSHOT 

 Normal Relocation Dose (DOSNRM) 

The normal relocation dose (DOSNRM) is a projected dose used to initiate normal relocation. If 

the total dose to individuals exceeds DOSNRM, those people are relocated (i.e., removed from 

the analysis) at a user specified normal relocation time (TIMNRM) in the early phase. 

DOSNRM is typically represented as the lower bound of the projected dose range provided in 

Table 1.1 of the EPA PAG Manual. 

Rationale for Distribution 

A triangular distribution is used because the mode (1 rem) is considered to be the most likely 

value based on EPA PAGs. It is assumed that OROs would attempt to meet the EPA PAG 

barring unforeseen circumstances. Using judgment, the upper bound for this distribution was 

increased by a factor of two over the mode to 2 rem. The lower bound of 0.5 rem was judged 

reasonable. A low threshold value causes the size of the affected area to increase, which would 

increase the number of people requiring relocation. The number of people affected would likely 

influence ORO decisions on the lower threshold value. The CDF is presented in Figure 5-34. 

DOSNRM is perfectly rank correlated with the hotspot relocation dose (DOSHOT) because 

MACCS requires DOSNRM to be less than DOSHOT. 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5

Cumulative Probability

Hotspot Relocation Dose (rem)
5-75

Figure 5-34 CDF of DOSNRM 

 Weather 

The deterministic offsite consequence values are the expected (mean) value of the probability 

distribution obtained from a large number of weather trials. The UA uses the same weathersampling strategy adopted for SOARCA, as reported in the MACCS Best Practices document, 

and also applied in the Peach Bottom UA. 

 MACCS Correlated Parameters 

Many of the parameters in the analysis have correlations. Some of these are perfectly rank 

ordered and others have rank correlation coefficients between -1.0 and +1.0. Unless specified 

in Table 5-19 below, parameters are not correlated. 

Table 5-19 MACCS correlated parameters. 

Input Parameter 

Perfectly RankOrder Correlated 

with 

Comments 

PROTIN(2) LPROTIN Long-term is tied to normal activity 

GSHFAC(2) LGSHFAC Long-term is tied to normal activity 

TIMHOT TIMNRM TIMHOT is always less than TIMNRM 

DOSHOT DOSNRM TIMHOT is always greater than TIMNRM 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.5 0.75 1 1.25 1.5 1.75 2

Cumulative Probability

Normal Relocation Dose (rem)
5-76

Input Parameter 

Perfectly RankOrder Correlated 

with 

Comments 

EFFTHR(1) EFFACA(1) Red bone marrow 

EFFTHR (2) EFFACA (2) Lungs 

EFFTHR (3) EFFACA (3) Stomach 

CYSIGA(1) CYSIGA(2-6) All weather conditions 

CYSIGA(1) CZSIGA(1-6) All weather conditions 

ESPEED(2)-1 ESPEED(2)-(2-5) Middle Phase of ESPEED for all Cohorts 

evacuating 

VDEPOS(1) VDEPOS(2-10) All particle size bins 

Co-58_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Co-60_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Rb-86_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Rb-88_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Sr-89_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Sr-90_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Sr-91_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Sr-92_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Y-90_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Y-91_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Y-91m_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Y-92_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Y-93_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Zr-95_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Zr-97_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Nb-95_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Nb-97_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Mo-99_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Tc-99m_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Ru-103_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Ru-105_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Ru-106_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Rh-103m_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Rg-105_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Te-127_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Te-127m_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Te-129_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Te-129m_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Te-131_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Te-131m_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 
5-77

Input Parameter 

Perfectly RankOrder Correlated 

with 

Comments 

Te-132_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

I-131_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

I-132_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

I-133_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

I-134_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

I-135_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Cs-134_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Cs-136_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Cs-137_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Ba-139_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Ba-140_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

La-140_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

La-141_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

La-142_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Ce-143_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Ce-144_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Pr-143_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Pr-144_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Nb-147_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Np-239_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Pu-238_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Pu-239_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Pu-240_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Pu-241_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Am-241_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Cm-242_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Cm-244_ICH(1) Co-58_ICH(2,4-9) Lifetime inhalation dose coefficient 

Linear correlation coefficients 

Input Parameter

Rank-Order 

Correlated 

With

Correlation 

Coefficient Comments 

PROTIN(2) PROTIN(3) 0.75 

Applies to all evacuation cohorts for 

normal activity and sheltering 

inhalation protection factors 

GSHFAC(2) GSHFAC(3) 0.8 

Applies to all evacuation cohorts for 

normal activity and sheltering 

groundshine shielding factors 
6-1

6. OFFSITE CONSEQUENCE ANALYSIS 

6.1 Introduction 

The Sequoyah SOARCA offsite consequence analysis includes the results of deterministic 

analyses and probabilistic uncertainty analyses. The deterministic cases have common 

MACCS input parameters as shown in Appendix B, but unique source terms. The deterministic 

cases are: 

• STSBO base case39 without random ignition, 

• An STSBO case, also without random ignition, with an early release, approximately 3 hrs 

after the initiating event, (STSBO ER), and 

• LTSBO base case. 

The probabilistic uncertainty analysis results are based on sampled MELCOR and MACCS 

input values. An uncertainty analysis was conducted for the STSBO cases without and with 

random ignition (STSBO w/o random ignition and STSBO with random ignition). Each 

realization represents a sampled parameter set that is unique. The uncertainty analysis 

comprised 467 realizations for the STSBO w/o random ignition and 432 for STSBO with random 

ignition for a total of 899 realizations. The selected uncertainty analyses which resulted in the 

maximum cesium release, the maximum LCF risk, and the maximum EF risk are discussed. 

Finally, a sensitivity analysis was conducted for the STSBO-ER scenario, in which the 

effectiveness of sheltering in place versus evacuation was examined under varied shielding 

assumptions. 

The results of the consequence analyses are presented in terms of individual LCF risk and 

individual EF risk for the population residing near the plant. The primary results are mean 

values of more than 1,000 weather trials. To examine the variation of risk with distance from the 

site, the reported risk measures are evaluated for residents within specified radial distance 

intervals (i.e., circular or annular areas with specified radii) centered on the reactor site. They 

are averaged over the entire residential population within each interval and over weather 

variability. These individual risk values are population weighted and are computed by dividing 

the predicted number of excess fatalities (early or latent) by the population living within the 

specified interval. These risk measures account for the distribution of the population within the 

distance interval and for the interplay between the population distribution and the wind rose 

probabilities. 

The results are presented as conditional risks, which are the risks predicated on the accident 

occurring. The scenario frequency-weighted risk is the product of the core damage frequency 

for the accident scenario and the conditional EF or LCF risk for that scenario. The scenario 

frequency-weighted risk is the average likelihood of an individual dying of latent cancer or from 

acute radiation exposure per year of plant operation (i.e., LCF risk per reactor year (pry) or EF 

 

39 “Base case” describes the STSBO without random ignition and the LTSBO scenarios in which all uncertain 

MELCOR parameters are set to their most likely value (mode). The STSBO Early Release was selected from the 

STSBO w/o random ignition set of UA realizations as an example of an early release. 
6-2

risk pry) as a result of the specific scenario under consideration. Selected results present both 

conditional and frequency-weighted risk values for comparison for each scenario. 

For the deterministic cases, individual LCF risk results are presented for four approaches to 

quantify the risk from radiation exposure at low doses and dose rates. Cancer dose-response 

curves for low doses and dose rates are subject to considerable uncertainty, so it is instructive 

to examine how much of the computed risk arises from low doses or dose rates. The primary 

approach is the use of the LNT model to quantify individual latent cancer fatality risk, which 

considers all doses and dose rates for estimating cancer risk. The LNT dose-response 

relationship suggests that any increase in dose, no matter how small, results in an incremental 

increase in risk. The alternate dose quantification approaches examine the contribution to LCF 

risk arising only from doses above a specified dose or dose-rate threshold. The three alternate 

dose quantification approaches used in this study are: 

• consideration of cancers arising only from dose rates above the US average natural 

background dose rate (310 mrem/yr) (NCRP), 

• cancers from dose rates above the US average natural background dose rate combined 

with average annual manmade (primarily medical) exposure as a dose threshold level 

(620 mrem/yr) [96], and 

• a dose and dose-rate threshold level based on the Health Physics Society’s Position 

Statement that, due to uncertainties, a quantified risk should not be assigned to dose 

rates less than 5 rem/yr with a lifetime dose limit of 10 rem [100]. 

A 10 mrem/yr dose threshold level was also investigated. It produced results that were 

modestly lower than results calculated using the LNT assumption; therefore, these results are 

not included. Finally, the UA and the sensitivity analysis results are based solely on the LNT 

approach. 

Like the SOARCA report NUREG-1935 [3], the individual LCF risks reported for Sequoyah do 

not include potential doses from food or water ingestion. MACCS does not include ingestion 

doses in the estimation of individual EF and LCF risks. Ingestion doses are considered to be 

societal, or population, doses and are difficult to apportion to individuals. This is because foods 

grown in a specific area are not necessarily consumed in that area. Instead, locally grown food 

is distributed to various parts of the country and, conversely, food consumed in a local area 

comes from other domestic and international sources. This complexity makes it very difficult to 

apportion ingestion doses to specific individuals. Therefore, individual doses only consider 

cloudshine, groundshine, and inhalation exposure pathways for which the exposure occurs 

locally. 

6.2 Sequoyah Source Terms 

Some of the key characteristics of a release of radiation to the environment are the magnitude 

and type of radioactive materials as well as the timing and duration of release. These influence 

the risk results of both the emergency and long-term phases of an accident. The long-term 

individual LCF risk is strongly correlated with the concentration of cesium deposited on the 

ground. A larger release provides more cesium source term, however the rate of release 

combined with local weather conditions influence where and when the cesium will deposit. As 

an example, a release of high magnitude and short duration is more likely to result in higher 
6-3

ground concentrations at certain locations than a long gradual release. This is because the 

longer release is more likely to disperse over a larger area as the wind direction changes. An 

early release can be important because of its potential to challenge emergency response 

protective actions such as evacuation. 

Table 6-1 lists source term environmental releases of the deterministic scenarios and 

summarizes the total release fraction by MELCOR chemical class. The environmental release 

fraction is that portion of the radionuclide inventory of each class that is released to the 

environment. The total release fraction is the result of integrating over the duration of the 

release. The start time is the beginning of the release to the environment, no matter how small. 

Some releases may begin with a small release, which subsequently increases significantly due 

to containment failure or some other release mechanism. The “increase” time describes the time 

when the release significantly increases in rate. For the Sequoyah project, all cases were 

truncated at 72 hours. 

Table 6-1 Source term releases for Sequoyah accident scenarios. 

Scenario Environmental Release Fraction by MELCOR Chemical Class Time (hr) 

Xe Cs Ba I Te Ru Mo Ce La Start* Increase** 

STSBO 

base case 

0.998 0.063 0.001 0.069 0.021 0.000 0.212 0.000 0.000 4.0 52.0

STSBO ER 1.000 0.039 0.004 0.084 0.093 0.002 0.141 0.000 0.000 2.6 3.6

LTSBO 

base case 0.997 0.023 0.009 0.092 0.087 0.000 0.001 0.001 0.000 23.8 28.8

*The “start” time indicates the timing of the first environmental release, no matter how small (e.g., release 

fraction on the order of 1E-9). 

**The “increase” time indicates the timing of the first significant increase in the rate of release. 

Figure 6-1 illustrates the progression of releases of the cesium and iodine classes over time for 

the STSBO w/o random ignition. The release begins at 4 hours, however the initial magnitudes 

are miniscule (~10-9). The cesium and iodine releases both increase rapidly at 52 hours. 

Ultimately, the cumulative cesium and iodine released to the environment equal 6.3% and 6.9% 

of the respective quantities contained in the Sequoyah Reactor unit 1, middle-of-cycle inventory. 

 
6-4

Figure 6-1 Cumulative environmental release fraction of cesium and iodine as a 

function of time for the STSBO base case scenario 

Figure 6-2 shows the cumulative iodine and cesium class release profile of the STSBO-ER 

scenario. The release starts at 2.6 hours, increases in multiple steps, and then increases 

relatively slowly from about 24 through 72 hours. For the STSBO-ER case, the total cesium 

release of 3.9 percent is about half of the total iodine release of 8.4 percent. 
6-5

Figure 6-2 Cumulative environmental release fraction of cesium and iodine as a 

function of time for the STSBO early release scenario 

Lastly, Figure 6-3 shows the LTSBO scenario release to the environment begins at 24 hours, 

increases in rate at 28 hours, and has another large increase at about 45 hours. The cesium 

and iodine class releases total 2.3 and 9.2 percent, respectively. 
6-6

Figure 6-3 Cumulative environmental release fraction of cesium and iodine as a 

function of time for the LTSBO base case 

6.3 Deterministic Analysis of Individual Latent Cancer Fatality 

Risk 

6.3.1 Summary of Deterministic Results 

For all deterministic cases analyzed, the individual early fatality risks were zero. The discussion 

below focuses on the individual latent cancer fatality risk. 

Table 6-2 presents the core damage frequencies, and the 0–10 mile radial interval (i.e., a 

circular area with a 10 mile radius centered on the Sequoyah plant) conditional and scenario 

frequency-weighted individual LCF risk results for the deterministic base case scenarios. The 

core damage frequencies are expressed as the probability of an unmitigated accident occurring 

per reactor year of operation (pry). The core damage frequencies for STSBO and LTSBO are 

estimated to be 2E-06 and 9E-06 per reactor year, respectively. The conditional individual LCF 

risks range from 4.8E-04 to 8.5E-04. The scenario frequency-weighted risks are the products of 

the core damage frequency for the scenario and conditional risk given that scenario has 

occurred. Taking the STSBO base case conditional LCF risk as an example, the scenario 

frequency-weighted risk is ~ 1E-9 pry. To simplify the presentation of results, the remainder of 

this section discusses conditional risk results only. The frequency of the STSBO scenarios 

(without random ignition and early release) were not quantitatively assessed. These STSBO 

scenarios are all weighted by the base STSBO core damage frequency. Thus, the reader is 

cautioned not to sum the unconditional risks for the variations on the STSBO in the table 

because this would count the risk twice. 
6-7

Table 6-2 The Sequoyah core damage frequency and conditional scenario-specific 

individual LCF risks for the 0 – 10 mile radial interval assuming LNT 

dose response. 

Scenario 

Core Damage 

Frequency 

(pry) 

Conditional Risk of LCF 

for an individual located 

within 10 miles 

STSBO base case without 

random ignition sources 2E-06* 

4.8E-04 

STSBO-ER 8.5E-04 

LTSBO base case 9E-06 5.7E-04 

* The core damage frequency is not equally likely for each scenario variation. This term reflects the 

overall frequency of an STSBO scenario. As discussed in Chapter 4, early releases were observed 

in roughly 20 percent of the STSBO w/o random ignition uncertainty analysis realizations. 

Table 6-3 and Figure 6-4 present individual LCF risks for exposures occurring during the 

emergency phase and for exposures occurring during both the intermediate and long-term 

phases (the latter category is simply termed long-term phase risk in the subsequent discussion). 

The emergency phase is the first seven days following the beginning of release into the 

environment. The intermediate phase follows the emergency phase and lasts one year. The 

long-term phase follows and lasts an additional 50 years. The emergency phase risks are 

calculated from doses received during the first seven days. The long-term phase risks include 

dose contributions from the long-term and intermediate phases. 

The LCF risks by phase are shown for 0-10 mile, 10-20 mile, and 0-50 mile radial distance 

intervals around the Sequoyah plant. The distance intervals presented are shown because the 

10-mile radius corresponds to the NRC’s Quantitative Health Objective (QHO) for latent cancer 

risk [107] and it also encompasses the Sequoyah plant EPZ. The second interval (10-20 miles) 

is adjacent to the EPZ and is presented to compare results to the EPZ, wherein nearly all of the 

population is evacuated, and the next most distant region, wherein a small fraction of the 

population (the shadow cohort) is assumed to evacuate. The 0-50 mile interval is used to 

identify regional impacts in regulatory analyses and is compatible with previous SOARCA 

reports. 

 
6-8

Table 6-3 Summary of the mean emergency and long term phase individual LNT, 

LCF risks for residents within 0 - 10, 10 - 20, and 0 - 50 mile intervals of 

the plant. 

Figure 6-4 shows key features of the data. In general, the emergency phase risks are small 

relative to the long term phase risks. Over all of the distance intervals, the total conditional LCF 

risks range between approximately 3E-04 to 9E-04, where the total is the sum of the long term 

phase and emergency phase risk values. The total risk magnitudes decrease as the distance 

intervals increase. Conversely, the 0-10 mile emergency phase risks are much lower than the 

corresponding risks for the 10-20 and 0-50 mile intervals. This is due to the effectiveness of 

emergency response actions. For the STSBO ER case, there is an increase in the emergency 

phase LCF risk relative to the STSBO and LTSBO base case emergency phase LCF risks. 

Each deterministic case is discussed in detail in the following subsections. 

Radius 

(miles)

Accident 

Phase

STSBO 

Base Case

Early 

Release 

LTSBO 

Base Case

Emergency 1.0E-07 6.7E-05 2.8E-06

Long Term 4.8E-04 7.8E-04 5.7E-04

Emergency 3.5E-05 2.3E-04 3.7E-05

Long Term 4.0E-04 5.8E-04 4.0E-04

Emergency 4.8E-05 1.4E-04 1.8E-05

Long Term 3.4E-04 4.5E-04 2.8E-04

0-10

10-20

0-50
6-9

Figure 6-4 Mean long term and emergency phase individual LNT, LCF risks for 

residents within 0 - 10, 10 - 20, and 0 - 50 mile intervals of the plant 

 

The solid bars in Figure 6-4 indicate risks from both the long term and intermediate phase (LTP 

in Figure 6-4 legend) exposures and the hatched bars indicate risk from emergency phase (EP) 

exposures. 

6.3.2 STSBO Base Case Scenario 

Figure 6-5 presents the mean individual LCF risk from the STSBO w/o random ignition scenario 

for specified intervals around the plant. In the 0-10 mile region, the emergency phase 

contribution to total individual LCF risk is 0.021%. For the 10-20 and 0-50 mile regions, the 

emergency phase contributes 8% and 12% of the total individual LCF risk, respectively. The 

emergency response is very effective within the EPZ, as demonstrated by the small contribution 

of the emergency phase to overall risk. Nearly all of the emergency phase risk is attributable to 

the 0.5 percent of the population that is modeled to not evacuate. 
6-10

Figure 6-5 Mean individual LCF risk from the Sequoyah STSBO base case for 

residents within 0 - 10, 10 - 20, and 0 - 50 mile intervals of the plant 

showing contribution from different accident phases 

Table 6-4 and Figure 6-6 show the total LCF risk as a function of alternate approaches to 

quantifying cancer risk from low doses and dose rates. As discussed in Section 6.1, these 

include the LNT (linear no threshold), a 310 mrem per year threshold (US natural background 

dose rate), a 620 mrem per year threshold (US background dose rates for natural and manmade radiation sources), and a threshold based on the HPS position statement. The 310 mrem 

and 620 mrem are annual thresholds. The HPS recommended approach has both annual (5 

rem) and lifetime (10 rem) components. 

Table 6-4 Individual LCF risk calculated using alternate low dose risk 

quantification approaches for the STSBO base case scenario. 

Interval 

(miles) LNT 310 mrem/yr 620 mrem/yr HPS 

0-10 4.8E-04 7.9E-05 4.0E-05 3.7E-09 

10-20 4.3E-04 1.2E-04 9.0E-05 1.2E-06 

0-50 3.9E-04 1.5E-04 1.1E-04 6.6E-07 
6-11

Figure 6-6 Mean individual LCF risk as a function of risk quantification threshold 

for the STSBO base case scenario 

Examining Table 6-4 and Figure 6-5, as the dose threshold increases the corresponding risks 

decrease. With non-LNT dose response approaches, only annual doses above the thresholds 

contribute to the LCF risk. 

As stated previously, the 0 – 10 mile interval emergency phase risk is a small fraction (0.021%) 

of the total LCF risk. The STSBO base case (Figure 6.1) produces a tiny release rate beginning 

at 4 hours that does not increase significantly until 52 hours, therefore the great majority 

(99.5%) of the EPZ residents evacuate prior to plume arrival. In effect, for this scenario and 

within the EPZ, virtually all of the LCF risk stems from the intermediate and long term phase 

risk. 

Assuming the LNT dose-response, the LCF risks fall modestly with increasing distance from the 

plant. For the 310 and 620 mrem per year dose thresholds, the LCF risk results increase with 

increasing distance from the plant. The habitability criteria are 2 rem committed dose during the 

intermediate phase (from 7 days to one year after the start of the accident), and 500 mrem for 

each year thereafter. Considering the habitability criteria and the dose thresholds, most of the 

dose comes from the first one or two years of exposure (i.e., the intermediate phase and 

possibly a few years of the long term phase for the 310 mrem/yr threshold). The increasing risk 

with distance implies that a proportionately larger fraction of the population closer to the plant 

may be relocated during the intermediate phase, compared with the population farther from the 

plant, due primarily to groundshine dose rates exceeding the habitability criterion. 
6-12

Referring to Figure 6-5, the population in the 10-20 and 0-50 mile intervals received larger 

emergency phase doses (implied by the higher risks) than the population in the 0-10 mile 

interval. The emergency phase doses also contribute to the total LCF risk. For the HPS dose 

response approach (the largest dose threshold), the total LCF risks are relatively very small in 

the 0-10 mile interval, increase in the 10-20 mile interval, and decrease again in the 0-50 mile 

interval. This implies that the dose thresholds are exceeded to a higher degree beyond the EPZ 

where most of the population is not evacuated and therefore receives an exposure during the 

emergency phase. 

Within the EPZ, the risks decrease from LNT by a factor of six for 320 mrem, twelve for 630 

mrem, and five orders of magnitude for the HPS dose threshold. Similarly, for the farther 

regions, the LCF risks decrease from by roughly three orders of magnitude with increasing dose 

threshold. 

6.3.3 STSBO Early Release Scenario 

The results from the STSBO early release (STSBO-ER) scenario differ from the previous 

results. Comparing Figure 6-5 with Figure 6-7 shows that the total LCF risks are somewhat 

higher for the STSBO-ER. Since the total release fractions of iodine and cesium (Figure 6-2) 

are roughly the same as the previous case and the main differences are the release timing and 

duration, the radioactivity released is dispersed over a larger area because of wind shifts during 

the more prolonged release. Thus, the ground surface concentrations are expected to be lower 

on average. This results in lower doses to more people. These lower doses are less likely to 

require remediation, and this increases the long term risk of health effects when evaluated with 

the LNT dose-response model. 

Figure 6-7 indicates that, like the other cases, the long term phase dominates the emergency 

phase contribution to total risks, however emergency phase risks as a fraction of total risks 

increase are greater than those in the previous scenario. The fractions for this scenario are 8%, 

28%, and 24% for the 0-10 mile, 10-20 mile, and 0-50 mile regions, respectively. Although the 

final magnitudes of the release fractions of cesium and iodine do not differ significantly from the 

other cases, the timing of release during the STSBO-ER scenario does. The major release 

begins much earlier, at 3.6 hours; whereas, the significant increases in the release rates for the 

STSBO and LTSBO base cases releases occur at 52 and 29 hours respectively. 

For the 0-10 mile region, the increase in the early contribution to the total LCF risk shows there 

is an increased risk to the evacuating cohorts in the EPZ corresponding to an increased 

possibility of part of the population evacuating under the plume. 
6-13

Figure 6-7 Mean individual LCF risk from the Sequoyah STSBO Early Release 

scenario for residents within 0 - 10, 10 - 20, and 0 - 50 mile intervals of 

the plant showing contribution from different accident phases 

Table 6-5 and Figure 6-8 present the total LCF risk as a function of dose threshold for the 

STSBO ER scenario. In this case, there are significantly higher results for the 0 – 10 mile 

interval for the non-LNT approaches than the previous scenario, which reflects a larger 

contribution from of the emergency phase dose. This is in large part due to the timing and 

duration of the release. Over all distance intervals, both the emergency and long term phase 

doses are greater for the STSBO ER than for the STSBO or LTSBO base cases. Unlike the 

STSBO base case dose threshold results, the non-LNT results have a maximum in the risk 

values for the 10-20 mile interval. This is consistent with a larger contribution to total dose from 

the emergency phase for areas beyond the EPZ. All of the dose threshold LCF risks are larger 

than for the STSBO base case. 

Table 6-5 Mean individual LCF risk calculated using low dose risk quantification 

approaches for the STSBO Early Release scenario. 

Interval 

(miles) LNT 310 mrem/yr 620 mrem/yr HPS 

0-10 8.5E-04 2.2E-04 1.6E-04 6.1E-05 

10-20 8.0E-04 3.8E-04 3.3E-04 7.0E-05 

0-50 5.9E-04 2.7E-04 2.3E-04 3.1E-05 
6-14

Figure 6-8 Mean individual LCF risk as a function of risk quantification threshold 

for the STSBO Early Release scenario 

6.3.4 LTSBO Scenario 

Figure 6-9 shows that for the LTSBO case, the long term phase again dominates the 

emergency phase contributions to total risks, with emergency phase risks as a fraction of total 

risks equal to 1%, 27%, and 22% for the 0-10, 10-20, and 0-50 mile intervals, respectively. In 

this case, the emergency response is effective in part because the release begins at 24 hours. 
6-15

Figure 6-9 Mean individual LCF risk from the Sequoyah LTSBO base case for 

residents within 0 - 10, 10 - 20, and 0 - 50 mile intervals of the plant 

showing contribution from different accident phases 

Table 6-6 and Figure 6-10 present the Total LCF risk as a function of dose threshold for the 

LTSBO scenario. The dose threshold comparison for the LTSBO is similar to the other cases. 

The risks for this scenario are between those for the STSBO-ER and the STSBO base case 

scenarios. 

Table 6-6 Total LCF risk as a function of dose threshold for the LTSBO scenario. 

Interval 

(miles) LNT 310 mrem/yr 620 mrem/yr HPS 

0-10 5.7E-04 1.4E-04 9.3E-05 6.7E-06 

10-20 6.0E-04 3.2E-04 2.9E-04 8.1E-05 

0-50 3.8E-04 1.8E-04 1.5E-04 3.0E-05 
6-16

Figure 6-10 Mean individual LCF risk as a function of risk quantification threshold 

for the LTSBO scenario 

6.4 Uncertainty Analysis – STSBO without Random Ignition 

This subsection describes the primary results for the uncertainty analysis of LCF and EF risk. 

The Sequoyah UA focuses on the LNT dose response model. The results in this section were 

generated from the statistical analysis of a relatively large number of individual results 

(realizations); therefore, the data include complementary cumulative distribution functions 

(CCDFs). 

6.4.1 LCF Risks for the Unmitigated STSBO Scenario without Random 

Ignition 

Table 6-7 and Table 6-8 show four statistics (mean, median, 5th percentile, and 95th percentile) 

for the mean individual LCF risk conditional on an accident occurring (per event) from the 467 

realizations from the MACCS uncertainty analysis. Results are provided at nine spatial intervals 

representing concentric circles and annular areas all centered on Sequoyah. The distance 

intervals are the radii of the corresponding inner and outer circles defining each area. The 

results in these tables are the mean value averaged over weather variability. Each of the 

statistics in the table represents the overall epistemic (state of knowledge) uncertainty on the 

mean for the groups of MELCOR (Level 2) and MACCS (Level 3) inputs that were treated as 

uncertain. The results show that mean conditional risks are on the order of 10-4 and diminish 

slightly with distance from the plant. 

Table 6-7 Mean individual LCF risk conditional on the STSBO w/o random ignition 
6-17

accident occurring (per event) for five intervals centered on Sequoyah. 

Table 6-8 Mean individual LCF risk conditional on the STSBO w/o random ignition 

accident occurring (per event) for four intervals centered on Sequoyah. 

For comparison between the deterministic analysis and the probabilistic uncertainty analysis, 

the mean value from Table 6-7 for the 0-10 mile ring (3.5E-04) is somewhat lower than the 

corresponding result of 4.8E-04 from the deterministic analysis shown in Table 6-2. 

Figure 6-11 and Figure 6-12 show the CCDFs for the same radial intervals summarized in 

Table 6-7 and Table 6-8. The points on the curves represent the mean LCF risk over variable 

weather for each of the 467 realizations representing epistemic uncertainty in this UA and are 

conditional on the accident occurring. The curves show that the conditional risks span the range 

of about 10-6 to more than 10-3 per event. 
6-18

Figure 6-11 Complimentary cumulative distribution function of mean, populationweighted LCF risk (w/o random ignition) within five intervals centered on 

Sequoyah 

Figure 6-12 Complimentary cumulative distribution function of mean, populationweighted LCF risk (w/o random ignition) within five intervals centered on 

Sequoyah 

Figure 6-13 shows the fraction of the total risk from the emergency phase for the set of LCF risk 

results shown in Figure 6-12. 
6-19

Figure 6-13 Complimentary cumulative distribution function of fraction of mean, 

population-weighted LCF risk (w/o random ignition) from the emergency 

phase for residents within five intervals centered on Sequoyah 

These results show that the contributions from the emergency phase to the individual latent 

cancer fatality risk are small in comparison to the contributions from the long term phase to the 

individual latent cancer fatality risk. This is consistent with the results from the deterministic 

STSBO w/o random ignition analysis shown in Figure 6-4. Table 6-9 shows the mean 

contribution of the emergency phase to the overall risk for the STSBO w/o random ignition 

scenario, and Table 6-10 shows the fractions of STSBO w/o random ignition MACCS 

realizations in which the emergency phase contributions to dose exceeded those of the long 

term phase. 

Table 6-9 Mean contribution of emergency phase to overall risk. 

Table 6-10 Fraction of STSBO w/o random ignition MACCS realizations 

in which the emergency phase contributions to dose exceed 

those of the long term phase. 

The curve for the distance range from 0 to 10 miles in Figure 6-13 has a different character than 

those for the other distance ranges because evacuation is very effective in reducing risk during 

the emergency phase for the population living within the EPZ. Most of the overall risk within 10 

miles is to the 0.5 percent of the public that is assumed to not evacuate; however, some of the 

risk is to the slowly evacuating cohorts for the realizations with relatively early releases. 

The core damage frequency for a STSBO at Sequoyah is 2×10-6 pry. Thus, the frequencyweighted risk of a STSBO, assuming that there are no random ignition sources within the 

containment is estimated to be about 6×10-10 to the population living within 10 miles of the 

reactor site. 

6.4.2 Regression Analysis of STSBO w/o Random Ignition Latent Cancer 

Fatality Risk 

Each of the four regression techniques was applied to the 467 successful realizations that were 

evaluated for the scenario where random ignition is assumed not to occur. Regression analyses 

for nine distance intervals were performed: five intervals with outer radii of 10, 20, 30, 40, and 

50 miles; and four intervals from 10 to 20 miles, 20 to 30 miles, 30 to 40 miles, and 40 to 50 

miles. However, only the results from 0 to 10, 10 to 20, and 0 to 50 miles are discussed in this 

report. The trends are similar at the distance intervals that are not discussed. 
6-20

Table 6-11 through Table 6-13 show the regression results for mean LCF risks from the 467 

realizations. The results are based on the LNT dose-response model at three distance intervals 

from the site: 0 to 10 miles, 10 to 20 miles, and 0 to 50 miles. The word ‘mean’ is used to 

indicate a statistical mean over variable weather. 

The first four major columns in the tables are labeled with the regression technique used to 

create the results. The last two columns contain average values of the main contribution of the 

parameter on the result metric and the conjoint influence of the parameter on the result metric. 

These are calculated as weighted averages of the overall contributions from the four regression 

techniques (R2 from rank regression and Si from the others) and a weighted average of the 

values of (Final R2) * (Ti – Si) for the three nonlinear regression techniques (i.e., all but rank 

regression). Appendix A explains the calculation of these metrics. 

Values of main contribution greater than 0.02 are considered significant, are highlighted, and 

are discussed in subsequent paragraphs. Conjoint contributions greater than 0.1 are considered 

significant and are highlighted. The parameters in the first column of the tables are ordered by 

the value in the column labeled Main Contribution, so the most important parameters appear in 

rank order at the top of the table. 

The first two parameters in all three regression tables are SV_frac, which indicates the open 

area of the relief valves on the primary side of the reactor and CFRISK(8), which represents 

residual cancer risk and accounts for all cancers that are not explicitly modeled. Residual 

cancers are based on doses to the pancreas, which is a surrogate organ to represent generic 

soft tissues. Residual cancers are a larger contributor to cancer risk than any of the cancer 

types that are explicitly modeled. The SV_frac parameter is important to the extent that it 

influences the depressurization of the reactor primary and subsequent containment failure 

related to a hydrogen burn. 

 
6-21

Table 6-11 Mean, individual, LCF risk regression results within a 0 – 10 

mile interval for all realizations based on LNT. 

 
6-22

Table 6-12 Mean, individual, LCF risk regression results within a 10 - 20 

mile interval for all realizations based on LNT. 

 
6-23

Table 6-13 Mean, individual, LCF risk regression results within a 0 – 50 

mile interval for all realizations based on LNT. 

The third parameter in the above tables is CFRISK(7), which is the cancer fatality risk factor for 

colon cancer. At least for this scenario, colon cancer turns out to be the most important single 

cancer type. 

The fourth parameter in Table 6-11 and Table 6-13 and the fifth parameter in Table 6-12 is 

DDREFA(8). It is the dose and dose rate effectiveness factor corresponding to residual cancers. 

This parameter has a minor conjoint influence with CFRISK(8) because the two parameters 

appear together in the relationship between organ dose and cancer risk for that organ. DDREFA 

appears in the denominator of the equation, so the risk diminishes as the parameter increases, 

which explains the negative value of the correlation coefficient (SRRC) in the tables for this 

parameter. 

The fifth parameter in Table 6-11 and seventh parameter in Table 6-12 is GSHFAC(2), which is 

the groundshine shielding factor for normal activity during the emergency phase and is perfectly 

correlated with the groundshine shielding factor during the long term phase. Normal activity is 

an average for the population over a normal week of activity, including time spent indoors at 

home and at work, outdoors, and commuting. Groundshine is not a particularly important dose 

pathway during the emergency phase, but it is generally the dominant dose pathway during the 

long term phase. Since the groundshine dose received by any individual during the long term 

phase is directly proportional to the long term groundshine shielding factor, it is important for 

latent cancer risk. This is especially so when the long term-phase dominates the emergencyphase contribution to risk, which is demonstrated to be true in Figure 6-13. 
6-24

The MELCOR rupture parameter is the sixth parameter in Table 6-11 and Table 6-13 and the 

fourth parameter in Table 6-12. This parameter controls the pressure at which the containment 

is modeled to rupture. Thus, it influences whether and when the containment fails. 

One other important parameter in Table 6-12 and Table 6-13 is CFRISK(4), which is the cancer 

risk factor for lung cancer. Lung cancer risk is primarily driven by inhalation of alpha-emitting 

radionuclides, and this occurs predominately during the emergency phase. 

The scatter plots provided in Figure 6-14 through Figure 6-17 show the trends between the five 

most important inputs for LCF risk within a 50-mile circular area, as discussed in the preceding 

paragraphs. The trends in these figures are very similar for the other circular and annular areas 

that were evaluated in this study. The plots qualitatively show the sign and strength of the 

correlation between the parameters and LCF risk, although the trend is sometimes hard to 

distinguish by eye because of the density of the dots. 

Figure 6-14 shows a significant correlation between the area fraction of one or more stuck-open 

valves with LCF risk. Values greater than one indicate multiple stuck-open valves. Though none 

of the individual valves necessarily have an open area fraction of 1, the sum of the open area 

fractions of the individual valves (SV_frac) can be greater than 1 because multiple values can 

stick open. The regression coefficient on SV_frac is positive in the rank regression results in 

Figure 6-15. This is supported by the slight positive trend in Figure 6-16, indicating a tendency 

for cancer risk to increase with the combined open area fraction. The open area fraction directly 

influences the magnitude of the source term, and thus the consequences, because it allows for 

early release of hydrogen that provides an efficient burn source that can contribute to 

containment failure simultaneous with RCS failure. 

Figure 6-15 shows a clear trend between CFRISK(8) and cancer fatality risk with a positive 

correlation coefficient, indicating that cancer fatality risk increases with CFRISK(8). This is the 

expected trend since cancer fatality risk is the sum of the product of the organ doses and risk 

coefficients. The number eight represents the risk coefficient for residual cancers, which 

represents all cancers not specifically treated, which turns out to be a larger contributor than any 

of the individual cancer risks. 

Figure 6-16 shows a similar trend as Figure 6-15, but for CFRISK(7), which is the cancer fatality 

risk coefficient for colon cancer, the most important single cancer type for the Sequoyah STSBO 

accident scenario. Risk increases with the value of CFRISK(7), as expected, for the reason 

given in the previous paragraph. 

Figure 6-17 shows a somewhat difficult-to-perceive negative trend between DDREFA(8), the 

dose and dose-rate effectiveness factor for residual cancers, with cancer fatality risk. The 

negative correlation indicates that cancer risk decreases with the value of DDREFA(8), which is 

expected since cancer risk has an inverse dependence on this parameter. 

Figure 6-18 shows a clear but modest, positive correlation between GSHFAC(2), representing 

the value of the groundshine shielding factor for normal activity during the emergency phase 

and the value of the long term groundshine shielding factor. This indicates that risk increases 

with the value of the groundshine shielding factor. This is an expected trend since groundshine 

is the dominant pathway during the long term phase and the long term phase contribution to risk 

usually exceeds the emergency-phase contribution to risk, as shown in Figure 6-13. 

Groundshine dose is proportional to this parameter. 
6-25

Figure 6-14 Scatter plot of mean, individual, LCF risk within a 0 - 50 mile interval for 

all realizations versus safety valve open area fraction, SV_frac, 

conditional on an STSBO accident occurring 

Figure 6-15 Scatter plot of mean, individual, LCF risk within a 0 – 50 mile interval for 

all realizations versus CFRISK(8), conditional on an STSBO accident 

occurring 
6-26

Figure 6-16 Scatter plot of mean, individual, LCF risk within a 0 – 50 mile interval for 

all realizations versus CFRISK(7), conditional on an STSBO accident 

occurring 

Figure 6-17 Scatter plot of mean, individual, LCF risk within a 0 – 50 mile interval for 

all realizations versus DDREFA(8), conditional on an STSBO accident 

occurring 
6-27

Figure 6-18 Scatter plot of mean, individual, LCF risk within a 0 – 50 mile interval for 

all realizations versus groundshine shielding factor, GSHFAC(2), 

conditional on an STSBO accident occurring 

6.4.3 Results for Early Fatality Risks 

Table 6-14 show four statistics (mean, median, 5th percentile, and 95th percentile) for mean 

(over weather variability) individual early fatality risk conditional on an accident occurring (per 

event) from the MACCS uncertainty analysis. Results are provided for seven spatial intervals 

centered on Sequoyah. The results in this table are the mean value averaged over weather 

variability. The statistics in the table represent the overall epistemic (state of knowledge) 

uncertainty for the groups of MELCOR and MACCS inputs that were treated as uncertain. The 

results show that mean conditional risks are approximately 10-6 within 1 mile and diminish 

rapidly with distance from the plant. There is no early fatality risk beyond 5 miles for any of the 

realizations. 

Table 6-14 Mean individual early fatality risk without random ignition, conditional on 

accident (per event) for the MACCS uncertainty analysis for seven 

distance intervals centered on Sequoyah. 

Figure 6-19 shows the complimentary cumulative distribution functions for the same circular 

areas summarized in Table 6-14. The points on the curves represent the mean early fatality risk 

over variable weather for each of the 467 realizations representing epistemic uncertainty in this 

The linked image cannot be displayed. The file may have been moved, renamed, or deleted. Verify that the link points to the correct file and location.
6-28

UA and are conditional on the accident occurring. The curves show that the conditional risks 

span the range from 0 to 10-4 per event. Realizations with zero risk are not shown on the plot 

because of the logarithmic scale. 

Figure 6-19 Complimentary cumulative distribution function of mean, populationweighted early fatality risk (STSBO w/o random ignition) within six 

distance intervals centered on Sequoyah 

Of the 467 successful MACCS realizations that were evaluated, only 47 realizations (about 

10%) had doses high enough to create an early fatality risk to the population within 5 miles. 

Thus, the small number of nonzero results, a regression analysis to determine which 

parameters are most important is not included in this report. 

6.5 Uncertainty Analysis - STSBO with Random Ignition 

6.5.1 Results for LCF Risks 

Table 6-15 and Table 6-16 show four statistics (mean, median, 5th percentile, and 95th

percentile) for the individual LCF risk conditional on an accident occurring (per event) from the 

432 realizations from the MACCS uncertainty analysis. Results are provided at nine spatial 

intervals centered on Sequoyah. The distances in the table are the inner and outer radii of the 

areas for the reported LCF risks. The risks are averaged over weather variability. Each of the 
6-29

statistics in the table represents the overall epistemic (state of knowledge) uncertainty for the 

groups of MELCOR and MACCS inputs that were treated as uncertain. The results show that 

mean conditional risks are on the order of 10-4 and diminish slightly with distance from the plant.

Table 6-15 Mean, individual STSBO with random ignition LCF risk, conditional on 

accident (per event) for the MACCS uncertainty analysis for five intervals 

centered on Sequoyah. 

Table 6-16 Mean, individual STSBO with random ignition LCF risk, conditional on 

accident (per event) for the MACCS uncertainty analysis for four 

intervals centered on Sequoyah. 

Figure 6-20 and Figure 6-21 show the complimentary cumulative distribution functions for the 

same intervals summarized in Table 6-15 and Table 6-16. The points on the curves represent 

the mean LCF risk over variable weather for each of the 432 realizations representing epistemic 

uncertainty in this UA and are conditional on the accident occurring. The curves show that the 

conditional risks span the range of less than 10-6 to more than 10-3 per event. 
6-30

Figure 6-20 Complimentary cumulative distribution function of mean, populationweighted LCF risk (assuming a random ignition source) within five 

intervals centered on Sequoyah 

Figure 6-21 Complimentary cumulative distribution function of mean, populationweighted LCF risk (assuming a random ignition source) within five 

intervals centered on Sequoyah 

Figure 6-22 shows the fraction of the total risk from the emergency phase for the set of LCF risk 

results shown in Figure 6-21. 
6-31

Figure 6-22 Complimentary cumulative distribution function of fraction of mean, 

population-weighted LCF risk (assuming a random ignition source) from 

the emergency phase for residents within five intervals centered on 

Sequoyah 

Similar to the results from the STSBO without random ignition, these results show that the 

contributions from the emergency phase to the individual latent cancer fatality risk are small in 

comparison to the contributions from the long term phase to the individual latent cancer fatality 

risk. This is consistent with the results from the deterministic STSBO with random ignition 

analysis shown in Figure 6-5. Table 6-17 shows the mean contribution of the emergency phase 

to the overall risk for the STSBO with random ignition scenario, and Table 6-18 shows the 

fractions of STSBO with random ignition MACCS realizations in which the emergency phase 

contributions to dose exceeded those of the long term phase. 

Table 6-17 Mean contribution of emergency phase to overall risk, STSBO with 

random ignition. 

Table 6-18 Fraction of STSBO with random ignition MACCS realizations in which 

the emergency phase contributions to dose exceed those of long term 

phase. 
6-32

As with the results from the STSBO w/o random ignition, the curve for the 0 to 10 mile interval 

has a very different character than those for the other distance ranges because evacuation is 

very effective in reducing risk during the emergency phase for the population living within the 

EPZ. Most of the overall risk within 10 miles is to the 0.5 percent of the public that is assumed 

to not evacuate; however, some of the risk is to the slowly evacuating cohorts for the 

realizations with relatively early releases. 

6.5.2 Regression Analysis of STSBO with Random Ignition Latent Cancer 

Fatality Risk 

Each of the four regression techniques was applied to the 432 successful realizations that were 

evaluated for the scenario where random ignition is assumed not to occur. Regression analyses 

for nine distance intervals were performed: five circular areas with outer radii of 10, 20, 30, 40, 

and 50 miles and four annuli (rings) from 10 to 20 miles, 20 to 30 miles, 30 to 40 miles, and 40 

to 50 miles. However, only the results from 0 to 10, 10 to 20, and 0 to 50 miles are discussed in 

this report. The trends are similar at the distance intervals that are not discussed. 

Table 6-19 through Table 6-21 show the regression results for mean LCF risks from the 432 

realizations. The results are based on the LNT dose-response model at three distance intervals 

from the site: 0 to 10 miles, 10 to 20 miles, and 0 to 50 miles. The word ”mean” is used to 

indicate a statistical mean over variable weather. 

The first two parameters in all three regression tables are CFRISK(8) and SV_frac, although 

their order is reversed in Table 6-20. Both parameters have large values in the Main 

Contribution column and also have a significant conjoint contribution. These are the same two 

parameters that were shown to be most significant in Subsection 6.4.2 and the reasons are the 

same. 

The other important input parameters are mostly the same ones identified in Subsection 6.4.2, 

namely DDREFA(8), CFRISK(7), and GSHFAC(2), although these appear in a slightly different 

order than in the previous subsection. Subsets of these parameters are shown to be important 

for the 10 to 20 and 0 to 50-mile intervals in Table 6-20 and Table 6-21, respectively. The 

reasons these parameters are important and the general discussion of their role in the 

consequence analysis is the same as that provided in Subsection 6.4.2. 

One new parameter is shown to be significant in Table 6-19 that did not appear in the previous 

tables, CFRISK(3), which represents the cancer fatality risk factor for breast cancer. 

Figure 6-23 through Figure 6-27 show scatter plots for each of the 5 most important parameters 

shown in Table 6-19. The figures are for the 50-mile circular area. The parameters and the 

trends are the same as those discussed in section 6.4.2. The difference in source terms with 

and without random ignition affects the cancer fatality risk, but has only a minor influence on the 

most important parameters affecting those results. 

 
6-33

Table 6-19 Mean, individual, LCF risk regression results within a 0 – 10 

mile interval for all realizations based on LNT. 

 
6-34

Table 6-20 Mean, individual, LCF risk regression results within a 10 – 20 

mile interval for all realizations based on LNT. 
6-35

Table 6-21 Mean, individual, LCF risk regression results within a 0 – 50 

mile interval for all realizations based on LNT. 

Figure 6-23 Scatter plot of mean, individual, LCF risk within a 0 – 50 mile 

interval for all realizations versus CFRISK(8), conditional on 

an STSBO accident occurring 
6-36

Figure 6-24 Scatter plot of mean, individual, LCF risk within a 0 – 50 mile 

interval for all realizations versus safety valve open area 

fraction, SV_frac, conditional on an STSBO accident 

occurring 

Figure 6-25 Scatter plot of mean, individual, LCF risk within a 0 – 50 mile 

interval for all realizations versus DDREFA(8), conditional on 

an STSBO accident occurring 
6-37

Figure 6-26 Scatter plot of mean, individual, LCF risk within a 0 – 50 mile 

interval for all realizations versus CFRISK(7), conditional on 

an STSBO accident occurring 

Figure 6-27 Scatter plot of mean, individual, LCF risk within a 0 – 50 mile 

interval for all realizations versus CFRISK(7), conditional on 

an STSBO accident occurring 
6-38

6.5.3 Regression Analysis of Early Fatality Risk Assuming Random 

Ignition 

Table 6-22 shows statistical results for mean individual early fatality risk conditional on an 

accident occurring (per event) from the MACCS uncertainty analysis at seven spatial intervals 

representing concentric circles centered on Sequoyah. The distance intervals in the table are 

the radii of the circular areas and mean indicates that results are averaged over weather 

variability and within the spatial interval. Each of the statistics in the table represents the overall 

epistemic (state of knowledge) uncertainty for the groups of MELCOR and MACCS inputs that 

were treated as uncertain. The results show that mean conditional risks are approximately 10-8

and diminish rapidly with distance from the plant. There is no early fatality risk for any of the 

realizations beyond 5 mi and there are only 12 realizations (less than 3%) with an early fatality 

risk within 5 mi. 

Table 6-22 Mean, individual EF risk with random ignition (STSBO-WRI), conditional 

on accident (per event) for the MACCS uncertainty analysis for five 

distance intervals centered on Sequoyah 

Figure 6-28 shows the complimentary cumulative distribution functions for the same circular 

areas summarized in Table 6-22. The points on the curves represent the mean LCF risk over 

variable weather for each of the 432 realizations representing epistemic uncertainty in this UA 

and are conditional on the accident occurring. The curves show that the conditional risks span 

the range from 0 to more than 10-6 per event. 
6-39

Figure 6-28 Complimentary cumulative distribution function of mean, 

population-weighted EF risk (with random ignition) within five 

intervals centered on Sequoyah 

Of the 432 successful realizations that were evaluated, only 12 realizations (about 3%) had 

doses high enough to create an early fatality risk to the population within 5 miles. Thus, a 

regression analysis to evaluate the most important variables would be essentially meaningless 

and therefore it was not performed. 

6.6 MACCS Results from Selected UA Realizations 

This section evaluates three individual UA realizations; two of them happen to be the same 

realization. The realizations considered are those for the maximum cesium release, the 

maximum LCF risk, and the maximum EF risk. These correspond to MELCOR realization 

numbers 110, 232, and 232, respectively, and both realizations are from the UA set without 

random ignition. Since the maximum LCF and EF risk realizations are the same, the term 

“maximum risk” is used in several of the figures to denote risks for both LCF and EF. 

Because these realizations were not discussed previously, information is provided to put these 

selected source terms in perspective with the larger set of MELCOR source terms. Cesium and 

iodine release fraction histories are provided in Figure 6-29 and Figure 6-30. These show that 

the release timing is very different for the two realizations. The maximum-risk realization has an 

early and protracted release for both cesium and iodine and has a very large iodine release; the 

maximum-cesium-release realization has a late but rapid release of both cesium and iodine. 

The iodine release fraction for the maximum risk realization is larger than for the maximum 

cesium release realization. Table 6-23 shows that the release fraction for all other chemical 

groups but cesium is larger for the maximum risk realization. In particular, the large cerium 
6-40

release (more than 5%) for the maximum risk realization should have a significant contribution 

to latent cancer fatalities. Also, the barium release of more than 7% should also have a 

significant contribution because this group contains Sr-90. 

Figure 6-29 Cesium release histories for the maximum cesium release 

and maximum risk realizations 

Figure 6-30 Iodine release histories for the maximum cesium release and 

maximum risk realizations 
6-41

Table 6-23 Integral release fractions for the maximum cesium release 

and maximum risk realizations. 

Figure 6-31 and Figure 6-32 show complimentary cumulative distribution functions for LCF risks 

within circular and annular intervals surrounding the Sequoyah site for the maximum cesium 

release realization, conditional on the occurrence of an accident. The probabilities shown in the 

figures represent weather variability. Conditional LCF risks shown in the figures range from 

about 10-4 to almost 10-3. Statistical values, including the mean, for the LCF risks corresponding 

to these same areas are provided in Table 6-24 and Table 6-25. Early fatality risks are zero for 

this realization. 

Figure 6-31 Complimentary cumulative distribution function of mean, populationweighted LCF risk (without random ignition source) for the maximumcesium-release realization within five intervals centered on Sequoyah. 

Probability represents weather variability. 
6-42

Figure 6-32 Complimentary cumulative distribution function of mean, populationweighted LCF risk (without random ignition source) for the maximumcesium-release realization within five intervals centered on Sequoyah. 

Probability represents weather variability 

Table 6-24 Mean, individual LCF risks for the maximum-cesium-release realization, 

conditional on accident (per event) for the MACCS uncertainty analysis 

for five intervals centered on Sequoyah. 

Table 6-25 Mean, individual LCF risks for the maximum-cesium-release realization, 

conditional on accident (per event) for the MACCS uncertainty analysis 

for four intervals centered on Sequoyah. 

Figure 6-33 and Figure 6-34 show complimentary cumulative distribution functions for LCF risks 

within circular and annular intervals surrounding the Sequoyah site for the maximum-risk 

realization, conditional on the occurrence of an accident. The probabilities shown in the figures 

represent weather variability. Conditional LCF risks shown in the figures range from less than 

10-3 to about 10-2. Statistical values, including the mean, for the LCF risks corresponding to 
6-43

these same areas are provided in Table 6-26 and Table 6-27. Mean EF risks are approximately 

5×10-5 within 1.3 miles and 3×10-5 within 2 miles of the site for this realization. No early fatality 

risks exist beyond 3 miles from the site for this realization. 

Figure 6-33 Complimentary cumulative distribution function of mean, populationweighted, LCF risk (without random ignition source) for the maximumrisk realization within five intervals centered on Sequoyah. Probability 

represents weather variability. 

Figure 6-34 Complimentary cumulative distribution function of mean, populationweighted, LCF risk (without random ignition source) for the maximumrisk realization within five intervals centered on Sequoyah. Probability 

represents weather variability. 
6-44

Table 6-26 Mean, individual LCF risks for the maximum risk realization, conditional 

on accident (per event) for the MACCS uncertainty analysis for five 

circular areas centered on Sequoyah. 

Table 6-27 Mean, individual LCF risks for the maximum risk realization, conditional 

on accident (per event) for the MACCS uncertainty analysis for four 

intervals centered on Sequoyah. 

6.7 Sensitivity Analysis 

A sensitivity analysis of the STSBO early release scenario was performed to evaluate potential 

impacts of sheltering in place instead of evacuating. This analysis considers all cohorts within 

the EPZ. It does not include variations for the shadow evacuees whom reside outside the EPZ 

and are assumed to evacuate in any event. To facilitate the analysis, the delay to evacuate 

parameter DLTEVA was set to 604,800 sec (7 days), such that the cohorts considered shelter 

throughout the entire emergency phase of the accident. Furthermore, the emergency phase 

normal and hot spot relocation doses, DOSNRM and DOSHOT, were both set to 10,000 rem to 

prevent residents from relocating. Residents are assumed to stay indoors, so groundshine and 

cloudshine protection factors for shelter were used. Long term phase parameters were not 

altered for this analysis. 

The strong seismic event that initiates the accident is expected to damage the integrity of some 

of the homes and facilities within the EPZ. Prediction of which structures could be damaged 

and to what degree is beyond the scope of this study. It is assumed that the structures are 

sufficient for occupancy, and that the shielding from radiation outside the structures is 

unaffected by the accident. However it is reasonable to assume that windows may be damaged 

and that residents may not be able to repair the damage under the shelter orders allowing 

increased penetration of outdoor air into the structures. 

To account for this, the inhalation and skin protection factors, PROTIN and SKPFAC, were 

varied between the typical sheltering protection factor of 0.33 to the evacuation protection factor 

of 0.98. The protection factors PROTIN and SKPFAC are normally assigned the same value. 

The inhalation protection factor affects LCF risk in the MACCS model whereas the skin 

protection has no effect on the reported risks. Table 6-28 presents the inhalation and skin 
6-45

protection factors for each sensitivity analysis. The sheltering protection factor of 0.33 used in 

the previous cases was taken as in the value for sensitivity 1 to reflect the bounding case of no 

damage to structures. Sensitivity 2 represents the inhalation and skin protection normally 

afforded by structures to be 50 percent compromised. Sensitivity 3 assumes that shelters are 

fully compromised, therefore the protection factors are set to 0.98, which are the same PROTIN 

and SKPFAC values used for evacuation. 

Table 6-28 Inhalation and skin protection factors of the sheltering 

sensitivity analysis. 

STSBO Early Release 

(Evacuate) 

Sensitivity 1 

(Shelter) 

Sensitivity 2 

(Shelter) 

Sensitivity 3 

(Shelter) 

0.33 0.33 0.66 0.98 

Figure 6-35 presents the emergency and long term phase LNT, LCF risk results for the 0 - 10 

mile interval. The figure shows that the emergency phase LCF risks increase dramatically for 

sheltering compared with the STSBO ER scenario in which 99.5 percent of the residents 

evacuate the EPZ. The emergency and long term phase data are shown to indicate the 

proportions of LCF risk by phase for each sensitivity case. The long term protection factors 

were not altered for this sensitivity, so the long term LCF risk contributions do not change. 

Figure 6-35 Summary of sensitivity analysis long term and emergency phase mean, 

individual, LNT, LCF risk results for the 0 - 10 mile region 
6-46

Table 6-29 provides selected results for the 0-10 mile interval, including the emergency phase 

LCF risk, the fraction of total LCF risk attributed to the emergency phase, and the sensitivity 

case risk values normalized to the STSBO ER results. 

Table 6-29 Sensitivity analysis results for emergency phase LCF risks for the 0 – 10 

mile interval, the emergency phase fraction of total LCF risk, and the 

emergency phase risks normalized to the STSBO early release risk. 

These sensitivity cases suggest that sheltering for seven days rather than evacuating for the 

STSBO ER scenario would result in large increases in the emergency phase doses to the point 

that they significantly exceed the late phase doses. The emergency phase dose contribution to 

the total LCF risk was eight percent for the STSBO-ER scenario. The equivalent contribution 

would increase from eight percent to 82 percent (intact inhalation protection, Sensitivity Case 1) 

up to 91 percent (completely degraded inhalation protection, Sensitivity Case 3). By 

normalizing the risk results to the STSBO ER scenario, the emergency phase risk associated 

with extended sheltering instead of evacuation increases by a factor from 52 to more than 110 

times greater with decreasing inhalation protection. Comparing total LCF risk, sheltering 

increases LCF risks by a factor of approximately six to twelve, increasing with the inhalation 

protection factor. 

The increase in LCF risk is sub-linear with respect to the inhalation protection factor. This is not 

surprising because the LCF risk during the sheltering period includes dose contributions from 

groundshine and cloudshine. A linear regression calculation of the emergency phase risk 

results shown in Figure 6-35 permits determination of the combined groundshine and 

cloudshine contributions to the emergency phase. The combined contribution to LCF risk is 

1.5E-03, which is 30 percent of the emergency phase LCF risk for Sensitivity 1. Under the 

assumptions of the sensitivity analysis, the risk contributions due to sheltering inhalation 

protection factor exceeds the risk contributions due to the other exposure pathways. 

Table 6-30 shows the early fatality risk results for selected intervals near the plant. The 

distance interval of 0-1.345 extends one mile beyond the Sequoyah EAB. The STSBO-ER 

evacuation scenario has zero EF risk results at all intervals, however the sensitivity analysis 

results in very small but non-zero EF risk values. As expected, the EF risks increase with 

decreasing inhalation protection. The sensitivity results indicate that the relative maximum EF 

risk occurs in the 0 – 2 mile distance interval. 

The absolute early fatality risk estimated to be on the order of 1E-11 is essentially zero. Overall, 

this EF risk is negligible even for these sensitivity cases. 

Result Early 

Release

Sensitivity 

1

Sensitivity 

2

Sensitivity 

3

Emergency Phase LCF Risk 6.7E-05 3.5E-03 5.5E-03 7.4E-03

Emergency Phase Fraction of 

Total LCF Risk 0.08 0.82 0.88 0.91

Emergency Phase Risk 

Normalized to STSBO ER 1 52 82 111
6-47

Table 6-30 Sensitivity analysis conditional early fatality risk results for selected 

radial intervals near Sequoyah. 

6.8 Summary 

The offsite consequence analysis results for Sequoyah SOARCA have been presented as 

individual LCF and EF risks for both deterministic and probabilistic uncertainty analysis results. 

In addition, regression techniques were performed on the uncertainty analysis to provide 

insights as to which parameters or combinations of parameters influence the risk results. Using 

LNT dose response, the LCF risks of both deterministic and uncertainty analysis range from 

about 3E-04 to 9E-04 for the 0 – 10 mile region and the LCF risks decrease with increasing 

distance from Sequoyah. Contributions from the long term phase risks dominate the emergency 

phase risks for the large majority of the LCF risk results. The EF risks are essentially zero. A 

sensitivity analysis examines the potential effect of sheltering in place instead of evacuating. 

The results show that evacuating greatly reduces the emergency phase contributions to risk as 

compared to sheltering in place, and even more so when structures are damaged from an 

earthquake. 

Result Radius 

(miles)

Early 

Release

Sensitivity 

1

Sensitivity 

2

Sensitivity 

3

0-1 0.0E+00 1.7E-08 2.0E-08 4.6E-08

0-1.345 0.0E+00 2.3E-06 2.7E-06 3.2E-06

0-2 0.0E+00 4.7E-06 5.7E-06 6.7E-06

0-3 0.0E+00 1.4E-06 1.6E-06 1.9E-06

0-5 0.0E+00 4.7E-07 5.6E-07 6.6E-07

Early Fatality 

Risk 
7-1

7. CONCLUSIONS 

Through the application of modern analysis tools and techniques, the SOARCA project 

developed a body of knowledge regarding the realistic outcomes of severe reactor accidents. 

SOARCA analyses of Peach Bottom and Surry pilot plants revealed insights into the accident 

progression for important scenarios in a BWR Mark I design and a PWR large dry 

(subatmospheric) containment design as well as the offsite consequences of potential 

radioactive releases. This analysis of station blackouts at the third SOARCA pilot plant, 

Sequoyah, expands on the SOARCA body of knowledge for the next most prevalent 

containment design in the U.S., the ice condenser. Compared to PWR large dry containments, 

ice condensers are smaller and have a lower design pressure; therefore, the containment 

cannot absorb as much energy despite the presence of ice for pressure suppression. Hydrogen 

combustion has long been known to be a potential challenge to the ice condenser containment. 

The Sequoyah SOARCA analysis examines phenomenology and modeling unique to the ice 

condenser design including the behavior of hydrogen and the potential for early containment 

failure from energetic hydrogen combustion. Beyond expanding the body of knowledge on 

realistic outcomes of severe accidents, this analysis has complemented and supported NRC’s 

activities to address lessons learned from the Fukushima Dai-ichi accidents, specifically NTTF 

item 5.2 (reliable hardened vents for containment designs other than Mark I and Mark II) and 

item 6 (hydrogen control and mitigation inside containment or in other buildings). 

The staff used updated and benchmarked standardized plant analysis risk (SPAR) models and 

available plant-specific external events information to identify the most probable of the very low 

probability station blackout (SBO) scenario variations for analysis. Similar to the Peach Bottom 

and Surry analyses, this group of scenarios includes the short-term station blackout (STSBO) 

and the long term station blackout (LTSBO). Both types of SBOs involve a loss of all AC power. 

SBO scenarios can be initiated by external events such as a fire, flood, or earthquake. The 

Sequoyah SOARCA analysis assumes that an SBO is initiated by a low probability severe 

seismic event because this is an extreme case in terms of timing and equipment failure. For the 

LTSBO, AC power is lost but the turbine-driven auxiliary feedwater pump (TDAFW) is available 

until batteries deplete. The contribution to core damage frequency for the LTSBO was 

estimated at one event per approximately 100,000 years of reactor operation (~1E-5/yr). For 

the STSBO, AC power is lost and the turbine-driven auxiliary feedwater pump is not available, 

and therefore the postulated scenario proceeds to core damage more rapidly (hence “short 

term”). The contribution to core damage frequency for the STSBO is lower, and was estimated 

at one event per approximately 500,000 years of reactor operation (~2E-6/yr). As a severe 

accident and consequence analysis, the Sequoyah focus was on the accident progression 

modeling and offsite consequence modeling coupled with the uncertainty analyses to guide the 

best selection for parameter inputs and to provide insights on the effects of selected 

parameters. 

SOARCA analyses were performed primarily with two computer codes, MELCOR for accident 

progression and MACCS for offsite consequences. MELCOR models the thermal-hydraulic 

response in the reactor coolant system, reactor cavity, containment, and confinement buildings; 

core heatup, degradation, and relocation; core-concrete attack; hydrogen production, transport, 

and combustion; fission product transport and release to the environment. MACCS models the 

atmospheric transport and deposition of radionuclides released to the environment as well as 

emergency response and long-term protective actions, exposure pathways, dosimetry, and 

health effects for the affected population. The Peach Bottom and Surry SOARCA studies were 

comprised of deterministic base case analyses using point estimates for input parameter values, 
7-2

followed by probabilistic uncertainty analyses (UAs) which sampled distributions representing 

input uncertainty to generate multiple results to represent the range of potential outcomes. For 

the Sequoyah SOARCA analysis, staff integrated probabilistic consideration of uncertainty into 

accident progression and offsite consequence analyses in parallel with deterministic 

calculations. 

Several variations of the STSBO were evaluated. The base case scenario was an evaluation of 

an unmitigated (i.e., without igniters) STSBO scenario. A sensitivity case of a mitigated STSBO 

was evaluated in which hydrogen igniters are modeled as operable. Two unmitigated (i.e., 

igniters are assumed inoperable) STSBO variations were run probabilistically with and without 

the presence of random ignition sources. Random ignition sources refer to the potential of the 

accident to lead to sparks possibly caused by structural materials sliding against or falling on 

other materials, static discharge, etc., but the timing and location of the spark(s) is inherently 

unknown. The staff modeled hundreds of variations of the STSBO with and without random 

ignition sources, each of which used different values of important input parameters40 selected 

from distributions that account for parameter uncertainty in both accident progression 

(MELCOR) and offsite consequences (MACCS). For the LTSBO scenario, staff has 

deterministically analyzed accident progression for a scenario without igniters or random ignition 

sources, and performed a number of deterministic sensitivity calculations to assess the impact 

of features including battery duration, hydrogen ignition criteria, and safety valve (SV) behavior. 

A sensitivity case of a mitigated LTSBO was also evaluated in which hydrogen igniters are 

modeled as operable. 

For the unmitigated STSBO (i.e., igniters inoperable), there are two potential containment 

outcomes: early failure (~3-12 hours from initiating event) due to hydrogen combustion or late 

failure (> 30 hours) from more gradual overpressure. If igniters are not available because of the 

loss of power, containment can fail soon after hot leg rupture or at the time of lower head failure 

due to hydrogen deflagration. However, if a random spark triggers ignition prior to reactor 

coolant system breach, the accident is less likely to proceed to early containment failure. An 

early containment failure can result in the potential for radionuclide release to the environment 

before the 0-10 mile EPZ population has time to complete evacuation; however, the 

consequence analysis shows that even under such conditions, health risk to the public is low. 

Sequoyah SOARCA analyses show that successful use of igniters can control hydrogen 

accumulation and limit the containment pressure. Hydrogen igniters can help consume oxygen 

faster leading to less energetic deflagrations and help avert early containment failure. 

The unmitigated LTSBO variations fall within the unmitigated STSBO variations in terms of 

release timing and magnitude, however for the unmitigated LTSBO, there are no cases of early 

containment failure. For the LTSBO, sensitivity calculations show that hydrogen combustioninduced failure occurs ~18-24 hours after accident initiation. The TDAFW system is very 

important in extending core cooling and allowing more time for implementation of additional 

mitigation. The ice is effective at mitigating the containment pressure rise by condensing hot 

steam and cooling non-condensable gases from the reactor pressure vessel and ex-vessel core 

concrete interaction. The ice also has the adverse consequence of reducing steam 

concentrations, which enhances conditions for large burns if the igniters are not available. Even 

though the benefit is limited because the ice can be completely melted within ~12 hours in the 

STSBO and ~34 hours in the LTSBO, it can significantly delay the timing of the slow long-term 

 

40 Because the unmitigated STSBO does not credit human actions, the UA also does not address human actions. 
7-3

overpressurization failure of the containment. Igniters would delay but not alleviate potential 

containment overpressure failure following lower head failure if no other mitigation systems are 

available. 

The Sequoyah plant analysis was unique in that it looked at the deterministic and uncertainty 

analyses in parallel. This allowed identification of important parameters that contribute to the 

outcome of the accident progression and consequence results. Regression and singlerealization analysis results show that uncertain accident progression parameters (of those 

included in the analysis) most important to release timing are those affecting the timing of the 

containment failure, which naturally included the containment rupture pressure. An early 

containment failure was most likely if the hydrogen was distributed throughout the containment 

prior to the development of a strong ignition source, which was influenced by an early failure of 

the pressurizer safety valve (low number of safety valve cycles) and a large flow area for failed 

safety valve. Uncertain accident progression parameters most important to release magnitude 

(cesium and iodine release fraction) are the ones that affect the timing of the containment failure 

and events that contribute to fission product revaporization, which included the number of safety 

valve cycles and the failed safety valve flow area. Uncertain accident progression parameters 

most important to hydrogen production include the melting temperature of the eutectic formed 

from fuel (UO2) and oxidized cladding (ZrO2), and the parameters that lead to reactor coolant 

system depressurization (i.e., few pressurizer safety valve cycles before failure and the 

subsequent flow area of the failed safety valve). 

Similar to the results from the Peach Bottom and Surry SOARCA analyses, the Sequoyah 

analyses show essentially zero individual early fatality risk and a low individual risk of latent 

cancer fatality for the affected population. Offsite radiological consequences were calculated for 

each scenario expressed as the average individual likelihood of an early fatality and latent 

cancer fatality. Even for STSBO variations leading to early containment failure in which the 

release to the environment begins prior to the completion of the EPZ evacuation, there is 

essentially zero individual early fatality risk and the individual latent cancer fatality risk is low. 

Individual LCF risk calculations are generally dominated by long-term exposure to small annual 

doses (below 2 rem in the year of the accident and below 500 mrem per year in subsequent 

years corresponding to the habitability criterion) for evacuees and relocated populations 

returning to their homes after the accident and being exposed to residual radiation over a long 

period of time. 

The use of dose response models that truncate annual doses below certain levels results in a 

further reduction to the individual LCF risks. Sensitivity calculations using the STSBO early 

release example source term that consider alternate emergency response actions such as 

sheltering-in-place instead of evacuating result in larger individual LCF risks (3-6 times higher 

within 10 miles depending on assumptions for inhalation protection factors). 

Regression analysis results show that uncertain input parameters (of those included in the 

analysis) that show importance to individual LCF risk in the STSBO cases include: failed safety 

valve flow area (and number of safety valve cycles experienced, in some cases), the cancer 
7-4

fatality risk factor for the “residual” organ41 (and sometimes other organs), dose and dose rate 

effectiveness factor for the “residual” organ, the groundshine shielding factors, and the 

containment rupture pressure. 

In summary, this analysis reinforces the results of past analyses of ice condenser containments 

showing that successful use of igniters is effective in averting early containment failure. Even 

for scenarios resulting in early containment failure (radioactive release to the environment prior 

to completion of evacuation for the 0-10 mile EPZ), resulting individual LCF risks are very small. 

Although earlier releases were calculated for the Sequoyah SOARCA STSBO than SOARCA 

Surry STSBO, health risks are projected to be low, with essentially zero individual early fatality 

risk and conditional individual latent cancer fatality risk results are similar to those calculated for 

Peach Bottom and Surry SOARCA scenarios. Sequoyah SOARCA results, while specific to 

Sequoyah, may be generally applicable for other PWRs with ice condenser containments. 

However, additional work would be needed to confirm this, because differences exist in plantspecific designs, procedures, and emergency response. 

 

41 MACCS uses eight cancer sites (organs), seven of which are specific (lung, red bone marrow, bone, breast, 

thyroid, liver, and colon) and the last of which (“residual”) is used as a surrogate for the remaining soft 

tissues not explicitly modeled. 
8-1

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A-1

APPENDIX A UNCERTAINTY QUANTIFICATION AND 

PROPAGATION 

 
A-2

 
A-3

UNCERTAINTY QUANTIFICATION AND PROPOGATION 

A.1 INTRODUCTION 

The assessment on the influence of the uncertainties relative to key input parameters on the 

potential consequences follows a single accident at the Sequoyah power plant. The inclusion of 

uncertainty is an inherent part of any risk analysis of complex systems. The approach chosen 

for this Sequoyah analysis is based on the methodologies used for the Peach Bottom and Surry 

UAs [1][2], with some changes implemented based on the lessons learned. 

A.1.1 Uncertainty Type 

When analyzing a complex system, it is useful to classify the uncertainty under consideration 

into aleatory uncertainty and epistemic uncertainty. Aleatory (or stochastic) uncertainty refers to 

the (apparent) inherent randomness in the properties or behavior of the system. This uncertainty 

is considered to be irreducible and is usually represented via a probability distribution. In 

classical risk analysis, the consideration of this type of uncertainty will lead to a determination of 

the risk associated with each output. 

Epistemic uncertainty derives from the lack of knowledge about a poorly known, but usually 

fixed, quantity (or at least a quantity that has a fixed representative value in the context of the 

analysis). This type of uncertainty is usually reducible by increasing the knowledge about the 

parameter under consideration. In risk analysis, epistemic uncertainty represents the uncertainty 

over the risk. The use of a probabilistic framework to characterize lack of knowledge uncertainty 

is not necessarily the best approach. Over several decades, several mathematical structures 

were developed to better represent this uncertainty type [3][4]. However, these methods are 

computationally intensive and lack the clarity of a simple probabilistic approach. Therefore, in 

this study, epistemic uncertainty is determined by the classical probabilistic approach. 

The main reason to separate uncertainty according to these two types is that it brings more 

insight to decision making. Estimates are made regarding which part of the uncertainty is 

irreducible and needs to be considered, and which part can be reduced with further study. 

Regression analyses are used to determine which inputs, amongst those that are uncertain, are 

driving the output uncertainty, i.e., where to focus future work. 

The analysis of complex systems typically requires answering the following four questions: 

1. What can happen? 

2. How likely it is to happen? 

3. What are the consequences if it happens? 

4. What is the confidence level in the answers to the first three questions? 

The first three questions are referred to as the Kaplan-Garrick ordered triplet [5]. The separation 

of uncertainty with respect to aleatory and epistemic classifications allows for the definition of a 

formal mathematical framework in which aleatory uncertainty is used to answer the first two 

questions (as they deal with randomness in future events and the conditions at the time of the 

event that may affect the consequences). The third question is answered by the models, which 

estimate (deterministically) the consequence, given a fixed set of inputs. Epistemic uncertainty 

answers the fourth (as confidence increases with the state of knowledge that directly depends 

on epistemic uncertainty). 
A-4

While it is desirable to keep a separation between epistemic and aleatory uncertainty, this is not 

always possible in practice. First, the separation requires a simulation to include two embedded 

loops (see Appendix A Section A.1.3). If high accuracy is necessary for both loops, the number 

of realizations may quickly become prohibitive. Second, while the definitions of aleatory and 

epistemic uncertainties are quite unambiguous, the characterization may be much more 

complex. It is not uncommon to find both an aleatory and an epistemic component in the 

uncertainty associated with a given input. Finally, some software does not allow for such 

separation. 

When the separation of aleatory and epistemic uncertainty is not feasible, the analysis of the 

results can still provide a great deal of insight. In the present analysis, when such a separation 

is not practical, the uncertainty will be represented as epistemic. This decision is driven by the 

fact that regression analysis (see Section A.2.2) may be applied to epistemically uncertain 

inputs. Note that it is still possible, once the regression analysis is performed, to separate the 

inputs into two groups in order to partly estimate how much of the total uncertainty is considered 

random and how much derives from a lack of knowledge. 

A.1.2 Uncertainty Characterization 

Each input that is considered uncertain in this analysis has its uncertainty represented with a 

probability distribution. The process through which the parameters are selected, and the 

associated uncertainty is represented, (via a probability distribution) is a crucial part of any 

probabilistic analysis as the results will be strongly affected by the choice. The uncertainty 

related to each parameter is unique and specific to that parameter. The methodology developed 

to build the uncertainty distributions can be summarized as follows: 

• Data and information was gathered and categorized based on relevance and reliability. 

For instance, observations and measurements were given more credit to elicitation. In 

the same spirit, data associated with the same mechanism or from Sequoyah data were 

considered more representative than proxies. 

• Depending on the amount of data available, an appropriate selection of distribution 

representation was selected. The choice varied from simple distribution fitting when 

enough observations were available, to Bayesian updating, and to expert elicitation or 

judgment when no data was available. 

• Consequent effort was made to document the rationale and assumptions to demonstrate 

the depth of the technical basis and because the results are interpreted conditionally 

upon those assumptions. 

A.1.3 Uncertainty propagation for source term and consequence 

analysis 

Monte Carlo methods were developed in the late 1940’s [6] as an answer to a specific problem: 

how can a function of a large number of inputs be estimated numerically. The problem of 

dimensionality quickly led to an impractical number of runs. The Monte Carlo technique consists 

of covering the input space by randomly sampling a value in that input space. A dense coverage 

insures that the approximation of the function is close enough to reality so that the appropriate 

conclusion can be reached. Demonstrations showed that the Monte Carlo approach will 
A-5

converge to the true solution as the sample size increases. Thus, the method essentially 

reduces a multidimensional integral (one dimension per variable) into a mono-dimensional one. 

The Monte Carlo method is the core of any sampling based approach, notably when the input 

space represents uncertainty with respect to the system. The original Monte Carlo method is 

characterized by sampling randomly in each direction, and is sometimes called Simple Random 

Sampling (SRS). 

Latin Hypercube Sampling (LHS), developed in the 1970’s [7] improves the Monte Carlo 

technique by stratifying each distribution in order to insure a dense coverage in each direction of 

the input space. LHS reduces the variance in each estimate without introducing a bias [8]. One 

limitation of LHS is that the stratification requires the sample size under consideration to be 

known at the beginning of the analysis. An additional limitation of this method is that a Latin 

Hypercube sample cannot be cut into parts because the location of any point is dependent on 

all other points. 

These two limitations of the LHS application preclude its use in the present context. MELCOR is 

a complex code that requires a change in the time-stepping or discretization for a realization to 

reach convergence. Some of these refinements are so demanding that they are computationally 

impractical. Previous uncertainly analyses [1][2] showed that rejecting some of the realizations 

due to a lack of convergence did not invalidate the coverage of the input space and did not bias 

the results toward a specific region. Such an analysis is once again necessary considering that 

it is not possible to have convergence for all realizations. However, the cost of the removal of 

these non-convergent realizations is that the use of LHS is not recommended and the SRS 

technique was used. MACCS is not affected by the same problem and each realization leads to 

a convergent result. Nevertheless, in order to maintain a consistent approach (and mainly in 

order to use bootstrapping to support stability analysis as will be explained in Section A.2.3) the 

SRS technique was also used for the MACCS analysis. 

The traditional method utilized to distinguish between aleatory and epistemic uncertainty is to 

use an inner loop for aleatory uncertainty and an outer loop for epistemic. The order of the loops 

could be reversed in theory, but a preference towards an inner aleatory loop and an outer 

epistemic loop is derived from what each uncertainty type represents. Aleatory uncertainty 

associated in the context of risk analysis is perceived intuitively as a probability and is 

represented as a summary statistic (e.g., mean or median) or a distribution. For a given 

epistemic set (i.e., for a specific value in the outer loop), risk can be represented conditionally 

on the assumption that one would have perfect knowledge about the value of the parameter. 

Epistemic uncertainty is then represented as a distribution on the representative value (e.g., 

mean or median) or a set of distributions showing confidence in the results given the current 

state of knowledge. The MACCS code was developed with this strategy in mind, and thus 

distinguishes between random inputs (mostly weather conditions) and epistemically uncertain 

inputs. This distinction is preserved and the outputs of interest (latent cancer fatality and prompt 

fatality at various locations or areas) are estimated as averages (i.e., probabilities). 

The MELCOR uncertainty engine considers only one loop and does not allow the separation 

between aleatory and epistemic uncertainty. The choice is to consider all uncertainties as 

potentially reducible and therefore epistemic. The distinction between epistemic and aleatory 

uncertainty can then be done partially at the regression analysis level. 
A-6

A.2 ANALYZING UNCERTAINTY RESULTS 

In the last step of a probabilistic approach, results are statistically analyzed (via uncertainty 

analysis) and the influence of input parameter uncertainty over the variance of each output 

under consideration is assessed (via regression analysis). Such analyses help to draw insights 

with respect to the results. Many techniques have been developed to perform such analyses, 

several of which are presented in Storlie et al. [9]. 

A.2.1 Uncertainty Analysis 

Uncertainty analysis usually refers to the determination of the uncertainty in the output of 

interest that derives from the uncertainty in the inputs. Its main purpose is to assess the extent 

of uncertainty in the results of interest given the uncertainty in the overall system. When a 

sampling-based approach is used to propagate uncertainty, a sample is generated for each 

output under consideration. Uncertainty analysis thus corresponds to a statistical analysis of the 

results of interest. 

Graphical representations such as probability density functions (PDF) and cumulative or 

complementary cumulative distribution functions (CDF and CCDF) are usually used to visualize 

the extent of the uncertainty under consideration. Statistical measures such as moments (mean, 

standard deviations) and quantiles (e.g., median, 5th and 95th percentile) are used to summarize 

the distributions in a more quantitative way. 

A.2.2 Regression Analysis 

The purpose of the regression analysis was to determine the contributions of individual 

uncertain inputs to the uncertainty of the analysis results. Several techniques can be used to 

estimate the influence of each uncertain input on the output uncertainty. Some methods are 

qualitative while some are more quantitative and can assess the importance of each input 

relative to the others with respect to uncertainty [11]. 

The four regression techniques applied in the Peach Bottom and Surry UAs [1][2] were also 

applied in this analysis to assess quantitatively the importance of uncertain inputs. The results 

of these four regressions are presented showing the influence of the uncertainty of each input 

parameter according to each of the techniques. The conclusions of these techniques are 

supported by the qualitative graphical representations of the relations using scatterplots. 

Rank regression, quadratic regression, recursive partitioning, and multivariate adaptive 

regression splines (MARS) were the four selected regression techniques used in this analysis to 

estimate the importance of the input parameters on the uncertainty of the outputs. The use of a 

set of regressions, instead of a single technique, was demonstrated beneficial in the previous 

analyses [1][2] and was motivated by the fact that there is no perfect regression approach 

capable of capturing all possible relationships from a given sample. Some regression 

techniques (such as linear regression) have the advantage of being robust (in the sense that 

they won’t overfit the model and lead to higher artificial ܴଶ values) but are unable to capture any 

complex relationship (nonlinear and non-monotonic influences, conjoint influences). Other 

techniques are more flexible, but may still include some assumptions on the nature of the 

relationship. Furthermore, such techniques can be less robust and might give importance to 

spurious relations. In particular, techniques considering conjoint influence can be used to 
A-7

quickly examine so many possibilities that they find a combination that appears to drive the 

uncertainty of the output of interest when this is not the case. 

Using a suite of regression techniques allows for better coverage of potential relations between 

input uncertainty and output uncertainty, while also increasing the confidence that an influence 

is not spurious if it is captured by multiple regressions. The results of each regression were 

studied by experts in the physical phenomenon under simulation to confirm that the regression 

results were expected in the physical sense based on the variation of the related input values. 

Although the use of multiple regression techniques leads to a more complex interpretation of the 

results, it was considered a necessary step since the application of a single regression 

technique could lead to a misinterpretation and erroneous conclusions. A short description of 

each selected technique follows, and more detailed description of the techniques can be found 

in [12] and [3]. 

Rank regression 

The rank regression technique uses a rank transformation over the input and output variables 

under consideration. The smallest value of a variable is given a rank of one, the next a rank of 

two, and so on up to the largest value having a rank of n (i.e. sample size). A stepwise linear 

regression is then applied to the rank-transformed data. The model is linear and additive and is 

shown in the following form: 

ܻ=ܽ଴ + ܽଵܺଵ + ܽଶܺଶ +⋯+ܽ௡ܺ௡ +ߝ=ܽ ଴+෍ܽ௜ܺ ௜+ ߝ

௡

௜ୀଵ

Eq. A-1 

Where ߝ represents (for this regression and the subsequent ones) the amount of uncertainty not 

explained by the model. 

The stepwise approach starts with trying to find the best fit with only one parameter and testing 

all possible input parameters. It then builds up from this initial fit by selecting the best fit with two 

parameters, conditional upon keeping the first parameter, and so on. An alpha value, 

representing the probability for each input effect to be spurious, is selected as a stopping 

criterion. The default value is set to approximately 15 percent, which means that if there is a 15 

percent chance (or more) for the variable to be spurious, it is not included. Rank regression is 

effective in capturing monotonic relationships between inputs and outputs. The non-parametric 

aspect makes it less sensitive to outliers. This technique is limited to additive models where no 

conjoint influences are considered and may perform poorly on non-monotonic relationships. 

Three metrics are included for each input variable used to display rank regression results. Two 

are based on the coefficient of determination, noted conventionally R2, which represents the 

amount of variance explained by the regression model. The coefficient of determination is a 

normalized value which varies between 0 (no variance explained) and 1 (all the variance 

explained). 

• R2

inc gives the cumulative coefficient of determination of the rank regression model when 

the ith variable has been added (that includes all variables up to the ith for the model). 
A-8

• R2

cont gives the gain in R2 when the ith variable has been added compared to the model 

with (݅ − 1) variables. It is a good indicator of the contribution of this specific variable in 

explaining the variance of the output in consideration. 

• Finally, standardized rank regression coefficients (SRRC) display the rank regression 

coefficients after they have been standardized to take out the unit influence. The rank 

regression coefficient is an indication of the strength of the influence. An absolute value 

close to zero means that the parameter does not have an influence, while an absolute 

value of one represents a very strong influence. The rank regression coefficient also 

indicates the positive or negative direction of the influence of this input variable on the 

considered output. A negative sign represents negative influence in which high values of 

the input lead to low values of the output and low values of the input lead to high values 

of the output. A positive sign represents positive influence where high values of input 

lead to high values of the output and low values of the input lead to low values of the 

output. 

Quadratic regression, recursive partitioning, and MARS techniques 

The three additional regression techniques considered (i.e. quadratic regression, recursive 

partitioning and MARS) are treated differently, as their models do not allow for a direct estimate 

of the contribution to each individual input to the variance of the output. For each of these 

models a coefficient of determination (R2) is estimated and can be used as an indicator of how 

the regression performed. Once the regression model is available, it can be used to generate a 

large number of realizations via a variance decomposition technique known as the Sobol 

decomposition. The Sobol decomposition is a technique that can estimate the contribution of 

each input and their potential interactions (i.e., conjoint influence) via an integral decomposition 

of the variance [9]. However, this technique requires a large number of realizations (tens of 

thousands) to be accurate enough (within a few percentages) and cannot usually be applied 

directly. However, the regression techniques described below lead to analytical models that can 

quickly be run a large number of times (over the course of seconds to minutes). Once the Sobol 

decomposition is applied, the importance of each variable (according to its uncertainty) can be 

assessed given the regression model. The answer is thus strongly dependent on the quality of 

the regression model and caution should be applied when the R2 value is relatively low. In some 

situations a regression technique (most likely the recursive partitioning) may over-fit and lead to 

an artificially high R2. In such case, scatterplots are relied on to confirm whether there is indeed 

a relation or not. 

The Sobol decomposition leads to different metrics than those used in stepwise linear 

regression. The two metrics selected for this analysis are described below: 

• Si represents the first order sensitivity index and describes how much of the variance of 

the selected output is explained by the input parameter under consideration by itself (i.e., 

without conjoint influence). This index therefore estimates the same quantity as R2

cont for 

the rank regression technique and it is acceptable to compare these two metrics. 

• The second metric, labeled Ti , represents the total order sensitivity index and estimates 

how much of the variance of the selected output is explained by the input parameter 

alone plus its interaction with the other uncertain parameters (i.e., conjoint influence). It 

has no analogue in the rank regression model as the additive model does not capture 
A-9

conjoint influences. The difference between Ti and Si provides an estimate of the 

conjoint influence for a single input on the output considered. 

Quadratic regression 

Quadratic regression techniques apply the same approach as linear regression, including 

individual input variables, the square of these variables, and second order multiplicative 

interaction terms. The prediction model is of the form: 

ܻ=ܽ଴ +෍ܽ௜ܺ௜

௡

௜ୀଵ

+෍ܾ௜ܺ௜

ଶ

௡

௜ୀଵ

+෍ ෍ ܿ௜ܺ௜ܺ௝

௡

௝ୀ௜ାଵ

+

௡

௜ୀଵ

ߝ

Eq. A-2 

Quadratic regression is not solely additive as it can capture second order interactions. It can 

also capture the parabolic influence measured by the square of variables in the regression 

model. However, a complex relationship between variables and the output, like asymptotic 

behavior, may still be hard to capture with this technique and the method remains parametric, 

making it sensitive to outliers. 

Recursive partitioning 

Recursive partitioning regression, also known as a regression tree, is a regression method that 

captures conjoint influences. A regression tree splits the data into subgroups in which the 

values are relatively homogeneous. The regression function is constructed using the sample 

mean of each subgroup. This approach results in a piecewise constant function over the input 

space under consideration. The predictive model is: 

ܻ=෍(݀௦ܫ௦(ܺ௜))௜ୀଵ,...,௡

௡௉

௦ୀଵ

ߝ +

Eq. A-3

Recursive partitioning is well adapted to the present study as it strives to capture the effect of 

thresholds (e.g., a low value for one parameter and a high value for another parameter, or when 

a certain parameter reaches a threshold value). MELCOR includes many such threshold 

conditions to initiate some events or processes. One of the drawbacks of this regression is that 

it considers so many potential relations that it tends to over-fit by capturing spurious 

correlations. Consequently, checking the relations only found by this regression using 

scatterplots is recommended and was completed. 

Multivariate Adaptive Regression Splines (MARS) 

MARS is a combination of (linear) spline regression, stepwise model fitting and recursive 

partitioning. A regression with a single input starts with a mean-only model and adds basis 

functions in a stepwise manner while adding the overall linear trend first. A second model using 

linear regression via least squares is fit to the data. This model is then added to the basis 

functions in a way that reduces the sum of square error (SSE) between the observations and 

predictions. A fourth basis function is then added to minimize the SSE again. This process is 

repeated until M (set by default at 200) basis functions have been added. 
A-10

At this point, the MARS procedure will try to simplify the model using stepwise deletion of basis 

functions while keeping the y-intercept and linear trend. The ܯ−2 candidate leading to the 

smallest increase of SSE will be selected. This deletion will be applied until regressed to the 

original linear model. 

Stepwise addition and deletion leads to the creation of two different ܯ−2 models. The “best” 

model is chosen using a generalized cross validation score which corresponds to a SSE 

normalized by the number of basis functions considered. With multiple inputs, the basis 

functions will consider main effects and multiple-way interactions. The options used for this 

analysis consider only two-way interactions to avoid the exponential cost of considering more 

interactions. 

MARS usually leads to similar results as linear regression with a greater accuracy, and with the 

inclusion of non-monotonic effects and conjoint influences. However, it performs poorly with 

discrete inputs due to the use of splines. 

Ranking the variables according to the four regression techniques 

A consequence of the use of multiple regressions is that the ranking of the inputs amongst 

themselves is not obvious when the different regressions disagree. A qualitative approach has 

been used in the past [1] based on the physics considered in the problem and expert 

knowledge, but such an approach introduces some subjectivity and is hard to document. A more 

quantitative approach was implemented for the SOARCA Surry Uncertainty Analysis [Reference 

2] and again for the current analysis, due to a strong belief that such a quantitative ranking is 

only an indicator and should be supported by expert opinion based on the physics of the 

problem. 

Two effects of the uncertainty in the input on the output of consideration are estimated in the 

present study. The main effect represents the influence of the uncertain input by itself and is 

estimated with ܴ௖௢௡௧

ଶ in the stepwise regression and ܵ௜ for the other three regression techniques. 

Then the effect of the uncertain input from its interaction with other variables, which is ignored 

by the stepwise regression as it is an additive regression, is estimated with ܶ௜ − ܵ௜ for the other 

three regressions. 

The first effect of the uncertainty in the input was considered the most important, and a decision 

was made to rank the variables according to this main effect. Stepwise regression provides an 

estimate of the main effect for each variable directly. For the other regressions techniques, the 

real effect has to be adjusted by the goodness of fit of the model (i.e. the ܴଶ value from the 

regression model). In order to accomplish this, each ܵ௜ value is multiplied by the ܴଶ value of the 

regression model. Finally, if a variable is not included in a given regression, it is supposed that 

its main effect is null and the corresponding value is set to 0. The four resulting estimates are 

combined to create a weighted average, represented by: 

Main influence = ோ೎೚೙೟

మ ା∑ ோೕ

మ.ௌ೔,ೕ య

ೕసభ

ସ

Eq. A-4 

where ܴ௖௢௡௧

ଶ is from rank regression, j is the index of the three non-linear regression techniques, 

and ܴ௝

ଶ is the final R2 for each non-linear technique. 
A-11

The variables are then sorted according to this ‘weighted average’ approach in decreasing 

value, such that the most important variable is listed at the top of the array. 

The conjoint influence is captured by estimating ܶ௜ − ܵ௜ for each of the last three regressions, 

and adjusted using the ܴଶ value as a weight. The weighted average is taken again, represented 

by: 

Conjoint influence= ∑ ோೕ

మ∗(்೔,ೕିௌ೔,ೕ) య

ೕసభ

ଷ

Eq. A-5 

If the average value is greater than 0.1 (meaning 10 percent of the regression is explained via 

conjoint influence with this input), the ܶ௜ are emphasized to draw attention to a potential 

important conjoint influence. As the first order indices (i.e., ܵ௜) and total order indices (i.e., ܶ௜) 

are estimated numerically using a Sobol decomposition, they are approximations. If no conjoint 

influence is present, it may happen that ܶ௜ is estimated slightly lower than ܵ௜. In such situations, 

the value of (ܶ௜ − ܵ௜) was set to 0. These two metrics are added to the summary tables for the 

four regression results, as an indicator on the importance of the input uncertainty onto the 

output uncertainty. This represents a best estimate of the input uncertainty influence toward the 

uncertainty of the output considered. 

For the ease of reading the tables, highlighting is applied in the main contribution and conjoint 

contribution columns to identify the best estimate importance of each input parameters, based 

on the overall analysis. The cutoff for main contribution effect was set at 0.02, and the cutoff for 

conjoint effect was set at 0.1. The reason for a difference in the contribution effects is that 

conjoint contribution influence looks at a larger range of possible interactions (for instance, with 

20 inputs variables, the main contribution looks at 20 potential relations while conjoint influence 

of 2 inputs looks at 190 potential relations) and is more likely to identify spurious correlations. 

Therefore, an approach that concentrates on the larger contribution was considered 

appropriate. The threshold values of 0.02 and 0.1 were selected based on the knowledge 

acquired during Peach Bottom UA [1] and the regressions tables for the present analysis, such 

that important parameters would be acknowledged and negligible influence would not be 

highlighted. 

Testing for potential over-fitting 

Recursive partitioning has a tendency to be more permissive and therefore to overfit. In other 

words, the regression technique may lead to an artificially high ܴଶ . When only recursive 

partitioning (or any other technique) finds some strong relation, it is necessary to check for the 

validity of the relation found. An approach consists in creating a random output for each 

realization and checking on the result of the regression technique. Two uniform random 

variables are generated (one continuous and one discrete). The four regression techniques 

were then used with the same inputs to regress these random outputs. The results can infer 

whether the selected regression technique can be trusted or not. Tests have shown that 

recursive partitioning can usually lead to an ܴଶ of around 0.6 to 0.7 all the time, with mostly 

conjoint influence captured (no high ܵ௜ values, only high ܶ௜ values). As a result, for any set of 

regression leading to only ܶ௜ influence, the results of the regression are discarded in the 

description. 
A-12

Scatterplots 

The use of scatterplots is a qualitative yet powerful technique that completes the suite of 

regression techniques applied in this analysis. Scatterplots display a set of points, one per 

realization, whose coordinates correspond to the value of one selected input for the x-axis and 

of the output considered on the y-axis. It confirms graphically that the relation estimated by any 

of the regression techniques is indeed present and not spurious. 

A.2.3 Stability Analysis 

As with any numerical method, Monte Carlo techniques will lead to different levels of accuracy, 

depending notably on the sample size. The purpose of stability analysis is to assess this level of 

accuracy and determine if the sample size is big enough or if more realizations may be required. 

The notion of stability is hard to capture quantitatively as it is inherently qualitative and often 

includes subjective judgments. While it can be expressed with a formal approach (for instance, it 

is not desirable for the confidence interval to be larger than a certain fraction of the standard 

deviation or a standard error), the ultimate criteria is whether there is enough confidence that 

the conclusion will not be affected by the accuracy of the Monte Carlo technique used. In order 

to address this, a decision was made to represent stability as a confidence interval around the 

statistics of interest and rely on the judgment of experts to conclude whether such an interval is 

acceptable within the context of this analysis instead of selecting an arbitrary cut-off. 

The method selected to estimate these confidence intervals is a classical percentile bootstrap 

[13]. This method requires the generation of a new sample of the same size (with replacement) 

from the original output sample. The operation is then repeated a large number of times (1,000 

iterations was used) to generate a set of possible output distributions. This leads to a 

distribution for each statistic (mean and quantiles in the present analysis). A 95 percent 

confidence interval using percentile bootstrap is obtained by looking at the location of the 2.5 

percentile for the lower bound and 97.5 percentile for the upper bound for each of the statistics. 

A more complete description of the bootstrap technique can be found in [13]. 

One advantage of the SRS technique is that it is easy to increase the sample size, as any new 

realization can be added to the existing set. Furthermore, a subset of the sample is a valid 

sample of the original distribution, which is one of the required assumptions for the use of the 

bootstrap technique. 

This property of SRS can also be used to determine an optimal sample size that will lead to an 

appropriate stable estimate of one output of interest (within the context of this analysis). Any 

output data can be split into n groups of samples of size ெ

௡ (where M represents the initial 

sample size). Each sample can then be used to assess the quantity (or quantities) of interest. 

The operation can be repeated a large number of times by creating different combinations of 

values within each sample. This technique is equivalent to a bootstrap with the exception that 

each sample has a size representing a fraction of the initial sample size. With such approach, 

confidence intervals can be generated for different sample sizes and compared. Since the 

sample size controls the accuracy of the Monte Carlo techniques (much as grid size controls the 

spatial accuracy of any numerical method) this approach can be used to estimate when a 

sample size is big enough to lead to adequate stable results for the selected output of interest 

(e.g., statistics). Furthermore, if the desired accuracy is not met using the initial sample size, 
A-13

such technique can be used to extrapolate a potential sample size that should meet the 

corresponding criterion. 

Regression analysis of failed realizations 

A certain number of MELCOR calculations fail to converge within a reasonable computational 

time for various reasons. An analysis is thus completed to determine whether there is a 

correlation between failed runs and having the realization set in a particular area of the input 

space. Such a situation would indicate that a particular value for one input (or combination of 

values for several inputs) would lead to failure, biasing the conclusion that could be drawn by 

only analyzing the successful runs. 

In order to detect a potential relation between input uncertainty and failure, a regression 

analysis (using the four regressions techniques described above) can be performed, using an 

indicator function set to 0 when the realization failed to run to completion and 1 when it ran up to 

the end as the output of interest. These regression results were compared to similar analyses 

performed on a uniform discrete and a uniform continuous random variable. These regression 

results were used as a baseline to indicate the ability of the regression techniques to find a 

relationship between regions of the input space and an unrelated random variable. Similar 

results between the random variable analyses and the success indicator function analysis would 

therefore suggest that failed realizations are randomly distributed within the input space. 

The regression comparison showed that slightly better models were fit to the realization success 

indicator than to either of the random outputs. However, none of the realization success 

indicator regressions resulted in models that suggest a relationship strong enough that it could 

not be due to random chance; while the regressions generated models, those models were 

weak. The only parameter indicated as important was priSVcycles, so further study was focused 

on this parameter. 

To determine whether there is a significant relationship between priSVcycles and realization 

success, two techniques were employed. First the Pearson product-moment correlation 

coefficient was calculated between priSVcycles and the realization success parameter. The 

correlation between was approximately 0.20. This suggests a weak relationship between the 

two parameters. Secondly, separate empirical CDFs for those priSVcycles samples associated 

with realization success and those samples associated with realization failure were calculated 

and compared. If a certain range of priSVcycles determined realization success, we would 

expect the two empirical distributions to be significantly different. The CDFs were compared 

using a two-sample Kolmogorov-Smirnov test with ߙ = 0.05. The test was unable to distinguish 

between the two distributions, supporting the conclusion that there is not a significant 

dependence between a region of the sample space and realization success. In combination 

these tests suggest a small, but questionably significant relationship between priSVcycles and 

realization completion. 

 
A-14

A.2.4 References 

[1] NUREG/CR-7155, "State-of-the-Art Reactor Consequence Analyses Project, Uncertainty 

Analysis of the Unmitigated Long-Term Station Blackout of the Peach Bottom Atomic 

Power Station,” U.S. Nuclear Regulatory Commission, Washington, DC, 2016. 

[2] “State-of-the-Art Reactor Consequence Analyses Project, Uncertainty Analysis of the 

Unmitigated Short-Term Station Blackout of the Surry Power Station,” Draft Report. 

ADAMS ML15224A001, U.S. Nuclear Regulatory Commission, Washington, DC, 2016. 

[3] Helton, J.C. “Uncertainty and Sensitivity Analysis in the Presence of Stochastic and 

Subjective Uncertainty” Journal of Statistical Computation and Simulation 197; 57 (1-4): 

3-76. 

[4] Helton, J.C. Johnson, J.D, Oberkampf W.L. Sallaberry C.J. “Representation of Analysis 

Results Involving Aleatory and Epistemic Uncertainty” Sandia Report SAND2008-4379 – 

Albuquerque, NM: Sandia National Laboratories 2008. 

[5] Kaplan, S. and B.J. Garrick, “On the Quantitative Definition of Risk," Risk Analysis, 1(1): 

p. 11-27, 1981. 

[6] Metropolis, N. and Ulam S., “The Monte Carlo Method” Journal of the American 

Statistical Association; 44(247):335-341, 1949. 

[7] McKay, M.D., Beckman, R.J., and Conover, W.J., “A Comparison of Three Methods for 

Selecting Values of Input Variables in the Analysis of Output from a Computer Code” 

Technometrics 21(2): 239-245, 1979. 

[8] Helton J.C. and Davis F.J., “Latin hypercube sampling and the propagation of 

uncertainty in analyses of complex systems” Reliability Engineering and System Safety 

81 (1) 23-69, 2003. 

[9] Storlie, C.B., et al., “Implementation and evaluation of nonparametric regression 

procedures for sensitivity analysis of computationally demanding models," Reliability 

Engineering & System Safety, 94(11): p. 1735-1763, 2009. 

[10] Sobol’, I. “Sensitivity Estimates for Nonlinear Mathematical Models.” Mathematical 

Modeling and Computational Experiment,” 1, 407-414. 1993. 

[11] Helton, J.C., et al., “Survey of Sampling-based Methods for Uncertainty and Sensitivity 

Analysis," Reliability Engineering & System Safety, 91(10-11): p. 1175-1209, 2006. 

[12] Storlie, C.B. and J.C. Helton, "Multiple predictor smoothing methods for sensitivity 

analysis: Description of techniques," Reliability Engineering & System Safety, 93(1): p. 

28-54, 2008. 

[13] Efron B. and Tibshirani R.J., “An Introduction to the Bootstrap” Monographs on Statistics 

and Applied Probability 57 - Chapman & Hall/CRC – ISBN 0-412-04231-2, 1993. 
B-1

APPENDIX B INPUT PARAMETERS FOR CONSEQUENCE 

ANALYSIS 

 
B-2
B-3

; 

INPUT PARAMETERS FOR CONSEQUENCE ANALYSIS 

The input parameters used for the Sequoyah SOARCA scenarios are shown in this appendix in 

tabular form. Table B-1 contains the more general ATMOS input parameters used for these 

scenarios. Table B-2 through Table B-4 contain specific inputs related to the source terms that 

were extracted from MELCOR results via the MELMACCS code. Table B-5 contains general 

EARLY input parameters. Table B-6 and Table B-7 contain parameters associated with the 

population distribution model that was used to treat emergency response. Table B-8 and 

Table B-9 contain the evacuation direction parameters. Table B-10 contains the CHRONC input 

parameters. Table B-11 contains the radionuclide inventory.

Table B-1 ATMOS input parameters used in the Sequoyah scenarios. 

Variable Description STSBO 

Base Case 

STSBO 

Early LTSBO 

APLFRC 

Method of 

Applying 

Release 

Fraction 

PARENT PARENT PARENT 

BNDMXH 

Boundary 

Weather Mixing 

Layer Height 

966 966 966 

BNDRAN 

Boundary 

Weather Rain 

Rate 

0 0 0 

BNDWND Boundary Wind 

Speed 1.66 1.66 1.66 

BRKPNT 

Breakpoint Time 

for Plume 

Meander 

3600 3600 3600 

BUILDH 

Building Height 

for all Plume 

Segments 

47.2 47.2 47.2 

CORINV 

Isotopic 

Inventory at 

Time of Reactor 

Shutdown 

MELMACCS 

Data (See 

Table B-11) 

MELMACCS 

Data (See 

Table B-11) 

MELMACCS 

Data (See 

Table B-11) 

CORSCA 

Linear Scaling 

Factor on Core 

Inventory 

1 1 1 

CWASH1 

Linear 

Coefficient for 

Washout 

1.89E-05 1.89E-05 1.89E-05 

CWASH2 

Exponential 

Term for 

Washout 

0.664 0.664 0.664 

CYSIGA 

Linear 

Coefficient for 

sigma-y 

Stability Class A 0.7507 0.7507 0.7507 

Stability Class B 0.7507 0.7507 0.7507 
B-4

Variable Description STSBO 

Base Case 

STSBO 

Early LTSBO 

Stability Class C 0.4063 0.4063 0.4063 

Stability Class D 0.2779 0.2779 0.2779 

Stability Class E 0.2158 0.2158 0.2158 

Stability Class F 0.2158 0.2158 0.2158 

CYSIGB 

Exponential 

Term 

for sigma-y 

 

Stability Class A 0.866 0.866 0.866 

Stability Class B 0.866 0.866 0.866 

Stability Class C 0.865 0.865 0.865 

Stability Class D 0.881 0.881 0.881 

Stability Class E 0.866 0.866 0.866 

Stability Class F 0.866 0.866 0.866 

CZSIGA 

Linear 

Coefficient for 

sigma-z 

Stability Class A 0.0361 0.0361 0.0361 

Stability Class B 0.0361 0.0361 0.0361 

Stability Class C 0.2036 0.2036 0.2036 

Stability Class D 0.2636 0.2636 0.2636 

Stability Class E 0.2463 0.2463 0.2463 

Stability Class F 0.2463 0.2463 0.2463 

CZSIGB 

Exponential 

Term 

for sigma-z 

 

Stability Class A 1.277 1.277 1.277 

Stability Class B 1.277 1.277 1.277 

 

Stability Class C 0.859 0.859 0.859 

Stability Class D 0.751 0.751 0.751 

Stability Class E 0.619 0.619 0.619 

Stability Class F 0.619 0.619 0.619 

DISPMD Dispersion 

Model Flag LRTIME LRTIME LRTIME 

DRYDEP Dry Deposition 

Flag 

Xe = 

.FALSE. 

Other 

Groups = 

.TRUE. 

Xe = 

.FALSE. 

Other 

Groups = 

.TRUE. 

Xe = 

.FALSE. 

Other 

Groups = 

.TRUE. 

ENDAT1 

Control flag 

indicating only 

ATMOS is to be 

run 

.FALSE. .FALSE. .FALSE. 

GRPNAM 

Names of the 

Chemical 

Classes (Used 

by WinMACCS) 

 

Chemical Class 

1 Xe Xe Xe 
B-5

Variable Description STSBO 

Base Case 

STSBO 

Early LTSBO 

Chemical Class 

2 Cs Cs Cs 

Chemical Class 

3 Ba Ba Ba 

Chemical Class 

4 I I I 

Chemical Class 

5 Te Te Te 

Chemical Class 

6 Ru Ru Ru 

Chemical Class 

7 Mo Mo Mo 

Chemical Class 

8 Ce Ce Ce 

Chemical Class 

9 La La La 

IBDSTB 

Boundary 

Weather 

Stability Class 

Index 

5 5 5 

IDEBUG 

Debug Switch 

for Extra 

Debugging Print 

0 0 0 

IGROUP 

Definition of 

Radionuclide 

Group Numbers 

1 = Xe 1 = Xe 1 = Xe 

2 = Cs 2 = Cs 2 = Cs 

3 = Ba 3 = Ba 3 = Ba 

4 = I 4 = I 4 = I 

5 = Te 5 = Te 5 = Te 

6 = Ru 6 = Ru 6 = Ru 

IGROUP 7 = Mo 7 = Mo 7 = Mo 

8 = Ce 8 = Ce 8 = Ce 

9 = La 9 = La 9 = La 

INWGHT 

Number of 

Samples for 

Each Bin Used 

for Nonuniform 

Weather Bin 

Sampling 

Bin 1 12 12 12 

Bin 2 12 12 12 

Bin 3 23 23 23 

Bin 4 145 145 145 

Bin 5 117 117 117 

Bin 6 64 64 64 

Bin 7 12 12 12 

Bin 8 0 0 0 

Bin 9 97 97 97 

Bin 10 117 117 117 
B-6

Variable Description STSBO 

Base Case 

STSBO 

Early LTSBO 

Bin 11 25 25 25 

Bin 12 12 12 12 

Bin 13 83 83 83 

Bin 14 43 43 43 

Bin 15 12 12 12 

Bin 16 0 0 0 

Bin 17 42 42 42 

Bin 18 15 15 15 

Bin 19 18 18 18 

Bin 20 17 17 17 

Bin 21 15 15 15 

Bin 22 12 12 12 

Bin 23 12 12 12 

Bin 24 12 12 12 

Bin 25 12 12 12 

Bin 26 12 12 12 

Bin 27 12 12 12 

Bin 28 7 7 7 

Bin 29 12 12 12 

Bin 30 7 7 7 

Bin 31 7 7 7 

Bin 32 12 12 12 

Bin 33 8 8 8 

Bin 34 11 11 11 

Bin 35 7 7 7 

Bin 36 7 7 7 

IRSEED 

Seed for 

Random 

Number 

Generator 

79 79 79 

LATITU Latitude of 

Power Plant 35 ̊ 13' 36" 35 ̊ 13' 36" 35 ̊ 13' 36" 

LIMSPA 

Last Interval for 

Measured 

Weather 

28 28 28 

LONGIT Longitude of 

Power Plant 85 ̊ 5' 28" 85 ̊ 5' 28" 85 ̊ 5' 28" 

MAXGRP 

Number of 

Radionuclide 

Groups 

9 9 9 

MAXHGT Flag for Mixing 

Height 

DAY_AND_

NIGHT 

DAY_AND_

NIGHT 

DAY_AND_

NIGHT 

MAXRIS 

Selection of 

Risk Dominant 

Plume 

20 1 8 

METCOD 

Meteorological 

Sampling 

Option Code 

2 2 2 

MNDMOD Plume Meander NEW NEW NEW 
B-7

Variable Description STSBO 

Base Case 

STSBO 

Early LTSBO 

Model Flag 

NAMSTB List of Pseudo 

stable Nuclides 

Isotope 1 I-129 I-129 I-129 

Isotope 2 Xe-131m Xe-131m Xe-131m 

Isotope 3 Xe-133m Xe-133m Xe-133m 

Isotope 4 Cs-135 Cs-135 Cs-135 

Isotope 5 Sm-147 Sm-147 Sm-147 

Isotope 6 U-234 U-234 U-234 

Isotope 7 U-235 U-235 U-235 

Isotope 8 U-236 U-236 U-236 

Isotope 9 U-237 U-237 U-237 

Isotope 10 Np-237 Np-237 Np-237 

Isotope 11 Rb-87 Rb-87 Rb-87 

Isotope 12 Zr-93 Zr-93 Zr-93 

Isotope 13 Nb-93m Nb-93m Nb-93m 

Isotope 14 Nb-95m Nb-95m Nb-95m 

Isotope 15 Tc-99 Tc-99 Tc-99 

Isotope 16 Pm-147 Pm-147 Pm-147 

NPSGRP 

Number of 

Particle Size 

Groups 

10 10 10 

NRINTN 

Number of Rain 

Intensity 

Breakpoints 

3 3 3 

NRNINT 

Number of Rain 

Distance 

Intervals 

5 5 5 

NSBINS 

Number of 

Weather Bins to 

Sample 

36 36 36 

NUCNAM Radionuclide 

Names 

MELMACCS 

Data (See 

Table B-11) 

MELMACCS 

Data (See 

Table B-11) 

MELMACCS 

Data (See 

Table B-11) 

NUCOUT 

Radionuclide 

Used in 

Dispersion Print 

Cs-137 Cs-137 Cs-137 

NUMCOR 

Number of 

Compass 

Sectors in the 

Grid 

64 64 64 

NUMISO Number of 

Radionuclides 69 69 69 

NUMRAD 

Number of 

Radial Spatial 

Intervals 

29 29 29 

NUMREL 

Number of 

Released Plume 

Segments 

40 72 56 

NUMSTB Number of 

Defined Pseudo 16 16 16 
B-8

Variable Description STSBO 

Base Case 

STSBO 

Early LTSBO 

stable 

Radionuclides 

OALARM 

Time to Reach 

General 

Emergency 

Conditions 

0 0 0 

PDELAY Plume Release 

Times 

MELMACCS 

Data (See 

Table B-2) 

MELMACCS 

Data (See 

Table B-2) 

MELMACCS 

Data (See 

Table B-2) 

PLHEAT Plume Heat 

Contents 

MELMACCS 

Data (See 

Table B-2) 

MELMACCS 

Data (See 

Table B-2) 

MELMACCS 

Data (See 

Table B-2) 

PLHITE Plume Release 

Heights 

MELMACCS 

Data (See 

Table B-2) 

MELMACCS 

Data (See 

Table B-2) 

MELMACCS 

Data (See 

Table B-2) 

PLMDEN Plume Mass 

Density 

MELMACCS 

Data (See 

Table B-2) 

MELMACCS 

Data (See 

Table B-2) 

MELMACCS 

Data (See 

Table B-2) 

PLMFLA Plume Mass 

Flow Rate 

MELMACCS 

Data (See 

Table B-2) 

MELMACCS 

Data (See 

Table B-2) 

MELMACCS 

Data (See 

Table B-2) 

PLMMOD 

Flag for Plume 

Rise Input 

Option 

DENSITY DENSITY DENSITY 

PLUDUR Plume Segment 

Durations 

MELMACCS 

Data (See 

Table B-2) 

MELMACCS 

Data (See 

Table B-2) 

MELMACCS 

Data (See 

Table B-2) 

PSDIST 

Particle Size 

Distribution by 

Group 

MELMACCS 

Data (See 

Table B-3) 

MELMACCS 

Data (See 

Table B-3) 

MELMACCS 

Data (See 

Table B-3) 

REFTIM 

Plume 

Reference Time 

Point 

0. for first 

0.5 for 

subsequent 

0. for first 

0.5 for 

subsequent 

0. for first 

0.5 for 

subsequent 

RELFRC 

Release 

Fractions of the 

Source Term 

MELMACCS 

Data (See 

Table B-4) 

MELMACCS 

Data (See 

Table B-4) 

MELMACCS 

Data (See 

Table B-4) 

RNDSTS 

Endpoints of 

Rain Distance 

Intervals 

 

Interval 1 2. 2. 2. 

Interval 2 5. 5. 5. 

Interval 3 10. 10. 10. 

Interval 4 15. 15. 15. 

Interval 5 20. 20. 20. 

RNRATE 

Rain Intensity 

Breakpoints for 

Weather 

Binning 

 

Intensity 1 2 2 2 

Intensity 2 4 4 4 

Intensity 3 6 6 6 

SCLADP Scaling Factor 1.0 1.0 1.0 
B-9

Variable Description STSBO 

Base Case 

STSBO 

Early LTSBO 

for A-D Plume 

Rise 

SCLCRW 

Scaling Factor 

for Critical Wind 

Speed 

1.0 1.0 1.0 

SCLEFP 

Scaling Factor 

for E-F Plume 

Rise 

1.0 1.0 1.0 

SIGYINIT 

Initial Sigma-y 

for All Plume 

Segments 

9.4 9.4 9.4 

SIGZINIT 

Initial Sigma-z 

for All Plume 

Segments 

22.2 22.2 22.2 

 

SPAEND 

Radial distances 

for grid 

boundaries 

Ring 1 0.09997863 0.09997863 0.09997863 

Ring 2 0.3454824 0.3454824 0.3454824 

Ring 3 0.7499951 0.7499951 0.7499951 

Ring 4 0.9999727 0.9999727 0.9999727 

Ring 5 1.34502 1.34502 1.34502 

Ring 6 2.000007 2.000007 2.000007 

Ring 7 2.500025 2.500025 2.500025 

Ring 8 2.99998 2.99998 2.99998 

Ring 9 4.000015 4.000015 4.000015 

Ring 10 4.999988 4.999988 4.999988 

Ring 11 7.500012 7.500012 7.500012 

Ring 12 9.999975 9.999975 9.999975 

Ring 13 12.5 12.5 12.5 

Ring 14 15.00002 15.00002 15.00002 

Ring 15 20.00001 20.00001 20.00001 

Ring 16 25. 25. 25. 

Ring 17 29.99999 29.99999 29.99999 

SPAEND Ring 18 40.00003 40.00003 40.00003 

Ring 19 50. 50. 50. 

Ring 20 70.00002 70.00002 70.00002 

Ring 21 100. 100. 100. 

Ring 22 150. 150. 150. 

Ring 23 200. 200. 200. 

Ring 24 275. 275. 275. 

Ring 25 350. 350. 350. 

Ring 26 425. 425. 425. 

Ring 27 500 500 500 

Ring 28 750.0001 750.0001 750.0001 

Ring 29 1000 1000 1000 

TIMBAS Time Base for 

Plume 600 600 600 
B-10

Variable Description STSBO 

Base Case 

STSBO 

Early LTSBO 

Expansion 

Factor 

VDEPOS Dry Deposition 

Velocities 

Aerosol Bin 1 5.0439E-04 5.0439E-04 5.0439E-04 

Aerosol Bin 2 4.629E-04 4.629E-04 4.629E-04 

Aerosol Bin 3 6.0644E-04 6.0644E-04 6.0644E-04 

Aerosol Bin 4 1.0224E-03 1.0224E-03 1.0224E-03 

Aerosol Bin 5 1.9999E-03 1.9999E-03 1.9999E-03 

Aerosol Bin 6 4.0915E-03 4.0915E-03 4.0915E-03 

Aerosol Bin 7 7.8925E-03 7.8925E-03 7.8925E-03 

Aerosol Bin 8 1.2941E-02 1.2941E-02 1.2941E-02 

Aerosol Bin 9 1.6024E-02 1.6024E-02 1.6024E-02 

Aerosol Bin 10 5.1507E-02 5.1507E-02 5.1507E-02 

WETDEP Wet Deposition 

Flag 

Xe = 

.FALSE. 

Other groups 

= .TRUE. 

Xe = 

.FALSE. 

Other groups 

= .TRUE. 

Xe = 

.FALSE. 

Other groups 

= .TRUE. 

XPFAC1 

Base Time for 

Meander 

Expansion 

Factor 

0.2 0.2 0.2 

XPFAC2 

Breakpoint for 

Expansion 

Factor Model 

0.25 0.25 0.25 

YSCALE 

Scale Factor for 

Horizontal 

Dispersion 

1 1 1 

ZSCALE 

Scale Factor for 

Vertical 

Dispersion 

1.67 1.67 1.67 

 
B-11

Table B-2 Plume parameters used in the Sequoyah LTSBO, STSBO 

Base Case, and STSBO Early scenarios. 

SQN LTSBO 

Plume 

Segment 

PDELAY 

(s) 

PLHEAT 

(J/s) 

PLHITE 

(m) 

PLMDEN 

(kg/m3) 

PLMFLA 

(kg/s) 

PLUDUR 

(s) 

1 8.57E+04 1.039E+07 3.967E+01 8.484E-01 3.009E+00 3.7199E+03 

2 8.57E+04 1.414E+07 0.000E+00 8.261E-01 3.904E+00 3.7199E+03 

3 8.94E+04 3.007E+07 3.967E+01 1.044E+00 7.724E+00 3.4800E+03 

4 9.29E+04 2.794E+07 3.967E+01 1.085E+00 6.688E+00 3.6000E+03 

5 9.65E+04 2.498E+07 3.967E+01 1.099E+00 5.976E+00 3.7200E+03 

6 1.00E+05 2.402E+07 3.967E+01 1.104E+00 5.796E+00 3.4801E+03 

7 1.00E+05 7.042E+05 0.000E+00 1.100E+00 1.707E-01 3.4801E+03 

8 1.04E+05 2.320E+07 3.967E+01 1.099E+00 5.855E+00 3.6000E+03 

9 1.04E+05 2.416E+05 0.000E+00 1.100E+00 6.118E-02 3.6000E+03 

10 1.07E+05 2.142E+07 3.967E+01 1.103E+00 5.510E+00 3.6000E+03 

11 1.07E+05 1.033E+06 0.000E+00 1.103E+00 2.656E-01 3.6000E+03 

12 1.11E+05 2.095E+07 3.967E+01 1.107E+00 5.367E+00 3.6080E+03 

13 1.11E+05 9.867E+05 0.000E+00 1.106E+00 2.531E-01 3.6080E+03 

14 1.14E+05 2.093E+07 3.967E+01 1.108E+00 5.317E+00 3.5920E+03 

15 1.14E+05 8.951E+05 0.000E+00 1.108E+00 2.274E-01 3.5920E+03 

16 1.18E+05 2.227E+07 3.967E+01 1.101E+00 6.038E+00 3.5999E+03 

17 1.18E+05 1.238E+06 0.000E+00 1.103E+00 3.389E-01 3.5999E+03 

18 1.22E+05 2.766E+07 3.967E+01 1.069E+00 7.520E+00 3.7200E+03 

19 1.22E+05 9.766E+06 0.000E+00 1.002E+00 2.623E+00 1.4400E+03 

20 1.25E+05 3.426E+07 3.967E+01 1.047E+00 9.102E+00 3.6000E+03 

21 1.29E+05 3.813E+07 3.967E+01 1.033E+00 9.556E+00 3.6000E+03 

22 1.33E+05 3.995E+07 3.967E+01 1.025E+00 9.842E+00 3.6000E+03 

23 1.36E+05 4.107E+07 3.967E+01 1.017E+00 1.008E+01 3.6000E+03 

24 1.40E+05 4.199E+07 3.967E+01 1.012E+00 1.025E+01 3.6000E+03 

25 1.43E+05 4.261E+07 3.967E+01 1.008E+00 1.038E+01 3.4801E+03 

26 1.47E+05 4.031E+07 3.967E+01 1.025E+00 9.659E+00 3.6000E+03 

27 1.50E+05 3.953E+07 3.967E+01 1.032E+00 9.409E+00 3.6000E+03 

28 1.54E+05 4.045E+07 3.967E+01 1.027E+00 9.650E+00 3.5999E+03 

29 1.58E+05 4.207E+07 3.967E+01 1.013E+00 1.011E+01 3.6000E+03 

30 1.61E+05 4.275E+07 3.967E+01 1.006E+00 1.031E+01 3.7200E+03 

31 1.65E+05 4.264E+07 3.967E+01 1.008E+00 1.027E+01 3.4801E+03 

32 1.68E+05 4.217E+07 3.967E+01 1.013E+00 1.012E+01 3.7199E+03 

33 1.72E+05 4.206E+07 3.967E+01 1.015E+00 1.008E+01 3.6000E+03 

34 1.76E+05 4.209E+07 3.967E+01 1.015E+00 1.008E+01 3.6000E+03 

35 1.79E+05 4.246E+07 3.967E+01 1.013E+00 1.016E+01 3.6000E+03 

36 1.83E+05 4.284E+07 3.967E+01 1.010E+00 1.026E+01 3.6000E+03 

37 1.87E+05 4.177E+07 3.967E+01 1.019E+00 9.957E+00 3.6000E+03 

38 1.90E+05 4.185E+07 3.967E+01 1.018E+00 9.969E+00 3.6000E+03 

39 1.94E+05 4.200E+07 3.967E+01 1.017E+00 1.000E+01 3.6000E+03 

40 1.97E+05 4.217E+07 3.967E+01 1.016E+00 1.004E+01 3.6000E+03 

41 2.01E+05 4.232E+07 3.967E+01 1.015E+00 1.007E+01 3.6000E+03 
B-12

SQN LTSBO 

Plume 

Segment 

PDELAY 

(s) 

PLHEAT 

(J/s) 

PLHITE 

(m) 

PLMDEN 

(kg/m3) 

PLMFLA 

(kg/s) 

PLUDUR 

(s) 

42 2.05E+05 4.247E+07 3.967E+01 1.014E+00 1.011E+01 3.6000E+03 

43 2.08E+05 4.262E+07 3.967E+01 1.013E+00 1.014E+01 3.6000E+03 

44 2.12E+05 4.276E+07 3.967E+01 1.013E+00 1.017E+01 3.6000E+03 

45 2.15E+05 4.289E+07 3.967E+01 1.012E+00 1.020E+01 3.6000E+03 

46 2.19E+05 4.302E+07 3.967E+01 1.011E+00 1.023E+01 3.6000E+03 

47 2.23E+05 4.313E+07 3.967E+01 1.010E+00 1.025E+01 3.6000E+03 

48 2.26E+05 4.325E+07 3.967E+01 1.009E+00 1.028E+01 3.6000E+03 

49 2.30E+05 4.336E+07 3.967E+01 1.009E+00 1.031E+01 3.6000E+03 

50 2.33E+05 4.346E+07 3.967E+01 1.008E+00 1.033E+01 3.6000E+03 

51 2.37E+05 4.356E+07 3.967E+01 1.007E+00 1.035E+01 3.6000E+03 

52 2.41E+05 4.366E+07 3.967E+01 1.007E+00 1.037E+01 3.6000E+03 

53 2.44E+05 4.375E+07 3.967E+01 1.006E+00 1.039E+01 3.6000E+03 

54 2.48E+05 4.384E+07 3.967E+01 1.005E+00 1.041E+01 3.6000E+03 

55 2.51E+05 4.392E+07 3.967E+01 1.005E+00 1.043E+01 3.6000E+03 

56 2.55E+05 4.399E+07 3.967E+01 1.004E+00 1.045E+01 3.6000E+03 

SQN STSBO Base Case 

Plume 

Segment 

PDELAY 

(s) 

PLHEAT 

(J/s) 

PLHITE 

(m) 

PLMDEN 

(kg/m3) 

PLMFLA 

(kg/s) 

PLUDUR 

(s) 

1 1.42E+04 4.91E+04 0.00E+00 1.11E+00 1.21E-02 3.60E+03 

2 1.78E+04 2.12E+04 3.97E+01 1.11E+00 5.25E-03 3.54E+03 

3 1.78E+04 4.23E+04 0.00E+00 1.11E+00 1.05E-02 3.54E+03 

4 2.14E+04 1.66E+04 3.97E+01 1.12E+00 4.10E-03 3.60E+03 

5 2.14E+04 3.30E+04 0.00E+00 1.12E+00 8.16E-03 3.60E+03 

6 2.50E+04 1.49E+04 3.97E+01 1.12E+00 3.68E-03 3.60E+03 

7 2.50E+04 2.96E+04 0.00E+00 1.12E+00 7.33E-03 3.60E+03 

8 2.86E+04 1.63E+04 3.97E+01 1.11E+00 4.03E-03 3.60E+03 

9 2.86E+04 3.24E+04 0.00E+00 1.11E+00 8.01E-03 3.60E+03 

10 3.22E+04 1.98E+04 3.97E+01 1.10E+00 4.90E-03 3.60E+03 

11 3.22E+04 3.95E+04 0.00E+00 1.10E+00 9.75E-03 3.60E+03 

12 3.58E+04 2.12E+04 3.97E+01 1.09E+00 5.23E-03 3.60E+03 

13 3.58E+04 4.21E+04 0.00E+00 1.09E+00 1.04E-02 3.60E+03 

14 3.94E+04 2.20E+04 3.97E+01 1.09E+00 5.44E-03 3.60E+03 

15 3.94E+04 4.38E+04 0.00E+00 1.09E+00 1.08E-02 3.60E+03 

16 4.30E+04 2.15E+04 3.97E+01 1.08E+00 5.30E-03 3.60E+03 

17 4.30E+04 4.27E+04 0.00E+00 1.08E+00 1.05E-02 3.60E+03 

18 4.66E+04 4.14E+04 0.00E+00 1.08E+00 1.02E-02 3.60E+03 

19 5.02E+04 4.03E+04 0.00E+00 1.08E+00 9.93E-03 3.60E+03 

20 1.87E+05 5.87E+07 3.97E+01 6.69E-01 1.88E+01 3.60E+03 

21 1.87E+05 8.49E+07 0.00E+00 6.95E-01 2.62E+01 3.60E+03 

22 1.91E+05 5.61E+07 3.97E+01 7.30E-01 1.63E+01 3.60E+03 

23 1.94E+05 5.73E+07 3.97E+01 8.12E-01 1.54E+01 3.60E+03 

24 1.98E+05 5.70E+07 3.97E+01 8.23E-01 1.52E+01 3.60E+03 

25 2.02E+05 5.30E+07 3.97E+01 9.03E-01 1.32E+01 3.60E+03 
B-13

SQN STSBO Base Case 

Plume 

Segment 

PDELAY 

(s) 

PLHEAT 

(J/s) 

PLHITE 

(m) 

PLMDEN 

(kg/m3) 

PLMFLA 

(kg/s) 

PLUDUR 

(s) 

26 2.05E+05 5.19E+07 3.97E+01 9.30E-01 1.27E+01 3.60E+03 

27 2.09E+05 5.16E+07 3.97E+01 9.34E-01 1.26E+01 3.60E+03 

28 2.12E+05 5.15E+07 3.97E+01 9.35E-01 1.25E+01 3.60E+03 

29 2.16E+05 5.13E+07 3.97E+01 9.37E-01 1.25E+01 3.60E+03 

30 2.20E+05 5.17E+07 3.97E+01 9.33E-01 1.26E+01 3.60E+03 

31 2.23E+05 5.13E+07 3.97E+01 9.37E-01 1.25E+01 3.60E+03 

32 2.27E+05 5.12E+07 3.97E+01 9.38E-01 1.25E+01 3.60E+03 

33 2.30E+05 5.11E+07 3.97E+01 9.39E-01 1.25E+01 3.60E+03 

34 2.34E+05 5.11E+07 3.97E+01 9.39E-01 1.25E+01 3.60E+03 

35 2.38E+05 5.10E+07 3.97E+01 9.39E-01 1.25E+01 3.60E+03 

36 2.41E+05 5.10E+07 3.97E+01 9.40E-01 1.25E+01 3.60E+03 

37 2.45E+05 5.09E+07 3.97E+01 9.40E-01 1.24E+01 3.60E+03 

38 2.48E+05 5.09E+07 3.97E+01 9.41E-01 1.24E+01 3.60E+03 

39 2.52E+05 5.09E+07 3.97E+01 9.41E-01 1.24E+01 3.60E+03 

40 2.56E+05 5.08E+07 3.97E+01 9.41E-01 1.24E+01 3.60E+03 

SQN STSBO Early 

Plume 

Segment 

PDELAY 

(s) 

PLHEAT 

(J/s) 

PLHITE (m) PLMDEN 

(kg/m3) 

PLMFLA 

(kg/s) 

PLUDUR 

(s) 

1 1.28E+04 2.88E+07 3.97E+01 9.79E-01 7.59E+00 3.60E+03 

2 1.28E+04 1.66E+07 0.00E+00 8.63E-01 4.46E+00 3.60E+03 

3 1.64E+04 2.78E+07 3.97E+01 1.09E+00 6.69E+00 3.66E+03 

4 2.01E+04 2.60E+07 3.97E+01 1.10E+00 6.22E+00 3.60E+03 

5 2.37E+04 2.32E+07 3.97E+01 1.10E+00 5.54E+00 3.54E+03 

6 2.72E+04 2.40E+07 3.97E+01 1.10E+00 5.83E+00 3.60E+03 

7 2.72E+04 8.16E+05 0.00E+00 1.06E+00 1.95E-01 3.60E+03 

8 3.08E+04 3.29E+07 3.97E+01 1.06E+00 8.40E+00 3.60E+03 

9 3.08E+04 1.08E+07 0.00E+00 1.07E+00 2.72E+00 2.58E+03 

10 3.44E+04 4.08E+07 3.97E+01 1.02E+00 1.02E+01 3.60E+03 

11 3.80E+04 4.42E+07 3.97E+01 9.99E-01 1.09E+01 3.60E+03 

12 4.16E+04 4.34E+07 3.97E+01 1.00E+00 1.06E+01 3.60E+03 

13 4.52E+04 4.19E+07 3.97E+01 1.01E+00 1.01E+01 3.60E+03 

14 4.88E+04 4.22E+07 3.97E+01 1.01E+00 1.02E+01 3.60E+03 

15 5.24E+04 4.28E+07 3.97E+01 1.01E+00 1.03E+01 3.60E+03 

16 5.60E+04 4.22E+07 3.97E+01 1.01E+00 1.01E+01 3.60E+03 

17 5.96E+04 4.19E+07 3.97E+01 1.02E+00 1.00E+01 3.60E+03 

18 6.32E+04 4.31E+07 3.97E+01 1.01E+00 1.03E+01 3.60E+03 

19 6.68E+04 4.36E+07 3.97E+01 1.01E+00 1.04E+01 3.60E+03 

20 7.04E+04 4.29E+07 3.97E+01 1.01E+00 1.03E+01 3.60E+03 

21 7.40E+04 4.32E+07 3.97E+01 1.01E+00 1.03E+01 3.60E+03 

22 7.76E+04 4.36E+07 3.97E+01 1.01E+00 1.04E+01 3.60E+03 

23 8.12E+04 4.38E+07 3.97E+01 1.01E+00 1.05E+01 3.60E+03 

24 8.48E+04 4.42E+07 3.97E+01 1.00E+00 1.05E+01 3.60E+03 
B-14

SQN STSBO Early 

Plume 

Segment 

PDELAY 

(s) 

PLHEAT 

(J/s) 

PLHITE (m) PLMDEN 

(kg/m3) 

PLMFLA 

(kg/s) 

PLUDUR 

(s) 

25 8.84E+04 4.42E+07 3.97E+01 1.00E+00 1.05E+01 3.60E+03 

26 9.20E+04 4.43E+07 3.97E+01 1.00E+00 1.06E+01 3.60E+03 

27 9.56E+04 4.45E+07 3.97E+01 1.00E+00 1.06E+01 3.60E+03 

28 9.92E+04 4.47E+07 3.97E+01 9.99E-01 1.07E+01 3.60E+03 

29 1.03E+05 4.48E+07 3.97E+01 9.98E-01 1.07E+01 3.60E+03 

30 1.06E+05 4.49E+07 3.97E+01 9.97E-01 1.07E+01 3.60E+03 

31 1.10E+05 4.51E+07 3.97E+01 9.97E-01 1.07E+01 3.60E+03 

32 1.14E+05 4.52E+07 3.97E+01 9.96E-01 1.08E+01 3.60E+03 

33 1.17E+05 4.53E+07 3.97E+01 9.95E-01 1.08E+01 3.60E+03 

34 1.21E+05 4.53E+07 3.97E+01 9.95E-01 1.08E+01 3.60E+03 

35 1.24E+05 4.54E+07 3.97E+01 9.94E-01 1.08E+01 3.60E+03 

36 1.28E+05 4.55E+07 3.97E+01 9.93E-01 1.08E+01 3.60E+03 

37 1.32E+05 4.56E+07 3.97E+01 9.93E-01 1.09E+01 3.60E+03 

38 1.35E+05 4.56E+07 3.97E+01 9.92E-01 1.09E+01 3.60E+03 

39 1.39E+05 4.57E+07 3.97E+01 9.92E-01 1.09E+01 3.60E+03 

40 1.42E+05 4.57E+07 3.97E+01 9.92E-01 1.09E+01 3.60E+03 

41 1.46E+05 4.58E+07 3.97E+01 9.91E-01 1.09E+01 3.60E+03 

42 1.50E+05 4.59E+07 3.97E+01 9.91E-01 1.09E+01 3.60E+03 

43 1.53E+05 4.69E+07 3.97E+01 9.83E-01 1.12E+01 3.60E+03 

44 1.57E+05 4.70E+07 3.97E+01 9.82E-01 1.12E+01 3.60E+03 

45 1.60E+05 4.72E+07 3.97E+01 9.80E-01 1.13E+01 3.60E+03 

46 1.64E+05 4.70E+07 3.97E+01 9.82E-01 1.12E+01 3.60E+03 

47 1.68E+05 4.69E+07 3.97E+01 9.83E-01 1.12E+01 3.60E+03 

48 1.71E+05 4.74E+07 3.97E+01 9.79E-01 1.13E+01 3.66E+03 

49 1.75E+05 4.76E+07 3.97E+01 9.77E-01 1.14E+01 3.60E+03 

50 1.79E+05 4.77E+07 3.97E+01 9.77E-01 1.14E+01 3.60E+03 

51 1.82E+05 4.77E+07 3.97E+01 9.76E-01 1.14E+01 3.60E+03 

52 1.86E+05 4.78E+07 3.97E+01 9.75E-01 1.14E+01 3.60E+03 

53 1.89E+05 4.79E+07 3.97E+01 9.74E-01 1.14E+01 3.60E+03 

54 1.93E+05 4.80E+07 3.97E+01 9.74E-01 1.15E+01 3.60E+03 

55 1.97E+05 4.80E+07 3.97E+01 9.73E-01 1.15E+01 3.60E+03 

56 2.00E+05 4.81E+07 3.97E+01 9.73E-01 1.15E+01 3.60E+03 

57 2.04E+05 4.81E+07 3.97E+01 9.72E-01 1.15E+01 3.60E+03 

58 2.07E+05 4.81E+07 3.97E+01 9.72E-01 1.15E+01 3.60E+03 

59 2.11E+05 4.86E+07 3.97E+01 9.69E-01 1.16E+01 3.60E+03 

60 2.15E+05 4.88E+07 3.97E+01 9.65E-01 1.17E+01 3.60E+03 

61 2.18E+05 4.87E+07 3.97E+01 9.66E-01 1.17E+01 3.60E+03 

62 2.22E+05 4.86E+07 3.97E+01 9.66E-01 1.17E+01 3.60E+03 

63 2.25E+05 4.86E+07 3.97E+01 9.66E-01 1.17E+01 3.60E+03 

64 2.29E+05 4.86E+07 3.97E+01 9.66E-01 1.17E+01 3.60E+03 

65 2.33E+05 4.86E+07 3.97E+01 9.65E-01 1.17E+01 3.60E+03 
B-15

SQN STSBO Early 

Plume 

Segment 

PDELAY 

(s) 

PLHEAT 

(J/s) 

PLHITE (m) PLMDEN 

(kg/m3) 

PLMFLA 

(kg/s) 

PLUDUR 

(s) 

66 2.36E+05 4.86E+07 3.97E+01 9.65E-01 1.18E+01 3.60E+03 

67 2.40E+05 4.86E+07 3.97E+01 9.64E-01 1.18E+01 3.60E+03 

68 2.43E+05 4.87E+07 3.97E+01 9.64E-01 1.18E+01 3.60E+03 

69 2.47E+05 4.87E+07 3.97E+01 9.64E-01 1.18E+01 3.60E+03 

70 2.51E+05 4.88E+07 3.97E+01 9.63E-01 1.18E+01 3.60E+03 

71 2.54E+05 4.88E+07 3.97E+01 9.62E-01 1.18E+01 3.60E+03 

72 2.58E+05 4.89E+07 3.97E+01 9.62E-01 1.19E+01 1.50E+03 

 
B-16

Table B-3 Plume parameters used in the Sequoyah LTSBO, STSBO 

Base Case, and STSBO Early scenarios. 

SQN LTSBO 

Class Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7 Bin 8 Bin 9 Bin 10 

Xe 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01 

Cs 1.94E-01 2.94E-02 5.27E-02 2.15E-01 2.97E-01 1.10E-01 5.75E-02 3.65E-02 7.60E-03 4.56E-04 

Ba 2.51E-01 3.95E-02 1.39E-02 5.64E-02 1.02E-01 1.27E-01 2.05E-01 1.66E-01 3.69E-02 2.25E-03 

I 1.06E-01 1.86E-02 5.48E-02 2.60E-01 3.98E-01 1.19E-01 2.59E-02 1.39E-02 2.86E-03 1.78E-04 

Te 4.20E-02 1.11E-02 8.05E-02 3.44E-01 3.94E-01 9.68E-02 1.86E-02 1.03E-02 2.16E-03 1.30E-04 

Ru 4.10E-01 6.67E-02 3.49E-03 9.77E-03 4.29E-02 1.05E-01 1.83E-01 1.46E-01 3.18E-02 1.93E-03 

Mo 4.24E-01 5.41E-02 1.85E-02 4.85E-02 7.91E-02 1.13E-01 1.44E-01 9.77E-02 2.04E-02 1.21E-03 

Ce 2.96E-01 4.57E-02 1.62E-03 5.42E-03 4.31E-02 1.28E-01 2.39E-01 1.95E-01 4.32E-02 2.63E-03 

La 2.61E-01 3.92E-02 1.29E-02 4.46E-02 8.24E-02 1.28E-01 2.17E-01 1.74E-01 3.85E-02 2.34E-03 

SQN STSBO Base Case 

Class Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7 Bin 8 Bin 9 Bin 10 

Xe 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01

Cs 1.06E-01 2.07E-02 1.06E-01 3.83E-01 3.15E-01 5.84E-02 8.97E-03 2.11E-03 2.98E-04 2.38E-05

Ba 1.13E-01 2.40E-02 1.03E-01 3.14E-01 2.50E-01 1.27E-01 5.87E-02 9.55E-03 9.79E-04 8.21E-05

I 2.10E-01 4.73E-02 1.58E-01 3.20E-01 1.89E-01 5.62E-02 1.51E-02 3.61E-03 5.23E-04 4.68E-05

Te 1.36E-01 2.23E-02 7.07E-02 2.06E-01 1.92E-01 2.06E-01 1.37E-01 2.75E-02 1.80E-03 1.10E-04

Ru 1.85E-01 1.66E-02 7.03E-02 2.20E-01 2.03E-01 1.71E-01 1.08E-01 2.40E-02 2.04E-03 1.58E-04

Mo 2.55E-03 1.42E-03 1.50E-02 5.71E-02 1.69E-01 3.31E-01 2.79E-01 1.24E-01 2.04E-02 9.40E-04

Ce 1.10E-01 2.29E-02 9.03E-02 2.69E-01 2.30E-01 1.55E-01 9.52E-02 2.56E-02 2.91E-03 1.62E-04

La 1.04E-01 2.26E-02 9.13E-02 2.72E-01 2.32E-01 1.55E-01 9.54E-02 2.57E-02 2.91E-03 1.60E-04

SQN STSBO Early 

Class Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7 Bin 8 Bin 9 Bin 10 

Xe 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01

Cs 8.98E-02 1.61E-02 6.86E-02 2.26E-01 2.95E-01 1.67E-01 7.11E-02 4.23E-02 2.10E-02 4.50E-03

Ba 3.57E-02 1.11E-02 3.78E-02 1.75E-01 3.16E-01 2.09E-01 9.79E-02 7.05E-02 3.96E-02 7.92E-03

I 8.90E-02 1.22E-02 4.71E-02 1.90E-01 3.27E-01 2.13E-01 7.33E-02 3.06E-02 1.47E-02 3.41E-03

Te 5.07E-02 6.46E-03 2.41E-02 1.38E-01 3.20E-01 2.96E-01 1.18E-01 3.24E-02 1.16E-02 2.47E-03

Ru 1.07E-01 2.59E-02 2.35E-02 5.27E-02 1.03E-01 1.44E-01 1.99E-01 2.06E-01 1.16E-01 2.32E-02

Mo 3.35E-03 1.68E-03 1.57E-02 5.11E-02 1.54E-01 3.43E-01 2.89E-01 1.22E-01 1.85E-02 8.96E-04

Ce 6.75E-02 2.47E-02 2.80E-02 7.59E-02 1.37E-01 1.53E-01 1.78E-01 1.95E-01 1.17E-01 2.36E-02

La 4.34E-02 9.74E-03 4.96E-02 2.15E-01 3.27E-01 1.95E-01 8.48E-02 4.84E-02 2.30E-02 4.28E-03
B-17

Table B-4 Release fraction parameters used in the Sequoyah LTSBO, 

STSBO Base Case, and STSBO Early scenarios. 

SQN LTSBO 

Plume 

Segment Xe Cs Ba I Te Ru Mo Ce La 

1 1.345E01 

5.956E04 5.350E06 1.945E03 4.638E04 3.164E06 6.541E05 1.198E10 1.890E10 

2 1.391E01 

5.276E04 4.879E06 1.837E03 4.345E04 2.542E06 5.141E05 9.824E11 1.517E10 

3 1.326E01 

7.270E04 6.198E06 2.170E03 5.129E04 4.403E06 9.454E05 1.606E10 2.596E10 

4 5.511E03 

3.165E05 1.786E07 8.271E05 1.624E05 1.360E07 3.935E06 4.661E12 7.176E12 

5 7.876E03 

1.437E04 9.613E07 3.041E04 1.189E04 3.422E07 1.569E05 2.239E11 2.192E11 

6 1.979E02 

4.069E04 1.686E05 8.615E04 5.671E04 8.022E06 4.714E05 2.028E06 4.039E08 

7 8.360E04 

1.779E05 4.160E07 3.759E05 2.574E05 4.288E07 2.117E06 6.702E09 1.464E10 

8 8.001E02 

1.874E03 3.367E03 3.661E03 2.461E03 5.810E05 2.340E04 6.771E04 1.686E05 

9 8.235E04 

1.870E05 3.842E05 3.604E05 2.378E05 5.740E07 2.395E06 7.672E06 1.950E07 

10 6.833E02 

1.068E03 2.394E03 1.838E03 1.150E03 2.927E05 1.566E04 4.708E04 1.254E05 

11 3.265E03 

5.003E05 1.111E04 8.570E05 5.357E05 1.364E06 7.370E06 2.184E05 5.827E07 

12 4.976E02 

5.956E04 7.265E04 8.274E04 4.681E04 9.711E06 9.845E05 1.418E04 3.955E06 

13 2.410E03 

2.888E05 3.662E05 4.053E05 2.309E05 4.888E07 4.748E06 7.149E06 1.989E07 

14 3.626E02 

4.666E04 2.802E04 5.836E04 2.917E04 3.796E06 8.355E05 5.442E05 1.626E06 

15 1.549E03 

1.998E05 1.197E05 2.496E05 1.249E05 1.622E07 3.576E06 2.326E06 6.950E08 

16 6.600E02 

9.692E04 2.658E04 1.233E03 4.730E04 3.611E06 1.639E04 5.125E05 1.773E06 

17 4.009E03 

6.435E05 1.793E05 7.928E05 3.162E05 2.436E07 1.094E05 3.456E06 1.185E07 

18 6.850E02 

6.516E04 1.131E04 1.221E03 2.896E04 1.629E06 9.843E05 2.184E05 9.414E07 

19 9.442E03 

1.005E04 1.845E05 1.732E04 4.506E05 2.632E07 1.557E05 3.560E06 1.500E07 

20 5.850E02 

4.014E04 4.015E05 1.160E03 1.803E04 6.252E07 4.754E05 7.799E06 4.736E07 

21 2.636E02 

1.572E04 9.173E06 5.734E04 8.828E05 1.401E07 1.358E05 1.824E06 2.013E07 

22 1.585E02 

9.278E05 2.970E06 3.693E04 6.909E05 4.147E08 5.651E06 6.513E07 1.743E07 

23 1.258E02 

9.285E05 1.653E06 3.834E04 8.222E05 1.876E08 3.923E06 4.423E07 2.302E07 

24 9.606E03 

1.181E04 1.059E06 4.311E04 8.636E05 8.993E09 2.903E06 3.412E07 2.426E07 

25 7.480E03 

2.987E04 7.544E07 6.920E04 9.121E05 4.424E09 2.218E06 2.841E07 2.386E07 

26 2.308E03 

2.472E04 2.594E07 5.131E04 4.260E05 9.459E10 6.490E07 8.789E08 7.881E08 
B-18

SQN LTSBO 

Plume 

Segment Xe Cs Ba I Te Ru Mo Ce La 

27 8.984E04 

4.096E04 3.260E07 9.208E04 7.157E05 2.183E10 2.642E07 3.621E08 3.454E08 

28 1.536E03 

1.124E03 1.928E06 4.437E03 4.917E04 2.474E10 4.970E07 6.822E08 6.702E08 

29 3.474E03 

2.336E03 8.354E06 1.271E02 2.276E03 3.784E10 1.168E06 1.618E07 1.613E07 

30 4.312E03 

2.366E03 2.275E05 1.519E02 6.558E03 3.347E10 1.674E06 2.249E07 2.266E07 

31 3.506E03 

1.517E03 3.364E05 1.010E02 9.989E03 2.037E10 1.656E06 2.130E07 2.157E07 

32 2.779E03 

9.217E04 4.074E05 6.010E03 1.218E02 1.310E10 1.580E06 2.051E07 2.085E07 

33 2.228E03 

5.760E04 4.365E05 3.508E03 1.154E02 7.276E11 1.423E06 2.012E07 2.048E07 

34 1.964E03 

4.141E04 4.820E05 2.324E03 9.086E03 4.366E11 1.352E06 2.100E07 2.138E07 

35 1.918E03 

3.420E04 5.964E05 1.830E03 7.053E03 2.910E11 1.442E06 2.389E07 2.436E07 

36 1.860E03 

3.205E04 7.128E05 1.662E03 5.404E03 2.910E11 1.518E06 2.596E07 2.646E07 

37 9.388E04 

1.740E04 4.601E05 8.538E04 2.031E03 0.000E+

00 

8.512E07 1.463E07 1.493E07 

38 8.088E04 

1.609E04 5.033E05 7.015E04 1.308E03 1.455E11 8.278E07 1.388E07 1.417E07 

39 7.404E04 

1.557E04 5.718E05 6.464E04 9.176E04 0.000E+

00 

8.583E07 1.366E07 1.393E07 

40 6.818E04 

1.454E04 6.484E05 6.755E04 6.724E04 0.000E+

00 

9.085E07 1.334E07 1.361E07 

41 6.294E04 

1.467E04 7.332E05 7.825E04 5.243E04 0.000E+

00 

9.775E07 1.296E07 1.324E07 

42 5.822E04 

1.595E04 8.271E05 9.188E04 4.441E04 0.000E+

00 

1.067E06 1.260E07 1.286E07 

43 5.397E04 

1.756E04 9.236E05 1.070E03 4.229E04 1.455E11 1.178E06 1.221E07 1.249E07 

44 5.010E04 

2.085E04 9.085E05 1.265E03 4.655E04 0.000E+

00 

1.312E06 1.186E07 1.212E07 

45 4.650E04 

1.918E04 7.585E05 1.235E03 5.603E04 0.000E+

00 

1.472E06 1.149E07 1.175E07 

46 4.307E04 

1.579E04 5.829E05 1.138E03 6.149E04 0.000E+

00 

1.654E06 1.109E07 1.135E07 

47 3.991E04 

1.234E04 4.346E05 8.714E04 5.927E04 0.000E+

00 

1.867E06 1.072E07 1.096E07 

48 3.713E04 

9.575E05 3.268E05 5.876E04 6.207E04 0.000E+

00 

2.112E06 1.037E07 1.061E07 

49 3.446E04 

7.968E05 2.555E05 4.010E04 6.812E04 0.000E+

00 

2.376E06 1.001E07 1.025E07 

50 3.198E04 

7.176E05 2.172E05 2.873E04 7.117E04 0.000E+

00 

2.664E06 9.686E08 9.903E08 

51 2.973E04 

6.952E05 2.054E05 2.196E04 7.207E04 0.000E+

00 

2.983E06 9.371E08 9.588E08 

52 2.760E04 

7.145E05 2.201E05 1.808E04 7.524E04 0.000E+

00 

3.334E06 9.069E08 9.284E08 

53 2.566E04 

7.717E05 2.640E05 1.613E04 7.084E04 0.000E+

00 

3.727E06 8.813E08 9.008E08 

54 2.386E04 

8.575E05 3.187E05 1.543E04 6.032E04 0.000E+

00 

4.161E06 8.533E08 8.748E08 
B-19

SQN LTSBO 

Plume 

Segment Xe Cs Ba I Te Ru Mo Ce La 

55 2.217E04 

9.647E05 3.858E05 1.560E04 5.303E04 0.000E+

00 

4.639E06 8.300E08 8.492E08 

56 2.056E04 

1.101E04 4.614E05 1.639E04 4.840E04 0.000E+

00 

5.173E06 8.044E08 8.243E08 

 SQN STSBO Base Case 

Plume 

Segment Xe Cs Ba I Te Ru Mo Ce La 

1 2.26E-07 2.64E-09 4.59E-11 4.62E-09 1.80E-09 2.48E-10 3.77E-10 4.40E-15 4.50E-15 

2 1.55E-07 1.30E-09 4.13E-11 2.32E-09 1.21E-09 2.13E-10 2.23E-10 4.04E-15 4.08E-15 

3 3.09E-07 2.58E-09 8.23E-11 4.62E-09 2.41E-09 4.25E-10 4.44E-10 8.05E-15 8.14E-15 

4 1.66E-07 1.42E-09 6.12E-11 2.36E-09 1.29E-09 3.23E-10 2.76E-10 6.25E-15 6.30E-15 

5 3.30E-07 2.83E-09 1.22E-10 4.70E-09 2.56E-09 6.43E-10 5.50E-10 1.25E-14 1.25E-14 

6 1.90E-07 2.06E-09 1.71E-09 3.15E-09 1.92E-09 6.42E-10 4.49E-10 4.47E-10 1.33E-11 

7 3.78E-07 4.10E-09 3.41E-09 6.28E-09 3.82E-09 1.28E-09 8.94E-10 8.90E-10 2.65E-11 

8 2.60E-07 2.31E-09 3.20E-09 3.39E-09 1.95E-09 6.34E-10 5.00E-10 8.46E-10 2.63E-11 

9 5.17E-07 4.60E-09 6.38E-09 6.74E-09 3.88E-09 1.26E-09 9.95E-10 1.68E-09 5.24E-11 

10 3.84E-07 2.42E-09 2.91E-09 3.45E-09 1.76E-09 5.44E-10 5.27E-10 7.62E-10 2.40E-11 

11 7.64E-07 4.82E-09 5.79E-09 6.86E-09 3.50E-09 1.08E-09 1.05E-09 1.52E-09 4.78E-11 

12 4.85E-07 2.12E-09 2.20E-09 3.19E-09 1.43E-09 3.99E-10 4.59E-10 5.71E-10 1.82E-11 

13 9.65E-07 4.21E-09 4.37E-09 6.35E-09 2.85E-09 7.95E-10 9.14E-10 1.14E-09 3.63E-11 

14 5.81E-07 1.80E-09 1.59E-09 3.15E-09 1.25E-09 2.83E-10 3.77E-10 4.11E-10 1.33E-11 

15 1.16E-06 3.57E-09 3.17E-09 6.27E-09 2.49E-09 5.62E-10 7.49E-10 8.17E-10 2.65E-11 

16 6.44E-07 1.44E-09 1.10E-09 3.16E-09 1.02E-09 1.89E-10 2.88E-10 2.80E-10 9.27E-12 

17 1.28E-06 2.87E-09 2.18E-09 6.28E-09 2.02E-09 3.75E-10 5.73E-10 5.56E-10 1.84E-11 

18 1.40E-06 2.25E-09 1.50E-09 6.41E-09 1.52E-09 2.51E-10 4.29E-10 3.79E-10 1.29E-11 

19 1.52E-06 2.06E-09 1.04E-09 7.21E-09 1.16E-09 1.69E-10 3.19E-10 2.59E-10 9.17E-12 

20 3.66E-01 6.25E-03 7.79E-05 1.16E-02 2.18E-03 1.94E-08 6.15E-04 3.36E-07 3.31E-07 

21 6.27E-01 6.26E-03 1.34E-04 1.61E-02 3.61E-03 2.11E-08 4.36E-04 5.53E-07 5.49E-07 

22 4.79E-03 1.04E-02 5.12E-06 4.48E-03 1.99E-04 3.35E-09 2.13E-03 8.21E-08 8.85E-08 

23 2.64E-04 1.13E-02 8.68E-06 1.63E-03 8.67E-05 2.15E-10 2.88E-03 1.35E-07 1.47E-07 

24 7.65E-05 1.02E-02 1.13E-05 7.58E-04 9.66E-05 5.52E-11 2.72E-03 2.19E-07 2.34E-07 

25 1.48E-05 3.67E-03 4.95E-06 2.23E-04 4.55E-05 8.56E-12 9.84E-04 1.11E-07 1.17E-07 

26 7.93E-06 2.61E-03 1.24E-05 3.30E-04 5.89E-05 3.22E-12 6.72E-04 9.59E-08 1.01E-07 

27 6.82E-06 1.72E-03 1.43E-05 6.63E-04 7.64E-05 2.40E-12 4.13E-04 1.14E-07 1.19E-07 

28 6.08E-06 1.14E-03 1.10E-05 7.95E-04 8.98E-05 2.43E-12 2.39E-04 1.31E-07 1.37E-07 

29 5.16E-06 8.20E-04 8.97E-06 7.24E-04 1.18E-04 3.50E-12 1.37E-04 1.40E-07 1.46E-07 

30 5.39E-06 7.58E-04 8.29E-06 9.44E-04 1.95E-04 4.65E-10 6.49E-02 1.56E-07 1.63E-07 

31 6.05E-06 7.13E-04 6.55E-06 1.14E-03 2.49E-04 2.70E-09 6.87E-02 1.37E-07 1.43E-07 

32 7.06E-06 8.33E-04 6.67E-06 1.33E-03 4.37E-04 4.45E-09 3.19E-02 1.40E-07 1.46E-07 

33 7.78E-06 1.08E-03 9.58E-06 1.64E-03 9.39E-04 6.18E-09 1.62E-02 1.58E-07 1.65E-07 

34 8.20E-06 1.24E-03 1.47E-05 2.10E-03 2.00E-03 8.01E-09 8.65E-03 1.81E-07 1.88E-07 

35 8.40E-06 1.11E-03 2.27E-05 2.68E-03 3.47E-03 9.57E-09 4.78E-03 2.02E-07 2.11E-07 

36 8.58E-06 8.08E-04 3.77E-05 3.32E-03 2.93E-03 1.11E-08 2.70E-03 2.23E-07 2.33E-07 

37 8.61E-06 6.18E-04 6.63E-05 3.97E-03 2.00E-03 1.23E-08 1.54E-03 2.39E-07 2.49E-07 

38 8.67E-06 5.35E-04 1.06E-04 4.60E-03 1.18E-03 1.26E-08 8.84E-04 2.47E-07 2.57E-07 
B-20

 SQN STSBO Base Case 

Plume 

Segment Xe Cs Ba I Te Ru Mo Ce La 

39 8.73E-06 5.12E-04 1.55E-04 5.21E-03 6.72E-04 1.29E-08 5.12E-04 2.53E-07 2.64E-07 

40 8.67E-06 4.74E-04 2.12E-04 5.11E-03 3.84E-04 1.28E-08 2.97E-04 2.55E-07 2.65E-07 

SQN STSBO Early 

Plume Segment Xe Cs Ba I Te Ru Mo Ce La 

1 2.07E-01 1.90E-03 3.26E-05 4.82E-03 3.12E-03 3.96E-05 1.68E-04 7.81E-10 8.86E-10

2 1.38E-01 1.32E-03 2.28E-05 3.79E-03 2.42E-03 2.25E-05 9.41E-05 4.48E-10 5.05E-10

3 1.98E-02 1.14E-04 1.93E-06 2.63E-04 1.66E-04 2.89E-06 1.20E-05 5.81E-11 6.52E-11

4 2.63E-03 1.37E-05 2.58E-07 2.73E-05 1.82E-05 6.89E-07 1.95E-06 1.46E-11 1.51E-11

5 3.90E-03 9.07E-05 2.71E-06 1.22E-04 1.07E-04 1.32E-05 2.00E-05 2.47E-10 2.43E-10

6 4.08E-02 1.29E-03 3.32E-05 1.63E-03 1.36E-03 1.71E-04 2.97E-04 3.21E-09 3.17E-09

7 1.27E-03 4.84E-05 1.28E-06 6.09E-05 5.29E-05 6.57E-06 1.11E-05 1.23E-10 1.21E-10

8 2.06E-01 3.01E-03 7.12E-04 4.62E-03 4.19E-03 1.03E-03 7.88E-04 1.07E-04 1.87E-06

9 4.17E-02 8.19E-04 1.32E-04 1.25E-03 1.16E-03 2.71E-04 2.13E-04 1.86E-05 3.20E-07

10 1.53E-01 1.31E-03 2.79E-04 1.87E-03 1.16E-03 3.09E-04 3.30E-04 3.87E-05 1.06E-06

11 8.10E-02 6.93E-04 6.30E-05 1.08E-03 2.98E-04 6.78E-05 1.58E-04 8.92E-06 7.43E-07

12 3.18E-02 3.22E-04 1.01E-05 4.74E-04 1.25E-04 1.00E-05 6.77E-05 1.65E-06 4.38E-07

13 1.30E-02 2.25E-04 2.87E-06 3.62E-04 1.22E-04 2.04E-06 3.18E-05 5.51E-07 3.04E-07

14 9.18E-03 7.84E-04 3.07E-06 1.52E-03 2.42E-04 8.38E-07 2.54E-05 4.91E-07 3.90E-07

15 8.83E-03 3.50E-03 7.75E-06 7.95E-03 7.82E-04 4.95E-07 2.43E-05 5.76E-07 5.08E-07

16 5.91E-03 3.42E-03 1.16E-05 1.16E-02 1.72E-03 2.15E-07 1.52E-05 4.56E-07 4.15E-07

17 4.52E-03 1.78E-03 1.82E-05 7.14E-03 3.82E-03 9.97E-08 9.65E-06 4.02E-07 3.73E-07

18 5.06E-03 1.20E-03 3.86E-05 4.84E-03 9.88E-03 6.75E-08 7.85E-06 4.93E-07 4.63E-07

19 4.48E-03 7.17E-04 6.01E-05 2.75E-03 1.67E-02 3.77E-08 5.01E-06 4.68E-07 4.44E-07

20 2.86E-03 3.80E-04 7.10E-05 1.25E-03 1.38E-02 1.46E-08 2.48E-06 3.28E-07 3.15E-07

21 2.48E-03 4.41E-04 1.03E-04 1.20E-03 8.70E-03 7.80E-09 2.08E-06 3.13E-07 3.03E-07

22 2.18E-03 3.99E-04 1.43E-04 1.68E-03 4.89E-03 4.54E-09 2.18E-06 3.02E-07 2.94E-07

23 1.89E-03 2.88E-04 1.90E-04 1.36E-03 2.71E-03 2.79E-09 2.58E-06 2.91E-07 2.85E-07

24 1.69E-03 2.38E-04 2.54E-04 8.97E-04 1.66E-03 1.75E-09 3.36E-06 2.93E-07 2.87E-07

25 1.40E-03 2.09E-04 2.38E-04 6.36E-04 1.18E-03 1.16E-09 4.10E-06 2.71E-07 2.67E-07

26 1.19E-03 1.99E-04 1.61E-04 5.37E-04 1.23E-03 9.31E-10 5.09E-06 2.61E-07 2.58E-07

27 1.05E-03 2.05E-04 1.01E-04 5.29E-04 1.12E-03 8.15E-10 6.26E-06 2.57E-07 2.54E-07

28 9.17E-04 2.21E-04 6.52E-05 4.89E-04 7.45E-04 8.15E-10 7.53E-06 2.50E-07 2.48E-07

29 8.02E-04 2.32E-04 4.83E-05 4.64E-04 5.51E-04 5.82E-10 8.77E-06 2.43E-07 2.41E-07

30 6.99E-04 2.20E-04 4.36E-05 5.17E-04 4.14E-04 5.82E-10 9.74E-06 2.35E-07 2.34E-07

31 6.11E-04 2.16E-04 4.70E-05 6.47E-04 3.28E-04 5.82E-10 1.03E-05 2.28E-07 2.27E-07

32 5.35E-04 2.20E-04 5.78E-05 6.37E-04 3.19E-04 5.82E-10 1.04E-05 2.21E-07 2.19E-07

33 4.70E-04 2.37E-04 7.37E-05 6.26E-04 3.38E-04 5.82E-10 1.00E-05 2.14E-07 2.13E-07

34 4.11E-04 2.63E-04 9.23E-05 6.53E-04 2.83E-04 5.82E-10 8.67E-06 2.06E-07 2.05E-07

35 3.61E-04 2.99E-04 1.17E-04 7.12E-04 2.56E-04 6.98E-10 6.51E-06 2.00E-07 1.99E-07

36 3.18E-04 3.47E-04 1.45E-04 7.30E-04 2.32E-04 5.82E-10 5.89E-06 1.94E-07 1.93E-07

37 2.80E-04 3.78E-04 1.26E-04 6.74E-04 1.63E-04 8.15E-10 6.42E-06 1.88E-07 1.88E-07

38 2.47E-04 3.29E-04 8.30E-05 6.84E-04 1.18E-04 4.66E-10 7.44E-06 1.83E-07 1.82E-07

39 2.18E-04 2.63E-04 5.32E-05 7.49E-04 9.02E-05 2.33E-10 8.83E-06 1.78E-07 1.78E-07

40 1.93E-04 2.12E-04 3.35E-05 8.48E-04 7.39E-05 2.33E-10 1.04E-05 1.74E-07 1.73E-07

41 1.72E-04 1.87E-04 2.08E-05 9.79E-04 6.51E-05 0.00E+00 1.20E-05 1.71E-07 1.70E-07
B-21

SQN STSBO Early 

Plume Segment Xe Cs Ba I Te Ru Mo Ce La 

42 1.53E-04 1.82E-04 1.33E-05 1.13E-03 6.03E-05 1.16E-10 1.40E-05 1.70E-07 1.69E-07

43 1.77E-04 2.45E-04 1.13E-05 1.62E-03 7.64E-05 1.16E-10 2.29E-05 2.22E-07 2.22E-07

44 1.54E-04 2.55E-04 7.52E-06 1.58E-03 7.22E-05 0.00E+00 3.00E-05 2.16E-07 2.16E-07

45 1.35E-04 2.60E-04 5.29E-06 1.35E-03 7.10E-05 0.00E+00 3.76E-05 2.16E-07 2.16E-07

46 1.09E-04 2.29E-04 3.63E-06 8.68E-04 6.54E-05 0.00E+00 4.01E-05 1.98E-07 1.97E-07

47 8.87E-05 2.05E-04 2.64E-06 6.01E-04 6.20E-05 0.00E+00 3.99E-05 1.86E-07 1.86E-07

48 8.63E-05 2.26E-04 2.43E-06 5.55E-04 7.21E-05 0.00E+00 4.69E-05 2.10E-07 2.10E-07

49 7.56E-05 2.37E-04 2.16E-06 5.08E-04 7.74E-05 0.00E+00 5.12E-05 2.12E-07 2.11E-07

50 6.43E-05 2.50E-04 1.97E-06 4.75E-04 8.27E-05 0.00E+00 5.57E-05 2.09E-07 2.08E-07

51 5.45E-05 2.59E-04 1.87E-06 3.36E-04 8.95E-05 0.00E+00 6.12E-05 2.06E-07 2.05E-07

52 4.64E-05 2.71E-04 1.84E-06 2.08E-04 9.76E-05 0.00E+00 6.71E-05 2.03E-07 2.02E-07

53 3.93E-05 2.87E-04 1.86E-06 1.36E-04 1.07E-04 0.00E+00 7.32E-05 2.01E-07 2.00E-07

54 3.32E-05 3.08E-04 1.92E-06 9.59E-05 1.17E-04 0.00E+00 7.98E-05 1.99E-07 1.98E-07

55 2.81E-05 3.30E-04 2.04E-06 7.38E-05 1.29E-04 0.00E+00 8.62E-05 1.97E-07 1.96E-07

56 2.37E-05 3.52E-04 2.24E-06 5.83E-05 1.41E-04 0.00E+00 9.28E-05 1.94E-07 1.93E-07

57 1.99E-05 3.71E-04 2.52E-06 4.51E-05 1.53E-04 0.00E+00 9.86E-05 1.92E-07 1.91E-07

58 1.67E-05 3.86E-04 2.87E-06 3.79E-05 1.67E-04 0.00E+00 1.05E-04 1.91E-07 1.90E-07

59 1.56E-05 4.20E-04 3.43E-06 3.77E-05 1.87E-04 4.66E-10 2.36E-02 1.98E-07 1.98E-07

60 1.42E-05 3.95E-04 3.98E-06 3.56E-05 1.61E-04 5.82E-09 5.93E-02 1.73E-07 1.72E-07

61 1.16E-05 3.51E-04 4.59E-06 3.26E-05 1.54E-04 1.06E-08 2.68E-02 1.64E-07 1.64E-07

62 9.66E-06 3.41E-04 5.13E-06 3.30E-05 1.77E-04 1.28E-08 1.27E-02 1.74E-07 1.74E-07

63 8.11E-06 3.54E-04 5.73E-06 3.43E-05 2.14E-04 1.46E-08 6.54E-03 1.90E-07 1.90E-07

64 6.85E-06 3.76E-04 6.46E-06 3.59E-05 2.57E-04 1.64E-08 3.57E-03 2.11E-07 2.11E-07

65 5.84E-06 4.03E-04 7.04E-06 3.78E-05 3.04E-04 1.78E-08 2.02E-03 2.29E-07 2.29E-07

66 5.07E-06 4.30E-04 7.45E-06 3.97E-05 3.52E-04 1.86E-08 1.18E-03 2.42E-07 2.42E-07

67 4.41E-06 4.56E-04 7.62E-06 4.21E-05 4.00E-04 1.87E-08 7.13E-04 2.49E-07 2.50E-07

68 3.76E-06 4.76E-04 7.51E-06 4.40E-05 4.46E-04 1.82E-08 4.55E-04 2.50E-07 2.50E-07

69 3.46E-06 4.85E-04 7.33E-06 4.53E-05 4.92E-04 1.72E-08 3.11E-04 2.47E-07 2.48E-07

70 3.04E-06 4.56E-04 7.14E-06 4.69E-05 5.39E-04 1.59E-08 2.21E-04 2.44E-07 2.45E-07

71 2.80E-06 3.69E-04 7.10E-06 4.97E-05 5.93E-04 1.56E-08 1.52E-04 2.44E-07 2.45E-07

72 1.13E-06 1.24E-04 3.01E-06 2.18E-05 2.64E-04 6.64E-09 4.68E-05 1.03E-07 1.03E-07

 
B-22

Table B-5 EARLY parameters used in the Sequoyah LTSBO, STSBO 

Base Case, and STSBO Early scenarios. 

Variable Description LTSBO STSBO 

Base Case STSBO Early 

ACNAME Latent Cancer 

Effect 

Cancer Type 1 LEUKEMIA LEUKEMIA LEUKEMIA 

Cancer Type 2 BONE BONE BONE 

Cancer Type 3 BREAST BREAST BREAST 

Cancer Type 4 LUNG LUNG LUNG 

Cancer Type 5 THYROID THYROID THYROID 

Cancer Type 6 LIVER LIVER LIVER 

Cancer Type 7 COLON COLON COLON 

Cancer Type 8 RESIDUAL RESIDUAL RESIDUAL 

ACSUSC 

Population 

Susceptible to 

Cancer 

1.0 for all 

cancers 

1.0 for all 

cancers 

1.0 for all 

cancers 

ACTHRE 

Linear DoseResponse 

Threshold 

0 0 0 

BRRATE 

Breathing Rate 

(for all activity 

types) 

0.000266 0.000266 0.000266 

CFRISK 

Lifetime Cancer 

Fatality Risk 

Factors 

Cancer Type 1 0.0111 0.0111 0.0111 

Cancer Type 2 0.00019 0.00019 0.00019 

Cancer Type 3 0.00506 0.00506 0.00506 

Cancer Type 4 0.0198 0.0198 0.0198 

Cancer Type 5 0.000648 0.000648 0.000648 

Cancer Type 6 0.003 0.003 0.003 

Cancer Type 7 0.0208 0.0208 0.0208 

Cancer Type 8 0.0493 0.0493 0.0493 

CIRISK 

Lifetime Cancer 

Injury Risk 

Factors 

 

Cancer Type 1 0.0113 0.0113 0.0113 

Cancer Type 2 0.000271 0.000271 2.71E-04 

Cancer Type 3 0.0101 0.0101 0.0101 

Cancer Type 4 0.0208 0.0208 0.0208 

Cancer Type 5 0.00648 0.00648 0.00648 

Cancer Type 6 0.00316 0.00316 0.00316 

Cancer Type 7 0.0378 0.0378 0.0378 

Cancer Type 8 0.169 0.169 0.169 

CRIORG Critical Organ for 

EARLY Phase L-ICRP60ED L-ICRP60ED L-ICRP60ED 

CSFACT Cloudshine 

Shielding Factors 
B-23

Variable Description LTSBO STSBO 

Base Case STSBO Early 

Evacuation 

Shielding Factor 

for Cohorts 1&2 

1 1 1 

Normal Activity 

Shielding Factor 

for Cohorts 1&2 

0.31 0.31 0.31 

Sheltering 

Shielding Factor 

for Cohorts 1&2 

0.31 0.31 0.31 

Evacuation 

Shielding Factor 

for Cohorts 3-8 

1 1 1 

Normal Activity 

Shielding Factor 

for Cohorts 3-8 

0.72 0.72 0.72 

Sheltering 

Shielding Factor 

for Cohorts 3-8 

0.65 0.65 0.65 

DCF_FILE 

Name of Dose 

Conversion 

Factor File 

FGR13GyEqu

ivDCF.INP 

FGR13GyEq

uivDCF.INP 

FGR13GyEqui

vDCF.INP 

DDREFA Dose-Dependent 

Reduction Factor 

Cancer Type 1 2 2 2 

Cancer Type 2 2 2 2 

Cancer Type 3 1 1 1 

Cancer Type 4 2 2 2 

Cancer Type 5 2 2 2 

Cancer Type 6 2 2 2 

Cancer Type 7 2 2 2 

Cancer Type 8 2 2 2 

DDTHRE 

Threshold for 

Applying DoseDependent 

Reduction Factor 

0.2 0.2 0.2 

DIST_SYMB 

Symbol assigned 

to Population 

Distribution. 

See Table B5 See Table B5 See Table B-5 

DIST_LABEL 

Label assigned to 

Population 

Distribution 

See Table B5 See Table B5 See Table B-5 

DLTSHL Delay from Alarm 

Time to Shelter 

Cohort 1 1800 1800 1800 

Cohort 2 0 0 0 

Cohort 3 0 0 0 

Cohort 4 1800 1800 1800 

Cohort 5 9900 9900 9900 

Cohort 6 13500 13500 13500 

Cohort 7 10800 10800 10800 

 Cohort 8 1800 1800 1800 
B-24

Variable Description LTSBO STSBO 

Base Case STSBO Early 

DLTEVA 

Delay from 

Beginning of 

Shelter to 

Evacuation 

Cohort 1 18000 18000 18000 

Cohort 2 10800 10800 10800 

Cohort 3 16200 16200 16200 

Cohort 4 2700 2700 2700 

Cohort 5 5400 5400 5400 

Cohort 6 10800 10800 10800 

 Cohort 7 5400 5400 5400 

Cohort 8 18000 18000 18000 

DOSEFA 

Cancer DoseResponse Linear 

Factors 

1 for all 

organs 

1 for all 

organs 

1 for all 

organs 

DOSEFB 

Cancer DoseResponse 

Quadratic 

Factors 

0 for all 

organs 

0 for all 

organs 

0 for all 

organs 

DOSHOT 

Hot-Spot 

Relocation Dose 

Threshold (rem) 

5 5 5 

DOSMOD Dose-Response 

Model Flag AT AT AT 

DOSNRM 

Normal 

Relocation Dose 

Threshold (rem) 

1 1 1 

DURBEG 

Duration of 

Beginning of 

Evacuation 

Phase 

Cohort 1 4500 4500 4500 

Cohort 2 7200 7200 7200 

Cohort 3 8100 8100 8100 

Cohort 4 1800 1800 1800 

Cohort 5 9000 9000 9000 

Cohort 6 14400 14400 14400 

 Cohort 7 2700 2700 2700 

Cohort 8 0 0 0 

DURMID 

Duration of 

Middle of 

Evacuation 

Phase 

 

Cohort 1 9900 9900 9900 

Cohort 2 6300 6300 6300 

Cohort 3 5400 5400 5400 

Cohort 4 9900 9900 9900 

Cohort 5 5400 5400 5400 

Cohort 6 3600 3600 3600 

Cohort 7 2700 2700 2700 
B-25

Variable Description LTSBO STSBO 

Base Case STSBO Early 

 Cohort 8 0 0 

EANAM1 

Text Describing 

the EARLY 

Assumptions 

SQN Early 

(EP 0-10 mile 

evacuation) 

SQN Early 

(EP 0-10 

mile 

evacuation) 

SQN Early 

(EP 0-10 mile 

evacuation) 

EANAM2 

Text Describing 

the Emergency 

Response 

 

Cohort 1 0-10 Schools 0-10 Schools 0-10 Schools 

Cohort 2 

0-10 Special 

Facilities 

(Medical) 

0-10 Special 

Facilities 

(Medical) 

0-10 Special 

Facilities 

(Medical) 

Cohort 3 0-10 Transit 

Dependent 

0-10 Transit 

Dependent 

0-10 Transit 

Dependent 

Cohort 4 

0-10 Early 

General 

Population 

0-10 Early 

General 

Population 

0-10 Early 

General 

Population 

Cohort 5 

0-10 Middle 

General 

Population 

0-10 Middle 

General 

Population 

0-10 Middle 

General 

Population 

Cohort 6 

0-10 Tail 

General 

Population 

0-10 Tail 

General 

Population 

0-10 Tail 

General 

Population 

Cohort 7 10-15 

Shadow 

10-15 

Shadow 10-15 Shadow 

 Cohort 8 

Nonevacuating 

population 

(0.005 of 0-

10, 0.8 of 10-

15, all >15) 

Nonevacuating 

population 

(0.005 of 0-

10, 0.8 of 10-

15, all >15) 

Nonevacuating 

population 

(0.005 of 0-10, 

0.8 of 10-15, 

all >15) 

EFFACA LD50 for Early 

Fatality Types 

A-RED MARR 

(Sv) 5.6 5.6 5.6 

A-LUNGS (Sv) 23.5 23.5 23.5 

A-STOMACH 

(Sv) 12.1 12.1 12.1 

EFFACB 

Shape Factor for 

Early Fatality 

Types 

A-RED MARR 6.1 6.1 6.1 

A-LUNGS 9.6 9.6 9.6 

A-STOMACH 9.3 9.3 9.3 

EFFACY 

Efficacy of the KI 

Ingestion 

Threshold or 

Piecewise for 

Cohorts 1-8 

0.7 0.7 0.7 

Efficacy of KI 

Ingestion Linear 

No Threshold of 

0.0 0.0 0.0 
B-26

Variable Description LTSBO STSBO 

Base Case STSBO Early 

Cohorts 1-8 

EFFTHR Threshold Dose 

to Target Organ 

A-RED MARR 2.32 2.32 2.32 

A-LUNGS 13.6 13.6 13.6 

A-STOMACH 6.5 6.5 6.5 

EIFACA D50 For Early 

Injuries 

PRODROMAL 

VOMIT 2 2 2 

DIARRHEA 3 3 3 

PNEUMONITIS 16.6 16.6 16.6 

SKIN 

ERYTHRMA 6 6 6 

TRANSEPIDER

MAL 20 20 20 

THYROIDITIS 240 240 240 

HYPOTHYROIDI

SM 60 60 60 

EIFACB Shape Factor for 

Early Injuries 

PRODROMAL 

VOMIT 3 3 3 

DIARRHEA 2.5 2.5 2.5 

PNEUMONITIS 7.3 7.3 7.3 

SKIN 

ERYTHRMA 5 5 5 

TRANSEPIDER

MAL 5 5 5 

THYROIDITIS 2 2 2 

HYPOTHYROIDI

SM 1.3 1.3 1.3 

EINAME 

Early Injury Effect 

Names and 

Corresponding 

Organ 

 

PRODROMAL 

VOMIT A-STOMACH ASTOMACH A-STOMACH 

DIARRHEA A-STOMACH ASTOMACH A-STOMACH 

PNEUMONITIS A-LUNGS A-LUNGS A-LUNGS 

SKIN 

ERYTHRMA A-SKIN A-SKIN A-SKIN 

TRANSEPIDER

MAL A-SKIN A-SKIN A-SKIN 

THYROIDITIS A-THYROID A-THYROID A-THYROID 

HYPOTHYROIDI

SM A-THYROID A-THYROID A-THYROID 

EISUSC Susceptible 

Population 

1. for all 

health effects 

1. for all 

health 

1. for all 

health effects 
B-27

Variable Description LTSBO STSBO 

Base Case STSBO Early 

Fraction effects 

EITHRE Early Injury Dose 

Threshold 

PRODROMAL 

VOMIT 0.5 0.5 0.5 

DIARRHEA 1 1 1 

PNEUMONITIS 9.2 9.2 9.2 

SKIN 

ERYTHRMA 3 3 3 

TRANSEPIDER

MAL 10 10 10 

THYROIDITIS 40 40 40 

HYPOTHYROIDI

SM 2 2 2 

ENDAT2 

Control flag 

indicating only 

ATMOS and 

EARLY are to be 

run 

.FALSE. .FALSE. .FALSE. 

ENDEMP 

Time Duration for 

the Emergency 

Phase (day) 

7 7 7 

ESPEED Evacuation 

Speed 

Initial Evacuation 

Phase, Cohort 1, 

3, 5 (m/s) 

1.34112 1.34112 1.34112 

ESPEED 

Middle 

Evacuation 

Phase, Cohort 1, 

3, 5 

0.89408 0.89408 0.89408 

Late Evacuation 

Phase, Cohort 1, 

3, 5 

8.941 8.941 8.941 

Initial Evacuation 

Phase, Cohort 2 1.34112 1.34112 1.34112 

Middle 

Evacuation 

Phase, Cohort 2 

1.34112 1.34112 1.34112 

Late Evacuation 

Phase, Cohort 2 8.941 8.941 8.941 

Initial Evacuation 

Phase, Cohort 4 2.2352 2.2352 2.2352 

Middle 

Evacuation 

Phase, Cohort 4 

1.34112 1.34112 1.34112 

Late Evacuation 

Phase, Cohort 4 8.941 8.941 8.941 

Initial Evacuation 

Phase, Cohort 6 0.89408 0.89408 0.89408 

Middle 

Evacuation 1.34112 1.34112 1.34112 
B-28

Variable Description LTSBO STSBO 

Base Case STSBO Early 

Phase, Cohort 6 

Late Evacuation 

Phase, Cohort 6 8.9408 8.9408 8.9408 

Initial Evacuation 

Phase, Cohort 7 8.9408 8.9408 8.9408 

Middle 

Evacuation 

Phase, Cohort 7 

8.9408 8.9408 8.9408 

Late Evacuation 

Phase, Cohort 7 8.9408 8.9408 8.9408 

Initial Evacuation 

Phase, Cohort 8 4.4704E-05 4.4704E-05 4.4704E-05 

Middle 

Evacuation 

Phase, Cohort 8 

4.4704E-05 4.4704E-05 4.4704E-05 

Late Evacuation 

Phase, Cohort 8 

4.4704E-05 4.4704E-05 4.4704E-05 

ESPGRD 

Speed Multiplier 

to Account for 

Grid-Level 

Variations in 

Road Network 

Table B-6 

Table B-7 

Table B-6 

Table B-7 

Table B-6 

Table B-7 

ESPMUL 

Speed Multiplier 

Employed During 

Precipitation 

0.8 0.8 0.8 

EVATYP Evacuation Type NETWORK NETWORK NETWORK 

GSHFAC Groundshine 

Shielding Factors 

Evacuation 

Shielding Factor 

for Cohorts 1&2 

0.5 0.5 0.5 

Normal Activity 

Shielding Factor 

for Cohorts 1&2 

0.05 0.05 0.05 

Sheltering 

Shielding Factor 

for Cohorts 1&2 

0.05 0.05 0.05 

Evacuation 

Shielding Factor 

for Cohorts 3-8 

0.5 0.5 0.5 

Normal Activity 

Shielding Factor 

for Cohorts 3-8 

0.26 0.26 0.26 

Sheltering 

Shielding Factor 

for Cohorts 3-8 

0.2 0.2 0.2 

IDIREC 

Direction in 

Network 

Evacuation 

Model 

Table B-8 Table B-8 Table B-8 

IPLUME Plume Model 

Dispersion Code 

3-Wind Shift 

w/o Rotation 

3-Wind Shift 

w/o Rotation 

3-Wind Shift 

w/o Rotation 

KIMODL Model Flag for KI NOKI NOKI NOKI 
B-29

Variable Description LTSBO STSBO 

Base Case STSBO Early 

Ingestion 

LASMOV Last Ring in 

Movement Zone 19 19 19 

N_POP_DIS

T 

Number of 

population 

distributions 

entered by user 

5 5 5 

NUMACA 

Number of Latent 

Cancer Health 

Effects 

8 8 8 

NUMEFA Number of Early 

Fatality Effects 3 3 3 

NUMEIN Number of Early 

Injury Effects 7 7 7 

NUMEVA 

Outer Boundary 

of 

Evacuation/Shelt

er Region 

14 14 14 

NUMFIN Number of Fine 

Grid Subdivisions 7 7 7 

ORGFLG 

Doses to be 

Calculated for 

Specified Organ 

All TRUE for 

FGR-13 

All TRUE for 

DOSFAC2 

except for ALower LI and 

L-Liver, which 

are FALSE 

All TRUE for 

FGR-13 

All TRUE for 

DOSFAC2 

except for ALower LI and 

L-Liver, 

which are 

FALSE 

All TRUE for 

FGR-13 

All TRUE for 

DOSFAC2 

except for ALower LI and 

L-Liver, which 

are FALSE 

OVRRID 

Wind Rose 

Probability 

Override 

.FALSE. .FALSE. .FALSE. 

POP_DIST 

Population 

Distribution 

assigned to the 

evacuation grid 

Table B-6 Table B-6 Table B-6 

POPFLG Population 

Distribution Flag FILE FILE FILE 

POPFRAC 

Population 

Fraction 

Ingesting KI 

0 0 0 

PROTIN [E] 

Evacuation 

Shielding Factor 

for Cohorts 1&2 

0.98 0.98 0.98 

PROTIN [N] 

Normal Activity 

Shielding Factor 

for Cohorts 1&2 

0.33 0.33 0.33 

PROTIN [S] 

Sheltering 

Shielding Factor 

for Cohorts 1&2 

0.33 0.33 0.33 

PROTIN [E] 

Evacuation 

Shielding Factor 

for Cohorts 3-8 

0.98 0.98 0.98 
B-30

Variable Description LTSBO STSBO 

Base Case STSBO Early 

PROTIN [N] 

Normal Activity 

Shielding Factor 

for Cohorts 3-8 

0.46 0.46 0.46 

PROTIN [S] 

Sheltering 

Shielding Factor 

for Cohorts 3-8 

0.33 0.33 0.33 

REFPNT 

Reference Time 

Point (ARRIVAL 

or SCRAM) 

ALARM ALARM ALARM 

RESCON 

Emergency 

phase 

resuspension 

coefficient 

0.0001 0.0001 0.0001 

RESHAF 

Resuspension 

Concentration 

Half-Life 

182000 182000 182000 

RISCAT Risk by WeatherCategory Flag .FALSE. .FALSE. .FALSE. 

RISTHR 

Risk Threshold 

for Fatality 

Radius 

0 0 0 

SKPFAC [E] 

Evacuation 

Shielding Factor 

for Cohorts 1&2 

0.98 0.98 0.98 

SKPFAC [N] 

Normal Activity 

Shielding Factor 

for Cohorts 1&2 

0.33 0.33 0.33 

SKPFAC [S] 

Sheltering 

Shielding Factor 

for Cohorts 1&2 

0.33 0.33 0.33 

SKPFAC [E] 

Evacuation 

Shielding Factor 

for Cohorts 3-8 

0.98 0.98 0.98 

SKPFAC [N] 

Normal Activity 

Shielding Factor 

for Cohorts 3-8 

0.46 0.46 0.46 

SKPFAC [S] 

Sheltering 

Shielding Factor 

for Cohorts 3-8 

0.33 0.33 0.33 

TIMHOT 

Hot Spot 

Relocation Time 

(hr) 

36 36 36 

TIMNRM 

Normal 

Relocation Time 

(48) 

48 48 48 

TRAVEL 

POINT 

Evacuee 

Movement Option

CENTER 

POINT 

CENTER 

POINT 

CENTER 

POINT 

WTFRAC 

Weighting 

Fraction 

Applicable to this 

Scenario 

Cohort 1 0.19283 0.19283 0.19283 

Cohort 2 0.0077 0.0077 0.0077 
B-31

Variable Description LTSBO STSBO 

Base Case STSBO Early 

Cohort 3 0.0015 0.0015 0.0015 

Cohort 4 0.38972 0.38972 0.38972 

Cohort 5 0.31178 0.31178 0.31178 

Cohort 6 0.07794 0.07794 0.07794 

 Cohort 7 0.2 0.2 0.2 

 Cohort 8 0.005 0.005 0.005 

WTNAME 

Type of 

Weighting for 

Cohorts 

SUMPOP SUMPOP SUMPOP 

 
B-32

Table B-6 SUMPOP distribution labels.

DIST_SYMB DIST_LABEL 

Population 1 S 0-10 Schools 

Population 2 M 0-10 Med Nursing 

Population 3 G 0-10 General Pop 

Population 4 2 10-15 Shadow 

Population 5 3 0-1000 Nonevac 

Table B-7 SUMPOP distributions.

Population 

Cohort 1 

(0-10 

Schools) 

Cohort 2 

(0-10 

Special 

Facilities) 

Cohort 3 

(0-10 

Transit 

Dependent)

Cohort 4 

(0-10 Early 

Gen Pop) 

Cohort 5 

(0-10 

Middle Gen 

Pop) 

Cohort 6 

(0-10 Tail 

General 

Pop) 

Cohort 7 

(10-15 

Shadow) 

Cohort 8 

(Non-Evac)

Population 

1: 0-10 

Schools (S) 

0.3934 0 0.0115 0.2976 0.2381 0.0545 0 0 

Population 

2: 0-10 Med 

Nursing (M) 

0 0.342 0.0124 0.3228 0.2582 0.0646 0 0 

Population 

3: 0-10 

General 

Pop (G) 

0 0 0.0188 0.4875 0.39 0.0975 0 0.0063 

Population 

4: 10-15 

Shadow (2) 

0 0 0 0 0 0 0.2 0.8 

Population 

5: 0-1000 

Nonevac (3) 

0 0 0 0 0 0 0 1 

 
B-33

Table B-8 Grid-level evacuation speed multipliers used in the Sequoyah LTSBO, 

STSBO Base Case, and STSBO Early scenarios, cohorts 1-8. 

Compass Sector

Radial Ring 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 

1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

2 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

3 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

4 1.1 1.1 1.1 1.1 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

5 1.1 1.1 1.1 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

6 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

7 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

8 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

9 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

10 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

11 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

12 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

13 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

14 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

15 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

16 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

17 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

18 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

19 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

 
B-34

Compass Sector 

Radial Ring 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 

1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

2 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

3 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.1

5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

8 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

9 1.2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

10 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

11 1.2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

12 1.2 1 1 1 1 1 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1 1 1

13 1 1 1 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3

14 1.2 1.2 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3

15 1.2 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3

16 1.2 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3

17 1.2 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3

18 1.2 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3

19 1.2 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3

Compass Sector 

Radial 

Ring 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 

1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

2 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

3 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

4 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

5 1 1 1 1 1 1 1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

6 1 1 1 1 1 1 1 1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

7 1 1 1 1 1 1 1 1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

8 1 1 1 1 1 1 1 1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

9 1 1 1 1 1 1 1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

10 1 1 1 1 1 1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.3 1.1 1.1

11 1 1 1 1 1 1 1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

12 1 1 1 1 1 1 1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.2 1.2

13 1.3 1.3 1 1 1 1 1 1 1.1 1.1 1.1 1.1 1.2 1.2 1.2 1.2

14 1.3 1 1 1 1 1 1 1 1.1 1.1 1.1 1.1 1.2 1.2 1.2 1.2

15 1 1 1 1 1 1 1 1 1.1 1.1 1.1 1.1 1.2 1.2 1.2 1.2

16 1.3 1.3 1 1 1 1 1 1 1.1 1.1 1.1 1.2 1.2 1.2 1.2 1.2

17 1.3 1.3 1.3 1.3 1 1 1.1 1.1 1.1 1.1 1.2 1.2 1.2 1.2 1.2 1.2

18 1.3 1.3 1.3 1.3 1 1.1 1.1 1.1 1.1 1.1 1.2 1.2 1.2 1.2 1.2 1.2

19 1.3 1.3 1.3 1.3 1 1.1 1.1 1.1 1.1 1.1 1.2 1.2 1.2 1.2 1.2 1.2
B-35

Compass Sector 

Radial 

Ring 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 

1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

2 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

3 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

4 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

5 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

6 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.2

7 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.2 1.2

8 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.2 1.2 1.2

9 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.2 1.2 1.2 1.2 1.2

10 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.2 1.2 1.2 1.2 1.2

11 1.1 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

12 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

13 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

14 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

15 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

16 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

17 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

18 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

19 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

 
B-36

Table B-9 Evacuation direction parameters used in the Sequoyah LTSBO, STSBO 

Base Case, and STSBO Early scenarios. 

Compass Sector 

Radial Ring 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 

1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

2 4 4 4 4 4 4 4 1 1 1 1 2 2 2 2 2

3 4 4 4 4 4 4 4 4 1 1 1 1 1 1 1 1

4 4 4 4 4 1 1 1 1 1 1 1 1 1 1 1 1

5 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

6 2 2 1 1 1 1 1 2 1 4 4 4 4 4 4 4

7 1 1 1 1 1 1 1 4 4 4 4 1 1 1 1 1

8 2 2 2 2 2 2 1 4 4 1 1 1 1 1 1 1

9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

10 1 1 1 2 2 1 1 2 2 2 2 2 2 1 4 4

11 1 1 1 2 2 1 1 2 2 2 2 1 4 4 4 4

12 1 1 4 2 2 2 1 1 1 1 1 4 4 4 4 4

13 1 1 4 4 1 4 4 4 4 4 4 4 4 4 4 4

14 1 1 1 1 4 4 2 1 4 2 2 2 2 1 1 1

15 1 1 1 1 1 1 4 2 2 2 2 2 1 1 4 4

16 1 2 1 1 4 4 4 2 2 2 1 1 1 4 4 4

17 1 1 1 1 1 4 4 2 2 1 1 1 1 1 1 1

18 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

19 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

 
B-37

Compass Sector 

Radial Ring 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 

1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

5 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1

6 1 2 2 2 2 2 2 2 1 1 4 4 4 4 4 4

7 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

8 1 1 1 1 1 1 1 1 1 4 4 4 4 4 4 4

9 1 1 2 2 2 2 2 2 2 2 1 4 4 4 4 4

10 4 4 4 4 4 1 2 2 1 4 4 4 4 4 4 4

11 4 2 2 2 2 1 2 2 2 2 2 2 2 2 1 1

12 4 2 2 2 2 1 1 1 1 1 1 1 2 2 1 1

13 4 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1

14 4 4 2 2 1 1 1 1 1 4 4 2 2 1 1 1

15 4 2 2 1 1 4 4 4 2 2 1 1 1 4 4 4

16 4 2 2 1 1 4 1 4 4 2 2 1 1 1 1 1

17 1 4 2 1 1 4 2 1 1 4 2 2 1 1 1 1

18 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

19 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

 
B-38

Compass Sector 

Radial Ring 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 

1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

4 1 1 1 1 1 2 2 2 2 2 2 1 1 1 1 1

5 1 1 1 1 1 1 1 1 1 1 1 1 1 4 4 4

6 4 4 4 1 1 1 1 1 1 1 1 1 4 4 4 2

7 1 1 1 1 1 4 4 4 2 2 2 1 1 2 2 2

8 4 4 4 4 4 4 4 4 2 2 2 1 1 1 1 1

9 4 4 4 4 4 4 4 2 2 2 2 1 1 1 1 1

10 4 4 4 4 4 4 1 1 1 1 1 1 1 1 1 4

11 1 4 4 4 4 4 4 1 1 1 4 4 4 4 4 4

12 1 1 1 1 1 1 4 1 1 1 1 1 4 1 1 1

13 1 1 1 1 1 1 4 4 1 1 1 1 1 2 2 1

14 1 2 2 1 1 1 1 1 1 2 1 1 1 4 1 1

15 2 2 1 1 1 1 1 1 1 1 4 1 1 1 1 1

16 1 1 1 4 1 1 2 1 1 1 4 2 2 1 1 1

17 4 4 4 1 1 4 2 2 1 1 1 1 1 1 4 4

18 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

19 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

 
B-39

Compass Sector 

Radial Ring 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

2 2 2 2 2 2 1 1 1 1 1 1 4 4 4 4 4

3 1 1 1 1 1 1 1 1 1 1 1 1 4 4 4 4

4 1 1 1 1 4 1 2 1 1 4 4 2 2 1 4 4

5 4 4 4 4 4 4 2 1 1 1 4 4 4 4 4 4

6 2 1 4 4 4 4 1 2 2 1 4 4 3 3 3 2

7 2 1 1 1 1 1 1 1 4 4 4 1 1 4 2 2

8 1 1 1 1 1 1 1 1 1 1 4 4 4 1 2 2

9 1 1 4 4 4 4 4 4 4 4 4 2 2 1 1 1

10 4 4 4 4 4 4 4 4 4 4 4 2 2 2 1 1

11 1 1 1 1 1 1 1 1 1 2 1 1 2 2 1 1

12 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2

13 1 1 1 1 1 2 2 1 1 4 4 4 4 2 2 1

14 1 4 4 4 2 2 1 1 4 4 4 4 2 2 2 2

15 1 1 4 4 2 2 1 1 4 1 1 1 4 2 2 2

16 1 1 1 1 4 2 1 1 1 4 4 2 2 1 1 1

17 2 2 1 1 1 4 2 1 1 4 4 2 2 1 1 1

18 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

19 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

 
B-40

Table B-10 CHRONC input parameters used in the Sequoyah LTSBO, STSBO Base 

Case, and STSBO Early scenarios. 

Variable Description LTSBO 

STSBO 

Base 

Case 

STSBO 

Early 

CHNAME CHRONC Problem 

Identification SQN SQN SQN 

CDFRM 

Farmland 

Decontamination 

Cost ($/ha) 

 

Level 1 48800 48800 48800 

Level 2 65300 65300 65300 

 Level 3 83100 83100 83100 

CDNFRM 

Non-farmland 

Decontamination 

Cost ($/person) 

Level 1 7600 7600 7600 

Level 2 24300 24300 24300 

Level 3 43200 43200 43200 

CRTOCR Critical Organ for 

CHRONC Phase 

LICRP60ED 

LICRP60E

D 

LICRP60ED 

DPRATE Property Depreciation 

Rate 0.2 0.2 0.2 

DLBCST 

Hourly Labor Cost for 

Decontamination 

Worker ($/man-yr) 

76000 76000 76000 

DPFRCT Farm Production 

Dairy Fraction N/A N/A N/A 

DSCRLT Long-Term Phase 

Dose Criterion 0.5 0.5 0.5 

DSCRTI Intermediate-Phase 

Dose Criterion (rem) 2 2 2 

DSRATE Societal Discount 

Rate for Property 0.06 0.06 0.06 

DSRFCT Decontamination 

Factors 

Level 1 3 3 3 

Level 2 5 5 5 

Level 3 15 15 15 

DUR_INTPH

AS 

Duration of the 

Intermediate Phase 

(yr) 

1 1 1 

EVACST 

Emergency Phase 

Cost of 

Evacuation/Relocatio

n ($/person-d) 

179 179 179 

EXPTIM Maximum Exposure 

Time (yr) 50 50 50 

FDPATH 

COMIDA2 vs. 

MACCS Food Model 

Switch 

NEW NEW NEW 
B-41

Variable Description LTSBO 

STSBO 

Base 

Case 

STSBO 

Early 

FRACLD Fraction of Area that 

is Land N/A N/A N/A 

FRCFRM Fraction of Area 

Used for Farming N/A N/A N/A 

FRFDL 

Fraction of 

Decontamination 

Cost for Labor 

 

Level 1 0.35 0.35 0.35 

Level 2 0.35 0.35 0.35 

 Level 3 0.35 0.35 0.35 

FRFIM 

Farm Wealth 

Improvements 

Fraction 

0.2 in 0.2 in 0.2 in 

FRMPRD Average Annual 

Farm Production N/A N/A N/A 

FRNFIM 

Nonfarm Wealth 

Improvements 

Fraction 

0.72 0.72 0.72 

FRNFDL Nonfarm Labor Cost 

Fraction 

Level 1 0.35 0.35 0.35 

Level 2 0.35 0.35 0.35 

 Level 3 0.35 0.35 0.35 

GWCOEF 

Long-Term 

Groundshine 

Coefficients 

Term 1 0.5 0.5 0.5 

Term 2 0.5 0.5 0.5 

KSWTCH Diagnostic Output 

Option Switch 0 0 0 

LBRRATE Long-Term Breathing 

Rate 0.000266 0.000266 0.000266 

LGSHFAC 

Long-Term 

Groundshine 

Protection Factor 

0.3 0.3 0.3 

LPROTIN Long-Term Inhalation 

Protection Factor 0.46 0.46 0.46 

LVLDEC 

Number of 

Decontamination 

Levels 

3 3 3 

NGWTRM 

Number of Terms in 

Groundshine 

Weathering Equation 

2 2 2 

NRWTRM 

Number of Terms in 

Resuspension 

Weathering Equation 

3 3 3 

POPCST 

Per Capita Cost of 

Long-Term 

Relocation ($/person) 

7750 7750 7750 

RELCST Relocation Cost per 

Person-Day 143 143 143 
B-42

Variable Description LTSBO 

STSBO 

Base 

Case 

STSBO 

Early 

RWCOEF 

Long-Term 

Resuspension Factor 

Coefficients 

Term 1 0.00001 0.00001 0.00001 

Term 2 0.0000001 0.000000

1 0.0000001 

Term 3 0.00000000

1 

0.000000

001 

0.0000000

01 

TFWKF 

Fraction Farmland 

Worker Time in 

Contaminated Zone 

Level 1 0.15 0.15 0.15 

Level 2 0.15 0.15 0.15

Level 3 0.15 0.15 0.15

TFWKNF 

Fraction Nonfarmland Worker 

Time in 

Contaminated Zone 

 

Level 1 0.15 0.15 0.15 

Level 2 0.15 0.15 0.15

 Level 3 0.15 0.15 0.15

TGWHLF 

Groundshine 

Weathering HalfLives 

Term 1 16000000 16000000 16000000 

Term 2 2.8E9 2.8E9 2.8E9 

TIMDEC Decontamination 

Times 

Level 1 364 364 364 

Level 2 364 364 364 

 Level 3 364 364 364 

TMPACT Time Action Period 

Ends (yr) 1 1 1 

TRWHLF 

Resuspension 

Weathering HalfLives 

 

Term 1 16000000 16000000 16000000 

Term 2 160000000 16000000

0 160000000 

Term 3 160000000

0 

16000000

00 

160000000

0 

VALWF Value of Farm Wealth 11287 11287 11287 

VALWNF Value of Nonfarm 

Wealth 

3.42714E+0

5 

3.42714E

+05 

3.42714E+

05 

 
B-43

Table B-11 Sequoyah radionuclide inventory 

Radionuclide Isotopic Group Core Activity (Ci) 

Kr-85 1 7.85E+05 

Kr-85m 1 2.94E+07 

Kr-87 1 5.91E+07 

Kr-88 1 7.85E+07 

Xe-133 1 1.95E+08 

Xe-135 1 4.82E+07 

Xe-135m 1 3.92E+07 

Cs-134 2 9.88E+06 

Cs-136 2 2.69E+06 

Cs-137 2 7.36E+06 

Rb-86 2 1.42E+05 

Rb-88 2 7.94E+07 

Ba-139 3 1.77E+08 

Ba-140 3 1.71E+08 

Sr-89 3 1.05E+08 

Sr-90 3 5.88E+06 

Sr-91 3 1.35E+08 

Sr-92 3 1.42E+08 

Ba-137m 3 7.01E+06 

I-131 4 9.06E+07 

I-132 4 1.36E+08 

I-133 4 1.93E+08 

I-134 4 2.21E+08 

I-135 4 1.84E+08 

Te-127 5 6.56E+06 

Te-127m 5 5.17E+05 

Te-129 5 2.13E+07 

Te-129m 5 3.11E+06 

Te-131m 5 1.53E+07 

Te-132 5 1.31E+08 

Te-131 5 7.95E+07 

Rh-105 6 6.46E+07 

Ru-103 6 1.21E+08 

Ru-105 6 7.44E+07 

Ru-106 6 2.74E+07 

Rh-103m 6 1.20E+08 

Rh-106 6 3.31E+07 

Nb-95 7 1.48E+08 

Co-58 7 8.85E+02 

Co-60 7 8.09E+04 

Mo-99 7 1.77E+08 

Tc-99m 7 1.56E+08 

Nb-97 7 1.66E+08 

Nb-97m 7 1.57E+08 

Ce-141 8 1.59E+08 
B-44

Radionuclide Isotopic Group Core Activity (Ci) 

Ce-143 8 1.56E+08 

Ce-144 8 9.57E+07 

Np-239 8 1.29E+09 

Pu-238 8 1.52E+05 

Pu-239 8 1.78E+04 

Pu-240 8 2.12E+04 

Pu-241 8 6.05E+06 

Zr-95 8 1.59E+08 

Zr-97 8 1.65E+08 

Am-241 9 6.17E+03 

Cm-242 9 1.74E+06 

Cm-244 9 1.28E+05 

La-140 9 1.75E+08 

La-141 9 1.62E+08 

La-142 9 1.57E+08 

Nd-147 9 6.27E+07 

Pr-143 9 1.56E+08 

Y-90 9 6.06E+06 

Y-91 9 1.32E+08 

Y-92 9 1.43E+08 

Y-93 9 1.57E+08 

Y-91m 9 7.94E+07 

Pr-144 9 9.67E+07 

Pr-144m 9 1.57E+06 
C-1

APPENDIX C ROADWAY CAPACITY ANALYSIS 
C-2

 
C-3

ROADWAY CAPACITY ANALYSIS 

C.1 Approach 

Evacuation routes are dispersed throughout the emergency planning zone (EPZ) such that 

addressing the infrastructure issues by quadrant provided a reasonable approach for 

determining evacuation time estimates (ETEs) which account for damage to bridges. All 

bridges within the EPZ were assumed to fail due to the earthquake. The roadway network was 

reviewed using aerial mapping to determine available routes to exit the EPZ, and the capacities 

of these routes were calculated. Topography of the area was reviewed using United States 

Geological Service mapping to identify low lying areas to also assess the potential for roadway 

impacts due to flooding should the upstream Watts Bar dam fail. From the ETE study, the 

number of vehicles exiting different areas of the EPZ was identified. Using the number of 

vehicles and capacity at the exit points of the EPZ, an estimate of the evacuation time was 

calculated. A description of the bridge locations, alternate routes, and evacuating vehicles is 

provided below. 

Data from the 2013 Sequoyah ETE 

SQN ETE 5 highest volume roadways 

SR 58 SB – 12,046 vehicles 

SR 319 SB – 10,976 vehicles 

US27/SR29 NB – 7,709 vehicles 

I-75 SB – 5,317 vehicles 

SR 58 NB – 3,351 vehicles 

Table 3-4 of the ETE study provides the following for the winter day scenario 

Total vehicles = 50,894 

Population = 127,883 (includes general public, schools, and transients) 

C.2 Major Roadways within the EPZ 

SR 58 

Bridge over Harrison Bay 

• Includes Harrison Bay State Park. 

• Alternate routes are Ooltewah Georgetown Road and SR 312 south to exit EPZ. 

• This bridge location is very near the location in the ETE report where an analysis of the 

road closure was performed. The results of the ETE road closure analysis increased the 

90 percent ETE by 1.5 hours and the 100 percent ETE by 3.5 hours. Similar results 

would be expected if this bridge is assumed to fail. The increase in ETE only affects the 

eastern side of the EPZ because the roadway network on the western side of the 

Chickamauga Lake is independent of the east side. 

I-75 

Due to limited access and bridge failures below, I-75 is assumed to be unavailable within the 

bounds of the EPZ. 

Bridge on I-75 near Green Gap. 

• I-75 passes under roadway crossing. Assume failure causes I-75 to close. 
C-4

• Alternate routes include frontage road south to exit EPZ. 

Interchange on I-75 just north of Ooltewah near south limit of EPZ. 

• I-75 passes over roadway crossing. Assume failure. 

• Affected area is mostly EPZ pass through traffic. There is only one interchange from 

SR64 onto I-75 and this is located along the eastern EPZ boundary near Cleveland. 

• Alternate routes include frontage road south to exit EPZ. 

Bridge on I-75 crossing Bancroft Road. 

• I-75 passes over Bancroft Road. Assume bridge failure. 

• Alternate routes include Bancroft Road south to exit EPZ. 

• Population north of I-75 would need to travel north along residential roads to SR 312 to 

exit the EPZ. 

SR 312 

No bridges or crossings assumed to fail 

SR 60 

No bridges or crossings assumed to fail 

SR 306/Freewill Road NW 

No bridges or crossings assumed to fail 

Ooltewah Georgetown Road 

No bridges or crossings assumed to fail 

West side of Chickamauga Lake 

SR 319 

Bridge at Dockside 

• Bridge over water inlet. Assume failure. 

• Alternate routes exist north and south of bridge. There should be a minor effect of less 

than 10 minutes extra travel to exit this area. 

Bridge over railroad near Hixon on EPZ boundary. 

• Roadway bridge crosses over railroad, assume failure. 

• Considerable area affected. 

• Alternate route is residential street a quarter mile north of bridge. 

• Reduced roadway capacity on alternate route, but additional travel distance is minimal. 

US 27 

Due to multiple assumed bridge failures below, assume entire length of road is unusable. 

• Interchange at SR 153 

• Interchange at Thrasher Pike 

• Interchange at Harrison Lane 

• Interchange at SR 153 
C-5

• Bridge over Daisy Dallas Road 

• Bridges over CR 2158 

• Bridge over Card Road 

• Interchange with SR 319 

• Bridge between SR 319 and Dayton Pike crossing 

• Bridge crossing Dayton Pike 

• Interchange with SR 111 

• Bridge over Possum Creek 

Using the ETE Table 3-4 data for vehicles by evacuation sector, an estimate of the vehicle 

evacuation routing was performed. The EPZ is split by Lake Chickamauga such that evacuation 

routing on each side of the lake is independent of the other side. For the analysis, the EPZ was 

divided into approximate quadrants with the lake serving as the north/south centerline. Next, 

the routing was reviewed with respect to the postulated damaged infrastructure. The following 

vehicles were then assigned to each quadrant. From data provided in the ETE study, vehicles 

were summed by EPZ quadrant as follows: 

• NW quadrant: 7,100 

• NE quadrant: 2,100 

• SE quadrant: 22,500 (Approximately 1,300 vehicles from Sector C-5 and part of C-3 

evacuate separately and 21,200 vehicles exit the along the south boundary of the EPZ) 

• SW quadrant: 19,200 (this section includes the Middle Valley and Hixon area bounded 

by the lake, SR 27, the north edge of Sector A-1, and A-3, and the south edge of the 

EPZ. 

Total: 50,900 vehicles 

C.3 Capacity Analysis 

MACCS allows input of speeds for each cohort and then allows the analyst to adjust the speed 

at the grid element level. Therefore, one section of the EPZ was used as the control section 

where the speed multiplier is 1.0 and sectors that have higher or lower speeds were then 

adjusted from the base speed. 

 

C.3.1 Southwest Half of the EPZ 

The large population in the SW EPZ quadrant controls the evacuation time for the west side of 

Lake Chickamauga. SR 27 bisects this quadrant and the west side of the EPZ. The ETE study 

converts population to vehicle count and shows approximately 16,800 evacuating vehicles for 

the area between SR 27 and Lake Chickamauga in the area of Middle Valley and Hixon. The 

EPZ is divided into alpha numeric sectors. Sectors A-1 and A-3 would typically evacuate to the 

north using SR 27; however, the bridges along SR 27 are assumed to fail in this area 

eliminating the use of the evacuation route and forcing evacuation in the southerly direction. 

Approximately 2,400 vehicles from these sectors could be affected. Thus, the total number of 

vehicles evacuating south from this area is 19,200. The area north and west of SR 27 is lightly 

populated and the exit routes have not been impacted. 

The ETE study provides estimates for the SSW area in Table 6-3 when wind is from the NNENE. The SSW area includes Middle Valley and Hixon. From the ETE study, the evacuation time 
C-6

for this area is approximately 3.75 hours for 90 percent of the public and 5 hours for 100 percent 

of the public. After the seismic event, the most likely roadways accessible to evacuees from the 

area of Middle Valley and Hixon include Hamill Road, Old Hixon Pike, Grubb Road, Lake Resort 

Drive, and Dayton Boulevard. These roadways do not have bridge crossings in this area and 

would provide alternate routes out of this section of the EPZ. 

Each of these routes is a single lane roadway with a capacity of 1,200 pc/h, which is developed 

from a base capacity of 1,600 pc/h with Highway Capacity Manual (HCM) adjustments for peak 

hour (0.88), heavy vehicles (0.86) and terrain (0.99). It is assumed this includes 10 percent 

heavy vehicles (trucks, buses, trailers, etc.). With approximately 19,400 vehicles, the exit time 

would be about 3.25 hours (rounded to quarter hour) using the exit capacity of these 5 locations. 

However, additional time is needed for evacuees to identify their current route is blocked, travel 

the rural backroads to find an unobstructed alternative route, and travel in congested traffic until 

they exit the EPZ. An additional 2 hours is assumed to address these actions, and to reflect 

additional capacity reduction which occurs in oversaturated roadways. The mobilization time of 

2.5 hours is added, which is in the upper range of the Figure 5-1 mobilization curve in the ETE 

study, but is reasonable to reflect delay due to the seismic event. The combined time of 7.75 

hours is used as the ETE for this section of the SW quadrant of the EPZ. This time would begin 

after the sirens for General Emergency. 

Dayton Pike, also called Dayton Boulevard near the south limit of the EPZ, is a 2 lane (one lane 

each direction) roadway that runs parallel to US 27 throughout the EPZ. Near the north and 

south limits of the EPZ, Dayton Pike crosses US 27 and would be assumed unavailable at these 

two points. Evacuees from the southwest quadrant of the EPZ would need to travel local routes 

for about the last mile to exit the EPZ to the south. Evacuees from the northwest quadrant of 

the EPZ would need to travel local routes for about the last 3 miles to exit the EPZ to the north. 

There is a low lying area around Dallas Bay where a review of the topography shows the 

elevations in this area could be susceptible to flooding making additional local roadways 

impassible. The larger roadways in the area were already removed from the analysis due to 

assumed bridge failures. 

C.3.2 Northwest Quadrant of the EPZ 

CR 1126 and Poe Road are identified as evacuation routes and do not have bridges, thus are 

assumed unobstructed. US 27 is assumed unavailable due to bridge failures, but there are atgrade crossings that will allow evacuees to travel unobstructed out of the EPZ. Because there 

is a relatively small population in this area and there are multiple evacuation options, even with 

the loss of US 27, the travel speeds for this quadrant are assumed to be 20 percent faster than 

the speeds established for the SW EPZ quadrant. This is consistent with the ETE study which 

shows under normal conditions, the ETE for 100 percent of the population of this area is 20 

percent shorter than the SW quadrant. This increase was applied through the use of speed 

multipliers in MACCS. There is a low lying area near the north end of Soddy Daisy along the 

stretch of Dayton Pike that passes near the Lake Chickamauga inlet. Review of the topography 

shows the elevations in this area are relatively flat and may be subject to flooding making 

additional local roadways inaccessible. 

 

C.3.3 Eastern Half of the EPZ 

The ETE study identifies a large population that will exit the SE quadrant of the EPZ, and this 

controls the evacuation time for the east side of the Lake Chickamauga. SR 58 runs 

north/south through the eastern side of the EPZ. Bridges on SR 58 and Snow Hill Road cross a 

lake finger and are assumed to fail. These failures will necessitate rerouting of traffic to southern 

EPZ exit points. Multiple bridges on US 74/I-75 are assumed to fail, and this prevents vehicles 
C-7

from evacuating east, with the exception of Sector C-5 and part of Sector C-3 (in the ETE) 

which are located east of US 74/I-75. Hickory Valley Road would remain accessible, but serves 

a very small population and in this analysis is not assumed to contribute to the evacuation. 

There are approximately 21,200 vehicles that would be rerouted to evacuate south out of the 

EPZ on the portion of the two-lane (each direction) SR 58 that is open, and on single lane (each 

direction) roadways of South Hickory Valley Road and Webb Road. This provides 4 lanes of 

outbound traffic from the SE quadrant of the EPZ. There is a low lying portion of Harrison Bay 

Road, immediately north of the bridge on SR 58, that appears likely to flood if the upstream dam 

were to fail. However, the traffic near the potentially flooded area is already assumed to travel 

north and would not be impacted if flooding occurred in this location. Snow Hill Road has a few 

low lying areas, but investigation found these appear elevated sufficiently such that they would 

not likely flood. Using the same roadway capacity data above, it would take about 4.5 hours to 

evacuate 21,200 vehicles. An additional 2 hours is added for evacuees to identify their current 

route is blocked, travel the rural backroads to find an unobstructed alternate route, and travel in 

congested traffic until they exit the EPZ. Mobilization time of 2.5 hours is also added as 

described earlier. The total evacuation time for the SE quadrant is 9 hours and begins after the 

sirens for General Emergency. 

 

Consistent with guidance in NUREG/CR-7002, the ETE study includes a roadway impact 

analysis. The study evaluated SR 58 as though it was completely closed, and evacuees were 

re-routed to accessible roadways. SR 58 bisects the EPZ on the east side of the lake and is the 

most heavily travelled evacuation route within the EPZ. The ETEs increased from 3.5 hours to 

about 5 hours for the 90 percent evacuation and from 5 hours to about 8.5 hours for the 100 

percent evacuation. This evacuation time compares well with the capacity analysis above. The 

capacity analysis also assumes bridge fail on I-75; but, because I-75 has limited access points 

within the EPZ, the SR 58 roadway closure generally represents the effects of the seismic event 

on evacuation of the eastern half of the EPZ. 

C.4 MACCS Input 

MACCS allows the input of speeds for the beginning, middle, and late phases of the evacuation. 

Speeds are provided for each cohort. The speeds identified above were used throughout the 

EPZ and were adjusted through the use of speed multipliers. Following the approach described 

above, speeds are established for the eastern half of the EPZ and were adjusted at the grid 

level using MACCS speed multipliers. Figure C-1 shows assumed bridge failures and low lying 

areas within the EPZ. 
C-8

Figure C-1 Assumed bridge failures and low lying areas within the EPZ