*U.S.NRC

United States Nuclear Regulatory Commission

Protecting People and the Environment

NUREG-1829

Vol. 1

Estimating Loss-of-Coolant

Accident (LOCA)

Frequencies Through the

Elicitation Process

Main Report

Office of Nuclear Regulatory Research
AVAILABILITY OF REFERENCE MATERIALS

IN NRC PUBLICATIONS

NRC Reference Material

As of November 1999, you may electronically access

NUREG-sedes publications and other NRC records at

NRC's Public Electronic Reading Room at

http://www.nrc.gov/readin-g-rm.html.

Publicly released records include, to name a few,

NUREG-series publications; Federal Register notices;

applicant, licensee, and vendor documents and

correspondence; NRC correspondence and internal

memoranda; bulletins and information notices;

inspection and investigative reports; licensee event

reports; and Commission papers and their

attachments.

NRC publications in the NUREG series, NRC

regulations, and Title 10, Energy, in the Code of

Federal Regulations may also be purchased from one

of these two sources.

1. The Superintendent of Documents

U.S. Government Printing Office

Mail Stop SSOP

Washington, DC 20402-0001

Internet: bookstore.gpo.gov

Telephone: 202-512-1800

Fax: 202-512-2250

2. The National Technical Information Service

Springfield, VA 22161-0002

www.ntis.gov

1-800-553-6847 or, locally, 703-605-6000

A single copy of each NRC draft report for comment is

available free, to the extent of supply, upon written

request as follows:

Address: U.S. Nuclear Regulatory Commission

Office of Administration

Mail, Distribution and Messenger Team

Washington, DC 20555-0001

E-mail: DISTRIBUTIONtanrc..ov

Facsimile: 301-415-2289

Some publications in the NUREG series that are

posted at NRC's Web site address

htti):/Iwww.nrc.aov/readina-rm/doc-collections/nureas

Non-NRC Reference Material

Documents available from public and special technical

libraries include all open literature items, such as

books, journal articles, and transactions, Federal

Register notices, Federal and State legislation, and

congressional reports. Such documents as theses,

dissertations, foreign reports and translations, and

non-NRC conference proceedings may be purchased

from their sponsoring organization.

Copies of industry codes and standards used in a

substantive manner in the NRC regulatory process are

maintained atThe NRC Technical Library

Two White Flint North

11545 Rockville Pike

Rockville, MD 20852-2738

These standards are available in the library for

reference use by the public. Codes and standards are

usually copyrighted and may be purchased from the

originating organization or, if they are American

National Standards, fromAmerican National Standards Institute

11 West 42nd Street

New York, NY 10036-8002

www.ansi.org

212-642-4900

Legally binding regulatory requirements are stated only

in laws; NRC regulations; licenses, including technical

specifications; or orders, not in

NUREG-series publications. The views expressed in

contractor-prepared publications in this series are not

necessarily those of the NRC.

The NUREG series comprises (1) technical and

administrative reports and books prepared by the staff

(NUREG-XXXX) or agency contractors

(NUREG/CR-XXXX), (2) proceedings of conferences

(NUREG/CP-XXXX), (3) reports resulting from

intemational agreements (NUREG/IA-XXXX), (4)

brochures (NUREG/BR-XXXX), and (5) compilations

of legal decisions and orders of the Commission and

Atomic and Safety Licensing Boards and of Directors'

decisions under Section 2.206 of NRC's regulations

(NUREG-0750).

are updated periodically and may differ from the last

printed version. Although references to material found

on a Web site bear the date the material was

accessed, the material available on the date cited may

subsequently be removed from the site.
*U.S.NRC

United States Nuclear Regulatory Commission

Protecting People and the Environment

NUREG-1829

Vol. 1

Estimating Loss-of-Coolant

Accident (LOCA)

Frequencies Through the

Elicitation Process

Main Report

Manuscript Completed: March 2008

Date Published: April 2008

Prepared by

R. Tregoning (NRC), L. Abramson (NRC)

P. Scott (Battelle-Columbus)

A. Csontos, NRC Project Manager

Office of Nuclear Regulatory Research

ABSTRACT

The NRC is establishing a risk-informed revision of the design-basis pipe break size requirements in 10

CFR 50.46, Appendix K to Part 50, and GDC 35 which requires estimates of LOCA frequencies as a

function of break size. Separate BWR and PWR piping and non-piping passive system LOCA frequency

estimates were developed as a function of effective break size and operating time through the end of the

plant license-renewal period. The estimates were based on an expert elicitation process which

consolidated operating experience and insights from probabilistic fracture mechanics studies with

knowledge of plant design, operation, and material performance. The elicitation required each member of

an expert panel to qualitatively and quantitatively assess important LOCA contributing factors and

quantify their uncertainty. The quantitative responses were combined to develop BWR and PWR total

LOCA frequency estimates for each contributing panelist. The distributions for the six LOCA size

categories and three time periods evaluated are represented by four parameters (mean, median, 5" and

9 5th percentiles). Finally, the individual estimates were aggregated to obtain group estimates, along with

measures of panel diversity.

There is general qualitative agreement among the panelists about important technical issues and LOCA

contributing factors, but the individual quantitative estimates are much more variable. Sensitivity studies

were conducted to examine the effects on the estimated parameters of distribution shape, correlation

structure, panelist overconfidence, panel diversity measure, and aggregation method. The group estimates

are most sensitive to the method used to aggregate the individual estimates. Geometric-mean aggregation

produces frequency estimates that approximate the medians of the panelists' estimates and also are

generally consistent with both operating experience and prior LOCA frequency estimates, except where

increases are supported by specific material aging-related concerns. However, arithmetic-mean and

mixture-distribution aggregation are alternative methods that lead to significantly higher mean and 95 h

percentile group estimates. Because the results are sensitive to the aggregation method, a particular set of

LOCA frequency estimates is not generically recommended for all risk-informed applications.

iii

FOREWORD

Estimated frequencies of loss-of-coolant accidents (LOCAs; i.e., pipe ruptures as a function of break size)

are used in a variety of regulatory applications, including probabilistic risk assessment (PRA) of nuclear

power plants. Currently, the U.S. Nuclear Regulatory Commission (NRC) is using such information

to establish a risk-informed alternative to the emergency core cooling system (ECCS) requirements

in Title 10, Section 50.46, of the Code of Federal Regulations (10 CFR 50.46). Current requirements

consider pipe breaks in the reactor coolant pressure boundary, up to and including breaks equivalent in size

to the double-ended rupture of the largest pipe in the reactor coolant system. One aspect of this

risk-informing activity is to evaluate the technical adequacy of redefining the design-basis break size

(the largest pipe break to which 10 CFR 50.46 applies) to a smaller size that is consistent with updated

estimates of pipe break frequencies.

To provide the technical basis for a risk-informed definition of the design-basis break size, this study

developed LOCA frequency estimates using an expert elicitation process. This process consolidated

operating experience and insights from probabilistic fracture mechanics studies with knowledge

of plant design, operation, and material performance. Expert elicitation is a well-recognized technique

for quantifying phenomenological knowledge when modeling approaches or data are insufficient.

The results from the expert elicitation provide LOCA frequency estimates for piping and non-piping

passive systems, as a function of effective break size and operating time through the end of the plant licenserenewal period, for both boiling- and pressurized-water reactors (BWRs and PWRs, respectively). The

panelists generally agreed on the important technical issues and LOCA-contributing factors. However, as

expected, the panelists' estimates exhibit both significant uncertainty and diversity. The uncertainty is reflected

in the estimated parameters (mean, median, 5th and 9 5

rh percentiles) of the individual LOCA frequency

distributions, and the diversity is captured by the confidence bounds on the group estimates. In addition,

this study considered the sensitivity of the results to various analysis approaches. The results are most

sensitive to the method used to aggregate the individual panelists' estimates to obtain group estimates. In this

study, geometric-mean aggregation produces group frequency estimates that approximate the medians of the

panelists' estimates and are also generally consistent with both operating experience and prior LOCA

frequency estimates except where increases are supported by specific material aging-related concems.

However, arithmetic-mean and mixture-distribution aggregation are alternative methods that lead to

significantly higher mean and 9 5th percentile group estimates.

Because the alternative aggregation methods can lead to significantly different results, a particular set of

LOCA frequency estimates is not recommended for all risk-informed applications. The purposes and

context of the application must be considered when determining the appropriateness of any set of

elicitation results. In particular, during the selection of the BWR and PWR transition break sizes for the

proposed 10 CFR50.46a rulemaking, the NRC staff considered the totality of the results from the

sensitivity studies, rather than only the summary frequency estimates from this study. The NRC anticipates

that a similar approach will be used in selecting appropriate replacement frequencies for the estimates provided

in NUREG/CR-5750, "Rates of Initiating Events at U.S. Nuclear Power Plants: 1987 - 1995," and for other

applications that require frequencies for break sizes other than those in NUREG/CR-5750.

Jennifer L. Uhle, Director

Division of Engineering

Office of Nuclear Regulatory Research

U.S. Nuclear Regulatory Commission

TABLE OF CONTENTS

ABSTRACT ......................................................................................................... .....................

F O R E W O R D ................................................................................................................................... v

EXECUTIV E SUM M ARY ............................................................................................................ xv

ACKN OW LEDGM ENTS ............................................................................................................ xxv

NOM ENCLATURE ................................................................................................................... xxvii

1. BACKGROUND ...........................................................................................................-........ 1-

1.1 M otivation ........................ ......................................................................................... -1

1.2 Previous Approaches for Estim ating LOCA Frequencies .................................................. 1-2

1.3 M odeling M itigation Effects .............................................................................................. 1-8

1.4 Other LOCA Initiating Events ........................................................................................... 1-9

1.5 Expert Elicitation .............................................................................................................. 1-10

1.6 R e fe re n c e s ......................................................................................................................... 1 -Il

2. OBJECTIV E AND SCOPE ................................................................................................... 2-1

2 .1 R efe re n c e s ............................................................ ............................................................. 2 -3

3. ELICITATION APPROACH ............................................................................................ 3-1

3 .1 P ilo t E lic itatio n ................................................................................................................... 3 - 1

3 .2 P an el S e le ctio n ................................................................................................................... 3 -4

3.3 Elicitation Training .................................................................................................... .. 3-5

3.4 Technical Issue Formulation .............................................................................................. 3-8

3.5 Base Case Developm ent ................................................................................................... 3-17

3.6 Elicitation Background Inform ation ................................................................................. 3-25

3.7 Flow Rate Correlation ...................................................................................................... 3-26

3.8 Elicitation Question Development ................................................................................... 3-27

3.9 Individual Elicitations ...................................................................................................... 3-31

3.10 Final Elicitation Responses ........................................................................................... 3-32

3.11 Review and Reporting .................................................................................................... 3-34

3 .12 R e fe ren ce s ...................................................................................................................... 3 -3 5

4. BASE CASE RESULTS .................................................................................................. 4-1

4.1 Piping Base Case Through-W all Cracking Frequencies .................................................... 4-1

4.2 Piping Base Case LOCA Frequencies ............................................................................... 4-2

4.3 Piping Base Case Sensitivity Studies ........................................ ..................................... 4-12

4.4 Non-Piping Base Case Results ........................................................................................ 4-25

4 .5 R efe re n c e s .............................................................................................. .......................... 4 -3 1

vii
5. ANALYSIS OF ELICITATION RESPONSES .............................................................. 5-I

5.1 A nalysis Fram ew ork ................................................................................. 5-I

5 .2 E licitatio n R esp o n ses ......................................................................................................... 5 -4

5 .3 In d ivid u al E stim ates .......................................................................................................... 5 -6

5.4 Group Estimates and Confidence Intervals ...................................................................... 5-14

5.5 Baseline LOCA Frequency Estimates .............................................................................. 5-16

5 .6 S en sitiv ity A n aly ses ........................................................................................................ 5-18

5 .7 R e fe re n c e s ...................................................................................................... ....... .......... 5 -2 6

6. QUALITATIVE RESULTS AND DISCUSSION ............................................................ 6-1

6 .1 B ase C ase E v alu ation s ....................................................................................................... 6 -1

6 .2 S afety C u lture E ffects ........................................................................................................ 6-2

6.3 P assive System Failure Insights ......................................................................................... 6-5

6 .4 R eferen ce s ........................................................................................................................ 6 -13

7. Q U A N T IT A T IV E R E SU LT S .............................................................................................. 7-1

7 .1 S afety C u ltu re ..................................................................................................................... 7 -2

7.2 Total BW R and PWR LOCA Frequencies (Baseline Estimates) ....................................... 7-5

7.3 Comparison of Piping with Non-Piping Contributions to Total

L O C A F req u en cies ........................................................................................................... 7 -8

7.4 Effect of Plant Operating Time on Total LOCA Frequencies .......................................... 7-10

7.5 Individual Uncertainty and Panel Diversity ..................................................................... 7-12

7 .6 S en sitiv ity A n a ly ses ......................................................................................................... 7 -18

7 .7 S um m ary E stim ates ......................................................................................................... 7-50

7.8 Steam Generator Tube Rupture and Small Break (Category I) LOCA Frequencies ....... 7-52

7.9 C om parison w ith Prior Estim ates ..................................................................................... 7-55

7.10 Comparison with Operating Experience Data ................................................................. 7-59

7 .1 1 R e fe re n ce s ....................................................................................................................... 7 -6 2

8. BASIS FOR FUTURE LOCA FREQUENCY UPDATES .................................................. 8-1

9. SUMMARY OF RESULTS AND CONCLUSIONS .................................................... 9-1

LIST OF APPENDICES

APPENDIX A: PANEL MEMBER QUALIFICATIONS .......................................................... A-I

APPENDIX B: MEETING MINUTES FROM GROUP PANEL MEETINGS ............ B-1

APPENDIX C: ELICITATION TRAINING EXERCISE RESULTS .................................... C-I

APPENDIX D: PIPING BASE CASE RESULTS OF BENGT LYDELL ................................. D-1

viii
APPENDIX E:

APPENDIX F:

APPENDIX G:

APPENDIX H:

APPENDIX I:

APPENDIX J:

APPENDIX K:

APPENDIX L:

PIPING BASE CASE RESULTS OF WILLIAM GALYEAN ......................... E-I

PIPING BASE CASE RESULTS OF DAVID HARRIS ................................... F-I

PIPING BASE CASE RESULTS OF VIC CHAPMAN ............................. G-l

DESCRIPTION OF NON-PIPING DATABASE ........................................ H-i

REACTOR VESSEL LOCA PROBABILITY BASE CASE ANALYSES (BWR

VESSELS AND PWR TOP HEAD NOZZLES) ........................................ 1-1

ELICITA TIO N QU ESTIO N S ........................................................................ J-1

GENERAL APPROACH AND PHILOSOPHY OF EACH PANEL

M E M B E R ............................................................................................... . . K -I

D ETA IL ED RESU LTS .................................................................................. L-1

APPENDIX M: PUBLIC COMMENT RESPONSES ......................................................... M-1

LIST OF FIGURES

Figure 1 BWR and PWR Error Factor Corrected LOCA Frequency Estimates ................ xxiii

Figure 1.I Sensitivity of Conditional Failure Probability to Failure Mode Assumption ......... 1-8

Figure 1.2 IGSCCEvents By Year .......................................... 1-9

Figure 3.1 Flowchart of the Overall Elicitation Process ......................................................... 3-2

F igure 3 .2 E licitation S tructure ............................................................................................. 3-11

Figure 4. I Leak Frequency Comparison Between Operating-Experience-Based .................... 4-2

and PFM-Based Analyses

Figure 4.2 Category 1 Piping Base Case Frequencies at 25 Years ................................. 4-7

Figure 4.3 Category 3 Piping Base Case Frequencies at 25 Years .......................................... 4-8

Figure 4.4 PWR Piping Base Case Frequencies at 25 Years of Plant Operation ................... 4-10

Figure 4.5 BWR Piping Base Case Frequencies at 25 Years of Plant Operation ........ 4-10

Figure 4.6 BWR- I LOCA Frequencies as a Function of Plant Operating Time ................... 4-13

for the Case of No ISI with Different Leak Detection Strategies

Figure 4.7 BWR-I LOCA Frequencies as a Function of Plant Operating Time ................... 4-14

for the Case of ISI with 90% POD with Different Leak Detection Strategies

Figure 4.8 Probability of Detection (POD) Curve Used by Vic Chapman ............................ 4-16

in ISI Sensitivity Analysis

Figure 4.9 Graphical Results from Vic Chapman's ISI Sensitivity Analysis ........................ 4-16

Figure 4.10 Through-W all Residual Axial Stress Distribution ................................................ 4-18

Used in Weld Overlay Analyses

Figure 4. 11 Effect of Weld Overlay Repairs on the Cumulative Category 1 ..................... 4-18

LOCA Frequencies for [2-inch Diameter BWR Recirculation System Piping

Figure 4.12 Pre and Post-Mitigation Recirculation LOCA Frequencies ................................. 4-19

ix
Figure 4. 13 PRAISE Analysis Showing Effect of Normal Operating .......... ....... 4-21

Stress on Various Size Precursor Leak and LOCA

Frequencies for a Single Weld Joint in 12-inch Diameter

Section of Recirculation System

Figure 4.14 Effect of a Hydro Test on the Cumulative LOCA Probabilities ........................... 4-23

for the Hot Leg with a SSE Load Included

Figure 4.15 Effect of a SSE Load on the Cumulative LOCA Probabilities ............................. 4-23

for the Hot Leg Without a Hydro Test

Figure 4.16 Effect of Material Properties (Fracture Toughness) on the Cumulative ............... 4-25

LOCA Probabilities for the PWR-1 Base Case

Figure 4.17 Steam Generator Tube Leaks as a Function of Event Date .................................. 4-26

Figure 4.18 PWR CRDM LOCA Frequencies at 25, 40, and 60 years .................................... 4-30

Figure 4.19 BWR Combined RPV LOCA Frequencies at 25, 40, and 60 years ..................... 4-30

Figure 5.1 Flowchart of the Analysis Fram ework ................................................................... 5-3

Figure 5.2 Flow chart of Required C alculations ....................................................................... 5-7

Figure 7.1 Effect of Industry Safety Culture on Category I LOCAs .................................. 7-3

Figure 7.2 Effect of Regulatory Safety Culture on Category 1 LOCAs ..................... 7-3

Figure 7.3 Effect of Industry Safety Culture on Category 4 LOCAs ...................................... 7-4

Figure 7.4 Total BW R and PW R LOCA Frequencies ............................................................ 7-6

(Means and 95t Percentile Values) as a Function of LOCA

Category at 25 Years of Plant Operations

Figure 7.5 Total BW R and PW R LOCA Frequencies ............................................................. 7-7

(Means and 9 5 th Percentile Values) as a Function of the Threshold

Break Diameter at 25 Years of Plant Operations

Figure 7.6 BWR Piping and Non-Piping LOCA Frequencies (Current-day Estimates) .......... 7-8

Figure 7.7 PWR Piping and Non-Piping LOCA Frequencies (Current-day Estimates) .......... 7-9

Figure 7.8 BWR and PWR Mean Total LOCA Frequencies at 25 and 60 Years .................. 7-12

Figure 7.9 Individual Estim ates of B W R ............................................................................... 7-13

Category I LOCA Frequency Percentiles

Figure 7.10 Individual Estim ates of BW R Category 3 ............................................................ 7-14

LOCA Frequency Percentiles

Figure 7.11 Individual Estim ates of PW R Category I ............................................................. 7-14

LOCA Frequency Percentiles

Figure 7.12 Individual Estim ates of PW R Category 6 ............................................................. 7-15

LOCA Frequency Percentiles

Figure 7.13 95% Confidence Intervals for Total BWR LOCA Frequencies .......................... 7-17

at 25 Years of Plant Operations

x
Figure 7.14 95% Confidence Intervals for Total PWR LOCA Frequencies ............................ 7-18

at 25 Years of Plant Operations

Figure 7.15 M ean Values for Selected Distributions by UEF < 100 ....................................... 7-21

(Median = 1 and LEF = 1000)

(Baseline Distribution is Truncated Split at 99.9%)

Figure 7.16 Mean Values for Selected Distributions by UEF < 1200 ..................................... 7-22

(Median = I and LEF = 1000)

(Baseline Distribution is Truncated Split at 99.9%)

Figure 7.17 Blanket I and Targeted 1 Coverage Interval Adjustments ................................... 7-24

For the Mean and 95 h Percentile

Figure 7.18 Targeted 3 Coverage Interval Adjustments .......................................................... 7-25

For the Mean, Median, and 95 h Percentile

Figure 7.19 BW R LOCA Frequencies with T3 Adjustment .................................................... 7-26

Figure 7.20 PWR LOCA Frequencies with T3 Adjustment ........................ 7-27

Figure 7.21 BWR LOCA Frequencies with Error-Factor Adjustment .................................... 7-30

Figure 7.22 PWR LOCA Frequencies with Error-Factor Adjustment ..................................... 7-31

Figure 7.23 Comparison of Baseline Results and Error-Factor Adjustment ........................... 7-31

Figure 7.24 Ratio of M onte Carlo to Baseline Results Assuming ........................................... 7-34

a Perfect Rank Correlation Structure

Figure 7.25 Ratio of PRC to IC Structure Results ................................................................... 7-35

Figure 7.26 Group Estimates of Mean PWR LOCA Frequencies at 25 Years ........................ 7-37

Figure 7.27 Group Estimates of Mean BWR LOCA Frequencies at 25 Years ........................ 7-37

Figure 7.28 Arithmetic-Mean and Geometric-Mean Group Estimates of .............................. 7-38

Mean BWR LOCA Frequencies at 25 Years

Figure 7.29 Arithmetic-Mean and Geometric-Mean Group Estimates of ............................... 7-39

Mean PWR LOCA Frequencies at 25 Years

Figure 7.30 Effect of Aggregation Point on PWR LOCA Frequency Estimates ..................... 7-43

at 25 Years of Operation

Figure 7.3 1. Comparisons of Baseline and MEF Aggregation Parameters for ......................... 7-44

PWR LOCA Frequencies at 25 Years

Figure 7.32 BWR LOCA Frequencies Determined by Mixture-Distribution .......................... 7-46

Aggregation: Current-day Estimates

Figure 7.33 PWR LOCA Frequencies Determined by Mixture-Distribution ......................... 7-46

Aggregation: Current-day Estimates

Figure 7.34 Measures of Panel Diversity for BWR LOCA Frequencies at 25 Years ......... 7-49

Figure 7.35 Measures of Panel Diversity for PWR LOCA Frequencies at 25 Years ......... 7-49

xi
Figure 7.36 PWR LOCA Frequencies with Error-Factor Adjustment (reproduced from ...... 7-51

Figure 7.21 )

Figure 7.37 PWR LOCA Frequencies with Error-Factor Adjustment ...................... 7-52

(reproduced from Figure 7.22)

Figure 7.38 Comparison of Elicitation Estimates for PW R ..................................................... 7-57

Medium Break LOCAs at 25 Years with Estimates from Prior Studies

Figure 7.39 Comparison of Elicitation Estimates for BWR .................................................... 7-58

Large Break LOCAs at 25 Years with Estimates from Prior Studies

LIST OF TABLES

Table I Total BW R and PW R LOCA Frequencies ............................................................. xxi

(After Overconfidence Adjustment using Error Factor Scheme)

Table 1.1 Comparison of Mean Results from Previous Studies ............................................ 1-6

(per Calendar Years)

T able 1.2 D EG B Frequencies for PW R s ................................................................................ 1-7

T able 3.1 LO C A Frequency Expert Panel .............................................................................. 3-5

T able 3.2 LO C A C ategory D efinitions ................................................................................... 3-9

Table 3.3 M aterial Aging Degradation M echanism s ............................................................ 3-13

Table 3.4 BW R LOCA-Sensitive Piping System s ............................................................... 3-14

Table 3.5 PW R LOCA-Sensitive Piping Systems ............................................................ 3-15

Table 3.6 Reactor Pressure Vessel (RPV) Failure Modes ................................................... 3-16

Table 3.7 Piping B ase C ase D efi nitions .............................................................................. 3-18

Table 3.8 Break Size to Leak Rate C orrelation .................................................................... 3-27

Table 4.1 Piping Base Case Frequency Results by Participant ..................... 4-6

Table 4.2 Tabular Results from Vic Chapman's ISI Sensitivity Analysis ........................... 4-17

Table 4.3 Average and Average Plus and M inus Two ......................................................... 4-27

Standard Deviations of the Adjusted Through-Wall Crack (TWC)

Frequency from the PTS Re-Evaluation Project

Table 4.4 BWR RPV LOCA Frequencies Broken Down by Major Subcomponent ............ 4-29

(Beltline and Feedwater Nozzles)

Table 4.5 PWR RPV (CRDM) LOCA Frequencies .............................................................. 4-29

Table 5.1 Elicitation Response Adjustm ent Schemes .......................................................... 5-21

Table 5.2 Sum m ary of M onte C arlo Trials ........................................................................... 5-23

Table 6.1 M ajor Piping and Non-Piping Contributors ......................................................... 6-6

to the Various Size LOCA Categories

Table 7.1 Total BW R and PW R LOCA Frequencies ............................................................. 7-6

xii
Table 7.2 Ratio of BW R and PW R Non-Piping ..................................................................... 7-9

Contribution to Piping Contribution

Table 7.3 Sensitivity A nalysis Sum m ary M atrix .................................................................. 7-19

Table 7.4 Overconfidence Adjustment Schemes .................................................................. 7-23

Table 7.5 Targeted (T3) Overconfidence Adjustment .......................................................... 7-27

Compared to Unadjusted Results (Baseline)

Table 7.6 Geometric M eans of the Panelists' Error Factors ................................................. 7-28

Current-day Estimates

Table 7.7 Total BW R and PW R LOCA Frequencies .......................................................... 7-29

(After Overconfidence Adjustment using Error-Factor Scheme)

Table 7.8 Current-Day Total BWR and PWR LOCA Frequencies ..................................... 7-29

and Upper Confidence Bounds (After Overconfidence Adjustment

using Error-Factor Scheme)

Table 7.9 Ratio of Adjusted Error Factor to Baseline Results .............................................. 7-30

Table 7. 10 Sum m ary of M onte C arlo Trials ........................................................................... 7-33

Table 7.11 Total BW R and PW R LOCA Frequencies ......................................................... 7-40

(Using Arithmetic-Mean Aggregation

Table 7.12 Ratio of AM to GM (Baseline) Group Estimates ................................................. 7-40

Table 7.13 Total BW R and PW R LOCA Frequencies .......................................................... 7-41

(Using Arithmetic-Mean Aggregation with Error-Factor

Overconfidence Adjustment)

Table 7.14 Current-day Total BWR and PWR LOCA Frequencies ....................................... 7-45

(Using Mixture Distribution for Aggregation)

Table 7.15 Ratios of M ixture-Distribution to Geometric-M ean ............................................. 7-47

Aggregated Results (not adjusted for overconfidence)

Table 7.16 Current-day Total BWR and PWR LOCA Frequencies ....................................... 7-48

(Using Mixture-Distribution Aggregation with Error-Factor

Overconfidence Adjustment)

Table 7.17 Total BW R and PW R LOCA Frequencies ........................................................... 7-51

(After Overconfidence Adjustment using Error-Factor Scheme)

(reproduced from Table 7.7

Table 7.18 PWR Steam Generator Tube Rupture Frequencies (Geometric Mean Value ...... 7-53

After Overconfidence Adjustment)

Table 7.19 Total PWR LOCA Frequencies without SGTR Contributions ............................. 7-55

(LOCA Categories 3 - 6 Reproduced from Table 7.17)

Table 7.20 Comparison of Current Elicitation and Selected Prior Mean Estimates ............... 7-56

xiii

EXECUTIVE SUMMARY

The emergency core cooling system (ECCS) requirements are contained in 10 CFR 50.46, Appendix K to

Part 50, and GDC 35. Specifically, ECCS design, reliability, and operating requirements exist to ensure

that the system can successfully mitigate postulated loss-of-coolant-accidents (LOCAs). Consideration of

an instantaneous break with a flow rate equivalent to a double-ended guillotine break (DEGB) of the

largest primary piping system in the plant generally provides the limiting condition in the required 10

CFR Part 50, Appendix K analysis. However, the DEGB is widely recognized as an extremely unlikely

event. Therefore, the staff is establishing a risk-informed revision of the design-basis break size

requirements for operating commercial nuclear power plants.

A central consideration in selecting a risk-informed design basis break size is an understanding of the

LOCA frequency as a function of break size. The most recent NRC-sponsored study of pipe break failure

frequencies is contained in NUREG/CR-5750. Unfortunately, these estimates are not sufficient for design

basis break size selection because they do not address all current passive-system degradation concerns

and they do not discriminate among breaks having effective diameters greater than 6 inch. There have

been two approaches traditionally used to assess LOCA frequencies and their relationship to pipe size: (i)

estimates based on statistical analysis of operating experience and (ii) probabilistic fracture mechanics

(PFM) analysis of specific postulated failure mechanisms. Neither approach is particularly suited to

evaluate LOCA event frequencies due to the rareness of these events and the modeling complexity. In

this study, LOCA frequency estimates have been calculated using an expert elicitation process to

consolidate operating experience and insights from PFM studies with knowledge of plant design,

operation, and material performance. This process is well-recognized for quantifying phenomenological

knowledge when data or modeling approaches are insufficient.

The principal objective of this study was to develop separate boiling water reactor (BWR) and pressurized

water reactor (PWR) piping and non-piping passive system LOCA frequency estimates as a function of

effective break size at three distinct time periods: current-day (25 years fleet average), end-of-plantlicense (40 years fleet average), and end-of-plant-license-renewal (60 years fleet average). These

estimates are based on the responses from an expert panel and one aim of this study was to obtain

estimates that represent a type of group consensus. Additionally, another objective was-to reflect both the

uncertainty in each panelist's estimates as well as the diversity among the individual estimates.

The elicitation focused on developing generic, or average, estimates for the commercial fleet and the

uncertainty bounds on these generic estimates rather than bounding values associated with one or two

plants. This approach is consistent with prior studies that did not consider plant-specific differences in

developing LOCA frequencies for use in probabilistic risk assessment (PRA) modeling. Consequently,

the elicitation panelists were instructed to consider broad differences among plants related to important

variables (i.e., plant system, material, geometry, degradation mechanism, loading,

mitigation/maintenance) in determining both the generic LOCA frequencies and especially the estimated

uncertainty bounds. It is the broad differences in these important variables that contribute most to passive

system failure and there is generally sufficient commonality among plants to make such a generic

assessment valuable.

The elicitation was solely focused on determining LOCA frequencies that initiate by unisolable primary

system side failures that can be exacerbated by material degradation with age. Therefore, active system

xv
failures (e.g., stuck open valve, pump seals, interfacing system LOCAs) and consequential primary

pressure boundary failures due to either secondary side failures or failures of other plant structures (e.g.,

crane drops) were not considered. Active system frequency contributions should be combined with the

passive system LOCA frequencies to estimate the total system risk. The effects of safety culture on

primary side failure (i.e., LOCA) frequencies were also considered.

This study developed LOCA frequency estimates consistent with historical small break (SB), medium

break (MB), and large break (LB) flow rate definitions. Additionally, three larger LOCA categories were

defined within the classical LB LOCA regime to examine trends with increasing break size, up to and

including, a DEGB of the largest piping system in the plant. Contrary to earlier studies, the six LOCA

categories are defined in terms of cumulative thresholds rather than break intervals because this definition

was more conducive to the elicitation structure. However, the differences between the interval and

cumulative estimates are typically much smaller than the estimates' uncertainties. It is therefore

reconmmended that the cumulative threshold estimates from any set of tabular results be used (e.g., Table

1) if interval-defined LOCA frequencies are desired.

Because the LOCA frequency estimates were intended to be both generic and consistent with historical

internal-event PRAs, the elicitation primarily considered normal plant operational cycles and loading

histories. The loads include representative constant stresses (e.g. pressure, thermal, residual) and

expected transient stresses (e.g. thermal striping, heat-up/cool-down, pressure transients) that occur over

the license-renewal period. The elicitation implicitly considered all modes of operation based on the

loading or operational experience associated with each piping system or non-piping component. Rare

event loading from seismic, severe water hammer, and other sources was also not considered in this

generic evaluation because of their strong dependency on plant-specific factors. However, a separate

research study was conducted to assess the potential impact of seismic loading on the break frequency

versus break size relationship. As part of that study, both unflawed and flawed seismic piping

contributions were considered. The results of that seismic LOCA analysis are summarized in Section 7.2.

Several important assumptions were made to guide the elicitation process. One such assumption is that

plant construction and operation comply with all applicable codes and standards required by the

regulations and the technical specifications. The specific impact on the LOCA frequencies of

purposefully violating these requirements was not considered. However, it was assumed that regulatory

oversight policies and procedures will continue to be used to identify and mitigate risk associated with

plants having deficient safety practices. While deviations from these requirements do represent some

percentage of the events included in the passive-system failure data, extrapolation of this data implicitly

assumes that similar future deviations will continue to occur with similar frequency.

Another important assumption is that current regulatory oversight practices will continue to evaluate

aging management and mitigation strategies in order to reasonably assure that future plant operation and

maintenance has equivalent or decreased risk. A related assumption inherent in this elicitation is that all

future plant operating characteristics will be essentially consistent with past operating practice. The

effects of operating profile changes were not considered because of the large uncertainty surrounding

possible operational changes and the potentially wide-ranging ramifications of significant changes on the

underlying LOCA frequencies.

The expert elicitation process employed in this study is an adaptation of the formal expert judgment

processes used to evaluate reactor risk (NUREG-1 150), to develop seismic hazard curves (NUREG/CR6372), and to assess the performance of radioactive waste repositories (NUREG/CR-541 t). The process

consisted of a number of steps. To begin, the project staff identified many of the issues to be evaluated

through a pilot elicitation. The panel members for this pilot elicitation were all NRC staff. A group of

twelve panel members was then selected for the formal elicitation. The staff gathered background

xvi
material and prepared an initial formulation of the technical issues which was provided to the panel. At

its initial meeting, the panel discussed the issues and, using the staff formulation as a starting point,

developed a final formulation for the elicitation structure. This structure included the decomposition of

the complex technical issues which impact LOCA frequencies into fundamental elements so that these

important contributing factors could be more readily assessed. Piping and non-piping base cases were

also defined for use in anchoring the quantitative elicitation responses. The base cases represent a set of

well-defined conditions which could cause a LOCA. A subset of the panel was created to develop

quantitative LOCA frequencies estimates associated with the base case conditions. At this initial meeting,

the panel was also trained in subjective elicitation of numerical values through exercises and discussion of

potential biases.

After this initial meeting, the staff prepared a draft elicitation questionnaire and iterated with the panel to

develop the final questionnaire. The panelists quantifying the base case conditions also developed their

initial estimates. A second meeting was then held with the entire panel to review the base case results,

review the elicitation questions, and finalize the formulation of remaining technical issues. At their home

institutions, the individual panel members performed analyses and computations to develop answers to

the elicitation questionnaire.

The elicitation questionnaire required panelists to assess the following technical areas: the base case

evaluation effort, utility and regulatory safety culture effects on LOCA frequencies, piping system LOCA

frequencies, and non-piping system LOCA frequencies. The utility and safety culture questions required

the panelists to compare future safety culture with the existing culture and predict the effect on LOCA

frequencies. These effects were considered separately from passive system degradation because the

panelist judged safety culture and degradation to be independent. The base case evaluation required the

panelists to assess the accuracy and uncertainty in the base case analyses, and to also choose a particular

base case approach for anchoring their elicitation responses. The piping and non-piping LOCA frequency

questions required each panelist to first identify important LOCA contributing factors (i.e., piping

systems, materials, degradation mechanisms, etc.) and select appropriate base case conditions for

comparison. The panelists were then required to provide the relative ratios between their important

contributing factors and the base case conditions based on their knowledge of passive system component

failure. Each relative comparison required mid value, upper bound, and lower bound values. The mid

value is defined such that, in the panelist's judgment, there is a 50% chance that the unknown true answer

lies above the mid value. The upper and lower bounds are defined such that there is a 5% chance that the

true answer lies above the upper bound or below the lower bound, respectively. Each panelist was also

required to provide their qualitative rationale supporting their quantitative values.

A facilitation team consisting of individuals knowledgeable about the technical issues (substantive

members), a staff member with extensive experience conducting expert elicitations (normative member)

and two recorders met separately with each panel member in day-long individual elicitation sessions. At

these sessions, each panel member provided answers to the elicitation questionnaire along with their

supporting technical rationales. The panelists were asked to self-select, based on their expertise, the

questions that they addressed. Consequently, several panelists only provided responses for either BWR

or PWR plants. After the elicitation sessions, the panel members returned to their home institutions

where they refined their responses based on feedback obtain during their session. Upon receipt of the

updated responses, the project staff compiled the panel's responses and developed preliminary estimates

of the LOCA frequencies. Along with the rationales, these preliminary estimates were presented to the

panel at a wrap-up meeting. Panel members were invited to fill in gaps in their questionnaire responses

and, if desired, to modify any of their responses based on group discussion of important technical issues

xvii
considered during the individual elicitations'. Final individual estimates of the LOCA frequencies were

then calculated and provided to the panel members for final review and quality assurance.

The qualitative insights provided by the panel are reasonably consistent. Panelists identified several

advantages and disadvantages with determining the base case LOCA frequencies through operatingexperience assessment or PFM analyses. However, there was a general consensus that operating

experience provides the best basis for evaluating current-day, small-break LOCA estimates. Hence, many

panelists used the operating-experience base cases to anchor their elicitation responses. The panel

members also generally expressed the opinions that the future safety culture will not differ dramatically

from the current culture, and that the utility and regulatory safety cultures are highly correlated. Many

panelists do believe that safety culture can significantly affect LOCA frequencies at specific plants, but

there is also an expectation that regulatory actions using existing enforcement measures will diminish

both the possibility and impact of deficient safety culture at particular plants. However because it was

thought that these plant-specific issues do not affect the generic averages, no specific adjustment to the

LOCA frequency estimates was applied to explicitly account for safety culture effects. This decision was

endorsed by the elicitation panelists.

There were several technical insights that were consistently identified. Many participants believe that the

number of precursor events (e.g., cracks and leaks) is generally a good barometer of the LOCA

susceptibility for the associated degradation mechanism. Welds are almost universally recognized as

likely failure locations because they can have relatively high residual stress, are preferentially-attacked by

many degradation mechanisms, and are most likely to have preexisting fabrication defects. Most

panelists also agreed that a complete break of a smaller pipe, or non-piping component, is generally more

likely than an equivalent size opening in a larger pipe, or component, because of the increased severity of

fabrication or service cracking. Therefore, the biggest frequency contributors for each LOCA size tend to

be systems having the smallest pipes, or component, which can lead to that size LOCA. The exception to

this general rule is the BWR recirculation system, which is important at all LOCA sizes due to IGSCC.

Many panelists thought that aging may have the greatest effect on intermediate diameter (i.e., 6 to 14-inch

nominal diameter) piping systems due to the large number of components within this size range and the

fact that this piping generally receives less attention than the larger diameter piping and is harder to

replace than the more degradation-prone smaller diameter piping.

The participants generally identified thermal fatigue, stress corrosion cracking (SCC), flow accelerated

corrosion (FAC), and mechanical fatigue as the degradation mechanisms that most significantly

contribute to LOCA frequencies in BWR plants. Generally, the most important BWR degradation

mechanism is intergranular SCC (IGSCC), although the panelist's recognize that mitigation has greatly

reduced the susceptibility of BWR plants to this mechanism over the past 20 years. With the exception of

FAC, similar degradation mechanisms and concerns were also deemed to be important in PWR plants.

Specifically, primary water SCC (PWSCC) is a principal concern. Many panelists believe that PWSCC

will be mitigated in PWR plants over the next 15 years, but that effective mitigation has yet to be

developed and implemented.

The panelists generally agreed on the important technical issues and LOCA-contributing factors.

However, the individual quantitative responses are much more uncertain and there are relatively large

differences among the panelists' responses. This is to be expected given the underlying scientific

uncertainty. The analysis of these responses was structured to account for the uncertainty in the

Each panelist's quantitative elicitation responses can be found through the "Electronic Reading Room" link on the NRC's

public website (http://www.nrc.,ov/) using the Agencywide Documents Access and Management System (ADAMS). The

document is found in ADAMS using the following accession number: ML080560005.

xviii
individual estimates and the diversity among the panelists. The quantitative responses were analyzed

separately for each panel member to develop individual BWR and PWR total LOCA frequency estimates.

A unified analysis format was developed to ensure consistency in processing the panelists' inputs. The

panelists' mid-value, upper bound and lower bound responses were assumed to represent the median, 9 5th,

and 5 th percentiles, respectively, of their subjective uncertainty distributions for each elicitation response.

The analysis structure was based on the assumption that all the responses correspond to percentiles of

lognormal distributions. These distributions were then combined using a lognormal framework. The

final outputs for each panelist are estimates for the means, medians and 5 th and 9 5th percentiles of the total

BWR and PWR LOCA frequencies. The panelists' estimates were then aggregated to obtain group

LOCA frequency estimates, along with measures of panel diversity.

The individual and group estimates for the means, medians, 5"h and 95 th percentiles of the LOCA

frequency distributions were calculated using the following principal assumptions and choices:

(i) The mid-value, upper bound, and lower bound supplied by each panelist for each elicitation

question were assumed to correspond to the median, 9 5th percentile and 5th percentile,

respectively, of a split lognormal distribution, with the mean calculated assuming that the

upper tail is truncated at the 99.9th percentile.

(ii) Only those panelists whose uncertainty ranges were relatively small were adjusted using an

error-factor adjustment scheme to account for possible overconfidence (Section 7.6.2.2).

(iii) Split lognormal distributions were summed by assuming perfect rank correlation among the

individual terms.

(iv) The individual estimates are the total LOCA frequency parameters (i.e., mean, median, 5 "h

percentile, and 9 5 th percentile) determined for each panelist.

(v) The group estimates of the total LOCA frequency parameters were determined using the

geometric means of the individual estimates.

(vi) Panel diversity was characterized with two-sided 95% confidence intervals based on an

assumed lognormal model for the individual estimates.

In the report, these six assumptions and choices define what are termed the summary estimates. The

report also calculates and discusses baseline estimates. These baseline estimates are calculated using all

the above assumptions, except no overconfidence adjustment (as described in (ii) above) is applied.

Because it is well-established that experts tend to be overconfident, the overconfidence adjustment is

deemed to result in improved LOCA frequency estimates

The resultant individual and group summary estimates are consistent with the elicitation objectives and

structure and are reasonably representative of the panelists' quantitative judgments. In particular, they are

not dominated by extreme results, either on the high or low end, and the geometric means of the

individual estimates approximate the medians of these estimates. The median is often used to represent

group opinion in elicitations, especially when the individual estimates differ by several orders of

magnitude, as they do in this study.

The LOCA frequency summary estimates for the current-day (25 years) and end-of-plant-license (40

years) periods are provided in Table 1 for both BWR and PWR plant types. The aggregated group

estimates for the median, mean, 5 th and 9 5 "h percentiles are presented. Frequency estimates are not

expected to change dramatically over the next 15 years for any size LOCA, or even over the next 35 years

for BWR LOCA Categories 1- 5 and PWR LOCA Categories I - 2 (see results in Appendix L). While

order of magnitude increases in BWR LOCA Category 6 and PWR LOCA Categories 3 - 6 over the next

35 years are expected, these increases are largely due to uncertainty about the future and the concern that

new degradation mechanisms could arise in the operating fleet. However, while aging will continue, the

panelists' consensus is that mitigation procedures are in place, or will be implemented in a timely manner,

xix
to alleviate significant increases in future LOCA frequencies for existing degradation mechanisms.

Because of the predicted stability in these estimates over the near-term, the current-day (25 year) results

can be used to represent the LOCA frequencies over the next 15 years of fleet operation.

The current-day median, mean, and 95 h percentile estimates are graphically presented in Figure 1. The

95% confidence intervals calculated for these parameters are also illustrated in this figure. The LOCA

frequencies as a function of threshold break diameter were estimated only for the six specified LOCA

categories in the elicitation. The plotted points are connected with straight lines in the figure for visual

clarity, but this should not be construed as a recommended interpolation scheme. Interpolation of

frequencies between category sizes can be done at the user's discretion depending on the conservatism

required by the application. Some common interpolation schemes are linear, multi-point nonlinear and

cubic spline. A step-wise or stair-step interpolation between two categories where the frequency for the

lower category size is used for all flow rates or corresponding break sizes between the two categories

provides the most conservative interpolation scheme. Note that any interpolation scheme does not reflect

the uncertainty in the interpolated frequencies.

A measure of the individual uncertainties in Table I and Figure 1 is given by the differences between the

medians and the corresponding 5h or 95th percentile estimates. Panel diversity is reflected in the

confidence bounds in Figure 1. The large widths of these confidence bounds (as much as 3 orders of

magnitude) reflects the significant diversity of the individual estimates in this study As the LOCA size

increased, the panel members generally expressed greater uncertainty in their predictions, and the

variability among individual panelists' estimates increased. This is to be expected because of the

increased extrapolation required from available passive-system failure data for larger LOCA sizes.

While it is acknowledged that operating experience-based estimates do not necessarily reflect the current

state, it is informative to compare such estimates with the elicitation frequencies for the smallest LOCAs

(Category 1). This comparison requires the least extrapolation of passive-system failure data since no

larger LOCAs have occurred. The BWR and PWR Category 1 LOCA frequencies (including the steam

generator tube rupture (SGTR) frequency for PWRs) were estimated up through December 2006. For

BWR plants, the average SB LOCA frequency based solely on the number of reported events was

estimated to be 5.5E-04 per calendar year. The mean elicitation BWR SB LOCA estimate is 6.5E-04 per

calendar year. The BWR elicitation estimate is less than 20 percent higher than the operating-experience

estimate which, given the uncertainty of these estimates, is not statistically significant.

For PWR plants, the average SB LOCA frequency estimated analogously is 3.6E-03 per calendar year.

The mean elicitation PWR SB LOCA estimate is 7.3E-03 per calendar year. The PWR elicitation

estimate is about 100 percent (or a factor of two) higher than the operating-experience-based estimate.

Additional insight into this difference can be gained by partitioning the PWR passive-system failure data

into frequencies for SGTRs and for all other passive-system SB LOCAs.

Based on reported failures, the mean SGTR LOCA frequency is 3.2E-03 per calendar year. This result is

almost identical to the current-day elicitation estimate of 3.7E-03 per calendar year. The frequency of all

other Category I PWR passive-system failures is 4.OE-04 per calendar year based on operating

experience. The corresponding elicitation estimate is 1.9E-03 per calendar year. While this value is 5

times greater than the operating-experience-based estimate, this difference is explained by the elicitation

panelists' estimation of the effect of PWSCC on small diameter component failures.

There are several prior studies that also estimated LOCA frequencies. Some care is needed when

comparing the elicitation LOCA frequency estimates with these earlier studies because the LOCA

categories are defined differently. Specifically, the current-day LOCA Category I and 2 estimates in

Table I are comparable to total system SB, MB, and LB LOCA frequencies, respectively, reported in

xx
NUREG/CR-5750. Additionally, current-day SGTR frequencies (Table 7.18) and PWR LOCA

frequencies for all other passive system failures (i.e., frequencies for breaks greater than 100 gpm (380

1pm) in Table 7.19) are comparable to NUREG/CR-5750 SGTR and PWR SB LOCA frequencies when

these failure modes are analyzed separately. The NUREG/CR-5750 LB LOCA frequency estimates are

best compared to the elicitation LOCA Category 4 frequency estimates because the pipe break sizes are

most similar.

After accounting for these differences, the elicitation LOCA frequency estimates are generally much

lower than the WASH-1400 estimates and more consistent with the NUREG/CR-5750 estimates. The SB

LOCA PWR elicitation estimates after subtracting the SGTR frequencies are approximately 3 times

greater than the NUREG/CR-5750 estimates, due to the aforementioned PWSCC concerns. However, the

total BWR and PWR SB LOCA frequency estimates are similar once the SGTR frequencies are added to

the NUREG/CR-5750 PWR results. The elicitation MB LOCA estimates are higher than the

NUREG/CR-5750 estimates by factors of approximately 4 and 20 for BWR and PWR plant types,

respectively. These increases are partly due to concerns about PWSCC of piping and non-piping (e.g.,

CRDM) components as well as general aging concerns with piping in this size range. The NUREG/CR5750 LB LOCA frequency estimates are slightly higher (less than a factor of 3) than the current elicitation

results for both PWR and BWR plants. The generally good agreement between the NUREG/CR-5750

and current elicitation estimates is somewhat surprising given the markedly different methodologies used

to arrive at these results.

Table 1 Total BWR and PWR LOCA Frequencies

(After Overconfidence Adjustment using Error-Factor Scheme)

Eff. Current-day Estimate (per cal. yr) End-of-Plant-License Estimate (per cal, yr)

LOCA Break (25 yr fleet average operation) (40 yr fleet average operation)

Plant Size Size

Type (gpm) (inch) 5 Ih Per. Median Mean 9 5th Per. 5 'h Per. Median Mean 9 5 h Per.

>100 1/2 3.3E-05 3.OE-04 6.5E-04 2.3E-03 2.8E-05 2.6E-04 6.21-04 2.2E-03

> 1,500 1 7/8 3.0E-06 5.0E-05 1.3E-04 4.8E-04 2.51-06 4.5E-05 1.2E-04 4.8E-04

>5,000 3 1A 6.OE-07 9.7E-06 2.9E-05 1.1E-04 5.4E-07 9.8E-06 3.2E-05 1I3E-04

BWR >25K 7 8.6E-08 2.2E-06 7.3E-06 2.9E-05 7.8E-08 2.3E-06 9.4E-06 3.7E-05

>100K 18 7.7E-09 2.9E-07 1.5E-06 5.9E-06 6.8E-09 3.1E-07 2.1E-06 7.9E-06

>500K 41 6.3E-12 2.9E-10 6.3E-09 1.8E-08 7.5E-12 4.OE-10 1.0E-08 2.80-08

>100 12 6.9E-04 3.9E-03 7.3E-03 2.3E-02 4.0E-04 2.6E-03 5.2E-03 1.8E-02

>1,500 I 5/8 7.6E-06 1.4E-04 6.4E-04 2.40-03 8.3E-06 1.6E-04 7.8E-04 2.9E-03

>5,000 3 2. 1 E-07 3.4E-06 1.6E-05 6. 1 E-05 4.8E-07 7.6E-06 3.6E-05 1.4E-04

PWR >25 K 7 1.4E-08 3.1E-07 1.6E-06 6.1E-06 2.8E-08 6.6E-07 3.6E-06 1.4-E05

>100K 14 4.IE-10 1.2E-08 2.0E-07 5.8E-07 1.0E-09 2.8E-08 4.8E-07 1.40-06

>500K 31 3.5E-Il 1.2E-09 2.9E-08 8.1E-08 8.7E-11 2.9E-09 7.50-08 2.1E-07

Sensitivity analyses were also conducted to examine the robustness of the quantitative results to the

analysis procedure used to develop the summary estimates. These sensitivity analyses investigated the

effect of distribution shape on the means as well as the effects of correlation structure, panelist

overconfidence, panel diversity measure, and aggregation method on the estimated parameters. The mean

calculation in the analysis procedure used a split lognormal distribution truncated at the 9 9 .9th percentile

to obtain reasonably conservative values compared with other possible choices. The correlation structure

in the analysis procedure assumed maximal correlation, which is reasonably representative of the

elicitation structure. The structure provides conservative 95" percentile estimates. However, based on

selected Monte Carlo simulations, an independent correlation structure leads to larger median and 5th

percentile estimates. The means are unaffected by the choice of the correlation structure. The analysis

procedure also used confidence intervals for the aggregated estimates to measure panel diversity. An

alternative approach used quartiles of the individual estimates, leading to comparable, but narrower

intervals.

xxi
The analysis procedure also adjusted those panelists' responses that had relatively narrow uncertainty

bands using an error-factor scheme to account for a known tendency for people, including experts, to be

overconfident when making subjective judgments. Sensitivity analyses examined the effects of other

overconfidence adjustments of the nominal subjective confidence levels supplied by the panelists. No

overconfidence adjustment was also investigated. While blanket overconfidence adjustments can result in

large, unsupportable increases in the mean and 95th percentile frequency estimates, no adjustment results

in modest decreases in these estimates. Therefore, the error-factor scheme, which adjusts only those

panelists who are most overconfident, is deemed to the most appropriate.

Finally, the largest sensitivity is associated with the method used to aggregate the individual panelist

estimates to obtain group estimates. The baseline and summary estimates were developed using

geometric-mean aggregation. In this study, the geometric-mean aggregation produces frequency

estimates that approximate the median of the panelists' estimates and therefore effectively leads to

consensus-type results. Therefore, the summary estimates in Table 1 are believed to be a reasonable

representation of the expert panel's current state of knowledge regarding LOCA frequencies.

The sensitivity analyses evaluated the effect of using alternative aggregation methods to calculate the

group estimates. Specifically, mixture-distribution and arithmetic-mean aggregation were evaluated. For

the panelists' responses in this study, these alternative aggregation methods can lead to significantly

higher mean and 9 5th percentile estimates than those obtained using geometric-mean aggregation.

Alternative LOCA frequency estimates that are higher than the summary estimates (Table I) can be

derived by using either the summary estimates with 95% confidence bounds (Tables 7.8), the arithmeticmean aggregated results (Table 7.13), or the mixture-distribution results (Table 7.16). These estimates

also incorporate the same overconfidence adjustment as the summary estimates.

Because alternative aggregation methods can lead to significantly different results, a particular set of

LOCA frequency estimates is not generically recommended for all risk-informed applications. The

purposes and context of the application must be considered when determining the appropriateness of any

set of elicitation results. In particular, during the selection of the BWR and PWR transition break sizes

for the proposed IOCFR50.46a rule making, the NRC staff considered the totality of the results from the

sensitivity studies rather than only the summary estimates from this study The NRC anticipates that a

similar approach will be used in selecting appropriate replacement frequencies for NUREG/CR-5750

estimates and for other applications where frequencies for break sizes other than those in NUREG/CR5750 are required. While the lack of clear application guidance places an additional burden on the users

of the study results, those users are in the best position to judge which study results are most appropriate

to consider for their particular applications.

xxii
BWR: Error Factor Adjustment

0

c)

>1

(-

L3

07

C) C..

0~a,

10-1

10-2

10-3

10-4

10-5

10-6

10-7

108

10-9

i0-ic

0.1 1 10

Threshold Break Diameter (in)

PWR: Error Factor Adjustment

100

10-1

10-2

10-3

>-10-4

0_

CL) 10-7

10-8

10-9

1 0-10

0.1 1 10 10

Threshold Break Diameter (in)

Figure 1 BWR and PWR Error-Factor Adjusted LOCA Frequency Estimates

xxiii

0

ACKNOWLEDGMENTS

This work was supported by the United States Nuclear Regulatory Commission (NRC) through the

Component Integrity Branch of the Division of Engineering of the Office of Nuclear Regulatory.Research

under several different contracts. Battelle Memorial Institute support was provided under NRC Contract

NRC-04-02-074 (NRC Job Code Y6538). Mr. William Galyean of Idaho National Engineering

Environmental Laboratory was supported under NRC Job Code Y6332. Dr. Cory Atwood was supported

under NRC Job Code Y6492. Dr. Alan Brothers was supported under NRC Job Code Y6604.

The authors would first like to thank the members of the expert elicitation panel whose insights formed

the basis for the results and conclusions reached in this report. The elicitation panel members also

provided valuable editorial and technical comments that have been incorporated into this summary

document. The panel members included:

0

0'

S

0

S

0

0

0

0

0

0

Mr. Bruce Bishop

Dr. Vic Chapman

Mr. Guy DeBoo

Mr. William Galyean

Dr. Karen Gott

Dr. David Harris

Mr. Bengt Lydell

Dr. Peter Riccardella

Mr. Helmut Schulz

Dr. Sampath Ranganath

Dr. Fredric Simonen

Dr. Gery Wilkowski

Westinghouse Electric Co. LLC

OJV Consultancy Limited

Exelon Nuclear

Idaho National Engineering Environmental Laboratory

Swedish Nuclear Power Inspectorate

Engineering Mechanics Technology, Inc.

ERIN® Engineering and Research, Inc.

Structural Integrity Associates, Inc.

Gesellschaft fur Reaktorsicherheit (GRS) mbh

Formerly GE Nuclear Energy/Now XGEN Engineering

Pacific Northwest National Laboratory

Engineering Mechanics Corporation of Columbus

A special debt of gratitude is expressed to the base case team members (Dr. David Harris, Mr. Bengt

Lydell, Mr. William Galyean, and Dr. Peter Riccardella) for the extra effort they provided to conduct the

base case and sensitivity analyses discussed in Sections 3 and 4 of this report. In addition, Dr. Riccardella

provided base case frequencies for a series of non-piping components and Mssrs. Galyean and Lydell

developed the non-piping precursor database. Summaries of the base case team members' analyses are

contained in Appendices D - I. Each appendix has been written by the responsible base case team

member, and these contributions are much appreciated and are vital to the technical basis of this NUREG.

Mr. Lydell and Dr. Harris also tirelessly discussed and corrected the authors' characterization of their

efforts for accuracy and clarity.

The authors would also like to recognize the facilitation team members who assisted with the

development of the technical issues and associated elicitation questions and who also participated in the

individual elicitations sessions. Along with the authors, Dr. Ken Jacquay of Casco Services, Inc. was a

principal member of the facilitation team

Other members included:

* Ms. Bennett Brady

* Mr. Frank Cherny

* Mr. Alan Kuritzky

" Mr. Arthur Salomon

NRC

NRC

NRC

NRC

The authors are also appreciative of Dr. Corwin Atwood of Statwood Consulting and Dr. Alan Brothers

of Pacific Northwest National Laboratory, who provided an external peer review of the elicitation

xxv
approach and analysis of results. They suggested a number of sensitivity studies to further validate and

clarify the conclusions reached in this report. They also provided many helpful comments that have been

used to clarify this report. Dr. Atwood, in particular, provided many valuable insights which were used to

develop and refine the techniques used to analyze the elicitation results.

Many NRC staff also provided valuable comments and contributions that were used to revise the draft

report, including Dr. Arthur Buslik, Mr. Stephen Dinsmore, Mr. Gary Hammer, Mr. Glenn Kelly, and Mr.

Arthur Salomon,. In particular, the authors would like to thank Dr. Arthur Buslik for his contribution to

Section 5.6.4.4 and Mr. Arthur Salomon for his reviewing and editing assistance. The authors are also

grateful to Dr. David Rudland of Engineering Mechanics Corporation of Columbus for assisting with

selected Monte Carlo calculations to evaluate the analysis of individual panelist responses.

Finally, the authors would like to thank Dr. Al Csontos of the NRC, who served as the program manager

over the final months of this program, and to Ms. Charlotte Matthews and Ms. Patricia Zaluski, both of

Battelle-Columbus, for their invaluable assistance in the preparation of this report.

xxvi
NOMENCLATURE

1. Symbols

a Flaw depth

a Crack depth having 50% chance of being detected

b Value of the upper bound supplied by a panelist

b' Value of the lower bound supplied by a panelist

bp(Y) pth percentile of distribution Y

C Parameter in fatigue crack growth relationship

CI, CR Coefficients used in probability of detection curve definition

D Diameter

DN Nominal pipe diameter

E Young's modulus

E(Y) Expected value of distribution Y

EF(Y) Error factor of distribution Y

f Fraction of welds inspected for cracks or flaws

f Inspection coverage/scope

g Median of the lognormal distribution U

g Group estimate of g

J J-integral fracture parameter

JD Deformation J

Jlo Plane strain J at crack initiation by ASTM813

JM Modified J

J-R J-resistance

k• path percentile of the standard lognormal distribution

kP pth percentile of the standard lognormal distribution

K Stress intensity factor

K, Mode I stress intensity factor

Kic Plane strain stress intensity factor at crack initiation

fPOD Probability of detection function

I ISI effectiveness factor

L Length

LBC Likelihood of a leak due to any degradation mechanism (base case)

LPL Likelihood of a perceptible leak

LTSL Likelihood of a technical specification leak

L5 0 Likelihood of a crack 50% through wall deep

m Median value

m Parameter in fatigue crack growth relationship

m(Y) Mean of distribution Y

n Number of panelists

nc Number of cracks or flaws

nf Number of failure events

N Normal operating stress

N Number of components

N Number of stress cycles

p Percentile

P Pressure

Pa Value of the percentile in the adjusted distribution corresponding to the original

error factor

PBc Conditional failure probability for the chosen seismic piping base case

xxvii
PFD Probability inspected welds will find existing flaw

PND Probability of not detecting a crack

PPL Conditional failure probability of a crack that has just formed a perceptible leak

PTSL Conditional failure probability of a crack leaking at the technical specification

limit

PTSL@SL-B Conditional failure probability of a crack leaking at the technical specification

limit assuming a Service Level B load

PTSL@St.D Conditional failure probability of a crack leaking at the technical specification

limit assuming a Service Level D load

P50 Conditional failure probability of a crack with a maximum depth of 50% of the

wall thickness

PL/F Conditional failure probability

p, Value of the percentile of the panelist's assumed lognormal distribution

corresponding to b'

r Error factor

rp Error factor of the panelist's adjusted lognormal distribution

R Stress ratio

Rc/F Number of non-through-wall cracks per leak event

S Sum of cyclic stress

S Seismic

s2 Sample variance

SD(Y) Standard deviation of distribution Y

t Wall thickness

T Thermal

T Tearing modulus

T Total time

V(Y) Variance of distribution Y

Z, Anchoring factor

Z, Adjustment ratios

CStrain

Probability of not detecting a crack regardless of depth

X Pipe failure frequency (through-wall crack)

Mean

v Parameter controlling slope of PND curve

0) Total frequency (cracks and leaks)

G Stress

GStandard deviation

0G Variance 0 DW Dead weight stress

0 No Normal operating stress

rte Thermal expansion stress

2. Acronyms and Initialisms

ACRS Advisory Committee on Reactor Safeguards

ADAMS Agencywide Document Access and Management System

AM Arithmetic mean

ANL Argonne National Laboratories

ASME American Society of Mechanical Engineers

ASTM American Society for Testing and Materials

BINP Battelle Integrity of Nuclear Piping

xxviii
BWR Boiling water reactor

B&W Babcock and Wilcox

CBP Conditional break probability

CD Compact disk

CDF Cumulative distribution function

CE Combustion Engineering

CFR Code of Federal Regulations

CFP Conditional failure probability

COD Crack opening displacement

CRD Control Rod Drive

CRDM Control Rod Drive Mechanism

CS Carbon steel

CV Correct value

CVCS Chemical Volume and Control System

CY Calendar year

DEGB Double ended guillotine break

DVI Direct Volume Injection

DW Dead weight

ECCS Emergency Core Cooling System

ECSCC External chloride stress corrosion cracking

EDY Effective degradation years

EF, Error factor after overconfidence adjustment

EFi Error factor of distribution Zi

EF0 Error factor before overconfidence adjustment

EF(Y) Error factor of Y

EFPY Effective full power years

Emc2 Engineering Mechanics Corporation of Columbus

EMT Engineering Mechanics Technology

EPRI Electric Power Research Institute

EQ Elicitation question

FAC Flow accelerated corrosion

FAD Failure assessment diagram

FDR Fabrication defect and repair

FS Flow sensitive

FW Feed water

GALL Generic aging lessons learned

GC General corrosion

GDC General Design Criterion

GE General Electric

GL Generic letter

GM Geometric mean

GRS Gesellschaft ftr Reactorsicherheit

HAZ Heat affected zone

HPCS High Pressure Core Spray

HPI/MU High Pressure Injection/Make-up

HWC Hydrogen water chemistry

[AEA International Atomic Energy Agency

IC Independent correlation

ICI In-core Instrumentation

ID Inside diameter

IGSCC Intergranular stress corrosion cracking

xxix
IHSI Induction heat stress improvements

INEEL Idaho National Engineering and Environmental Laboratory

IPE Individual plant evaluation

IPIRG International Piping Integrity Research Group

IQR Interquartile range

IRS Incident Reporting System

ISI In-service inspection

IS LOCA Interfacing system loss of coolant accident

LAS Low alloy steel

LB Large break

LB Lower bound

LBB Leak before break

LC Localized corrosion

LEF Lower error factor

LER Licensee Event Report

LERF Large early release frequency

LIV Loop Isolation Valve

LLNL Lawrence Livermore National Laboratory

LOCA Loss of coolant accident

LOOP Loss of offsite power

LPCS Low Pressure Core Spray

LPHSW Last pass heat sink welding

LQ Lower quartile

LTOP Low temperature over pressurization

MA Material aging

MB Medium break

MERIT Maximizing Enhancements in Risk Informed Technology program

MF Mechanical fatigue

MITI Ministry of International Trade and Industry (Japan)

MRP Materials Reliability Program

MSIP Mechanical Stress Improvement Process

MSIV Main Steam Isolation Valve

MV Mid value

NB Nickel-based weld

NDE Non-destructive examination

NG Nuclear grade

NPP Nuclear power plant

NPS Nominal pipe size

NRC Nuclear Regulatory Commission

NSSS Nuclear steam supply system

NUPEC Nuclear Power Engineering Test Center (Japan)

NWC Normal water chemistry

OBE Operational basis earthquake

OD Outside diameter

OECD Organization for Economic Co-operation and Development

OPDE OECD Piping Data Exchange

ORNL Oak Ridge National Laboratories

PFM Probabilistic fracture mechanics

PIFRAC Pipe fracture mechanics material property database

PIV Pressurizer isolation valve

PNNL Pacific Northwest National Laboratories

xxx
POD Probability of detection

PORV Power operated relief valve

PRA Probabilistic risk assessment

PRC Perfect rank correlation

PSA Probabilistic safety assessment

PSI Pre-service inspection

PSL Pressurizer Spray Line

PTS Pressurized thermal shock

PVP Pressure Vessel and Piping

PWHT Post weld heat treatment

PWR Pressurized water reactor

PWSCC Primary water stress corrosion cracking

QA Quality assurance

QC Quality control

RCIC Reactor Core Isolation Cooling

RCP Reactor Cooling Piping

RCPB Reactor Coolant Primary Boundary

RCS Reactor cooling system

RES Office of Nuclear Regulatory Research

RH Reactor head

RHR Residual Heat Removal

RI-ISI Risk informed in-service inspection

RPV Reactor Pressure Vessel

RR Rolls Royce

RS Residual stress

RV Random variable

RWCU Reactor Water Cleanup

SAM Seismic anchor motion

SB Small break

SCC Stress corrosion cracking

SCSS Sequence Coding and Search System

SG Steam generator

SGTR Steam generator tube rupture

SI Stress improvement

SIS Safety Injection System

SKI Swedish Nuclear Inspectorate

SLB Service Level B

SLC Standby Liquid Control

SLD Service Level D

SRM Staff requirements memorandum

SRV Safety relief valve

SQUIRT Seepage Quantification of Upsets in Reactor Tubes

SS Stainless steel

SSE Safe shutdown earthquake

TBS Transition break size

TF Thermal fatigue

TGM Trimmed geometric mean

TGSCC Transgranular stress corrosion cracking

TMI Three Mile Island

TS Thermal stratification

TSL Technical specification leakage

xxxi
TWC Through-wall crack

UA Unanticipated mechanism

UB Upper bound

UEF Upper error factor

UQ Upper quartile

US United States

USNRC United States Nuclear Regulatory Commission

VTC Video Teleconference

WH Water hammer

WO Weld overlay

WOR Weld overlay repair

WOG Westinghouse Owners Group

xxxii
1. BACKGROUND

1.1 Motivation

The emergency core cooling system (ECCS) requirements are contained in 10 CFR 50.46, Appendix K to

Part 50, and GDC 35. Specifically, ECCS design, reliability, and operating requirements exist to ensure

that the system can successfully mitigate postulated loss-of-coolant accidents (LOCAs). Loss-of-coolant

accidents are defined in 10 CFR 50.46(c) as hypothetical or postulated "accidents that would result from

the loss of reactor coolant, at a rate in excess of the capability of the reactor coolant makeup system,

from breaks in pipes in the reactor coolant pressure boundary up to and including a break equivalent in

size to the double-ended rupture of the largest pipe in the reactor coolant system ". In addition, 10 CFR

Part 50, Appendix K, paragraph (I)(C)(1), states that "In analyses of hypothetical loss-of-coolant

accidents, a spectrum of possible pipe breaks shall be considered. This spectrum shall include

instantaneous double-ended breaks ranging in cross-sectional area up to and including that of the largest

pipe in the primary coolant system. " The LOCA definition in t0 CFR Part 50, Appendix A, expands this

definition to consider "breaks in the reactor coolant pressure boundary, up to and including a break

equivalent in size to the double-ended rupture of the largest pipe of the reactor coolant system." The

consideration of instantaneous breaks with a flow rate equivalent to a double-ended guillotine break

(DEGB) of the largest primary system piping generally provides the limiting condition in the required 10

CFR Part 50, Appendix K analysis.

A DEGB of the largest primary system piping is widely recognized as an extremely unlikely event.

Furthermore, the consideration of this event in nuclear plant design and operation requires significant

resources that may not be commensurate with the associated risk. Focusing resources on more risksignificant events in a manner consistent with Regulatory Guide 1.174 [1.1] could potentially improve

plant safety and allow those regulatory requirements that have negligible impact on plant safety to be

relaxed. In an effort spur a risk-informed reevaluation of the regulatory requiremenits, the Commission

provided NRC staff with direction to proceed with a study of risk-informing the technical requirements of

10 CFR Part 50 [1.2]. The staff provided its plan and schedule for this work in SECY-99-264, "Proposed

Staff Plan for Risk-Informing Technical Requirements in 10 CFR Part 50," dated November 8, 1999, In

this plan, the risk-informed reevaluation of 10 CFR 50.46 requirements was prioritized. The Commission

approved this plan on February 3, 2000 [1.3]. Since that time, the staff has provided five status reports to

the Commission:SECY-00-0086, SECY-00-0198, memorandum to the Commission dated February 5,

2001, SECY-01-0133, and SECY-02-0057 [1.4]. A staff requirements memorandum (SRM) was

provided on March 3 1, 2003 [1.5] addressing the latest status report, SECY-02-0057. This SRM provides

explicit staff direction on risk-informing 10 CFR 50.46, Appendix K, and GDC 35. Most relevantly, the

SRM directed the staff to consider a risk-informed revision of the design-basis break size requirements

and certain rion-functional changes to the design basis of operating commercial nuclear power plants.

A central consideration in selecting a risk-informed design basis break size is an understanding of the

LOCA frequency as a function of break size. The most recent NRC-sponsored study of pipe break failure

frequencies is contained in NUREG/CR-5750 [1.6]. These frequencies are currently the basis for

initiating event frequencies in many plants' probabilistic risk assessment (PRA) models. However, there

are several concerns with utilizing the NUREG/CR-5750 LOCA frequency estimates for the riskinformed reevaluation of 10 CFR 50.46. First, several degradation mechanisms have emerged at plants

within the last few years which were not previously evident within the service period covered by the

NUREG/CR-5750 estimates. These include primary-water stress-corrosion cracking (PWSCC) of

pressurized water reactor (PWR) alloy 82/182 welds, PWR vessel head degradation at Davis Bessie,

hydrogen combustion failures of the type experienced at the Hamaoka and BrunsbUttel plants, and

control-rod-drive mechanism (CRDM) cracking.

1-1
Second, LOCAs can also occur from non-pipe break passive failures (e.g. CRDM nozzles, valve bodies,

vessel head degradation, and steam generator tubes). The NUREG/CR-5750 estimates only focused on

piping failures. Therefore, LOCA contributions from non-piping passive system failures must also be

included in the initiating event estimates. Third, forward-looking LOCA frequency estimates are required

to understand the future ramifications associated with possible changes to the 50.46 regulation up to the

end of the license-renewal period for approved plants. The NUREG/CR-5750 estimates are unavoidably

based solely on the historical operating performance and did not explore the future relevance of these

estimates.

Finally, the NUREG/CR-5750 estimates defined LOCA break sizes in a manner consistent with current

PRA classification using small break (SB), medium break (MB), and large break (LB) LOCA categories

that are loosely based on plant mitigation requirements for each break size. The large break category

represents the cumulative frequency of a rupture with an equivalent diameter greater than 6 inches. These

frequencies will not be representative of the failure frequency of a DEGB in the largest primary system

piping, which is approximately 30 inches in diameter for most PWR plants., Therefore, frequency

estimates of breaks larger than an equivalent 6-inch diameter pipe need to be considered when riskinforming 10 CFR 50.46.

A review of the NUREG/CR-5750 and other historical LOCA frequency estimation techniques follows.

The strengths and weaknesses of each technique are highlighted with respect to many of the concerns

enumerated above. The proper consideration of the role of mitigation in LOCA frequency estimates is

discussed as well as a description of non-historical failure modes that are important to consider. The

relative merits of expert elicitation are also outlined and are the basis for its use to estimate LOCA

frequencies in this study.

1.2 Previous Approaches for Estimating LOCA Frequencies

There have been two approaches traditionally used to assess LOCA frequencies as a function of pipe size:

estimates based on statistical analysis of operating experience and probabilistic fracture mechanics (PFM)

analysis of specific postulated failure mechanisms. Both of these approaches have unique strengths and

weaknesses for determining LOCA frequencies. In many ways, the two methods are complementary

although combined or comparative analyses utilizing both approaches are rare.

1.2.1 Operating-Experience-Based LOCA Estimates

There are several distinct advantages to using operating experience to directly calculate LOCA

frequencies. Operating experience can provide a historical evaluation of actual initiating event

frequencies. This evaluation is more realistic as the LOCA size decreases because actual ruptures of

smaller diameter Class I piping and non-piping (i.e., steam generator tubes) systems have occurred.

Additionally, operating experience can be used to identify the degradation mechanisms in piping systems

and non-piping components which have led to defect repair or material replacement under operating plant

conditions. Operating experience can also provide, in the long term, information on the effectiveness of

specific mitigation techniques and some indication of the likelihood of precursor events (e.g., cracks and

leaks) prior to failure for specific degradation mechanisms. Detailed evaluation of operating experience

can also be used to define how operating conditions, service environment, design characteristics, and

fabrication techniques can lead to degradation. All this information is valuable for estimating LOCA

frequencies.

However, some natural deficiencies make it challenging to directly calculate LOCA frequencies from

operating data. With the exception of steam generator tube rupture (SGTR), there have been few passive

failures of Class 1 piping and non-piping systems. Additionally, most of these failures that have occurred

1-2
have resulted in leak rates below the SB LOCA threshold. No MB or LB LOCAs have occurred.

Because pipe break LOCAs have been sparse, the LOCA frequencies are typically estimated using

rupture precursor events such as partial or through-wall cracking. These events, for larger class 1 piping,

have also been relatively sparse. Therefore, it is important to capture all precursor failure events for

estimating passive-system failure rates. However, it has been difficult to construct a comprehensive

database because precursor failure information comes from a variety of information sources.

Additionally, the precursor events in the operating experience must be conditionally related to LOCA

frequencies, and not all LOCAs evolve from detectable precursor events (e.g., hydrogen explosions). The

relationship between precursor and non-precursor events and associated LOCA failure frequencies is a

function of the aging mechanism. This relationship, for LOCA determination, cannot be explicitly

developed from the operating experience.

Another limitation is that past operating experience is also not necessarily representative of future system

performance. New material degradation mechanisms can systematically increase generic LOCA

frequencies with time compared with frequencies based on operating experience. Aging mechanisms can

require significant incubation time before degradation precursor or failure events are observed. However,

once the incubation period is over, degradation can occur relatively rapidly and lead to an increased failure

frequency compared with prior operating experience. The failure frequency can then remain elevated until

effective mitigation procedures are developed to reduce the failure propensity of the degradation

mechanism.

Conversely, industry-wide mitigation programs can produce systematic decreases in LOCA frequencies

compared to historical levels. However, it is difficult to judge the effectiveness of the generic mitigation

programs using operating experience until sufficient time has past so differences in pre- and postmitigation precursor event frequencies can be evaluated. The effects on the LOCA frequencies due to the

emergence of a new degradation mechanism followed by industry-wide mitigation is illustrated by the

intergranular stress corrosion cracking (IGSCC) experience in boiling water reactor (BWR) plants [1.7].

Precursor cracking events increased dramatically, starting in the late 1970s as wide-spread IGSCC was

discovered in the BWR fleet. It was then several years before effective mitigation strategies were

generically adopted. While the effectiveness of these mitigation strategies was demonstrated through other

means, several more years and inspection cycles were required to judge the mitigation effectiveness using

operating experience. Recent PWSCC [1.8] in PWR plants is another aging mechanism with similar

characteristics to IGSCC. It is anticipated that precursor cracking events will increase as PWSCC is

observed, followed by a decreased number of cracking events once effective mitigation measures have

been applied throughout the PWR fleet. Because future LOCA frequencies may not be constant, it is

crucial to consider the effects that aging mechanisms may have on historical piping precursor and failure

rates.

1.2. 1.1 WASH-1400 -The first systematic study of piping failure in the nuclear industry is contained

within WASH- 1400, which was completed in 1975 [1.9]. At that time, the total number of years of

reactor operating experience was less than 200. Therefore, the pipe LOCA frequencies were based on

failure data from other but similar industries. WASH-1400 examined data from naval nuclear reactors,

experimental reactors, United Kingdom military experience, commercial power plants, and the oil and gas

transmission pipeline industry. The most comprehensive data was obtained from the oil and gas pipeline

industry and formed the basis of the WASH- 1400 LOCA frequency estimates, after normalizing to

account for nuclear plant pipe lengths.

It was certainly realized in the WASH-1400 study that transmission pipeline materials, quality assurance,

in-service inspection (ISI), operating conditions, failure modes, and environments in non-nuclear

industries were vastly different from, and in most cases inferior to, commercial nuclear requirements.

Therefore, the WASH-1400 analysis provided a conservative estimate of the pipe break LOCA frequency.

1-3
However, this estimate was considered the best available, given the relatively scant nuclear experience

available at the time of this study. The WASH-1400 pipe break estimates formed the basis of

NUREG-1150 estimates in 1987 [1. 10]. The NUREG-l 150 estimates were calculated by performing a

Bayesian update of the WASH- 1400 estimates with the information that no additional LOCAs failures

had occurred in the interim.

1.2. 1.2 NUREG/CR-5750 - The next NRC-sponsored evaluation of pipe break LOCA frequencies was

provided by Appendix J of NUREG/CR-5750, "Rates of Initiating Events at U.S. Nuclear Power Plants:

1987 - 1995" [ 1.6]. In this study, the authors evaluated nuclear piping failures and estimated separate

frequencies for BWR and PWR reactors. For BWR plants, only U.S. experience was considered for a total

of 710 reactor calendar years. The PWR database combined U.S. and "Western-style" PWR data from

international experience for a total of 3,362 reactor calendar years. The authors utilized different methods

to calculate pipe break frequencies as a function of break size. The SB LOCA estimates were calculated

using a Bayesian update of the WASH-1400 SB LOCA estimates with the information that no SB LOCAs

occurred between the WASH-1400 and NUREG/CR- 5750, Appendix J studies. This is analogous to the

earlier update for the NUREG- 1150 estimates.

The MB and LB LOCA frequencies were derived from precursor leak frequencies determined from

operating experience. The leak frequency was multiplied by a Beliczey and Schulz conditional pipe break

probability (CBP) which is inversely related to pipe diameter [1.11]:

CBP = 2.5/DN (I.1)

In Equation 1.1, DN is the nominal pipe diameter in millimeters. The NUREG/CR-5750, Appendix J

analysis capped this expression at 0.01 for piping greater than 250 mm to impart some conservatism to

the estimates. The advantage of this approach is that there had been several reported leaks of primary

pressure boundary piping, but no failures. Therefore, operating experience could be utilized directly to

determine the pipe leak frequency and only the CBP given a precursor leak needed to be estimated. One

concern is that the Beliczey and Schulz expression was developed for fatigue crack failures only. It is not

expected to be applicable for other aging mechanisms. A bigger concern with this precursor approach is

that it ignores failure contributions from existing flaws or degradation that does not result in a leak. There

are many potential initiating events which do not exhibit a precursor leak. Recent hydrogen combustion

failures of residual heat removal piping at Hamaoka [ 1. 12] and auxiliary coolant system piping at

Brunsbiittel [ 1. 13] represent one such mechanism. Flow accelerated corrosion (FAC), which induced a

rupture of an 18 inch diameter feedwater suction pipe elbow at Surry 2 in 1986 [1.14], is another

mechanism which can lead to rupture prior to precursor leaking.

The NUREG/CR-5750, Appendix J approach also applied a mitigation factor of 1/20 to the BWR leak

rate data to account for the effectiveness of IGSCC mitigation strategies. The IGSCC mechanism was the

prevalent BWR piping failure mechanism within the precursor database. This mitigation factor was

justified in light of analysis of the operating experience [L 15] and quantitative estimates of the

improvement in reliability for all mitigation strategies [1.16]. There are several different IGSCC

mitigation techniques that have been applied including: hydrogen water chemistry addition, weld

overlay, increased inspection, and material replacement with a less susceptible austenitic grade of piping

(L or NG grades) [1.16]. Some plants have applied single or multiple mitigation strategies and the

effectiveness of each particular strategy will obviously vary and may not be well-characterized by a single

mitigation factor. Additionally, the Germans have experienced IGSCC in some of the low carbon, less

susceptible steels [1.17]. Therefore, this mitigation factor may also not be applicable to describe the

future effectiveness of IGSCC mitigation measures up to the end of the license-renewal period.

1-4
1.2.1.3 Barsebick-1 Estimation - In the same time period as the NUREG/CR-5750 evaluation, the

Swedish Nuclear Inspectorate (SKI) initiated an effort to develop an international piping failure database

[1.18]. This differed from earlier studies in several fundamental ways. Most importantly, it was the first

study to concentrate solely on pipe failure and it included class 2 and class 3 piping failures as well as

class 1, or primary system piping. Secondly, several data sources were utilized to corroborate each event

and determine the metallographic root failure cause whenever possible. Thirdly, it was constructed to

allow specific queries by material engineers and PRA practitioners. The resultant database has been

employed to develop initiating event frequencies, evaluate emerging failure trends, and judge the

effectiveness of mitigation strategies.

The SKI effort culminated in 1997 with a guide for using the database to evaluate piping reliability in

terms of important influence and attribute factors [ 1. 15]. The report stresses the need to consider each

failure mechanism separately and develop estimates from the database with respect to the influence

factors for that mechanism. For instance, FAC is most severe in carbon steel piping at pipe tees and

elbows. Pipe failure frequencies associated with FAC are then estimated per reactor year and per the

number of carbon steel pipe tees and elbows in the plant using the database. Once the relevant attributes

of a specific plant are determined (i.e. the number of carbon steel pipe tees and elbows), plant-specific

event frequencies can be estimated. This process is repeated for all potential failure mechanisms (e.g.,

IGSCC, vibration fatigue, water hammer, etc.). Finally, all the relevant mechanisms are combined to

estimate total LOCA frequencies [1.15].

This framework was followed to develop failure rates in the Barsebdick-I reactor coolant pressure

boundary piping [1.19]. Barsebdick-1 is a third generation BWR by ABB-Atom that was closed in 1999.

The study collected all the plant-specific attributes of the Barsebick-I plant. The attributes include the

pipe materials, geometry, pipe length, number of pipe welds, number of pipe .connections, and the

connection type. Only medium and large LOCA frequencies were determined by considering those

failure mechanisms represented within the database.

While the initial SKI-effort database developments and the Barsebdck study were completed in 1998, the

database has been maintained and updated through the current period. There is currently a three-year

OECD-sponsored Piping Database Exchange (OPDE) project involving the U.S. and twelve other

European and Asian countries that is expanding the coverage of the database for events occurring during

the 1990's, adding current events, and improving the accuracy and completeness of all included events.

Specific attention is focused on including indications of non-leaking flaws discovered during pre-service

and in-service inspections. The 1998 version of the database included 1,880 U.S. piping failures and a

total of 2,416 failures worldwide. Currently, in June 2004, the database contains nearly 3,200 U.S. piping

failures and 4,600 worldwide failures. Therefore, the database completeness has been enhanced by the

inclusion of over 70% more US events and almost double the number of international events since 1998.

1.2. 1.4 Comparison of Operating-Experience-Based LOCA Estimates - The pipe break LOCA

frequencies from the WASH-1400, NUREG/CR-5750, Appendix J, and the Barsebdck-1 study using the

SKI pipe database are compared in Table 1. 1. As previously noted, these estimates differ dramatically in

the methodology used, the completeness and relevance of the underlying database, and the time period of

study. The WASH-1400 estimates are understandably higher because the oil and gas transmission

industry does not have the same rigorous design practice, quality assurance, and inspection as the nuclear

industry. Also, pipe failure from external damage (e.g., mechanical damage from construction

equipment, external corrosion, etc.) represents the largest contributing failure mechanism in this industry.

The WASH-1400 estimates also reflect the necessary conservatism because of the lack of operating

experience at the time of this study.

1-5
The application of actual service data in NUREG/CR-5750, Appendix J and the SKI-pipe database in the

Barsebdck- I study reduced the estimated break frequencies by an order of magnitude compared to

WASH-1400 (Table 1.1). There is roughly a factor of two difference between these two studies, with the

Barsebdck study predicting more MB LOCA events and fewer LB LOCA events for BWRs. This

difference is primarily due to the differences in the analysis methodology and the application of plant

specific data for Barsebdick-1. The BWR frequencies in both the NUREG/CR-5750 and Barsebick-I

studies reflect the successful mitigation strategies adopted to combat IGSCC in the early and mid-1980's.

Table 1.1 Comparison of Mean Results from Previous Studies

(per Calendar Years)

Reactor Type Analysis SB LOCA MB LOCA LB LOCA (x I05) (x 10s1) (X 10 -5)

BWR WASH-1400 300 80 30

NUREG/CR-5750 40 2.6 2.4

Barsebdick-1 NA 3.4 0.8

PWR WASH- 1400 300 80 30

NUREG/CR-5750 40 3.0 0.36

1.2.2 PFM LOCA Estimates

The PFM models are attractive because they can parametrically assess the effects of possible mitigation

strategies, including inspection, on future piping system performance for particular degradation

mechanisms. This is a valuable feature for assessing the possible severity of emerging degradation

mechanisms where there is both a dearth of operating experience and a lack of understanding of

mitigation effects. However, it is difficult to utilize PFM models for calculating absolute LOCA

frequency estimates because many of the input variables and model assumptions are overly simplistic and

may not adequately represent true plant conditions. This oversimplification also leads to increased

uncertainty in the results when the input uncertainties are taken into account.' It is also difficult to

benchmark PFM models using the sparse piping failure information. Instead, it is often necessary to

benchmark PFM models using the limited existing data for large non-leaking flaws or for very small

leaks. The models can then be used to calculate the probabilities of much larger leaks or complete

failures as a function of operating time. The PFM failure rate predictions for future operation can only be

considered to be realistic if estimates of current precursor or failure rates are consistent with operating

experience.

The international community (e.g., SKI in Sweden and GRS in Germany) has sponsored several PFMbased predictions of passive system failure rates [1.20, 1.21]. The NRC and the nuclear industry have

also sponsored PFM-based research over the last 20 years in an attempt to develop LOCA frequency

estimates from first principles including an estimate of pipe failure probability in Westinghouse PWR

reactor coolant loops [1.22] and a reliability analysis of stiff versus flexible piping [ 1.23]. Additionally,

joint international research and investigation of primary piping system integrity has been conducted by

the International Piping Integrity Research Group programs (IPIRG1 and IPIRG2) [1.24, 1.25] as well as

the Battelle Integrity of Nuclear Piping program (BTNP) [1.26].

There was also a large body of research sponsored during the 1980's to evaluate the leak-before-break

propensity of BWR and PWR plants [1.27-1.30]. These studies estimated the frequencies of DEGB in

BWR main steam, feedwater, and reactor recirculation piping, and in PWR reactor coolant loop piping.

Also, the frequencies of seismic-induced direct and indirect DEGB in PWR plants were estimated. Direct

DEGB is induced by fatigue crack growth due to the combined effects of thermal, pressure, seismic, and

other cyclic loads. Indirect DEGB occurs when the failure of other passive components due to seismic

1-6
loading causes a consequential failure in the primary piping system. An example would be the failure of

PWR steam generator supports during an earthquake, leading to additional primary coolant piping loads

and possible piping failure. Table 1.2 is a summary of the median and 9 0 th percentile estimates of the

DEGB frequencies for PWRs. The results reflect contributions from all earthquakes, not just those with

SSE or greater magnitudes.

Table 1.2 DEGB Frequencies for PWRs

DEGB Frequencies [1/Reactor Year]

Plant Direct DEGB Indirect DEGB

Type Median 90th Percentile Median 900h Percentile

CE PWRs 6 E-14 to5 E-13 4 E-12 to 7 E-ll 5 E-17 to 6 E-6 3 E-14 to 5 E-5

Westinghouse PWRs 2 E-13 to 3 E-I1I 8 E-10 to I E-9 5 E-8to 5 E-6 I E-6 to 5 E-5

B&W PWRs < I E-10 6E-II to2 E-7 8 E-9to IE-5.

Several of the more well-known US computer codes arising from these various efforts include PRAISE

[1.3 1], SRRA [L1.32], PSQUIRT [1.33], and PROLBB [1.34]. Each code has distinct capabilities, and

there are marked differences in structure, approach, modeling assumptions, and the degree of

benchmarking with failure data. The PRAISE models have been benchmarked with the through-wall

fatigue cracks in a PWR feedwater line near the steam generator and with the small leak in a BWR

recirculation line inlet nozzle safe end [ 1.35] and with the observed leak probabilities due to IGSCC

[1.36]. The SRRA code [1.32] has been benchmarked with observed repair data for flow-assisted

corrosion and directly compared with the PRAISE results for fatigue [1.31]. Crack initiation, sub-critical

crack growth, leak rate calculation, and fracture/failure analysis are the four major components required

for life prediction using PFM, yet the various codes handle these modules using vastly different

approaches, or not at all.

For instance, the SRRA code uses a limit-load failure criterion while the PROLBB code uses an elasticplastic fracture mechanics based criterion. This single difference can result in extensive output variability

in the pipe rupture probability. Figure 1.1 depicts the conditional pipe failure probability for a

hypothetical through-wall crack as a function of leak rate. To develop the graphs, the crack size is

increased systematically and the associated leak rate and conditional failure probability are calculated.

The graph on the left assumes that the pipe fails upon reaching limit load, i.e., the crack is in high

toughness wrought stainless steel. The graph on the right assumes that the crack is in lower toughness

stainless steel weld metal such that failure occurs below the pipe limit stress. This single difference

results in up to a 5 order of magnitude difference in the conditional failure probabilities. For a

conservatively assumed rupture loading event frequency of 10-3 per year, the failure frequency is about

10-40 per calendar year for the base metal and about 10-7per calendar year for the weld metal, assuming

loading at 50% of service level A and a 1 gpm (3.8 1pm) leakage detection threshold. This threshold is

approximately the level required to locate unidentified leakage within nuclear plants. Either analysis

could be realistic for a given weld joint depending on the actual pipe material, fabrication practice, and

crack location.

The PFM analysis must also model each potential aging mechanism of concern (FAC, IGSCC, PWSCC,

corrosion fatigue, vibration fatigue, etc.) to develop comprehensive LOCA estimates. Each mechanism

potentially requires a different degradation/cracking model and corresponding set of material properties to

accurately calculate the LOCA frequency. Probabilistic fracture mechanics codes for all of the above

mechanisms also utilize different approximations for input parameters such as the applied load magnitude

and spectrum, pipe boundary conditions, residual stress contribution, and initial flaw distribution. While

the codes are complex, many simplifying assumptions are necessary and the model of actual plant

conditions is, at best, approximate. Given these modeling realities, and the inherent sensitivity of fatigue

1-7
and crack growth calculations to the initial conditions and modeling assumptions, it is not surprising that

the results from the various models can vary dramatically.

10, 0

, 10" 0- 1 BWR-3 (b=se metal) > 10 B M R-3 (weld mteW) . •-

010'1

10.o 1 i'50%- "vice '.1 of S1 Lvul A .- 50% of Service Level A 0

10,I

10 (D 1 D"

10 10.7

10- 10' 100% of Se.ce Level A

010. "1 1 001, of Service Level A 1

i 1010"

1 ~10"• .I

"10 1" 1 0 10" 0 10" 10' 10' 1•

Leak Rate, gpm Leak Rate, oom

Figure 1.1 Sensitivity of Conditional Failure Probability to Failure Mode Assumption

1.3 Modeling Miti-gation Effects

Once degradation mechanisms are discovered and assessed, it is expected that mitigation measures will be

eventually developed and applied in order to ultimately decrease the passive system degradation rate to

historical levels, or lower. Mitigation includes such measures as inspection, material replacement, repair,

and changes in operating conditions. Therefore, the expected impact of a new degradation mechanism on

LOCA frequencies is a short term increase followed by a decrease due to mitigation after some time

period. This effect, and the benefits of successful mitigation, are demonstrated by the response to the

IGSCC cracking phenomena in the early-1980s (Figure 1.2). There was a period of two to three years

when the cracking mechanism and its prevalence was discovered in susceptible systems. There was

another two to three year period before mitigation was developed and fully implemented. During the

approximately four year period of diagnosis and mitigation of the IGSCC problem, the instantaneous

discovery rate was approximately one order of magnitude higher than historical levels. However, since

mitigation has been employed, the occurrence of IGSCC is less than pre-1980 values [1.19].

It can be difficult to account for the effects of mitigation in either operating-experience-based or PFM

LOCA frequency estimates. The pre-mitigation data is part of the operating experience and must be

properly screened when estimating frequencies for plants which have implemented mitigation measures.

While the general effect of mitigation on IGSCC is apparent, t'he effect of individual mitigation measures

at specific plants is less obvious. This is relevant because several different IGSCC mitigation options are

practiced, often in combination, and it is difficult to quantify the effects of individual measures from the

data alone. The frequencies estimated by the NUREG/CR-5750 and Barsebdck-1 studies did take into

account the general effect of IGSCC mitigation when calculating frequencies (Table 1.1). However, both

these studies had the benefit of several years of mitigation experience as a basis for their analyses when

the studies were performed.

In PFM-based analysis, an explicit treatment of the effects of individual mitigation measures on the

degradation rate is possible. The PFM approach can be especially valuable for comparing the relative

differences between pre- and post-mitigation failure probabilities. An ASME-sponsored effort [1.37]

examined the effects of mitigation techniques such as stress improvement and weld overlay for BWR

recirculation piping subject to IGSCC. However, mitigation measures are often simplistically modeled

1-8
and absolute predictions of post-mitigation degradation rates, and failure frequencies, are subject to the

same limitations discussed earlier for PFM models. A common mitigation technique usually considered

in PFM analysis is the effect of inspection on the failure frequencies. The impact of inspection is a

function of the periodicity, the accuracy of the inspection technique, and operator skill. The accuracy and

operator skill are typically modeled by an assumed relationship between the probability of detection

(POD) and defect size. Unfortunately, for many inspection techniques, this relationship is highly

uncertain and the curves themselves are often developed under idealized laboratory conditions.

Therefore, they may not be representative of actual field experience. Furthermore, the POD relationship

chosen for the models can significantly influence the predicted failure rates. The possible impact

associated with inspection accuracies have been documented by an ASME Research Task Force [1.37].

3.5

> 2

I.s I r i t

2.5 -.i.-- ......-

0 IC LI

" _ __ _ I i I _______,

T 12

0 .~ - . N ~O e~ - • - ;.

* , ; 0 0 0 , 

Figure 1.2 IGSCC Events by Year

1.4 Other LOCA Initiatina Events . .......... ... . 1 . ...

The LOCA frequencies are also influenced by active component failure. Active components are defined

as those components subject to periodic maintenance and those that contain moving parts which are

subject to either wear or failure (e.g., pump seals, SRV, PORV, etc.). Active component failure was not

considered in this study. Therefore, the active-system LOCA frequencies should be combined with the

passive system LOCA frequencies from this study in order to estimate total system risk. Because active

component failures occur regularly, their frequency can be estimated from the operating experience

database as in [1.6].

Steam generator tube rupture is a passive component failure which was also considered in this study.

Because tube ruptures have occurred with enough regularity to be represented in the passive-systemfailure database, historical rupture frequencies can be estimated as in Reference 1.6. However, the

applicability of operating experience to future steam.generator tube failure rates is unclear. For instance,

it is important to understand how steam generator replacement, secondary and primary side environmental

changes, and other factors may affect the future failure rates. For these reasons, these failure modes were

also considered in the elicitation.

1-9
There is an entire class of other non-piping, passive system failure modes which has recently been

recognized and that has not been considered in previous LOCA frequency studies. Recent experience has

demonstrated that degradation, and therefore LOCAs, can occur in non-piping components such as

CRDM housings, BWR stub tubes (Hamaoka), and the RPV reactor head (Davis Besse). These

degradation mechanisms and failure locations have also not been explicitly represented in historical

piping-based operating-experience databases. However, the impact of these emerging LOCA initiators is

an important consideration in determining total LOCA frequency. Therefore, these non-piping failure

frequencies must be combined with historical piping failure frequencies to determine comprehensive

passive-system LOCA frequencies.

Finally, in both piping and non-piping components, it is important to identify and assess the LOCA

severity of degradation mechanisms with long incubation times which have yet to surface in the operating

experience and may not be incorporated in PFM models. The identification and assessment of these

future mechanisms is only possible by understanding the long-term interrelationship between the passive

component materials, operating environment, and loading conditions.

1.5 Expert Elicitation

Expert elicitation has attributes which can build on the strengths and compensate for the weaknesses

associated with purely operating-experience-based or PFM-based approaches to estimate LOCA

frequencies. These attributes make this technique a natural choice for estimating LOCA frequencies.

Expert elicitation is a formal process for providing quantitative estimates of the frequencies of physical

phenomena when the required data is sparse and when the subject is too complex to adequately model.

Furthermore, the scientific uncertainty about the phenomenadis so large that, in the absence of adequate

data, validated models or computer codes cannot be developed. If the issue is also important, i.e., it has

significant regulatory implications and may also be controversial, then devoting the substantial resources

required for an expert elicitation may be justified.

Expert elicitation is a structured process which enhances its accuracy, consistency, credibility, and thus

acceptability compared to informal, less-structured processes. The emphasis on a structured

decomposition of the issues improves accuracy and credibility, thus making the results more acceptable to

the stakeholders. Expert elicitation reduces the likelihood of bias and enhances the consistency and

comparability of the results. The emphasis on documentation leads to improved scrutiny and acceptance

of the results. The main drawbacks in using a formal expert elicitation process are the increased time and

resources required. Because of the structure, there is also reduced flexibility to make changes as the

process proceeds.

Expert elicitation is a well-established technique [ 1.38] which has been used on a number of occasions to

evaluate technical issues related to nuclear safety. Examples include: NUREG- 1150 [1.10], the

determination of flaw density and size distributions in reactor pressure vessels [ 1.39], the evaluation of

the high level radioactive waste repository [1.40, 1.41], and in the construction of a probabilistic seismic

hazard curve [ 1.42].

Expert elicitation was selected to develop passive system LOCA frequency estimates because data

sparseness and subject complexity are characteristic of pipe break LOCA frequency estimation. Data

sparseness is evidenced by the fact that no Class 1 nuclear pipe break LOCA events have occurred.

Existing pipe break LOCA frequency estimates from NUREG/CR-5750, Appendix J, vary from 4x10-4

per calendar-year for SB LOCAs to 4x 10.6 per calendar-year for PWR LB LOCAs. On the average, this

translates into one SB LOCA every 2,500 years per plant and one PWR LB LOCA every 250,000 years

per plant.

1-10
Complexity is evidenced by the large number of pipe system variables which must be considered to

accurately evaluate the full spectrum of pipe breaks using the PFM or statistical methods discussed in

previous sections. Variables include piping design and layout; piping fabrication; materials; degradation

mechanisms; stress; service environment; application of codes and standards; inspection type, quality and

schedule; and plant operating history. These variables serve as input for both operating-experience and

PFM models. In particular, the importance of a particular degradation mechanism depends on the effect

of the relevant combinations of these variables.

As previously discussed, the number and interaction of these variables and the sparseness of data severely

hinders accurate frequency assessment using operating-experience approaches (Section 1.2.1). The PFM

approach (Section 1.2.2) suffers because small changes in the input variable assumptions can dramatically

affect the predicted piping reliability. Also, the underlying physical modeling of degradation and failure

mechanisms (crack initiation, leak rates, complex crack growth, and piping instability) is unavoidably

simplistic. These limitations lead to a great deal of uncertainty when utilizing only operating experience

or PFM analysis to estimate LOCA frequencies. Using expert elicitation to develop these estimates

fosters a more comprehensive approach by using individuals with relevant technical expertise to identify

and focus on important variables which affect LOCA frequencies. The panelists then are required to

combine insights gained from operating experience, PFM modeling, and other considerations to develop

their LOCA frequency estimates.

1.6 References

1.1 USNRC, Regulatory Guide 1. 174: An Approach for Using Probabilistic Risk Assessment In

Risk-Informed Decisions On Plant-Specific Changes to the Licensing Basis, July 1998.

1.2 Staff Requirements - SECY-98-300 - Options for Risk-Informed Revisions to 10 CFR Part 50 -

"Domestic Licensing of Production and Utilization Facilities," dated June 8, 1999.

1.3 Staff Requirements - SECY-99-264 - Proposed Staff Plan for Risk Informing Technical

Requirements in 10 CFR Part 50, dated February 3, 2000.

1.4 U.S. Nuclear Regulatory Commission (USNRC), SECY-02-0057, "Update to SECY-01-0133,

'Fourth Status Report on Study of Risk-Informed Changes to the Technical Requirements of 10

CFR Part 50 (Option 3) and Recommendations on Risk-Informed Changes to 10 CFR 50.46

(ECCS Acceptance Criteria)', dated March 29, 2002.

1.5 Staff Requirements - SECY-02-0057 - Update to SECY-01-0133, "Fourth Status Report on

Study of Risk-Informed Changes to the Technical Requirements of 10 CFR Part 50 (Option 3)

and Recommendations on Risk-Informed Changes to 10 CFR 50.46 (ECCS Acceptance

Criteria)", dated March 31, 2003.

1.6 Poloski, J.P, Marksberry, D.G., Atwood, C.L., and Galyean, W.J., "Rates of Initiating Events at

U.S. Nuclear Power Plants: 1987-1995," NUREG/CR-5750, U.S. Nuclear Regulatory

Commission, February 1999.

1.7 "Investigation and Evaluation of Stress-Corrosion Cracking in Piping of Light Water Reactor

Plants," NUREG-053 1, U.S. Nuclear Regulatory Commission, February 1979.

1.8 Rao, G.V., Seeger, D.E., Jr., Hoffman, J.A., DeFlitch, C., Rees, R.A., and Junker, W.R.,

"Metallurgical Investigation of Cracking in the Reactor Vessel Alpha Loop Hot Leg Nozzle to

Pipe Weld at the V.C. Summer Nuclear Generating Station," WCAP- 15616, Westinghouse

Electric Company LLC, January 2001.

1-11
1.9 "Reactor Safety Study: An Assessment of Accident Risks in U.S. Commercial Nuclear Power

Plants," WASH-1400, U.S. Nuclear Regulatory Commission, October 1975.

1. 10 "Severe Accident Risks: An Assessment for Five U.S. Nuclear Power Plants," NUREG- 1150,

U.S. Nuclear Regulatory Commission, December 1990.

1.11 Beliczey, S., and Schulz, H., "Comments on Probabilities of Leaks and Breaks of Safety-Related

Piping in PWR Plants," International Journal of Pressure Vessel and Piping, Vol. 43, pp. 219 -

227, (1990).

1.12 "Manual Shutdown of Unit-I of the Hamaoka Nuclear Power Station," Nuclear Power Safety

Press Release Information, ANRE/MITI, Chubu Electric Power Company, November 8, 2001.

1.13 "Unique Brunsbhuttel Core Spray was Vulnerable to Gas Explosion," Nucleonic Week, Vol. 43,

No. 10, March 7, 2002.

1.14 Licensee Event Report (LER)-28186020, "Surry 2 Feedwater Failure," March 31, 1987.

1.15 Nyman, R., "Hegedus, D., Tomic, B., Lydell, B., "Reliability of Piping System Components:

Framework for Estimating Failure Parameters from Service Data," SKI Report 97:26, Swedish

Nuclear Power Inspectorate, December 1997.

1 16 Danko, J.C., "Boiling Water Reactor Pipe Cracking: The Problem and Solution," Processing of

Materials in Nuclear Energy, American Society for Metals, Metals Park, OH, 1983.

1.17 Wilkowski, G.M., Rudland, D., Wolterman, R., Krishnaswamy, P., Rahman, S., and Scott, P.,

"Technical Evaluation of Probabilistic LBB Codes and Approaches," Draft Technical Report,

November 30, 2001.

1.18 Nyman, R., Erixon, S., Tomic, B., and Lydell, B., "Reliability of Piping System Components,

Volume 1: Piping Reliability - A Resource Document for PSA Applications," SKI Report 95:58,

Swedish Nuclear Power Inspectorate, December 1995.

1.19 Lydell, B., "Failure Rates in Barsebdck-1 Reactor Coolant Pressure Boundary Piping: An

Application of a Piping Failure Database," SKI Report 98:30, Swedish Nuclear Power

Inspectorate, May 1999.

1.20 "Reliability of Piping System Components. Vol. 2: PSA LOCA Data Base Review of Methods

for LOCA Evaluation Since the WASH-1400," SKI Report 95:59, Swedish Nuclear Power

Inspectorate, 1996.

1.21 Gesellschaft Fir Anlagen und Reaktorsicherheit (GRS) mbh; German Risk Study Phase B, GRS72, Verlag, TUV; Cologne, 1989.

1.22 Woo, H.H, Mensing R.W. and Benda, B.J.,"Probability of Pipe Failure in the Reactor Coolant

Loops of Westinghouse PWR Plants, Volume 2: Pipe Failure by Crack Growth, Load

Combination Program," NUREG/CR-3662, Vol. 2, U.S. Nuclear Regulatory Commission,

August 1984.

1.23 Lu, S.C. and Chou, C. K., "Reliability Analysis of Stiff Versus Flexible Piping, Final Project

Report, NUREG/CR-4263, U.S. Nuclear Regulatory Commission, May 1985.

1.24 Wilkowski, G. M., and others, "International Piping Integrity Research Group (IPIRG) Program,"

NUREG/CR-6233, Vol. 4, June 1997.

1.25 Hopper, A., and others. "The Second International Piping Integrity Research Group (IPIRG-2)

Program," NUREG/CR-6952, March 1997.

1-12
1.26 Scott, P., and others, "The Battelle Integrity of Nuclear Piping (BINP) Program Final Report -

Vol. I: Summary and Implications of Results," NUREG/CR-6837, June 2005.

1.27 T. Loo and R.W. Mensing, "Probability of Pipe Failure in the Reactor Coolant Loops of

Combustion Engineering PWR Plants," NUREG/CR-3663, U.S. Nuclear Regulatory

Commission, September 1984.

1.28 Holman, G.S. and Chou, C.K. "Probability of Pipe Failure in the Reactor Coolant Loops of

Westinghouse PWR Plants," NUREG/CR-3660, U.S. Nuclear Regulatory Commission, July

1985.

1.29 Holman, G.S. and Chou, C.K. "Probability of Pipe Failure in the Reactor Coolant Loops of

Babcock & Wilcox PWR Plants," NUREG/CR-4290, U.S. Nuclear Regulatory Cormnission, May

1986.

1.30 T.Lo, S.E. Bumpus, D.J. Chinn, R.W. Mensing, and G.S. Holman, "Probability of Failure in

BWR Reactor Coolant Piping," NUREG/CR-4792, U.S. Nuclear Regulatory Commission, March

1989.

1.31 Harris, D.O., and Dedhia, "A Probabilistic Fracture Mechanics Code for Piping Reliability

Analysis (pcPRAISE code)," NUREG/CR-5864, U.S. Nuclear Regulatory Commission, 1992.

1.32 Bishop, B.A., "Westinghouse Structural Reliability and Risk Assessment (SRRA) Model for

Piping Risk Informed In-Service Inspection," WCAP- 14572 Revision 1, Supplement I,

Westinghouse Electric Company LLC, October 1997.

1.33 Paul, D.D., Ahmad, J., Scott, P.M., Flanigan, L.F., and Wilkowski, G.M., "Evaluation and

Refinement of Leak-Rate Estimation Models," NUREG/CR-5128, Rev. 1, U.S. Nuclear

Regulatory Commission, June 1994.

1.34 Rahman, S., Ghadiali, N., Paul, D., and Wilkowski, G., "Probabilistic Pipe Fracture Evaluations

for Leak-Rate Detection Applications," NUREG/CR-6004, U.S. Nuclear Regulatory

Commission, April 1995.

1.35 Simonen, F.A. and Woo, H.H., "Analyses of the Impact of Inservice Inspection Using a Piping

Reliability Model," NUREG/CR-3869, U.S. Nuclear Regulatory Commission, July 1984.

1.36 Holman, G.S., "Application of Reliability Techniques to Prioritize BWR Recirculation Loop

Welds for In-Service Inspection," NUREG/CR-5486, U.S. Nuclear Regulatory Commission,

December 1989.

1.37 "Risk-Based Inspection - Development of Guidelines, Light Water Reactor (LWR) Nuclear

Power Plant Components," NUREG/GR-0005, Vol. 2, Part 1, U.S. Nuclear Regulatory

Commission and American Society of Mechanical Engineers (CRTD Vol. 20-2), July 1993.

1.38 Meyer, M.A., and Booker, J.M., "Eliciting and Analyzing Expert Judgment: A Practical Guide,"

NUREG/CR-5424, U.S. Nuclear Regulatory Commission, January 1990.

1.39 Simonen, F.A., Doctor, S.R., Schuster, G.J., and Heasler, P.G., "A Generalized Procedure for

Generating Flaw-Related Inputs for FAVOR Code," NUREG/CR-6817, March 2004.

1.40 Bonano, E.J., Hora, S.C., Keeney, R.L., and von Winterfeldt, D., "Elicitation and Use of Expert

Judgment in Performance Assessment for High-Level Radioactive Waste Repositories,"

NUREG/CR-5411, U.S. Nuclear Regulatory Commission, May 1990.

1.41 Kotra, J.P., Lee, M.P., Eisenberg, N.A., and DeWispelare, A.R., "Branch Technical Position on

the Use of Expert Elicitation in the High-Level Radioactive Waste Program, "NUREG/CR-1563,

U.S. Nuclear Regulatory Commission, 1996.

1-13
1.42 Budnitz, R.J., Apostolokis, G., Boore, D.M., Cluff, L.S., Coppersmith, K.J., Cornell, C.A., and

Morris, P.A., "Recommendations for Probabilistic Seismic Hazard Analysis: Guidance on the

Use of Experts," NUREG/CR-6372, U.S. Nuclear Regulatory Commission, 1997.

1-14
2. OBJECTIVE AND SCOPE

The principal objective of the expert elicitation process was to develop separate, generic BWR and PWR

piping and non-piping passive system LOCA frequency estimates as a function of effective break size and

operating time through the end of the plant license-renewal period. These estimates are based on the

responses from an expert panel. Another objective of the elicitation process was to reflect both the

uncertainty in each panelist's estimates as well as the diversity among the individual estimates. These

objectives are consistent with staff direction [2.1]. This section discusses several important choices and

assumptions that were adopted as part of the elicitation process to achieve these objectives.

The staff was directed to provide realistically conservative LOCA frequency estimates rather than

bounding values associated with one or two plants [2:1]. Therefore, the elicitation focused on developing

generic, or average, estimates for the commercial fleet and uncertainty bounds on these generic estimates.

The BWR and PWR LOCA frequency estimates were not partitioned further to describe differences

related to design class, vendor, or specific plant operating characteristics. This approach is consistent

with prior LOCA frequency studies that did not consider plant-specific differences in developing LOCA

frequencies for use in PRA modeling [2.2, 2.3]. However, the elicitation panelists were instructed to

consider broad differences among plants related to important variables (i.e., plant system, material,

geometry, degradation mechanism, loading, mitigation/maintenance) in determining both the generic

LOCA frequency estimates and especially the uncertainty bounds. It is the broad differences in these

important variables that are most important to passive system failure and there is generally sufficient

commonality among plants to make such a generic assessment meaningful.

Panelists were specifically instructed not to consider differences that only exist at a few plants. For

instance, if a particular plant vendor design was judged by a panelist to be more LOCA-sensitive than

other designs and encompasses, say, 20 PWRs, then the panelist was expected to consider this in his

estimates as well as in the uncertainty bounds. However, if the same plant design only applies to one or

two plants, then this should not have been considered in the estimates. It is understood that unique plant

features and safety culture may also influence LOCA frequencies and plant-specific estimates may fall

outside the generic fleet estimates. However, accident frequency increases stemming from deficient

safety practices are expected to be identified and mitigated through regulatory oversight policies and

procedures.

The elicitation was solely focused on determining LOCA frequencies that initiate by unisolable primary

system side failures that can be exacerbated by material degradation with age. Therefore, active system

failures (e.g., stuck open valve, pump seals, interfacing system LOCAs) and consequential primary

pressure boundary failures due to either secondary side failures or failures of other plant structures (e.g.,

crane drops) were not considered. While such LOCA frequency contributions may be an important

consideration when evaluating total plant risk, the assessment of these risk contributions was beyond both

the original scope and the expertise of the selected panelists.

This study developed LOCA frequency estimates consistent with historical.SB, MB, and LB flow-rate

definitions. Additionally, three larger LOCA categories within the classical LB LOCA regime were

defined for the elicitation. The purpose of these additional LOCA categories was to examine trends with

increasing break size, up to and including, a DEGB of the largest piping in the plant. The consideration

of the consequences of such a break is a requirement of current t0 CFR 50.46, and associated Appendix

K and GDC 35 requirements. The SB, MB, and LB LOCA categories have historically been defined on

the basis of flow rate. Simple correlations were developed to relate the rupture size to the expected flow

rate required for the ECCS make-up system. Although the correlations developed in this study are

2-1
different from those used in the past, they provide a mechanism to compare these LOCA estimates with

prior estimates.

The elicitation considered three distinct time periods: current-day, end-of-plant-license, and end-of-plantlicense-renewal. For purposes of the elicitation, these time periods were quantified as 25 years

(approximate current fleet average), 40 years, and 60 years, respectively, after plant operation

commences. The current-day estimates were intended to represent current plant conditions and are

therefore equivalent to instantaneous LOCA frequency estimates. However, the 40-year and 60-year

estimates were not explicitly defined in the elicitation to represent either instantaneous frequencies at 40

and 60 years of operation or averaged frequencies between 25 - 40 years and 40 - 60 years of plant

operation. Because the interpretation of the future time period estimates was not explicitly defined, the

panelists were free to assess the relevance of each type of estimate and then provide either averaged or

instantaneous frequencies as they deemed most appropriate. Each panelist was also asked to discuss

possible differences between averaged and instantaneous frequencies for the future time periods. The

lack of an explicit definition, while important for understanding the context of the results, is not of

practical significance because future changes in generic LOCA frequencies are generally expected to be

gradual.

As noted above, the LOCA frequency estimates were intended to be both generic and consistent with

historical estimates that have been used to evaluate core damage frequency and large early release

frequency (LERF) metrics. For these reasons, the elicitation primarily considered normal plant

operational cycles and loading histories consistent with current internal-event PRA. Therefore, separate

frequencies for each unique mode of plant operation were not estimated. Rather, the estimated

frequencies implicitly consider all modes of operation based on the loading or operational history

associated with each piping system or non-piping component. Additionally, consideration of normal

plant operational cycles and loading histories was limited to representative constant stresses (e.g.,

pressure, thermal, and residual) and expected transient stresses (e.g., thermal striping, heat-up/cool-down,

and pressure transients) that occur over the extended licensing period. Therefore, only loading events

with a frequency greater than approximately 0.01 per calendar year were explicitly addressed.

Rare event loading from seismic, severe water hammer, and other such sources was also not considered in

this generic evaluation because of their strong dependency on plant-specific factors. Furthermore, the

assessment of these risk contributions was beyond the expertise of many of the selected panelists. As

with other consequential LOCAs, the contribution to the LOCA frequency from rare event loading may

be an important consideration when evaluating total plant risk.. Consequently, a separate research effort

was conducted to assess the potential impact of seismic loading on the LOCA break frequency versus

break size relationship [2.4]. As part of that study, both unflawed and flawed piping failure contributions

were considered. The results of that seismic LOCA analysis are also summarized in Section 7.2.

The generic BWR and PWR estimates were determined by first estimating the separate LOCA frequency

contributions associated with BWR piping, BWR non-piping, PWR piping, and PWR non-piping failures

for each panelist who provided the required responses. These individual piping and non-piping

frequencies were then combined to estimate parameters of the total pas sive system LOCA frequency

distributions for BWR and PWR plants at each distinct LOCA category and time period. The median,

mean, 5th and 95th percentiles were the parameters chosen to represent the LOCA frequency distributions

and reflect each panelist's uncertainty. Finally, these individual estimates were aggregated to provide

group estimates. These aggregated group estimates are intended to provide a consensus-type group

estimate. Additionally, confidence intervals were determined to reflect the diversity among the panelists'

estimates. The group estimates along with the diversity measures for each LOCA parameter are the

primary quantitative results produced by the elicitation process.

2-2
Several important assumptions were made to guide the elicitation process. One such assumption is that

plant construction and operation comply with all applicable codes and standards. Therefore, all LOCAsensitive passive-system components were assumed to be designed and fabricated using approved

materials in accordance with ASME or similarly applicable requirements. Also, component inspections

were assumed to be conducted using applicable ASME Section XI periodicity and quality requirements.

Other additional inspections required by the NRC to evaluate specific degradation mechanisms (e.g.,

IGSCC, PWSCC, or CRDM inspections) were also assumed to occur as required. Plant operation was

generally assumed to occur within the expected parameters required by the regulations and the technical

specifications.

The specific impact on the LOCA frequencies of using counterfeit materials and fabrication techniques,

avoiding inspections, and blatantly deviating from approved operating criteria was not considered.

However, it was assumed that regulatory oversight policies and procedures will continue to be used to

identify and mitigate risk associated with plants having deficient safety practices such as these.

Deviations from good safety practices do represent some percentage of the events included in the passivesystem failure data and extrapolation of this data implicitly assumes that similar future deviations will

continue to occur with similar frequency.

Another important assumption is that current regulatory oversight practices will continue to evaluate

aging management and mitigation strategies in order to reasonably assure that future plant operation and

maintenance has equivalent or decreased risk. A related assumption inherent in this elicitation is that all

future plant operating characteristics will be essentially consistent with past operating practice. The

effects of operating profile changes were not considered because of the large uncertainty surrounding

possible operational changes and the potentially wide-ranging ramifications of significant changes on the

underlying LOCA frequencies. For instance, significant power upgrade allowances may change plant

performance and relevant operating characteristics (e.g., temperature, environment, flow rate) to a degree

which significantly impacts future LOCA frequencies. Because operating experience provides

fundamental information used to determine the LOCA frequency estimates, these types of changes might

undermine the applicability of the operating experience data. Consequently, the relationship between

plant operating profiles and LOCA frequencies should be considered when evaluating possible riskinformed changes based on the elicitation study results.

2.1 References

2.1 Staff Requirements - SECY-02-0057 - Update to SECY-01-0133, "Fourth Status Report on Study of

Risk-Informed Changes to the Technical Requirements of 10 CFR Part 50 (Option 3) and

Recommendations on Risk-Informed Changes to 10 CFR 50.46 (ECCS Acceptance Criteria)", dated

March 31, 2003 ADAMS Accession #ML030910476.

2.2 "Reactor Safety Study: An Assessment of Accident Risks in U.S. Commercial Nuclear Power Plants,"

WASH- 1400, U.S. Nuclear Regulatory Commission, October 1975.

2.3 Poloski, J.P, Marksberry, D.G., Atwood, C.L., and Galyean, W.J., "Rates of Initiating Events at U.S.

Nuclear Power Plants: 1987-1995," NUREG/CR-5750, U.S. Nuclear Regulatory Commission,

February 1999.

2.4 Chokshi, N.C., Shaukat, S.K., Hiser A.L., DeGrassi, G., Wilkowski, G., Olson, R., and Johnson, J.J.,

"Seismic Considerations For the Transition Break Size," NUREG-1903, U.S. Nuclear Regulatory

Commission, February 2008.

2-3

3. ELICITATION APPROACH

The expert elicitation process used with the panel is an adaptation of the formal expert judgment

processes used in NUREG- 1150 [3.11 ]'to estimate core damage frequencies [3.2] and to assess the

performance of radioactive waste repositories [3.3]. The elicitation process used for this project consisted

of a number of steps (Figure 3.1). To begin, the project staff identified the issues to be evaluated through

a pilot elicitation (Block 2 in Figure 3.1) and selected a panel of twelve members (Block 1). The staff

then gathered background material and prepared an initial formulation of the technical issues (Block 3)

that was provided to the panel. At its initial meeting, the panel discussed the issues and, using the staff

formulation as a starting point, developed a final formulation for the elicitation (Block 3). This

formulation included an elicitation structure (Block 5) for decomposing the technical issues and the

development of base cases (Block 6) which are used in the subsequent anchoring of all the elicitation

responses. A base case team was established (Block 7) as a subset of the entire panel to estimate the

LOCA frequencies associated with the base case conditions. At this initial meeting, elicitation training

(Block 4) was also conducted using exercises and a discussion of biases to educate the panel about the

subjective elicitation of numerical values. After the first meeting, the base case team developed

preliminary estimates for the base case frequencies. The staff also prepared a draft elicitation

questionnaire (Block 8) which was reviewed by the panelists (Block 9) and revised based on comments

received (Block 8). A second meeting was held to review the base case estimates, review the elicitation

questions, and finalize remaining technical issue formulation issues. The elicitation questions and base

case estimates were finalized based on feedback received at that meeting (Blocks 8 and 10).

At their home institutions, the individual panel members performed analyses and computations to develop

their answers to the elicitation questionnaire. A facilitation team consisting of substantive experts, a

normative expert and two recorders met separately with each panel member in day-long elicitation

sessions (Block 11). At these sessions, each panel member provided-answers to the elicitation

questionnaire along with their supporting technical rationales (Block 12). The panel members then

returned to their home institutions where they refined their responses based on feedback from the

elicitation session. Upon receipt of the updated responses, the project staff compiled the panel's

responses and developed preliminary estimates of the LOCA frequencies (Block 13). Along with the

rationales, the preliminary estimates were presented to the panel at a third meeting (Block 14). Panel

members were invited to fill in gaps in their questionnaire responses and, if desired, to modify any of their

responses after this meeting (Block 15). Based on these updates, final estimates of the LOCA frequencies

were calculated and provided to the panel members for review (Block 16). The project staff developed a

draft report on the elicitation process and results (Block 17). A fourth meeting via video teleconferencing

was held to discuss the draft report which was revised based on feedback received during this meeting

(Blocks 19 and 20). Separately, an external peer review of the analysis of the elicitation responses from

the panelists (Sections 5 and 6) was conducted (Block 18). In addition, public comment is being solicited

(Block 21) prior to the development of the final LOCA frequencies to be used in the redefinition of the

ECCS requirements in 10 CFR 50.46 (Block 22). More detail on each of these general steps is provided

below.

3.1 Pilot Elicitation

The study was initiated with a pilot elicitation conducted using only NRC staff (Block 2). The primary

objectives of this pilot study were to identify technical issues for consideration during the subsequent

formal elicitation and to test a possible elicitation framework. Additionally, interim LOCA frequency

estimates were developed to support the study conducted by NRC staff on the feasibility of risk-informing

10 CFR 50.46, Appendix K, and GDC 35. Specifically, estimates were sought to explore the potential of

eliminating the design requirements to mitigate a simultaneous LOCA and loss-of-offsite-power (LOOP)

3-I
event. The technical issues identified by the staff and discussed during this pilot study provided the

foundation for subsequent discussions during the elicitation. The pilot elicitation was also valuable for

identifying strengths and weaknesses in the process. The NRC staff used the lessons learned from this

pilot study to help formulate a suggested formal elicitation structure for subsequent consideration by the

elicitation panelists. The results of this feasibility study and the staff's pilot elicitation have been reported

[3.4], but a synopsis of salient points is provided below.

12. Initial Panelists' ResponsesI

Figure 3.1 Flowchart of the Overall Elicitation Process

3-2
The pilot elicitation was structured similarly to the formal elicitation. Panelists were chosen to provide

expertise in relevant technical areas. A kick-off meeting was held to discuss objectives and provide

background information. An issue development meeting was then conducted to define the problem,

identify important technical issues for consideration, develop elicitation questions, conduct elicitation

training, and identify baseline LOCA frequencies for subsequent adjustment during the elicitation. The

elicitation questionnaire was then independently answered by each panel member, the analyzed results

were presented to the panelists, and the panelists provided feedback of the elicitation process.

As part of the issue development, the panelists developed a structure of classifying piping systems and

non-piping components separately and identified corresponding materials, degradation mechanisms, and

mitigation measures. The panelists then eliminated piping systems, non-piping components, and

degradation mechanisms from consideration based on consensus opinion of those issues that were not

important. Thepanelists utilized historical LOCA definitions and size classification from NUREG/CR5750 [3.5] for consistency and agreed that the NUREG/CR-5750 LOCA frequencies would be the

baseline for the exercise. The group evaluated the time period from the current period (2002) up to the

end of the plant license-renewal period. The panelists were asked to determine the highest frequencies

over this time period, but these maximums were typically associated with the end of the plant licenserenewal period. Separate BWR and PWR estimates were developed. It should also be noted that the pilot

study considered both active and passive system LOCA frequencies simultaneously.

The elicitation questions were structured so that each piping system or non-piping component was

evaluated separately. There were two types of questions that provided redundant information. One type

of question asked each participant to provide the relative percentage change between the NUREG/CR5750 frequency estimates and the frequencies associated with each piping system or non-piping

component. The other type of question asked for the relative ratio between MB and SB LOCAs and

between LB and MB LOCAs for each piping system or non-piping component. There were also six

global questions posed that were not related to any particular piping system. These included a

consideration of the importance of the following issues on future LOCA frequencies: current IGSCC

mitigation; hydrogen-combustion failures; future mitigation and degradation mechanisms; leak-detectionsystem accuracy; contribution of non-precursor degradation; and future ISI techniques. All questions

were posed in a questionnaire and the results were analyzed and summarized.

There were several features of the pilot elicitation structure that were identified as advantageous for use

during the formal elicitation. First, it was evident that classifying issues with respect to piping systems

and non-piping components was appropriate. Next, it was valuable to identify those variable classes (e.g.,

material and degradation mechanisms) that affect the LOCA frequencies. Finally, it was useful to assign

all relevant variables within each of these variable classes to each piping system and non-piping

component. For example, all relevant materials and degradation mechanisms associated with the surge

line were identified.

There were also several deficiencies uncovered in the pilot elicitation which required refinement prior to

the formal elicitation. First, the understanding of elicitation questionnaire and the quality of the responses

suffered in the pilot elicitation because there was no direct interface with each panelist during the

completion of the elicitation questionnaire. Also, it was obvious that not all important variable classes

had been appropriately identified during issue development and more comprehension was necessary.

Next, the global questions should have been considered as a function of piping system, non-piping

component, material, and degradation mechanism; and not independently from these variables. The use

of NUREG/CR-5750 estimates to provide baseline values for adjustment was also realized as a limitation

because the underlying conditions associated with these estimates were not sufficiently decomposed.

Many panelists also thought that projecting estimates over a single 35-year period was too difficult and

3-3
that breaking up the time period into shorter intervals would have made the assessment easier. Finally, it

was evident that active and passive system LOCAs should be considered separately due to their different

failure mode characteristics. All these deficiencies were addressed during the development of the formal

elicitation process.

3.2 Panel Selection

Panel selection (Block I) is a critical step in the process. The success of the elicitation is a direct result of

the broad expertise of the panel members, and the ability of each member to provide corollary

information in their specific areas of expertise. This information exchange enhances the general

understanding of the remaining panel members. Potential panel members were sought within industry,

academia, national laboratories, contracting agencies, other government agencies, and international

agencies. Initially, a pool of 55 nominally qualified people was established by querying knowledgeable

sources within the industry and NRC. Twenty-five people were solicited for the panel from this pool.

They were sent information about the objective, scope, and approach of the elicitation exercise as

background and were asked to submit resumes and also to evaluate their relevant technical areas of

expertise for the exercise. Based on this feedback, the final panel of 12 was chosen to achieve both

technical and organizational variety, and ensure a diversity of opinion, expertise, and backgrounds.

The elicitation panel members are listed in Table 3.1. The organizational diversity is apparent. Two of

the panel members represent the European regulatory community; three of the panel members represent

commercial vendors and owner's groups; four members are primarily NRC consultants; and three

members have conducted extensive relevant research for both the commercial nuclear industry owner's

groups and individual plants. Panel members were also chosen to represent a range of relevant technical

specialties: PFM, piping design, piping fabrication, operating experience, materials, degradation

mechanisms, operating mitigation practices, stress analysis, nondestructive evaluation, etc. All panel

members have at least twenty-five years of experience in these relevant technical areas pertaining to

commercial nuclear power applications. Each member's relevant qualifications'are summarized in

Appendix A.

3-4
Table 3.1 LOCA Frequency Elicitation Panel

Panel Member Organization

Mr. Bruce Bishop Westinghouse Electric Co LLC

Dr. Vic Chapman OJV Consultancy Limited

Mr. Guy DeBoo Exelon Nuclear

Mr. William Galyean Idaho National Engineering Environmental Laboratory

Dr. Karen Gott Swedish Nuclear Power Inspectorate

Dr. David Harris Engineering Mechanics Technology, Inc.

Mr. Bengt Lydell ERIN® Engineering and Research, Inc.

Dr. Peter Riccardella Structural Integrity Associates, Inc

Mr. Helmut Schulz Gesellschaft fur Reaktorsicherheit (GRS) mbh

Dr. Sampath Ranganath Formerly GE Nuclear Energy/Now XGEN Engineering

Dr. Fredric Simonen Pacific Northwest National Laboratory

Dr. Gery Wilkowski Engineering Mechanics Corporation of Columbus

A facilitation team was assembled to guide the panel through the elicitation process. The team consisted

of one normative member, six substantive members, and two recorders. All but two of the facilitation

team members were NRC staff. The substantive members were chosen to provide the same broad

relevant technical knowledge and background required of the panel. The facilitation team role was to

formulate the elicitation objectives and scope; coordinate and provide background technical information;

develop the elicitation questions; guide and record the individual elicitation sessions; analyze and

summarize the panel's findings; and develop the final LOCA frequency distributions from the panel's

responses.

3.3 Elicitation Traininq

A basic premise in using an elicitation process is that the panel responses as a whole have no significant

systematic bias. While individual responses can be highly uncertain and differ drastically, they do not

systematically over- or underestimate the quantities of interest. Many elements of the elicitation

procedure are designed to achieve this goal. These include the following:

" Constructing the panel with experts from all relevant technical areas and

institutional/organizational affiliations (Section 3.2).

* Conducting elicitation training to identify possible sources of bias (Section 3.3.1) and conduct an

exercise involving "almanac-type" questions with known answers (Section 3.3.2).

" Providing operating experience data and base case scenarios for anchoring and validating

responses to the panel (Section 3.5).

" Formulating the elicitation questions to avoid response bias (Section 3.8).

" Conducting individual elicitation sessions to eliminate the possibility of group dynamics

influencing panelist responses (Section 3.9).

From Appendix C, an analysis of the training exercise results supports the basic premise of no significant

systematic bias. Elicitation training is an important tool to eliminate or minimize bias. It can also

increase the accuracy and consistency of the responses provided by the panelists.

3-5
The elicitation training had three specific purposes:

(i) to discuss sources of bias in the elicitation procedure;

(ii) to familiarize the panelists with the type of responses which they will be asked to make; and

(iii) to provide the panelists with practice in making elicitation responses using a training exercise.

3.3.1 Motivational and Cognitive Biases

The panelists were introduced to sources of bias with the purpose of reducing biases in their individual

subjective judgments. There are two sources of bias: motivational and cognitive. Motivational biases are

due to emotional and psychological factors while cognitive biases are due to limitations on how

information is processed by the human brain [3.6].

Motivational biases can result when a panelist's thoughts and responses are altered by the elicitation

process. There are four types of motivational biases.

1. Social pressure can lead to groupthink when panelists may suppress their doubts or differing

opinions in order to attain consensus. It can also be manifested in a panelist's response to verbal

and non-verbal feedback from an interviewer. In addition, a panelist's responses might be

influenced by his perception of what might be acceptable to his employer or society at large.

2. Misinterpretation can occur if the elicitation question structure is inconsistent with a panelist's

thought process. For example, a panelist used to thinking in deterministic terms may be unsure

how to respond if asked to think in probabilistic terms. Misinterpretation can also occur if a

panelist is guided by the interviewer's viewpoint rather than his own.

3. Misrepresentation can be due to incorrect assumptions about the data or the models used to

analyze the issue.

4. Wishful thinking can be the result of an institutional bias, e.g., a manager who underestimates the

risk of a hazardous activity. This type of bias is relatively uncommon.

Cognitive biases can result when a panelist's thinking is illogical or does not conform to normative rules

such as the axioms of probability. There are four types of cognitive biases.

I. Inconsistency can occur when there are multiple issues, assumptions, definitions or algorithms

involved and a panelist does not keep them all in mind simultaneously. For example,

inconsistency would occur if the panelist's sum of the probabilities of a set of mutually exclusive

and exhaustive events does not add to one. Inconsistency is the most common cognitive bias.

2. An anchoring bias can occur when a panelist makes a relative comparison to a base case and does

not sufficiently adjust his response with respect to the base case estimates.

3. An availability bias can occur when a panelist's opinion is overly influenced by the recent

occurrence of a dramatic event, e.g., the accidents at TMI or Chernobyl.

4. Underestimation of uncertainty can occur when a panelist is asked for uncertainty bounds on his

estimates. It is well-established that people are often overly-confident when dealing with highly

uncertain issues [3.6], see Section 3.3.2.

3.3.2 Training Exercise

Elicitation training was conducted as part-of the first group meeting with the panel. Meeting minutes for

the various panel meetings are provided in Appendix B. The training exercise (Block 4) consisted of

asking the panelists a number of quantitative questions with known answers, but in a subject area with

which they are relatively unfamiliar. The purpose of asking these "almanac-type" questions is twofold:

3-6
(i) to accustom the panelists to the types of responses that they will be required to provide in their

elicitations, and

(ii) to demonstrate to the panelists that, although individually they may be highly uncertain about

their responses, the group response is closer to the correct answer than the individual responses.

For each question, the panelists were asked to supply three numbers: a mid-value (MV), an upper bound

(UB) and a lower bound (LB). The MV is defined such that, in the panelist's judgment, there is a 50%

chance that the correct answer lays above the MV and a 50% chance that it lays below the MV. The UB

and LB are defined such that there is a 5% chance that the correct answer lays above or below them,

respectively. In other words, the MV corresponds to the median of the panelist's subjective distribution

of the correct answer, and the UB and LB correspond to the 9 5 "h and 5"' percentiles, respectively. The

interval (LB, UB) is a subjective 90% coverage interval for the correct answer. The elicitation exercise

consisted of four questions.

1. According to the 2000 census, how many American men age 65 or over were there in the

U.S.?

2. How many Americans men age 65 or over suffered from the following chronic conditions in

1995: arthritis, cataracts, diabetes, hearing loss, heart disease, and prostate disease? Express

your answer in terms of a rate per 1,000 men.

3. What is the ratio of the rate for men 45-64 years old to the rate for men 65 and older for each

of the conditions listed in Question 2?

4. What is the ratio of the rate for men under 45 years old to the rate for men 45-64 years old for

each of the conditions listed in Question 2?

Note that Questions 3 and 4 ask about ratios. This corresponds to the type of questions asked in the

elicitation, where almost all the questions concern the ratios of LOCA frequencies with respect to the base

case conditions.

The results of the elicitation training exercise were consistent with several of the basic premises

underlying the elicitation structure and methodology (see Appendix C). .Recall that the (LB, UB)

intervals are supposed to contain the correct answer 90% of the time. One useful evaluation is to compare

the actual coverage interval to its nominally prescribed value of 90%. This provides a measure of how

well the panelists are calibrated. As presented in Appendix C, between 15 and 17 responses were

provided for each of the four questions. Although there were only 12 members on the panel, members of

the facilitation team were also invited to participate in the exercise.. The results for the four questions

above are as follows.

1. Question 1: Fourteen of 17 intervals (82%) covered the correct answer.

2. Question 2: Fifteen responders provided intervals for each of the six conditions. Of the 90

intervals, 55 (61%) covered the correct answer.

3. Question 3: Sixteen responders provided intervals for each of the six conditions. Of the 96

intervals, 69 (72%) covered the correct answer.

4. Question 4: Sixteen responders provided intervals for each of the six conditions. Of the 96

intervals, 68 (7 1%) covered the correct answer.

The result for the first question was close to the nominal 90%, most likely because this question dealt

with demographic data for which the responders had a relatively good understanding. The results for the

other three questions were consistent with the well-established observation that the actual coverage

3-7
probability for a subjective interval is generally less than its nominal coverage probability [3.6]. In other

words, people are generally overconfident and underestimate the uncertainty in their subjective estimates.

(Sensitivity analyses examining the potential magnitude of this over confidence are discussed in Section

5.6.1).

The coverage intervals for the last two questions were more accurate than for the second question. This

may stem from that fact that Questions 3 and 4 asked for relative ratios rather than absolute numbers.

This trend is consistent with the assumption driving the basic structure of this elicitation, namely, that

relative values are easier to assess than absolute values (see Section 3.8.1). See Appendix C for the

correct answers to the training exercise, as well as a full discussion of the panel and facilitation team

responses.

3.4 Technical Issue Formulation

The formal elicitation was begun in February 2003 with a three-day meeting of the panel and facilitation

team. The five principal objectives of this meeting were to define the scope and objectives of the

elicitation (Section 2); provide background information about previous LOCA frequency estimates

(Section 1); construct an approach for determining LOCA frequencies (Section 3); identify significant

technical issues affecting LOCA frequencies (Section 3.4); and conduct elicitation training (Section 3.3).

Subsequent discussion to finalize technical issues also was held in a follow-up two day meeting in June

2003.

3.4.1 Important Definitions

The first step in the decomposition was to define key technical terms and issues (Block 3), including the

definition of a LOCA to ensure consistent understanding within the panel. For the purpose of this

elicitation, the panel members defined a LOCA as:

"A breach of the reactor coolant pressure boundary which results in a leak rate beyond

the normal makeup capability of the plant."

The panel next defined various LOCA size categories (Table 3.2). The LOCA size categories are largely

consistent with historical definitions developed for small break (SB), medium break (MB), and large

break (LB) LOCAs during the WASH-1400 evaluation [3.7]. These definitions were retained in

subsequent exercises to characterize plant risk [3.1] and determine initiating event frequencies [3.5]. One

distinction is that, historically, break size frequencies were defined over a range of flow rates for SB (100

to 1,500 gpm [380 to 5,700 Ipm]) and MB (1,500 to 5,000 gpm [5,700 to 19,000 lpm]) LOCAs. In this

exercise, the panel chose to work with threshold values for each LOCA category. The cumulative

threshold definitions were chosen instead of interval LOCA definitions because the former are more

consistent with the various PFM and operating experience analyses that the panelists performed.'

Additionally, three additional categories which fall within the historical LB LOCA regime were defined.

These additional categories were developed to evaluate frequencies associated with the larger break sizes

up to the DEGB of the largest primary system piping. These additional categories also reflect regions

where different plant responses may be required to mitigate LB LOCA events of increasing size. LOCA

Category 6 was chosen to correspond to the flow rate which would result from the complete rupture of the

largest PWR primary piping system in the plant, i.e., a hot leg. For a BWR plant, the LOCA Category 6

Interval-based estimates for the mean LOCA frequencies can be determined by simply subtracting adjacent cumulative LOCA

categories. However, this technique cannot be used for determining other percentiles of the distribution. Because the differences

between the results for consecutive LOCA categories are much smaller than the uncertainties, cumulative threshold (see tabular

results in Section 7) and interval-based LOCA frequency estimates are statistically identical.

3-8
flow rates are only applicable for a catastrophic rupture of the reactor pressure vessel. There are no

primary BWR piping systems that can generate a LOCA of this size upon failure. LOCA Categories 4

and 5 were determined so that the ratios between successive LB LOCA threshold values were

approximately equal between Categories 3 to 6. The flow rate increases by either a factor of 4 or 5 for

each successive category between Categories 3 to 6.

Table 3.2 LOCA Category Definitions

LOCA Flow Rate Threshold, LOCA

Category gpm (Ipm) Classification

I > 100 (380) SB

2 > 1,500 (5,700) MB

3 > 5,000 (19,000) LB

4 > 25,000 (95,000) LB a

5 > 100,000 (380,000) LB b

6 > 500,000 (1,900.000) LB c

In addition to considering different LOCA sizes, the panel members also considered the possibility that

future LOCA frequencies may be time dependent. Three different time periods were defined: 0-25, 25-

40, and 40-60 years of plant operation. The 0-25 year period is representative of the current plant

experience considering that the current fleet average age is approximately 25 years. Frequency estimates

at 25 years are synonymous with the current-day LOCA frequency estimates. The elicitation panelists

were expected to estimate current-day, or instantaneous LOCA frequencies.at the end of this first time

period.

The 25-40 year period is representative of future operation up to the end of the original design life and

plant license. This period allows LOCA frequency changes over the next 15 years to be evaluated. The

40-60 year period is representative of future operation between the expiration of the original plant license

up through the plant license-renewal period. This period evaluates LOCA frequency changes within the

next 35 years. The 40-year and 60-year estimates were not explicitly defined in the elicitation to

represent either instantaneous frequencies at 40 and 60 years of operation or averaged frequencies

between 25 - 40 years and 40 - 60 years of plant operation. Panel members could choose to consider

either average values over the time periods, maximum values within the time periods, or values at the

ends of the time periods. No distinction was made so that the panelists had the most flexibility to assess

the relevance of each type of estimate, and provide either averaged or instantaneous values accordingly.

Each panelist was also asked to discuss possible differences between averaged and instantaneous

frequencies for the future time periods. The lack of an explicit definition, while important for

understanding the context of the results, is not of practical significance because future changes in generic

LOCA frequencies are generally expected to be gradual.

The panel members considered issues that affect both piping and non-piping passive system failures. The

contributions from active system components (e.g. stuck open valves, gasket and seal LOCAs) were not

considered. The contributions from active components should be assessed separately and added to the

passive system frequencies developed in order to estimate the total LOCA initiating event frequencies

from both passive and active component failures. The panel members used existing ASME rules to

identify the boundaries between piping and non-piping components. However, this distinction was

somewhat academic since total LOCA estimates (i.e., sum of the piping and non-piping contributions)

were determined from individual panelist responses. Therefore, piping and non-piping boundary

differences among the panelists do not affect the final estimates.

3-9
3.4.2 Safety Culture Issue Formulation

Issues related to the effect of safety culture on the LOCA frequencies were often raised during technical

issue formulation (Block 3). While none of the panelists are organizational safety culture experts, they all

have relevant experience and opinions on how safety culture variability can affect LOCA frequencies.

The panelists believe that safety culture affects are only weakly correlated with other variables that

influence LOCAs, including the effects of aging mechanisms. These considerations were also thought to

be independent of specific piping systems and non-piping components. Therefore, the panelists decided

to consider the effect of safety culture LOCA contributions independently from age-related contributions.

For the purposes of the elicitation, safety culture was defined to encompass a number of economic,

political, social, and psychological issues. Issues discussed fell into the broad categories of deregulation;

decommissioning; human error; new-plant construction; technology improvements; knowledge transfer;

lessons-learned from operating experience; public perception; the regulatory environment; and

management philosophy. These factors can have either positive, negative, or neutral attributes with

respect to their effect on LOCA initiating event frequencies. The objective of the group discussion was to

provide the panelists with a clear understanding of the safety culture issues for assessment during their

individual elicitations.

Some specific issues discussed include the likelihood that human error can occur during maintenance and

mitigation operations and impact LOCA frequencies. An example is the failure to adhere to proper

procedures or misinterpretation of obvious inspection indications of degradation. The effect of an aging

workforce and the ability to provide sufficient knowledge transfer to the less experienced replacement

workers was also discussed. The effect of lessons learned from past experiences on decreasing the

response time needed to mitigate the impact of new degradation mechanisms was also debated. For

example, the industry experience with mitigating IGSCC in the early 1980's may provide some useful

strategies for mitigating current PWSCC concerns. Additionally, with respect to management

philosophy, the merits of the adoption and implementation of a risk-informed management strategy were

discussed. Some additional discussion topics are summarized in Appendix B.

The elicitation questions asked panelists to individually consider the effect of these and other significant

safety culture issues on LOCA initiating-event frequencies. The elicitation structure was defined so that

each panelist only addressed relative changes in the future safety culture relative to the existing safety

culture (Section 3.8.3). The panel also decided during the initial issue formulation meeting to consider

industry and regulatory safety cultures separately. However, it was also identified that this relationship

could be strongly correlated. The elicitation questions asked panelists to consider the correlation between

the industry and regulatory safety culture in their assessment. Also, panelists were asked to assess the

relationship between safety culture and LOCA-size category.

More detail on the safety culture elicitation questions and structure is provided in Section 3.8.3. The

qualitative rationale that identifies and discusses important safety culture issues is provided in Section

6.2. This rationale supports the quantitative estimates supplied by the panelists to the elicitation

questions. These results are summarized in Section 7.1 along with a discussion on how these results were

considered when determining the LOCA initiating event frequencies.

3.4.3 General Issue Development Structure

The remainder of the elicitation focused solely on the contribution of aging-related passive system

degradation to the LOCA frequency estimates. The panel developed a structure (Block 5) for considering

passive system failures which contribute to LOCAs (Figure 3.2) due to normal operating conditions and

loading, and transients expected over the extended plant operating life. The total passive system

frequencies were first divided by the panel into piping and non-piping contributions. The panel next

agreed that the design and operating characteristics of each piping system and each major non-piping

3-10
component (e.g., main coolant pumps, steam generators, pressurizer, and valves for PWRs) could impact

the underlying LOCA frequencies. Non-piping components were further subdivided into relevant

subcomponents (e.g., valve bonnet, valve bonnet bolts, valve casing, etc.) that possess unique operating

and design characteristics. For a given LOCA-sensitive piping system or non-piping subcomponent. the

panel identified five variable classes (i.e., geometry, loading history, materials, aging mechanisms, and

mitigation and maintenance practices) that contain all the principal variables that affect passive system

LOCA frequencies.

The elicitation structure (Figure 3.2) developed by the panel provided flexibility to the LOCA frequency

estimation process. Panel members could develop LOCA frequencies based on either piping system or

non-piping component level information (top-down approach in Figure 3.2), or by considering the

individual effects of specific variable combinations within a given system or component and then

combining important contributions (bottom-up approach in Figure 3.2). Separate elicitation questions

were developed for each analysis approach (Section 3.8.4). The panel members either used one of these

two approaches, a combined approach, or an alternative approach in their elicitation responses. More

specific details on the issue formulation follow. Other information is available in Appendix B.

Figure 3.2 Elicitation Structure

3.4.4 Variable Assignments

The panel then developed a list of relevant variables within each of these variable classes that could affect

the LOCA frequencies. For instance, under the aging mechanism variable class, mechanisms that both

have been experienced or could surface in the future were identified (Table 3.3). Table 3.3 summarizes

the mechanisms developed during the group brainstorming sessions. The mechanisms are segregated by

the primary mechanism type. Additional sub-categories are used to identify either specific degradation

mechanisms under the appropriate main category or features associated with the main category. For

3-11
example, fatigue degradation was separated into low cycle fatigue which is primarily driven by thermal

loading fluctuations due to plant heat-up and cool-down cycles and high cycle mechanical fatigue which

could result from general loading functions on the piping, possibly due to an adjacent source of vibration.

Both crack initiation and crack growth portions of life are important contributors to fatigue life, although

crack initiation occupies a greater percentage of the life in the high-cycle fatigue regime. Stress corrosion

cracking (SCC) is listed as a main category and it includes IGSCC which was prevalent in BWRs in the

late 1970's, transgranular SCC (TGSCC) which affects casting components, PWSCC which has more

recently surfaced in PWRs, and external chloride SCC (ECSCC). Similar discussions ensued at the kickoff meeting for identifying the remaining degradation mechanisms listed in Table 3.3. A synopsis of

those discussions can be found in the detailed minutes for the kick-off meeting in Appendix B. In

addition, more details on the development of the variables within the other relevant piping system

variable classes, i.e., geometry, loading, etc. is provided in Appendix B along with complete tables for all

five of these variable classes.

3.4.5 Piping System Variable Combinations

The panel next identified those specific variable combinations that are active for each LOCA-sensitive

piping system. For example, the recirculation piping in BWR plants is stainless steel and is susceptible

only to certain aging mechanisms, like SCC, thermal fatigue, etc. The geometry, loading spectrum, and

maintenance practices applicable to the BWR recirculation system were also defined. A similar exercise

was conducted for all the BWR (Table 3.4) and PWR (Table 3.5) LOCA-sensitive piping systems to

identify and correlate the relevant variables associated with each piping system.

3-12
Table 3.3 Material Aging Degradation Mechanisms

Main Category Sub- Sub- Sub- SubCategory 1 Category 2 Category 3 Category 4

Low Cycle Thermal Fatigue Crack Crack Growth

Initiation

High Cycle Mechanical Fatigue Vibration Pressure Temperature

Stress Corrosion Cracking IGSCC TGSCC ECSCC PWSCC

Localized Corrosion Pitting Crevice

Corrosion

General Corrosion Boric Acid

(ID or OD)

Fretting Wear

Material Aging Thermal Dynamic Radiation Creep

Fabrication Defects and Repair

Hydrogen Embrittlement

Flow Sensitive Erosion/ FAC

Cavitation

Unanticipated (New)

Mechanisms

IGSCC = Intergranular stress corrosion cracking

TGSCC = Transgranular stress corrosion cracking

ECSCC = External chloride stress corrosion cracking

PWSCC = Primary water stress corrosion cracking

FAC = Flow accelerated corrosion

3-13
Table 3.4 BWR LOCA-Sensitive Piping Systems

System Piping Piping Safe End Welds Sig. Degrad. Sig. Loads. Mitigation/

MatIs. Size (in) Matis. Mechs. Maint.

RECIRC 304 SS, 316 4,10,12, 304 SS, SS, UA, FDR. SCC, RS, P, S, T, DW, ISI wTSL.

SS, 20,22,28 316 SS, NB LC, MA SUP, SRV, 0 REM

347 SS A600

Feed Water CS 10, 12 304 SS, CS, UA, FDR, MF, T, TFL. WH,P, S. IS[ w TSL.

(typ), 12 - 316SS NB TF. FS, LC, SRV, RS, DW, 0 REM

24 GC, MA

Steam Line CS-SW 18,24,28 CS CS UA, FDR. FS. WH, P, S, T, RS, ISI w TSL,

GC, LC, MA DW, SRV, 0 REM

HPCS, CS (bulk), 10, 12 304 SS, CS, UA. FDR, SCC, RS, T, P, S, DW, ISI w TSL,

LPCI 304 SS, 316 316 5S, SS, TF. LC, GC, TS, WH, SUP, REM

SS A600 NB MA SRV, 0

RHR CS, 304 SS, 8-24 CS, CS, UA. FDR, SCC, RS, T, P, S. DW, ISI w TSL,

316SS 304SS, SS, TF, FS, LC, TS, O SUP, SRV REM

316SS NB GC, MA

RWCU 304SS,316 8-24 CS, CS, UA.FDR,SCC. RS, TS, T, P. S, ISI w TSL,

SS, CS 304 SS, SS, TF, FS, LC. DW. SUP, SRV. REM

316SS NB GC, MA 0

CRD piping 304 SS, 316 < 4 Stub tubes Crevice UA, FDR, MF, RS, T. P, S. DW, ISI w TSL,

SS - A600 and A182 to SCC V, 0, SRV REM

(low temp) SS* head

SLC 304 SS, 316 <4 304 SS, SS, UA, FDR, MF, RS, T, P, S. DW, ISI w TSL,

SS 316SS NB SCC V, 0, SRV REM

INST 304 SS, 316 <4 304 SS, SS. UA, FDR, MF, RS, T, P, S, DW, ISI w TSL,

SS 316SS NB SCC, MA V, 0, SRV REM

Drain lines 304 SS, 316 <4 304 SS, S S, UA, FDR. ME, RS, T P, S. DW, [SI w TSL,

SS, CS 316SS. NB SCC. LC, GC V, O, SRV REM

CS

Head spray 304 SS, 316 < 4 304SS, SS, UA, FDR, SCC, RS, P, S, T, DW, ISI w TSL,

SS, CS 316 SS, CS NB TF, LC, GC SRV, O REM

SRV lines CS 6,8, 10, CS CS UA. FDR, MF, RS, PS. T, DW, ISI wTSL,

28 FS. GC, LC, SRV, 0 REM

MA

RCIC 304.SS, 316 6,8 304 SS, SS UA, FDR, SCC, RS. P, S. T, DW, ISI w TSL.

I SS, CS I 316SS NB LC, MA SRV, O REM

304 SS = 304 series stainless steel

316 SS = 316 series stainless steel

347 SS = 347 series stainless steel

A600 = Alloy 600

CS = carbon steel

CS - SW = seam welded carbon steel

NB = Nickel-based weld (Alloy 82/182)

UA = unanticipated mechanisms

MA = material aging

LC = local corrosion

FDR = fabrication defect and repair

SCC = stress corrosion cracking

MF= mechanical fatigue

TF = thermal fatigue

FS = flow sensitive (inc. FAC and erosion/cavitation)

ISI w TSL = Current ISI procedures with technical

specification leakage detection requirements

considered.

RS = residual stress

P = pressure

S = Seismic

T = Thermal

DW dead weight

SUP= support loading

SRV = SRV loading

WIHI water (and steam) hammer

o = overload

V = vibration

TFL = thermal fatigue loading from striping

TS = thermal stratification

REM = all remaining mitigation strategies possible

(e.g., not unique to piping system)

HPCS = high pressure core spray

LPCS = low pressure core spray

RHR = residual heat removal

RWCU = reactor water cleanup

CRD = control rod drive

SLC = standby liquid control

SRV = safety relief valve

RCIC - reactor core isolation cooling

3-14
Table 3.5 PWR LOCA-Sensitive Piping Systems

System Piping Piping Safe End Welds Sig. Degrad. Sig. Loads. Mitigation/

Matis. Size (in) Matis. Mechs. Maint.

RCP: Hot Leg 304 SS, 316 30-44 A600, 304 A82 TF, SCC, MA, P, S, T, RS, ISI w TSL.

SS. C-SS, SS, 316 SS, 304 SS, FDR. UA DW, 0, SUP REM

SSC-CS CS - CS 316 SS.

SW CS

RCP: Cold 304 SS, 316 22-34 A600, 304 A82 TF, SCC, MA, P, S, T, RS, ISI w TSL,

Leg/Crossover SS, SS, 316 SS, 304 SS, FDR, UA DW, 0, SUP REM

Leg C- SS, SSC- CS 316 SS.

CS, CS - SW CS

Surge line 304 SS, 316 10- 14 A600, A82 TF, SCC, MA, P, S, T. RS, TSMIT, ISI w

SS, C-SS 304 SS, 304 SS. FDR, UA DW, 0, TFL, TSL, REM

316SS, 316SS TS

SIS: ACCUM 304 SS, 316 10- 12 A600, A82 TF, SCC, MA. P, S, T, RS, ISI w TSL,

SS, C-SS 304 SS. 304SS, FS. FDR, UA DW, 0 REM

316SS, 316SS (FAC)

SIS: DVI 304 SS, 316 2-6 A600, A82 TF. SCC. MA, P, S T. RS, ISIw TSL,

SS 304 SS, 304 SS, FS, FDR, UA DW, 0 REM

316SS, 316SS (FAC)

Drain line 304 SS. 316 <2" MF, TF, GC. LC, P, S, T. RS, ISI w TSL,

SS, CS FDR, UA DW, 0, V, TFL REM

CVCS 304 SS, 316 2-8 A600 A82 SCC, TF, MF. P. S, T, RS, ISI w TSL,

SS (B&W and FDR. UA DW, 0, V REM

CE)

RHR 304 SS. 316 6-12 SCC, TF, MA, P, S, T, RS, ISI w TSL,

SS FDR, UA DW, 0, TFL REM

SRV lines 304 SS, 316 I -6- TF, SCC, MF, P. S, T, RS, ISI w TSL,

SS FDR, UA DW, 0. SRV REM

PSL 304 SS, 316 3-6 A82 TF, SCC, MA, P, & T, RS, ISI w TSL,

SS FDR, UA DW, 0, WH, REM

TS

CRDM A600 4-6 SCC, TF, MF, P, S, T, RS, HREPL, ISI w

LC, FDR. UA DW, 0 TSL, REM

RH 304 SS, 316 <2 A600 MF, SCC, TF, P, S, T, RS, ISI w TSL,

SS FDR, UA DW, 0, V, TS REM

ICI 304 SS,316 < 2 A600 MF, SCC, TF, P, S. T, RS, ISI w TSL.

SS FW, FDR, UA DW, 0, V REM

INST 304 SS, 316 <2 MF, SCC, TF. P, S, T, RS, ISI w TSL.

SS FDR, UA DW, 0, V REM

304 SS = 304 series stainless steel

316 SS = 316 series stainless steel

A600 = Alloy 600

A82 = Alloy82

SSC-SC = Stainless steel clad carbon steel

CS = carbon steel

CS - SW = seam welded carbon steel

UA = unanticipated mechanisms

MA material aging

GC = general corrosion

LC local corrosion

FDR = fabrication defect and repair

SCC = stress corrosion cracking

MF = mechanical fatigue

TF = thermal fatigue

FS = flow sensitive (inc. FAC and erosion/cavitation)

ISI w TSL = Current ISI procedures with technical

specification leakage detection requirements

considered.

B&W = Babcock and Wilcox

CE = Combustion Engineering

RS = residual stress loading

P = pressure loading

S = seismic loading

T = thermal loading

DW = dead weight loading

SUP = support loading

SRV = SRV loading

WH = water (and steam) hammer

o = overload

V = vibration

TFL = thermal fatigue loading from striping

TS = thermal stratification

REM = all remaining mitigation strategies possible (eg. not

unique to piping system)

TSMIT= thermal stratification mitigation

HREPL = RPV head replacement

RCP = reactor coolant piping

SIS = safety injection system

DVI = direct volume injection

CVCS = chemical volume and control system

RHR = residual heat removal

SRV = safety relief valve

PSL = pressurizer spray line

CRDM = control rod drive mechanism

RH = reactor head

ICI = in-core instrumentation

3-15
Table 3.6 Reactor Pressure Vessel (RPV) Failure Modes

Failure Modes Material Degradation Mitigation/ Comment

Mechanisms Maintenance

Vessel Head high strength LC Human error Removal leading to human

Bolts steel error during refueling

CRDM SS welded, bolted, threaded +

connections seam weld

Nozzles SSC-CS

RPV wastage SSC-CS LC Boric acid wastage (upper

& lower head, shell)

RPV Corrosion SSC-CS MF Initiate at cladding cracks

Fatigue (upper & lower head, shell)

RPV= reactor pressure vessel

SS = stainless steel

SSC-CS = stainless steel clad carbon steel

LC = local corrosion

GC = general corrosion

3.4.6 Non-Piping System Failure Modes

A similar exercise was conducted for the non-piping LOCA-sensitive components and subcomponents.

Table 3.6 illustrates the variables considered important for the RPV in BWRs and PWRs. The panel

identified failures in the RPV shell (due to wastage or corrosion fatigue), the head bolts, the CRDM

connections, and the nozzles as passive system LOCA-sensitive RPV subcomponents. The group

generally did not have information on component geometries, loading, and mitigation/maintenance

practices at this meeting and salient information was researched later as necessary by each panelist prior

to the elicitation. However, several specific failure scenarios were discussed for each of these

subcomponents. An RPV shell failure could occur by boric acid wastage from the outer shell or due to

corrosion fatigue within the shell emanating from fissures in the stainless steel cladding. The CRDM

connections are those that connect to the reactor CRDM nozzles. Failure would imply a failure of this

connection which could be bolted, welded, or threaded depending on the RPV design. Vessel head bolt

failure is only thought possible due to either human error during refueling as the head is removed and

reinstalled or a common cause degradation mechanism such as boric acid corrosion that is occurring

simultaneously in several bolts. Nozzle failure includes both the larger main coolant loop nozzles, as well

as the smaller CRDM and instrumentation penetration nozzles. Nozzle failure is possible due to SCC or

thermal fatigue. Other RPV failures were discussed as well, and similar discussions were held for all the

LOCA-sensitive non-piping components and subcomponents. See Appendix B for more details, and for

similar tables for other non-piping components. While these initial failure scenarios were discussed for

all the non-piping components during the issue development meeting, the panelists were invited to

consider other possible failure scenarios that should be considered during the elicitation.

3.4.7 Reference Case Development

Reference cases were developed for each BWR and PWR piping system by assigning specific values to

each variable category (e.g., material, geometry, loading, mitigation, and aging mechanism) within Tables

3.4 and 3.5. Similar cases were also developed for the PWR piping systems (Appendix B). The reference

cases were defined to create a simplified set of conditions that could be used as a starting point to assess

LOCA frequencies within each system. The reference cases are representative of possible conditions that

could lead to a LOCA, but they do not necessarily represent the most important LOCA contributor for a

given piping system. The actual BWR and PWR reference cases are described in Appendix B. The

development of the reference case conditions is analogous to the development of the base case conditions

3-16
(Section 3.5). However, the reference cases are not associated with a quantified LOCA frequency. See

Section 3.5 for more information on the base-case development. More information on the use of the

reference cases within the elicitation is provided in Section 3.8.4.

3.5 Base Case Development

The elicitation structure provided the panel members with a way to prioritize and assess important

contributing variables to the generic LOCA frequency distributions. The most challenging aspect for

each panelist was to quantify the frequencies associated with the important contributing variables that

they individually identified. In order to improve the accuracy of the assessment, piping and non-piping

base case frequencies were developed to provide the panelists with quantitative estimates for a set of well

defined conditions that they could use for anchoring their responses. A clearly defined set of conditions

and assumptions were developed by the elicitation panel for each base case (Block 6). These conditions

were then analyzed by a member of the base case team (Block 7) using either PFM or classical statistical

and/or Bayesian assessment of operating experience to predict the frequencies associated with each piping

and non-piping base case. The assumptions, approach, and results of these analyses were then presented

to the elicitation panelists so that they clearly understood the basis for the base case estimates.

This development and use of base cases was employed so that the panelists did not have to provide

absolute frequencies during the elicitation. Instead, they chose appropriate base cases and provided a

relative ratio to express the difference between the base cases and the important contributing variables

that they identified. This technique is based on the premise that relative ratios are easier to assess, and

therefore more accurate, than absolute numbers. The rationale for this premise is also discussed in

Sections 3.3.2 and 3.8.1. The decision to use and the application of these base cases was made by each

individual panelist. Some panelists utilized them extensively, others considered the relative trends

expressed by the base case estimates, and others chose not to utilize them at all. However, in general, the

panelists using the base case estimates chose to anchor their responses to base case results based on

operating experience instead of the PFM results. Therefore, the differences in absolute frequencies

between the operating-experience base cases and the PFM base cases (as discussed in Section 4) are not

important in assessing variability among individual elicitation responses.

3.5.1 Piping Base Cases

The objective of the piping base case study was to analyze various PWR and BWR piping systems and

materials using both operating experience and fracture mechanics analyses with shared information and a

common set of conditions and assumptions. The insights from these analyses were provided to the

panelists to help them formulate their opinions on the comparative LOCA likelihood between the base

case conditions and other systems and degradation mechanisms not explicitly modeled. It was

impractical to model all possible LOCA sensitive components, materials, and degradation mechanisms.

Therefore, the goal in the base case development was to consider several important variables in BWR and

PWR plants including piping size, aging mechanisms, and piping materials. Several aging mechanisms

were chosen based on operating experience and a consideration of those mechanisms that provide the

greatest LOCA challenge. A range of piping sizes were evaluated to provide information on the spectrum

of pipe break sizes considered in the elicitation. The most common piping materials and welds were

considered.

Finally, the following sensitivity analyses were conducted to evaluate their impact on the predicted basecase frequencies:

* Effect of different leak detection strategies for ISI

* Effectiveness of ISI assessment,

* Impact of weld overlay on IGSCC mitigation,

3-17
* Effectiveness of IGSCC mitigation techniques,

* Impact of applied loading history,

* Effect of large seismic loading and hydro test loads, and

" Impact of degraded material properties.

See Section 4.3 for a description of these sensitivity analyses and the results.

3.5. 1. 1 Piping Base Case Definition - Five different base cases were defined for the piping systems:

two BWR and three PWR cases (Block 6). The variables established for analysis are summarized in

Table 3.7. The loading spectrum for each of these systems was assumed to be the normal steady-state and

transient loading histories expected over 60 years of operation. Maintenance and mitigation was assumed

to be typical ISI for all systems with leak detection resolution as required by the technical specification

limits. The BWR systems were assumed to be operating with normal water chemistry (NWC) and the

recirculation system base case assumed that a weld overlay (WO) had been applied after 20 years of

operations to mitigate IGSCC2

. The inspection technique and periodicity for the recirculation base case

are as specified in generic letter (GL) 88-01 [3.8].. It should be noted that both Bengt Lydell and Bill

Galyean used the post-IGSCC mitigation cracking rates as the basis for their BWR-1 analysis. This was

counter to the original BWR-1 definition in Table 3.7, and results in lower LOCA frequency predictions

than the defined case since NWC had largely been eliminated by this time. Separate LOCA frequency

estimates associated with each of the piping bases cases were calculated for each defined LOCA category

at 25, 40, and 60 years of plant operations

Table 3.7 Piping Base Case Definitions

Plant System Pipe Pipe Safe End Weld Aging Maintenance

Type Size Material Material Material Mechanisms & Mitigation

(in)

BWR Recirculation 12, 304 SS NB NB IGSCC ISI, NWC,

28 WO

Feedwater 12 CS NA NA FAC, TF ISI, NWC

PWR Hot leg 30 304 SS NB NB PWSCC IST

Surge line 10 304 SS NB NB PWSCC, TF ISI

HPI make-up 4 CS or SS NA NA TF IS1

HPI = high pressure injection SS = stainless steel

CS = carbon steel NB = nickel-based

TF = thermal fatigue NA not applicable

3.5. 1.2 Piping Base Case Estimation - Four panelists were selected to separately calculate the

frequencies associated with each base case defined in Table 3.7: Victor Chapman, William Galyean,

David Harris, and Bengt Lydell (Block 7). These four panelists are referred to as the base case team.

Both Chapman and Harris were selected to utilize a PFM-based approach, while Galyean and Lydell were

chosen to utilize Bayesian and other statistical analysis techniques to extrapolate frequencies from

accumulated operating experience.

2 Most BWRs no longer operate with normal water chemistry as there have been improvements in reactor water conductivity to

mitigate IGSCC. Various sensitivity analyses were conducted to evaluate the effectiveness of various IGSCC mitigation

techniques (See Section 4.3).

3-18
There were several ground rules established by the elicitation panelist for the base case frequency

estimations. These are enumerated as follows:

* Base case members should collaborate and use common information as the basis for the

estimates.

" All base case calculations should model the established conditions (Table 3.7) as closely as

possible so that methodologies and results can be more easily compared.

" Each base case team member could choose any methodology for estimating the base case

frequencies.

* The PFM analysis should model service conditions as closely as possible. In particular, the

predicted crack leaking rates should be commensurate with operating experience.

* The base case team members should provide best estimate base case frequencies. Explicit

uncertainty calculations were not required.

* Frequencies should be calculated for the entire piping system, and not just on a per weld basis.

" Some additional sensitivity analyses should be conducted to evaluate the effect of variables not

modeled in the base case calculations.

Separate calculations were conducted by each base case team member to establish a range of base case

frequencies. As mentioned, these absolute frequencies are used to anchor panelist responses to the

elicitation questions. However, the differences among the results stemming from the four different

calculations provided valuable insights as well. The elicitation panelists, by understanding the details of

each approach, could identify the qualitative and quantitative effects of important assumptions and

modeling differences. The panelists could also assess the underlying uncertainties associated with each

calculation methodology. The sensitivity results could also be used to assess the impact of other variables

on the base case frequencies.

Periodic meetings were held among the base case team members. The purpose of these meetings was to

identify and share necessary background information, resolve technical ambiguities, discuss and resolve

problems, and review progress. It was apparent early in the process that the Table 3.7 base case

definitions were not sufficient to fully perform the calculations. Therefore, additional technical details

were standardized including the piping layout and configuration, weld census, applied stress information,

and the model BWR and PWR plant types. Additionally, the failure data associated with the base case

piping systems was available to the team members through a searchable database developed by SKI in

1998 [3.9]. The BWR base cases assumed a BWR/4 plant type. The PWR hot leg and surge line base

cases were modeled based on a three-loop Westinghouse plant while the HPI make-up line is

representative of a Babcock & Wilcox (B&W) plant. More details about the refined base case definitions

and a summary of background information are provided in Appendices D - G.

Each base case team member then conducted initial base case calculations and sensitivity analyses. The

initial sensitivity analyses included the effect of ISI, the impact of seismic events at 25, 40, and 60 years

of plant operation for some of the systems, the effect of proof testing, and the effect of weld overlay on

the BWR- I base case, i.e., recirculation system. More details about the individual calculations and

sensitivity analyses are contained in Appendices D - G. Once the initial base case frequencies were

obtained, results were shared among the base case team members and reasons for differences were

discussed.

Then, each base case team member prepared a presentation to summarize his calculation methodology

and results. The goal of these presentations was to make the underlying details of each calculation

apparent and provide the remaining panelists with sufficient information to independently assess the

calculations in support of their elicitation responses (Block 9). Therefore, the presentations had identical

formats so that the various approaches could be easily compared. The presentations explained the general

3-19
assumptions, general approach, input variable determination and assumptions, detailed calculation

procedure, results, and results of sensitivity analyses. Additionally, some major differences among the

four methodologies were summarized. A summary of the approaches followed by the four base case team

members in developing their piping base case frequencies is provided next.

3.5.1.2.1 Bengt Lydell's Base Case Estimates - This analysis was one of two base case estimates

using available operating experience to develop the base case frequencies. Base Case Report 2 (see

Appendix D) develops BWR and PWR LOCA frequency distributions using a "bottom-up approach".

Statistical analysis of the relevant operating experience is used to quantify the precursor failure rate and

rupture frequency (i.e., LOCA frequency) of individual welds. Here the precursor failure rate is defined

as the rate at which particular degradation is found and repaired/replaced in service based on historical

data. This is an attribute-influence approach in that the statistical analysis of operating experience is

restricted to consideration of the unique material combinations and degradation susceptibilities of primary

pressure boundary piping systems.

As there are few relevant events to estimate the rupture frequency in a purely statistical manner,

engineering judgment is required in the estimation of the conditional rupture probabilities given that a

precursor failure exists. Conditional rupture probability relationships were based on an analysis of

operating experience trends. Therefore, these CFPs were not assumed a priori. While Bengt Lydell

conditional probabilities vary somewhat as a function of degradation mechanism and LOCA size, the

average failure frequency decrease is approximately 1⁄2/2 order of magnitude between successive LOCA

categories. See Appendix D for more detail.

The rupture frequency is then simply the precursor failure rate multiplied by the conditional rupture

probability. The failure rate and rupture frequency (i.e. LOCA frequency) for an entire system is

calculated by concatenating the individual weld failure rates and rupture frequencies. As a final step,

Markov model theory is used to evaluate the influence of alternate strategies for ISI and leak detection on

the frequency of leaks and ruptures, and to calculate age-dependent LOCA frequencies. See Appendix D

for a more detailed description of this analysis (Block 10).

3.5.1.2.2 Bill Galyean's Base Case Estimates - This analysis also relied primarily on operating

experience to develop the base case frequencies, but in contrast to the previous method used a "top-down"

approach. The approach (see Appendix E) starts with the straightforward calculation of a best estimate

LOCA frequency using the number of failures (i.e., LOCAs) divided by the total number of reactor

operating years. The U.S. nuclear power operating experience consists of zero Category 1 (i.e., greater

than 100 gpm [380 lpm]) LOCAs over the entire operating experience, which totals 2,647 LWR

(calendar) years through April 2003. This operating history data was used to update a non-informative

prior distribution in a Bayesian calculation, to produce a posterior probability distribution on the total

LOCA frequency. The overall LOCA frequency calculated in this manner is approximately 1.9E-4/LWRyear (0.5/2600 LWR-years). This total frequency is then partitioned using the relative ratios of crack and

leak events from the available service data, to pipe and non-pipe passive component contributor

categories. Then, within the piping and non-piping categories, the frequency is further partitioned using

the available crack and leak data into the base case piping systems and degradation mechanisms.

The partitioned frequencies representative of the base case conditions are used as the base case

frequencies for LOCA Category 1. The frequencies for the other LOCA categories were calculated using

the common assumption that the frequency of a LOCA decreases as pipe size increases. A '/2 order of

magnitude factor was used to scale the frequencies for successive LOCA categories. This assumption is

consistent with historical LOCA frequency estimates and is also supported by research by Beliczey and

Schulz [Beliczey, S., and Schulz, H., "Comments on Probabilities of Leaks and Breaks of Safety-Related

3-20
Piping in PWR Plants," International Journal of Pressure Vessel and Piping, Vol. 43, pp. 219 - 227,

(1990)]. See Appendix E for a more detailed description of this analysis (Block 10).

3.5.1.2.3 Dave Harris's Base Case Estimates - This analysis was one of two base case estimates

using primarily PFM to establish base case frequencies. This approach utilized the PRAISE code for all

calculations [3.10]. Separate analyses were conducted for the five different piping base cases and a

number of sensitiyity calculations were also performed. Individual welds were analyzed and then

converted into piping system related frequencies. Benchmarking of operating experience for throughwall flaws was conducted where possible. Included.below is a synopsis of conditions modeled and the

analyses conducted for each base case.

The PWR hot leg (PWR- I) base case was modeled to separately consider fatigue crack growth of preexisting defects and PWSCC initiation and growth. Stresses from an earlier PRAISE study were used.

The case of PWSCC growth from pre-existing defects was selected as the base case. Sensitivity studies

were made of the effects of hydrostatic testing, seismic events, and degraded material properties. The

PWR surge line (PWR-2) base case considered initiation and growth of fatigue cracks. Stresses provided

in NUREG/CR-6674 [3.11] were used although all seismic loads were removed. To obtain results for

leaks greater than 100 gpm [380 lpm], an alternative procedure was required that involved extrapolating

the lengths of through-wall cracks obtained from a PRAISE run to determine the failure probability given

the existence of a through-wall crack. The HPI/make-up nozzle (PWR-3) was modeled with a failed

thermal sleeve. Stresses for an intact thermal sleeve from NUREG/CR-6674 [3.11] were employed, but a

fatigue crack was considered to initiate immediately with a depth of 0.12 inches (3.0 mm). This depth

corresponds to the initiation depth of applicable fatigue crack initiation relationships used within

PRAISE.

Two piping sizes were considered for the BWR recirculation system (BWR-t). The smaller lines (12

inch) are more prone to fail due to IGSCC than the larger lines (28 inch), but the larger lines are the only

contributor to the larger LOCA categories. Initiation and growth of IGSCC cracks was modeled. Default

residual stresses were employed, except for the 12 inch line which was repaired with a weld overlay after

20 years of service. The weld overlay greatly improves the residual stress situation, and employs a

material that is less prone to cracking. The application of the overlay was found to provide a large

reduction in the failure probability. Applied stresses in the 12 inch line were varied in order to provide

agreement with field observations of leaks, with a mean normal operating stress of 12 ksi (83 MPa)

providing the best agreement. This stress also provided good agreement with field observations of partthrough cracks. While the 12 ksi (83 MPa) mean normal operating stress results in better agreement with

field observations of cracking, a 20 ksi (138 MPa) stress value was used for the BWR-I base case

estimates. The 20 ksi (138 MPa) value was chosen for the base case estimates to be consistent with the

stresses utilized in the other base case analyses. This choice is somewhat arbitrary however, because the

base case frequencies using either 12 ksi (83 MPa) or 20 ksi (138 MPa) normal stresses are similar. This

consistency occurs because although the per joint failure frequency is higher for the 20 ksi (138 MPa)

stress level, the number of joints that are subject to this higher stress level is much lower than the number

of joints that are subject to the 12 ksi (83 MPa) stress level. See Appendix F for more discussion of this

phenomenon.

The feedwater system (BWR-2) was modeled considering fatigue crack initiation and growth. Flow

accelerated corrosion (FAC) is an important LOCA mechanism, but a probabilistic model is not available

for this mechanism. System stresses were again obtained from NUREG/CR-6674 [3.11]. As for the

surge line analysis, in order to obtain results for leaks greater than 100 gpm (380 lpm), an alternative

procedure was required that involved extrapolating the lengths of through-wall cracks obtained from a

PRAISE run for leaks (any through-wall crack). See Appendix F for a more detailed description of this

base case analysis (Block 10).

3-21
3.5.1.2.4 Vic Chapman's Base Case Estimates - This analysis also relied primarily on a PFM

approach for establishing the base case frequencies, but using the RR PRODIGAL code [3.12]. RR

PRODIGAL is a basic fatigue failure probability model developed by Rolls Royce (RR) for the British

Naval Nuclear program. The PRODIGAL code first simulates the weld construction in order to

determine the defect distribution and density for both buried and surface breaking defects at the onset of

service life. A failure probability using standard linear elastic fracture mechanics methods is then

determined for both the buried and surface breaking defects. Failure is achieved when the defect either

exceeds the R6 failure criteria [3.13], or simply grows through to the full thickness of the weld. The

failure probability for all initial defects is then combined to form the total failure probability. The specific

procedures used to develop the base case frequencies are as follows:

I Evaluate the basic fatigue failure probability using the RR-PRODIGAL code using the transient data

supplied.

2 Evaluate an elastic crack opening displacement (COD) as a function of defect size.

3 Use expert judgment to extend this COD beyond the elastic limit.

4 Evaluate the mean defect cross-sectional area for a given defect size using its associated COD.

5 Evaluate the mean leak rate for a given defect size using correlations developed for the elicitation..

6 Use expert judgment to assess the defect length distribution at failure.

7 Multiply Steps 5 and 6 to obtain the conditional probability of a leak rate greater than the prescribed

leak rates for LOCA Categories I through 6.

8 Multiply the conditional probability of Step 7 with the basic fatigue failure probability in Step I to

arrive at the required final probability of a leak greater than the prescribed leak rate for each of the

leak categories.

Note, for steps 2, 3, 4 and 5 above, the probabilities for the mean leak rate are determined as a function of

assumed defect size for an assumed leak. The defect length probability is multiplied by this to obtain the

conditional probability of leakage as a function of LOCA category assuming that a leak exists. The RRPRODIGAL code supplies the leak frequency. Individual welds were analyzed and then converted into

piping system related frequencies using this method. See Appendix G for a more detailed description of

this base case analysis (Block 10).

3.5. 1.3 Panel Review of Base Case Analysis - The base case team members made separate

presentations to the panel at the second elicitation meeting. The presentations consisted of the

assumptions, approach, results, and sensitivity analyses utilized by each team member for each base case.

The panelists provided feedback and a discussion of the differences among the four chosen methodologies

and the subsequent effect on the differences in the base case frequency estimates (Block 9). Refinements

to the base case calculations were suggested by the panelists to possibly minimize differences in the initial

base case frequency estimates and ensure that the defined base case conditions were appropriately

modeled. Refinements included conducting additional calculations to compare operating-experience and

PFM predictions of leaking crack frequencies at 25 years of operating life for each base case. Bengt

Lydell was selected to estimate leak rates from operating experience because of his access and knowledge

of the most extensive piping precursor database. David Harris was selected to calculate leak frequencies

using the PRAISE PFM code. It was suggested that additional benchmarking could be performed based

on this comparison. Revised stress histories for several base case systems were also developed. Crack

initiation and growth rate information for PWSCC cracking was also reevaluated for the appropriate base

cases. All refinement calculations were subsequently completed after this meeting and provided to the

panelists for use in the elicitation.

The elicitation panelists also identified additional sensitivity analyses toexarnine the impact of changes in

key variable and assumptions on the base case results. These analyses were important to inform the

panelists' subsequent elicitation responses. Sensitivity analyses included an evaluation on the effect of

3-22
different ISI leak detection strategies for all the base cases. For the BWR-l recirculation system base

case, sensitivity analyses compared IGSCC crack growth rates with and without a weld overlay; assessed

operating experience to quantify the effectiveness of IGSCC mitigation techniques; and quantified the

effect of variations in the applied loading history. For the PWR-I hot leg base case, sensitivity analyses

examined the effect of degraded material properties on base case frequencies and the effect of large

seismic and hydro test loading. A study of the effect of ISI on PFM surge line cracking frequencies

associated with thermal fatigue (part of PWR-2 base case) was performed. Finally, the effect of applied

loading history variations on thermal fatigue cracking in high pressure injection make-up lines (PWR-3

base-case) was evaluated.

The basecase frequency results after all refinements were completed are summarized in Section 4.

Section 4.3 summarizes these sensitivity analyses and the results. Additional details on the base

case refinements and sensitivity calculations specified by the panelists are contained in meeting minutes

(Appendix B). More details on the base case and sensitivity calculations are contained in the descriptions

provided by each base case team member (Appendices D - G).

3.5.2 Non-Piping Base Cases

In general, the non-piping elicitation structure, available background information and base case

development was completely analogous to the corresponding evaluation of piping system contributions to

the LOCA frequencies. When the elicitation started, less data had been collected specifically for nonpiping system failures. A non-piping failure precursor database was developed from licensee event

reports and provided to the panelists to correct this initial deficiency. This database is analogous to the

piping precursor failure database that was provided, although it is not as comprehensive. Details of the

non-piping precursor database are provided in Appendix. H.

Additionally, several panelists had experience evaluating specific non-piping degradation and failure

mechanisms that have occurred in service. These evaluations were used to develop specific non-piping

base cases that were available for anchoring subsequent elicitation responses in a completely analogous

manner to the piping base cases. These non-piping base case evaluations included: SGTR frequency

determination, current pressurized thermal shock (PTS) analysis, BWR vessel rupture due to beltline

cracking, BWR feedwater nozzle thermal fatigue cracking, and PWR CRDM failure due to PWSCC.

Information on the analysis details for each of these non-piping base cases is provided later in this section.

Appendix I provides additional details and results for BWR vessel rupture, feedwater cracking, and PWR

CRDM failure. Results for the non-piping base case analyses are provided in Section 4.4 of the report. It

should also be noted that the piping base case evaluations were also available for anchoring non-piping

elicitation responses. Some panelists choose these for anchoring in appropriate situations.

The elicitation structure for evaluating non-piping LOCA frequency contributions was also analogous to

the structure chosen to estimate piping LOCA frequency contributions. Discussion of non-piping LOCA

frequency contributions can be found in the minutes to the Elicitation Kick-Off meeting in Appendix B,

pages B-30 through B-36. The specific failure modes (or scenarios) for the five non-piping components

considered to contribute to the LOCA frequencies, i.e., RPV, pumps, valves (BWRs and PWRs) and

pressurizer and steam generators (PWRs), can be found in Tables B. 1. 13 through B. 1. 17 of Appendix B.

There were only two notable differences between the piping and non-piping evaluations. The first

difference is that several panelists performed separate base case evaluations for the defined piping.

scenarios while a single panelist performed each non-piping evaluation. As mentioned, the multiple

piping evaluations were used to identify differences in the estimated LOCA frequencies associated with

the base cases, based on the analysis approach and assumptions. It was not necessary to repeat this

exercise for similar non-piping scenarios. The second difference is that some of the non-piping failure

3-23
modes considered were distinct from important piping failure modes and did not lend themselves to

classical modeling approaches. Some examples include common cause bolting failures resulting from

maintenance that could lead to vessel head, pump or valve bonnet, or steam generator manway failures. It

is for these types of failure modes that elicitation is most valuable.

3.5.2. 1 Non-piping Precursor Database - Therefore, the first step in the non-piping base case

development was to create a separate precursor database for LOCA-sensitive non-piping components.

This database was based primarily on licensee event reports (LER) contained in the sequence coding and

search system (SCSS) database. The non-piping database identified all LOCA precursor events (e.g.,

cracks or leaks) between 1990 and 2002. These events were catalogued by the relevant non-piping

component (i.e. valve, pump, RPV, pressurizer, or steam generator) and subcomponent (e.g. body,

bonnet, nozzle, etc. for valves) identified by the panel during issue formulation (Section 3.4 and

Appendix B). The aging mechanism and calendar year associated with each event was identified in the

database as was the type of precursor event. Relevant precursor events include leaks, surface cracks,

through-wall-cracks, and bolted connection failures. This database of 216 events was provided to the

panel for use during the elicitation analogously to the database of precursor piping events. More

information about the non-piping LOCA precursor database development and its contents is contained in

Appendix H.

3.5.2.2 Steam Generator Rupture Estimates - The historical SGTR frequency was defined as one

non-piping base case. Only ruptures resulting in flow rates greater than 100 gpm (380 1pm) were

considered. This distinction is consistent with the Category I LOCA definition in this elicitation.

Ruptures of this size provide the most definitive data on passive system failures in smaller components

and represent the majority of pressure-retaining boundary passive system failures that have resulted in

flow rates greater than 100 gpm (380 1pm). The non-piping precursor database was used to estimate this

frequency. Additionally, steam generator leakage incidents, which are much more prevalent than

ruptures, were examined as a function of time to illustrate possible trends. Results of this analysis are

contained in Section 4.

3.5.2.3 Non-Piping Base Cases Analyzed using PFM - A number of other specific failure scenarios

were also identified as being particularly important. These were analyzed using PFM to provide

additional anchoring frequencies. These included ejection of CRDM nozzle tubes, evaluation of low

temperature over pressurization (LTOP) events in BWRs, and examination of non-LOCA transient

contributions to PTS events in PWRs. Only the non-LOCA PTS transients were considered for anchoring

because the LOCA transient frequencies are based on the assumed LOCA initiating event frequencies

which are being reevaluated herein.

One of the panelists, Dr. Pete Riccardella, analyzed the LOCA frequencies associated with BWR vessel

rupture and CRDM penetration ejection. The BWR analysis considered pressure vessel failures due to

both manufacturing flaws within the welds and service-induced flaws from SCC that grow into the low

alloy steel. An NRC-approved PFM code (VIPER) was utilized [3.14]. Irradiation embrittlement was

modeled and the vessel leak and fracture frequencies associated with both normal operation and LTOP

loading were calculated. The most important risk contribution from the calculation is an LTOP event

(1,150 psi [7.9 MPa] at 88'F [3 l°Cj) which has an assumed event frequency of IE-3. The current

analysis builds on earlier research [3.15] which was used as technical justification for reducing inspection

requirements for BWR reactor vessels. More details on the analysis procedure are contained in Appendix

1.

3-24
The study of CRDM ejection frequencies utilized a similar PFM program (MRPER) developed under a

materials reliability program to evaluate CRDM cracking [3.16]. The analysis predicts crack initiation

using a Weibull analysis based on observed leaking or cracked nozzles statistics through the spring of

2003. Probabilistic crack growth rates are based on laboratory PWSCC measurements and analysis was

conducted for four characteristic plant types (i.e., B&W, CE, Westinghouse 2-loop, and Westinghouse 4-

loop). The effect of inspection type, interval, and POD was also considered. More details on this analysis

are contained in Appendix 1.

The final non-piping base case built on parallel work being undertaken to risk-informthe PTS screening

criteria in 10 CFR 50.61 [3.17]. This study is evaluating PWR vessel failure probabilities as a function of

the level of irradiation embrittlement to develop screening criteria for minimizing the PTS risk. Four

PWR plants were investigated: Palisades, Beaver Valley, Calvert Cliffs, and Oconee. These plants span

the plant vendors and design variables, and also have unique embrittlement characteristics. For each

plant, probabilistic risk assessment, including a consideration of human errors, was used to determine the

most likely event scenarios that result in safety system actuation. Structural integrity assessment has

revealed that both high and low pressure safety system injection can induce high thermal stresses into the

vessel. Therefore, the plant-specific thermal-hydraulic transient associated with each safety system

actuation scenario was considered.

Vessel failure probability is predicted by first considering the probability of crack initiation from weld

and baseplate flaws which are created during vessel manufacturing. The likelihood of crack arrest prior

to the crack broaching the vessel thickness is then considered. Finally, the vessel failure frequency is

calculated as a function of the level of material embrittlement. The final PTS screening criteria will be

chosen such that the vessel failure probability is.appropriately low. More details on the PTS reevaluation

are available in [3.17]..

The PTS challenges are appropriate to consider in the elicitation because they could lead to vessel failure

and a resulting large LOCA. However, some care is required in this assessment. The biggest PTS risk

contribution is usually from SB, MB, and LB LOCA initiating events, especially as material

embrittlement increases. Further, the PTS risk is proportional to the LOCA frequencies. Because of this

dependency, the total PTS risk cannot be used as a base case for determining the frequency of other

pressure vessel or non-piping failures. However, there are other loading transients which contribute to

PTS risk. These transients include stuck open primary and secondary valves and feed and bleed

operations. The stuck-open valve and feed and bleed transient contribution to PTS risk ranges from 1% to

80% depending on the plant type and the embrittlement level. The panelists were only asked to consider

the frequency associated with PTS risk from these transients for possible anchoring during the elicitation

process.

3.6 Elicitation Background Information

The elicitation panel identified certain other generic information necessary for the elicitation.

Additionally, individual panel members requested specific information to supplement this generic

information. All information generated by these requests was catalogued and made available to all panel

members to aid in developing their elicitation responses. In order to facilitate communications among the

panel members, a repository of this information was maintained by Engineering Mechanics Corporation

of Columbus (Emc 2

) on an ftp site. Background information stored on this site includes references on

relevant prior studies, a complete set of BWR and PWR piping isometric drawings, weld census

information for the different piping systems, information on service stresses and transients, the piping and

non-piping databases, and information pertaining to the piping and non-piping base case development.

Additionally, all presentations, meeting minutes, elicitation questions, action items, and response tables

3-25
were included. A complete listing of the ftp site contents is also maintained at the site. This information

will be saved and catalogued after the elicitation has been completed.

3.7 Flow Rate Correlation

As previously described (Section 3.4.1) cumulative LOCA size categories 3 were defined in the elicitation

as a function of flow rate of the makeup water supply (Table 3.2). This approach is historically consistent

with plant response and mitigation action requirements that are a function of the flow rate as well as the

break location. However, the panel expertise was concentrated in the areas of structural analysis,

materials, and fracture mechanics, not thermal-hydraulics. Therefore, it was more natural for the panelists

to develop their responses as a function of the effective break area. In order to facilitate the elicitation

responses, it was therefore necessary to develop a correlation between the makeup flow rate and effective

break area. The break areas were then converted to equivalent break sizes by assuming circular openings.

Three separate correlations were developed for PWR liquid, BWR liquid, and BWR steam lines. The

correlations are a function of reactor type and the pipe length/diameter (LID) ratio. A large L/D ratio

implies saturation at the break plane. At small L/D ratios, the fluid does not reach these conditions. The

leakage rate is also assumed to correspond to the flow rate required for the makeup water supply to

mitigate the postulated break. Makeup water supply is assumed to exist at 70'F (21QC) under atmospheric

pressure.

For the BWR steam lines, the correlations are assumed to be independent of pipe size and are modeled

using equations by Todreas and Kazimi [3.18] with the upstream value for specific heat. The BWR liquid

correlations were developed by assuming that the upstream pressure is approximately the steady-state

operating pressure. For small L/D ratios (used for hole sizes > 9 inches (230 mm) diameter), the Moody

correlation was employed [3.19]. For large L/D ratios (used for hole sizes < 9 inches (230 mm)

diameter), the Zaloudek correlation was used [3.20]. The PWR liquid correlations were developed under

the assumption that the upstream pressure is saturated at a temperature of 600F (315C). Once again, the

Moody correlation was used for small L/D (used for hole sizes >7 inches (180 mm) diameter) while the

Zaloudek correlation was employed for large L/D (used for hole sizes < 7 inches (180 mm) diameter).

The correlations which relate flow rates and LOCA size categories to the effective break sizes in each

PWR and BWR system are summarized in Table 3.8. The break size corresponds to a partial fracture for

pipes with larger diameters than the break size, a complete single-ended rupture in pipes with the same

inside diameter, or a DEGB in pipes having inside diameters 142 times the break size. All panelists used

these correlations to relate their elicitation responses determined for the effective break sizes to the

appropriate LOCA size category. It is important to stress that breaks can occur in either LOCA

sensitivity piping or non-piping systems and components. For a BWR plant, the LOCA Category 6

effective break sizes are only applicable for a catastrophic rupture of the reactor pressure vessel. There are

no BWR piping systems that can generate a LOCA of this size upon failure.

3 That is the LOCA frequency associated with each LOCA category is the frequency of a break of the cited or larger size.

3-26
Table 3.8 Break Size to Leak Rate Correlation

LOCA Flow Rate BWR: Steam BWR: Liquid PWR: Liquid

Category (gpm)

Flow Rate Eff. Flow Rate Eff. Flow Rate Eff.

Flux Break Flux Break Flux Break

(gpm/in2 ) Size (gpm/in 2

) Size (gpm/in 2

) Size

I (in) (in) (in)

1 100 355 1/2 595 1/2 687 1/2

2 1500 355 2 1/4 595 1 3/4 687 1 1/2

3 5000 355 4 1/4 595 3 1/4 687 3

4 25,000 355 9 1/2 595 7 1/4 687 6 3/4

5 100,000 355 19 375 18 1/2 641 14

6 500,000 355 42 1/4 375 41 1/4 641 31 1/2

It should be noted that these correlations are different from those used in NUREG/CR-5750 [3.5] and in

other past LOCA efforts. The earlier correlations date to the NUREG-1 150 [3.1] plant risk study. The

BWR correlations used in NUREG-I 150 were developed from a matrix of calculations conducted using

existing thermal-hydraulic codes [3.21] specifically developed for each plant studied in NUREG-1 150. A

variety of break areas were postulated in certain systems and different combinations of mitigation

equipment were assumed to be operational. Calculations were performed for each plant. The reported

correlation of break area to flow rate is an amalgam of these results. It is assumed that the PWR

correlations are similarly based, although their development is not as well-documented.

These prior correlations were not adopted for this exercise because there was concern about their generic

applicability given the plant specific nature of the calculations performed. There is also concern about the

accuracy of the thermal-hydraulic codes existing at the time of the NUREG-1 150 study. Discharge leak

rates are highly uncertain. They are difficult to develop and are a function of upstream conditions, break

location, plant configuration, and mitigation reliability, as well as break area. Based on these

considerations, the simple closed-form approximate solutions are sufficient for the generic correlations

required for this elicitation. Application of the elicitation LOCA frequency results may require plant

specific calculations to evaluate break flow rate history and required mitigation response for assumed

break locations and sizes.

3.8 Elicitation Question Development

The elicitation questions were developed in concert with the panel members (Block 8). One objective

was to develop questions that were precise enough to be unambiguous, yet general enough so that the

question set could be minimized. Additionally, careful language was chosen to avoid terminology

specific to only a subset of the technical specialties represented by the panelists. Another objective was to

make sure that a variety of approaches could be used to develop answers to the questions. Initial

questions were formulated after the kick-off meeting. These questions were modified based on panel

member feedback prior to the base case review meeting. Modifications continued after the base case

review meeting and up through the first several individual elicitations. Elicitation questions were posed

in the following areas: piping base case evaluation, safety culture, PWR piping, BWR piping, PWR nonpiping, and BWR non-piping.

3-27
3.8.1 Elicitation Question Philosophy

None of the quantitative questions required the panelists to assess absolute LOCA frequencies. The

premise behind this philosophy is that an assessment of the relative likelihoods for comparable LOCA

frequency contributors is a more natural reflection of knowledge and experience than is an assessment of

the absolute frequencies of each contributor. A relative assessment allows a direct comparison of the

effects on LOCA frequencies resulting from specific combinations of materials, loading characteristics,

aging mechanisms, and mitigation procedures for all the LOCA-sensitive components. Assessments of

these interactions and comparisons of these relationships best match the panel expertise. An assessment

of absolute LOCA frequencies would not only require consideration of these relationships, but would also

require estimation of the frequencies associated with each postulated set of conditions. However, all of

the conditions rarely occur, and there is little supporting data for the members to assess the absolute

frequencies. Consequently, assessing relative frequencies should lead to more accurate results. The

elicitation questions have been structured accordingly, to require relative rather than absolute

assessments. More detail on the elicitation questions pertaining to the base case, safety culture, piping,

and non-piping evaluations follows.

3.8.2 Piping Base Case Elicitation Questions

The piping base case evaluation questions required each panel member to address the accuracy and

applicability of the four base case calculations. First, each panelist was required to assess how well each

calculation modeled the base case conditions defined by the panel (Table 3.7). Each panel member was

then asked to comment on the differences among the four base case team members' results for each base

case, and assess how modeling differences and variability in model input information led to these

differences. Additionally, each panelist was asked to evaluate the reasonableness of the differences in the

base case results and determine if the differences provide an accurate measure of the uncertainty inherent

in LOCA estimates. Each panel member was then asked to assess the accuracy of each base case

calculation and to choose, if desired, a specific set of results for anchoring his or her future elicitation

responses. The actual elicitation base case evaluation questions are contained in Appendix J (Block 8).

3.8.3 Safety Culture Elicitation Questions

The first set of quantitative questions focused on the influence of safety culture. As detailed in the issue

formulation section (Section 3.4.2), the panelists decided to evaluate the effects of safety culture

separately from a consideration of other variables which affect the LOCA frequencies (e.g., piping

system, material, degradation mechanism, etc.). This was justified because safety culture effects were

judged by the panel to be independent of these other variables, and are strictly a function of general

regulatory, general nuclear industry, and plant specific attitudes and practices.

The elicitation questions asked each panelist to assess future safety culture with respect to the current

safety culture. The future time period was decomposed into two intervals: the next 15 years (25 to 40

years), and the following 20 years (40 to 60 years). The current safety culture always serves as the

baseline and two ratios were required, one for each time period, to predict relative future safety culture

changes with respect to this baseline. The questions asked panelists to first separately consider the effects

of utility and regulatory safety culture. Then, the panelists were asked to assess the degree of correlation

between the utility and regulatory safety cultures. The questions also required the panelists to quantify

the relationship between safety culture and LOCA size category, if a relationship exists. Panelists were

asked for each question to qualitatively identify and discuss the strongest issues or factors which

influence their quantitative estimates. The safety culture evaluation questions are contained in Appendix

J.

3-28
3.8.4 Piping and Non-piping Elicitation Questions

Two parallel question sets were developed to determine piping and non-piping LOCA frequency

contributions due to passive system failure. The question sets were structured to support either a bottomup or a top-down philosophy (Figure 3.2). The bottom-up approach requires a fundamental consideration

of explicit combinations of variables (i.e., loading, geometry, materials, degradation mechanisms, and

mitigation), which lead to LOCAs in relevant piping systems and non-piping components (Figure 3.2).

The bottom-up approach is consistent with historical PFM analysis of LOCA challenges as well as the

Barsebkck-1 analysis [3.22].

The top-down approach allowed a more global consideration of piping system and non-piping componentcontributions without explicit consideration of individual degradation mechanisms, materials, etc. This

approach is more consistent with a LOCA frequency development using classical operating-experience

analysis as in NlUREG/CR-5750 [3.5]. These two alternative structures were developed to support

approaches commensurate with the specific technical expertise and philosophy of individual panelists.

Panelists were free to choose the top-down or bottom-up approach, a combination of the approaches, or

they could develop their own methodology. Panelists were encouraged to attempt both the top-down and

bottom-up question sets to evaluate the consistency of their responses. A few panelists did answer both

question sets and were able to iterate their final responses so that both the top-down and bottom-up

quantitative results matched their qualitative expectations. Also, several panel members did pursue

alternative approaches that they developed. A summary of the various philosophies and approaches from

each panelist is provided in Appendix K.

The piping and non-piping top-down and bottom-up questions were identically structured. This

commonality allowed the panelists to utilize similar approaches for addressing passive systems using

either approach. In each question set, the assessment structure is consistent with the relative assessment

philosophy described previously. The panel member first is asked to identify important LOCAcontributing factors. Then, he is asked to select an appropriate base or reference case for comparison with

each LOCA-contributing factor. He is then asked to make a relative assessment between the important

contributing factor and the chosen base or reference case conditions for each LOCA category and

operating time period (25, 40, or 60 years). The assessment continued for all important LOCAcontributing factors. These contributions of each individual factor are then analytically combined to

develop the final LOCA frequency estimates for each panelist (Section 5).

Reference cases (Section 3.4.7 and Appendix B) were developed by the panel analogously to the base

cases for the purpose of making relative assessments within piping systems that do not have an associated

base case. The reference cases, like the base cases, represent a specific set of defined conditions for each

important LOCA variable class (Section 3.4.7). Base case conditions have been previously summarized

in Table 3.7. If a panelist chose to use the reference cases, he was required to first make a relative

assessment between the base case and reference case conditions. This is necessary because only the base

case conditions are associated with fundamental, absolute frequencies. There was no explicit calculation

of the frequencies associated with the reference cases. Once this assessment was made, the reference case

associated with a piping system could be used for assessing issues within that piping system.

3.8.4. 1 Top-Down Questions: Specific Considerations -This structure of issue decomposition,

selection of important issues, relative comparison with base or reference cases, and final combination of

contributions allows the absolute LOCA frequencies to be developed for each panelist starting from only

the operating-experience and/or PFM-based estimates of LOCA frequencies associated with the

simplified base case conditions. No other absolute LOCA frequency quantification was required.

The only difference between the top-down and bottom-up question sets discussed previously is the

decomposition and consideration of the important contributing factors. The top-down approach asked the

3-29
panelist to identify the most important piping systems or non-piping components. The relative

comparison is made between the chosen base case condition and the important piping system/non-piping

component. This analysis is continued for all the piping systems and non-piping components.

The following example illustrates the top-down approach. A panel member first lists the important piping

systems (or non-piping components) for a PWR plant. He chooses the instrument lines, drain lines,

CVCS, hot leg, cold leg, and RHR system and has appropriate rationale supporting this selection. Then,

for the RHR system, he chooses a base case. In this example, he picks the hot leg cracking due to

PWSCC because he feels that this mechanism is the most likely to cause a LOCA in the RHR. He then

compares the LOCA contributions of the RHR to PWSCC cracking in the hot leg as a function of LOCA

size. He then determines the following ratios for LOCA Categories 1 - 6, respectively: 100, 50, 20, 10,

5, and 0. These estimates are based on the panelist's determination that PWSCC cracking in the RHR is

100 times more likely to result in a Category I LOCA than PWSCC hot leg cracking. However, Category

5 LOCAs are only expected to be five times more likely. The RHR system cannot support a Category 6

LOCA which is why this ratio is zero. The panelist determined these ratios by assessing precursor

differences between the RHR and hot leg and the knowledge that most of the precursors were due to

mechanical fatigue which would tend to cause smaller LOCAs instead of complete piping failure. The

panelist also considered the loading environment, and number of welds in the RHR system compared

with the hot leg in making this assessment. This panelist would conduct a similar analysis for all the

important PWR piping systems listed above.

3.8.4.2 Bottom-up Questions: Specific Considerations - The bottom-up approach asked the panelist

to identify important combinations of piping materials, degradation mechanisms, loading, geometry, and

mitigation measures within a given piping system or non-piping component. The relative comparison is

then made between the base or reference case condition and the selected variable combination. This

assessment continues for all important variable combinations for all relevant piping systems or non-piping

components.

The following example is applicable to the bottom-up approach. A panelist is assessing BWR LOCA

contributions. This panelist believes that IGSCC cracking under typical hydrogenated water chemistry

and inspection procedures provides the greatest LOCA contribution. He chooses the recirculation system

BWR base case for comparison. This base case evaluated IGSCC LOCA frequencies in normal water

chemistry with a weld overlay applied after 20 years of operation. Based on laboratory studies, the

panelist believes that crack growth rate of existing IGSCC cracks is similarly retarded using either a weld

overlay or hydrogenated water chemistry. However, the weld overlay also contributes an additional

margin of two to the failure stress for typical IGSCC crack sizes. In the recirculation system, the lower

applied failure stress transient is 10 times more likely than the transient require to fail a joint with a weld

overlay. Therefore, the panelist determines that the LOCA frequencies associated with the hydrogenated

water chemistries without an overlay are a factor of 10 higher than the base case LOCA frequencies in the

recirculation system for all LOCA sizes. The panelist then repeats this assessment for the IGSCC LOCA

susceptibility of other piping systems and then similarly considers LOCA contributions from other

important degradation mechanisms.

3.8.4.3 Additional Non-piping Considerations - As mentioned, the non-piping questions were

structured analogously to the piping questions. However, an additional level of decomposition was

necessary for the non-piping considerations. For each non-piping component (i.e., vessel, steam

generator, pump, valves, and pressurizer), LOCA-sensitive subcomponents (e.g., tubes, manway, tube

sheet, shell for the steam generator) were identified. This subcomponent decomposition is analogous to

the piping decomposition by system and was necessary due to the different types of failures associated

with each non-piping subcomponent. As with the piping systems, the subcomponents have specific

3-30
combinations of materials, geometry, degradation mechanisms, loading, and mitigation which affect the

LOCA likelihood. The subcomponent decomposition is summarized in Section 3.4.6.

The only other difference between the piping and non-piping questions is that specific failure scenarios

were explicitly developed for various non-piping subcomponent variable combinations. The failure

scenarios represent a combination of factors that could lead to non-piping LOCA failures. This

distinction was necessary because the non-piping failure scenarios were more disparate than the piping

considerations and it was not efficient to decompose those failures into single sets of appropriate variable

classes as was done for the piping systems. An example is the improper torque application of the reactor

head bolts due to human error that is not discovered during either bolt tension testing or initial system

pressurization. Another failure scenario could be CRDM nozzle ejection due to PWSCC cracking which

is not found during planned inspections. Many different failure scenarios were developed by the panelists

during the issue formulation (Section 3.4.6 and Appendix B). Each panelist was also free to develop and

evaluate other relevant scenarios.

Other than the additional decomposition and the distinction between the terminologies of variable

combinations (piping) and failure scenarios (non-piping), the piping and. non-piping questions are

identically structured. As previously discussed, the top down non-piping approach focused on assessing

those important failure scenarios for relevant non-piping subcomponents regardless of the non-piping

component type. The bottom-up approach required a more detailed assessment of possible failure

scenarios in each non-piping component and subcomponent.

3.8.5 Elicitation Response Requirements

All elicitation questions, except the base case evaluation, required both a qualitative and quantitative

assessment. For each quantitative assessment, the panelists were requested to provide a MV, a LB, and

an UB. As in the training exercise (see Section 3.3.2), the MV is defined such that, in the panel member's

opinion, the unknown true value for that particular question has a 50% chance of falling above or below

the MV. The LB is defined such that the true value has a 5% chance of falling below the bound. The UB

is similarly defined such that the true value has a 5% chance of being above the bound. The MV, LB, and

UB were interpreted in subsequent analysis (Section 5) as the median, 5 th and 9 5 th percentiles,

respectively, of a subjective distribution for the true value of the answer.

There were examples created for each elicitation question to clarify the question requirements for the

panelists and to illustrate possible approaches that could be used to answer the questions.

3.9 Individual Elicitations

Each panel member had between one and four months to prepare their elicitation responses after the base

case review meeting was held and the elicitation questions were finalized. During this time period,

individual elicitation sessions were conducted separately between each panel member and the facilitation

team (Block 11). With the exception of the last elicitation session with Mr. Helmut Schulz of GRS in

Germany, each of the sessions took place at the NRC headquarters in Rockville, MD. Mr. Schulz's

elicitation was conducted via video teleconference (VTC). Each session lasted a full day.

The elicitation sessions addressed each elicitation question in order; starting with the base case evaluation

questions, then the safety culture questions, followed up by the piping and non-piping related questions.

The objectives for the individual elicitation sessions were to:

1. Obtain and discuss the quantitative and qualitative responses to the elicitation questions.

2. Identify any inconsistencies between the quantitative and qualitative responses.

3. Provide additional clarification to the elicitation questions, as necessary.

4. Identify necessary follow-on work for each panel member.

3-31
5. Solicit feedback about the process.

The primary objective of the sessions was to obtain the quantitative responses to each question and

understand the qualitative rationale used as the basis for these responses (Block 12).

The elicitation framework (Section 3.8) was structured to provide the panelists flexibility for developing

the quantitative questionnaire responses. The flexibility was desirable so that each panelist could select a

specific approach --- consistent with his experience --- that would promote the greatest confidence in the

supplied responses. However, the framework was sufficiently standardized so that the individual results

could be analyzed using a common framework (Section 5) to develop the combined LOCA frequency

estimates (Section 7). Not surprisingly, no: two panelists used exactly the same approach. Some chose to

modify the base case results developed by the panel or even develop their own base case scenarios. A

few panelists even provided absolute values instead of the ratios relative to a chosen base case result as

requested. In these cases, absolute values were converted to equivalent relative ratios for comparison

with other panelist's results.

The philosophy used by each panel member to answer the elicitation questionnaire is summarized in

Appendix K. Because no two panel members used identical approaches to obtain quantitative estimates,

it was important that the facilitation team fully understood each participant's approach so that their results

could be subsequently analyzed. Most of the panelists provided the facilitation team with written

responses to the elicitation questions in advance of the interview. These responses were typically the

starting point for the review and discussion held during the interview. The facilitation team's role was to

evaluate the responses to ensure that they reflected the panel members' judgments and were consistent

with the elicitation question requirements. While preparing their elicitation question responses, the panel

members were asked to self-assess and not answer questions in areas where they had little or no expertise.

In areas where they had limited technical expertise, the panel members were urged to make estimates, but

to reflect their uncertainty in the UBs and LBs.

The discussion and review of the panelist's responses quite often revealed inconsistencies between the

quantitative estimates and the supporting qualitative beliefs. These inconsistencies were explored to

understand their genesis and possible resolution. Additionally, other weaknesses or incomplete areas in

the initial question responses were discussed. Sometimes, response deficiencies resulted from a lack of

understanding of particular elicitation questions and their requirements. These remaining ambiguities

were clarified during the interview.

Each elicitation was taped to provide a record of the elicitation. Minutes from each elicitation were also

recorded. The audio tapes and minutes were also used to clarify information and opinions. Approaches

were also developed with each panelist for subsequent follow-on work. A list of action items was also

developed for both the panel members and the facilitation team to reflect the additional work necessary to

resolve inconsistencies, complete missing information, and strengthen weak or ambiguous information

provided in these initial elicitation responses. This action item list was used by each panelist to help

refine his analysis. Each panel member then had another one to four months to revise his initial input to

address the follow-on work identified during the elicitation.

3.10 Final Elicitation Responses

After the individual elicitations were completed, and each panel member's refined elicitation responses

were analyzed (Block 13), a third meeting was held with the entire panel. The purpose of this meeting

was to summarize and discuss, the important quantitative and qualitative results arising from the

individual elicitations. The qualitative insights provided for each of the elicitation questions was a focal

point. These were discussed among the panelists so that all could share in this combined knowledge.

3-32
Each panelist benefited by understanding insights that he may not have explicitly considered in the

development of his elicitation responses. Additionally, each panelist gained from the insights in areas

outside of his expertise (Block 14).

The quantitative LOCA frequencies associated with important piping systems and all the non-piping

components were presented separately. The qualitative insights were also illustrated in the context of the

quantitative results. Important degradation mechanisms, loading, and other rationale associated with the

LOCA frequencies for each system were illustrated as underlying rationale. Also, the rationale associated

with quantitative outliers was also presented in those situations where it was unique and had possibly not

been considered by others. This discussion was used to highlight issues and concerns from individual

panelists so that the entire panel could assess their merits. Quantitative comparisons among piping

systems and non-piping components were also provided so that the panelists could understand the

implications of responses that they developed for the decomposed technical issues. Quite often, panelists

did not calculate total LOCA frequencies to understand these interdependencies.

Another objective of this meeting was to thoroughly explain the methodology used to combine all the

quantitative piping and non-piping LOCA frequencies for the decomposed piping systems and non-piping

subcomponents to develop comprehensive LOCA estimates from each panelist's elicitation responses.

The facilitation team reviewed these individual calculations with each panelist so that each could

understand how his initial responses had been used in this development. Sometimes, the raw input had to

be converted to fit the uniform analysis (Section 5. 1) and response (Section 5.2) frameworks developed

for this calculation. Also, additional assumptions were sometimes necessary to fill in missing information

in the panelists' responses. The facilitation team made assumptions or added missing information,

whenever possible, in a manner consistent with the philosophy, approach, assumptions, qualitative

insights, and other quantitative results already provided by the panelist. The most common reason, by far,

for augmenting the results was to provide complete coverage intervals for every elicitation response.

While panelists often did not provide every coverage interval, they typically did assess coverage intervals

in other places in the questionnaire. If necessary, the facilitation team created the missing coverage

intervals to be consistent with these other responses. Imputed data was never used to compensate for an

individual panelist's self-reported gaps in expertise. Any imputed data was also subject to panelist review

and acceptance prior to final use. Additionally, panel members were also required to ensure that their

responses were correctly transferred to the response framework (Section 5.2) and verify the results

calculated from their input responses.

Another purpose of this meeting was to discuss the quantitative frequencies associated with the nonpiping base cases. The qualitative conditions for all the non-piping base cases had been developed by the

panel in previous meetings. Also, the quantitative calculations had been either directly provided to the

panelists or made available on the ftp site for consideration. However, these quantitative calculations had

yet to receive the same scrutiny as the piping base case results. The analysis methodology and results

used to determine the SGTR frequencies, the CRDM ejection frequencies, BWR vessel failures due to

normal operation and LTOP loading, and the PWR vessel PTS analysis were shared. There was also a

presentation provided for the non-piping database development. The panel asked questions and provided

feedback to gain a thorough understanding of the non-piping base case calculations and results similar to

the piping base case evaluation in the previous meeting. Some concern was raised during this evaluation

that not all panelists may have properly addressed common cause or conditional failures stemming from a

single SGTR or CRDM ejection. Subsequent elicitation questions were therefore developed to explicitly

address this concern. More information on this meeting is contained in Appendix B.

A number of final action items were developed from this meeting. First, each panelist was asked to

review the calculations that the facilitation team performed on his elicitation responses. Each panelist

was to ensure that no errors were made in the entry of his responses, and was also asked to comment on

3-33
the appropriateness of assumptions or missing information added by the facilitation team during the

analysis of his results. Each panelist was asked to provide alternative assumptions or missing information

if desired. The panelists were also asked to separately consider conditional and common cause failures

for steam generator and CRDM ejections.

Finally, all the panelists were given one more opportunity to revise their elicitation responses based on the

discussion of the qualitative insights and rationale, a more thorough understanding of the non-piping base

case calculations, and a more complete understanding of the analysis procedures used by the facilitation

team in processing their raw input. Most panelists did not choose to change their preliminary estimates.

However, a few did modify their preliminary responses based on knowledge gained at this final meeting

(Block 15). Any necessary iteration was again conducted with each panelist to ensure that the final

responses are consistent and reflect the qualitative rationale. These final responses were then analyzed

once again to determine the LOCA frequency estimates provided by each panelist (Block 16). Detailed

results from elicitation for the various panelists are provided in Appendix L.

3.11 Review and Reporting

A multilayer review was conducted using both the panelists and external reviewers to validate the

accuracy and acceptability of the reporting and the analysis of the panelists' responses. First, a draft

report (Block 17) was created and circulated to the panelists for review. The panelists provided review

and feedback (Block 19) on every report section in order to increase the clarity and understanding of the

elicitation process, analysis, and results presentation. However, they conducted the most substantive

technical review of the background (Section 1), approach (Section 3), base case results (Section 4),

qualitative results (Section 6), and the summary and conclusions (Section 9). Several panelists provided

additional background information (Section 1) to ensure that the operating experience and PFM

descriptions were comprehensive and balanced. The piping and non-piping base case team members

contributed summary descriptions of their approaches (Section 3.5) and ensured that the base case results

and sensitivity analyses were accurately and clearly reported (Section 4). The piping base case team

members also provided some rationale to explain differences among the results.

The panelist review of and contributions to the qualitative results section (Section 6) was vital to ensure

that the results were summarized in a manner that reflects the important insights, yet also presents

dissenting views when appropriate. Group consensus of these insights was not explicitly sought during

the elicitation process; however, many of the reported insights were shared by the majority. The panelists

also reviewed the summary and conclusions (Section 9) that pertain to the base case and qualitative

results to ensure that they were complete and accurate. A revised report (Block 20) was developed which

addressed the feedback received from the panelists. The panelists were given the opportunity to conduct

a final review of this report and additional final comments have been incorporated as warranted.

In parallel with the panelists' review, an external peer review (Block 18) was conducted of the baseline

elicitation response analysis (Section 5.5), the sensitivity analyses (Section 5.6), and the corresponding

results (Section 7) contained in the draft report (Block 17). These are the portions of the process that the

elicitation panelists are not qualified to review. Two peer reviewers were chosen: Dr. C. Atwood of

Statwood Consulting and Dr. A. Brothers of Pacific Northwest National Laboratory. Dr. Atwood is a

statistician with experience in estimating rare event frequencies for PRA use and is also an author of

NUREG/CR-5750 [3.5]. Dr. Brothers is a decision analyst with knowledge of elicitation processes and

procedures.

3-34
The draft report was initially provided to the peer reviewers so that they could read Sections 5 and 7. A 1

1⁄2/2 day kick-off meeting was then held. The purpose of the kick-off meeting was to present and clarify the

analysis procedures, sensitivity analysis matrix, and results in detail so that the reviewers thoroughly

understood the approach. Some initial errors found in the analysis procedure were also discussed at the

meeting. Ideas for conducting additional sensitivity analyses and alternative response analysis and

aggregation schemes were also evaluated during the meeting. After the meeting, the facilitators were in

contact with the reviewers to answer any additional questions. The peer reviewers provided some

preliminary feedback within a week of the kick-off meeting and then provided their initial review a month

after the kick-off meeting.

The peer reviewers provided several recommendations and insights which are detailed in References 3.23

and 3.24. Dr. Brothers stated that while this was not a focal point of his review, the elicitation process

appears adequate and sound for determining the stated objectives. Dr. Atwood found some errors in the

baseline analysis procedure (Section 5.5) and provided an exact formulation for means of summed

distributions that were initially estimated. These errors were corrected and summation approximations

eliminated wherever possible: The accuracy of remaining approximations has been evaluated using

selected Monte Carlo analysis as requested in Reference 3.23. Both peer reviewers also suggested

additional sensitivity analyses to verify other analysis assumptions and approximations. All important

sensitivity analyses have been conducted and the calculational methodology has been updated as

warranted to reflect these findings. The updated procedure is reflected in the analysis section (Section 5)

of this revised report (Block 20). The elicitation responses were reevaluated using the updated analysis

procedure (Block 16) to develop the final LOCA frequency estimates (Block 22).

One focal point of the peer review recommendations pertains to the aggregation of the individual LOCA

frequency estimates (Section 5.4). Aggregation schemes other than the baseline methodology (Section

5.5) could be appropriate depending on the assumptions and interpretation of the individual total LOCA

frequency estimates. The most reasonable alternate aggregation scheme creates a mixture distribution

from the individual responses. This aggregation was conducted as an additional sensitivity analysis

(Section 5.6) for comparison with the baseline results (Section 7.6) and is included in the revised report

(Block 20).

The revised report (Block 20) was made available for public comment (Block 21). All public comments

were addressed and this final version of the report has been revised as necessary to address the.more.

pertinent of those public comments. The public comments and associated responses to those comments

are included in this report as Appendix M. Now that the public comment period is over and the public

comments resolved, the elicitation process is completed and the LOCA frequency estimates contained in

this report are considered final (Block 22).

3.12 References

3.1 "Severe Accident Risks: As Assessment for Five U.S. Nuclear Power Plants," NUREG-1 150, US

NRC, December 1990.

3.2 T.A. Wheeler, et al., "Analysis of Core Damage Frequency From Internal Events: Expert Judgment

Elicitation," NUREG/CR-4550, Vol. 2, Sandia National Laboratories, 1989.

3.3 E.J. Bonano, et al., "Elicitation and Use of Expert Judgment in Performance Assessment for HighLevel Radioactive Waste Repositories," NUREG/CR-5411, Sandia National Laboratories, 1990.

3.4 Memorandum from A.C. Thadani to S.J. Collins, Transmittal of Technical Work to Support Possible

Rulemaking on a Risk-Informed Alternative to 10 CFR 50.46/GDC 35, dated July 31, 2002.

3-35
3.5 Poloski, J. P., Marksberry, D. G., Atwood, C. L., and Galyean, W. J., "Rates of Initiating Events at

U.S. Nuclear Power Plants: 1987-1995," NUREG/CR-5750, US NRC, February 1999.

3.6 M.A. Meyer and J.M. Booker, "Eliciting and Analyzing Expert Judgment," NUREG/CR-5424, Los

Alamos National Laboratory, 1990.

3.7 "Reactor Safety Study: An Assessment of Accident Risks in U.S. Commercial Nuclear Power

Plants," WASH-1400, US NRC, October 1975.

3.8 Generic Letter 88-01, "NRC Position on IGSCC in BWR Austenitic Stainless Steel Piping," January

25, 1988.

3.9 Lydell, B., "OPDE Database Coding Guideline and Quality Control Manual," May 2002.

3.10 D.O. Harris and D. Dedhia, WinPRAISE." PRAISE Code in Windows, Engineering Mechanics

Technology, Inc. San Jose, California, Technical Report TR-98-4-1, 1998.

3. 11 Khaleel, M. A., and others, "Fatigue Analysis of Components for 60-Year Plant Life," NUREG/CR6674, June 2000.

3.12 Chapman, 0. J. V., and Simonen, F. A., "RR-PRODIGAL - A Model for Estimating the

Probabilities of Defects in Reactor Pressure Vessel Welds," NUREG/CR-5505, August 1998.

3.13 Milne, I., and others, "Assessment of the Integrity of Structures Containing Defects," CEGB Report

R/H/R6 - Revision 3, 1986.

3.14 VIPER Version 1.2, Structural Integrity Associates., Report # SIR-95-098 Rev. 1, Feb. 1999.

3.15 EPRI Report, "BWR Reactor Pressure Vessel Shell Weld Inspection Recommendations (BWRVIP05)," TR- 105697, September 1995.

3.16 Materials Reliability Program, MRP-105, "Probabilistic Fracture Mechanics Analysis of PWR

Reactor Pressure Vessel Top Head Nozzle Cracking," EPRI Report 1007834 (EPRI Licensed

Material), May, 2004.

3.17 Memorandum from A.C. Thadani to S.J. Collins, "Technical Basis for Revision of the Pressurized

Thermal Shock (PTS) Screening Criteria in the PTS Rule (10 CFR 50.61)," December 31, 2002.

3.18 Todreas, N. E., and Karimi, M. S., Nuclear Systems I Thermal Hydraulic Fundamentals, Taylor and

Francis, 1993.

3.19 Moody, F. J., "Maximum Two-Phase Vessel Blowdown from Pipes," J. Heat Transfer, 88:285,

1966.

3.20 Zaloudek, F. R., "The Low Pressure Critical Discharge of Steam-Water Mixtures from Pipes," HW68934, Hanford Works, Richland, WA, 1961.

3.21 "Additional Information Required for NRC Staff, Generic Report on Boiling Water Reactors,"

General Electric, NEDO-24708A. Class 1, Revision 1, December 1980.

3.22 Lydell, B., "Failure Rates in Barsebdick-1 Reactor Coolant Pressure Boundary Piping: An

Application of a Piping Failure Database," SKI Report 98:30, Swedish Nuclear Power Inspectorate,

May 1999.

3.23 Atwood, C. L., "Review of Draft Report on LOCA Frequency Estimates by Expert Elicitation,"

NRC ADAMS Accession No. ML051430327, May 2005.

3.24 Brothers, A., "Review of Draft LOCA Frequency Estimates by Expert Elicitation," NRC ADAMS

Accession No. ML05143043 t, May 2005.

3-36
4. BASE CASE RESULTS

As indicated earlier (Section 3.5), base case frequencies were' developed by a subset of the panel. These

frequencies were then provided to the other panelists as possible anchoring frequencies for use during

their elicitations (Section 3.5). Alternatively, panelists were free to develop a different approach as the

basis for their elicitation responses (Section 3.9). Base case frequencies were developed for five piping

systems, two BWR systems and three PWR systems. Base case frequencies were also developed for a

number of non-piping failure scenarios including BWR vessel failure, CRDM ejection, SGTR, and nonLOCA PTS transients. There were also two databases available containing precursor failure information

for LOCA-sensitive piping systems and non-piping components as discussed in Section 3 that could be

used for anchoring during the elicitations. The results of the associated base case analysis and sensitivity

studies are provided in this section.

4.1 Piping Base Case Through-Wall Cracking Frequencies

Bengt Lydell (BL) and David Harris (DH) developed through-wall cracking (TWC) frequencies

associated with each base case up to 25 years of average plant life. The purpose of this exercise was to

provide the most direct comparison between operating experience and PFM results and provide

information for benchmarking the PFM results. Bengt Lydell used a comprehensive operating-experience

data base (Section 3.5.1.2.1) to obtain direct estimates of the TWC frequencies. David Harris (Section

3.5.1.2.3) determined frequencies using the PRAISE code. Some of the PFM results were adjusted, or

benchmarked, through manipulation of variable input parameters (Appendix F) in order to more closely

match the operating-experience TWC frequencies.

Figure 4.1 is a comparative plot of the leak frequency results after 25 years of service. In this figure, the

hot leg (PWR-1), surge line (PWR-2), HPIIMU line (PWR-3), recirculation system (BWR-1), and

feedwater system (BWR-2) base cases are all represented. These through-wall cracking frequencies

represent LOCA precursor events due to the material degradation mechanisms specified in the base case

definitions (Section 3.5.1.1). The flow rate associated with these through-wall cracks range from

effectively zero up to the technical specification leak rate limit of approximately 1 gpm (3.8 1pm). Leaks

bigger than I gpm (3.8 lpm) are assumed to either be detected by the leak detection systems or result in a

LOCA.

There is relatively good agreement between the operating-experience (Bengt Lydell) and PFM (David

Harris) results for the PWR-3 and BWR- 1 base cases. This is to be expected because the PFM results

were effectively benchmarked against the operating-experience estimates for both of these base cases. It

is worth noting that IGSCC events in the BWR recirculation system (BWR-1) and thermal fatigue

cracking in the HPI make-up nozzles (PWR-3) have relatively high service frequencies and'there is

correspondingly a larger body of TWC data associated with these cracking mechanisms. However, there

is significant disagreement between the operating-experience and PFM results for the other three base

case results.

The PWR-I and PWR-2 base cases represent cracking in the hot leg due to PWSCC and in the surge line

due to PWSCC and thermal fatigue, respectively. The PFM results are quite sensitive to initial input

variable distributions for these base cases. Relatively minor changes can result in TWC frequency

differences of several orders of magnitude (Appendix F). The through-wall cracking PFM frequencies

(Figure 4. 1) could have been calibrated to provide better agreement between the PWR- I and PWR-2

through-wall cracking operating-experience results. However, there is the least amount of failure data

available, and consequently the lowest frequencies and highest uncertainty associated with these

operating-experience estimates. Therefore, the PFM through-wall crack frequencies (Figure 4.1) were

4-I
chosen to provide more realistic and comparable Category I LOCA estimates with the operatingexperience results (Sections 3.5.1.2 and 4.2).

The most significant difference between the PFM and operating -experience TWC frequencies occurs for

the feedwater system base case (BWR-2). The PFM estimate is approximately 4 to 5 orders of magnitude

less than the operating-experience estimate. There are at least two factors that contribute to this

difference. The most important is that the BWR-2 operating-experience frequency estimate was based on

20 reported incidents in BWR feedwater systems. However, none of these events resulted in through-wall

leakage. Therefore, the operating-experience estimate is undoubtedly conservative. Additionally, the

PFM result only considered thermal fatigue frequency contributions and not FAC as required for the

BWR-2 base case. There is no FAC degradation model in the PRAISE code. However, FAC is not a

mechanism that typically results in TWC prior to failure, so differences due to this inconsistency are not

expected to be large.

Operating experience could be analyzed more closely in an attempt to resolve this difference, but this was

not a principal concern since both the assumptions and approach associated with both estimates were

provided for panel consideration. It should be stressed that one principal objective of the base case

evaluations was to illustrate the variability which is possible using different estimation schemes. The

panelists were asked to consider this variability in their individual elicitations.

1.OE+O0

PWR-1 PWR-2 PWR-3 BWR-1 BWR-2

1.OE-01

a..

f) 1.OE-02

I-~1.OE.04

U-

.v 1.OE-05 j

(D0

= 1.OE-06

4.)

*Closed diamonds - Dave Harris

I.. 1.E-05

0 Open diamonds - Bengt Lydell 1.OE-08

1.0E-09

Figure 4.1 Leak Frequency Comparison Between Operating-Experience-Based and PFM-Based

Analyses

4.2 Piping Base Case LOCA Frequencies

Piping base case LOCA frequencies were developed by four of the panel members: Bill Galyean (BG),

Bengt Lydell (BL), Dave Harris (DH), and Vic Chapman (VC). As previously described in Section 3.5,

base case frequencies were intended to be developed for three time intervals: 0-25 years (current-day),

25-40 years (end of plant-license), and 40-60 years (end-of-plant- license-renewal). However, only BL

4-2
and DH predicted base case frequencies beyond 25 years. Bill Galyean and VC provided estimates only

for 25 years, although BG has indicated (Appendix E) that his base case frequencies are assumed to be

largely constant with time. Frequencies were developed for all six LOCA categories for the hot leg base

case (PWR-l). For the surge line base case (PWR-2), two of the analyses were conducted up to LOCA

Category 5 while the other two stopped at LOCA Category 4. It should be noted that a double-ended

rupture in the surge line would briefly qualify as a Category 5 LOCA until the pressurizer completely

drains. The flow rate for the remainder of this hypothetical transient would be classified as a Category 4

LOCA. Two of the analyses explicitly considered this initial higher flow rate in classifying the biggest

PWR-2 breaks as a Category 5 LOCA. For all other base cases, estimates were made up to the largest

LOCA size amenable to each piping system.

All four base case team members developed LOCA frequencies for the PWR piping base cases. All team

members except VC developed frequencies for the BWR piping base cases. Two of the panelists based

their estimates on existing operating experience (BG and BL) while the remaining two (DH and VC) used

PFM analyses to develop their estimates. Where possible, DH's PFM calculated through-wall cracking

frequencies were benchmarked with values estimated from operating experience (Section 4.1). Vic

Chapman's PFM results were not similarly benchmarked. More detailed information on the approaches

utilized by the four base case team members is provided in Section 3.5.1.2 and in Appendices D through

G.

The piping base case frequencies (per calendar year) are summarized in Table 4. 1. Note that the

operating experience-based frequency estimates (BL and BG) were based on calendar years of plant

operation where various reactor states from full power to shutdown occur. The PFM-based estimates (VC

and DH) were based on reactor years of operation where it is assumed that the reactor is at full power

over the entire year. The PFM estimates in Table 4. 1, and subsequent tables, have been multiplied by a

conversion factor of 0.8 to account for the approximate fleet average shut-down time per year [4.1]. This

adjustment assumes that the LOCA risk during shut-down time is zero, but it ensures that all frequencies

are reported on a consistent per calendar year basis. This adjustment is a minor consideration in tight of

the uncertainty in these estimates, but is performed for consistency.

It should be noted that these base case estimates (Table 4. 1) are those that each base case team member

believes most accurately represent the base case definitions (Section 3.5. 1). These estimates evolved

during the course of the elicitation and were not finalized prior to the onset of the individual elicitations.

Therefore, interim estimates were provided to the panel members for anchoring during their individual

elicitations. These interim estimates are not reported. The interim results of BL and DH were generally

within a factor of 2 of their final estimates (Table 4.1) although a few cases did vary by a factor of 10 or

more. In some cases the interim results for BG and VC varied by an order of magnitude from their final

estimates. However, the differences between the interim estimates and those in Table 4.1 are insignificant

compared to both the uncertainty associated with any single estimate and the variability among estimates

for a single base case. Even so, the panel members were provided the option of updating their original

elicitation responses once the final base case results (as reported in Table 4.1) were completed.

Several interesting observations can be made with respect to these results. First, some of the base case

frequency estimates are extremely low, less than 1E-10 per calendar year. These low frequencies are

most often associated with the PFM analyses. The prediction of low frequencies, by itself, does not imply

that the analysis is any more flawed than one which predicts higher frequencies. An analogous situation

is the following. Suppose someone buys one lottery ticket in each of three successive months, and each

ticket has one chance in a million of winning. The probability of winning all three times is IE-18. This is

truly an incredible event, but the calculation of its probability is correct nonetheless. However, lower

frequency estimates are typically more sensitive to the accuracy and variability of critical underlying

modeling approaches and assumptions because changes are more likely to have a proportionally bigger

4-3
impact. Therefore, the only direct conclusion that can be drawn from an extremely low LOCA frequency

estimate is that the occurrence of such an event is incredible based on the assumptions, input distributions,

and modeling approach used for the prediction.

The results in Table 4.1 are also interesting because large differences exist among some of base case team

member estimates. Figure 4.2 graphically depicts these differences for the Category 1 piping base case

frequencies (per calendar year) at 25 years of plant operations. The variability among the majority of the

Category I LOCA estimates (Figure 4.2) is approximately one to two orders of magnitude for all five

piping base cases. Additionally, the variability between the two operating-experience-based analyses

(i.e., the BG and BL results) is always relatively small, less than an order of magnitude for all five base

case Category I estimates (Figure 4.2). However, the VC's PWR-l and PWR-3 base case estimates, and

DH's estimate for the BWR-1 base case are greater than two orders of magnitude different from the other

results.

An understanding of the underlying modeling is necessary to both explain the low frequency estimates

and provide some reasons for the large discrepancies among individual base case estimates. For instance,

VC's PFM estimates for the PWR-1 base case (hot leg cracking due to PWSCC) predicted Category 1

LOCA frequencies of less than IE-10 per calendar year. However, VC only considered failures due to

fatigue from preexisting flaws, and not PWSCC. The cyclic stresses due to normal startup/shutdown

cycles plus an assumed seismic event were very low. Thus, the estimated failure frequency associated

with fatigue crack growth from a relatively small number of preexisting flaws is extremely low.

Conversely, the PFM evaluations made by DH considered both fatigue and PWSCC crack growth

mechanisms. Dave Harris's analysis also allowed flaws to initiate during operation. These additional

considerations substantially elevated DH's Category 1 LOCA frequency estimates (Figure 4.2). The

implication to draw from these differences is not that LOCA hot leg frequencies are extremely low, but

rather, that the LOCA frequency contribution due to fatigue crack growth from preexisting flaws is

extremely low.

The large, seven orders of magnitude difference between the VC and DH PFM predictions for PWR-3

(HPIIMU nozzle) can also be attributed to modeling considerations. Dave Harris considered accelerated

initiation and elevated stress due to a failed nozzle thermal sleeve. This is typically the condition evident

when service cracking has been apparent. The DH results are also benchmarked by the through-wall

cracking frequency for PWR-3 (Figure 4.1). However, VC analyzed the main weld of the nozzle

assuming that the thermal sleeve was intact. The intact sleeve significantly decreases the thermal

transients, thus leading to the extremely low failure frequencies calculated by VC. David Harris

conducted a sensitivity analysis by considering an intact thermal sleeve with a similar transient stress

history to VC. The LOCA frequencies predicted by DH for these more similar conditions was also

extremely low and varied from VC's results by only three orders of magnitude. Other, unexplained

differences in the modeling accounts for these remaining differences, but the important conclusion is that

these nozzles are not likely to crack if the thermal sleeve remains intact.

The PWR-2 base case considered both thermal fatigue and PWSCC for the surge line. Both DH and VC

modeled thermal fatigue, but neither modeled PWSCC for this base case. Thermal fatigue is apparently a

much more important contributing factor than PWSCC since this contribution, by itself, is close to the

operating-experience estimates. Also, the models have much less uncertainty. The increased impact of

thermal fatigue is due to the higher cyclic stresses present in the surge line. Hence, the two PFM

estimates are much closer, differing by less than one order of magnitude for the Category 1 LOCAs.

The variability between the two operating-experience-based analyses (i.e., the BG and BL results) is less

than an order of magnitude for all five base case Category I estimates (Figure 4.2). This consistency is

not surprising given the similarity of the approaches. While BG and BL used different operating4-4
experience databases, all the information in BG's database was contained within the more expansive

database used by BL. Additionally, both BG and BL used similar conditional pipe rupture relationships

(see Appendices D and E) between Category I and larger LOCA sizes although, as described in Section

3.5.1.2, each had a distinct basis to justify their relationship.

Therefore, the most significant difference in the approaches was how the initial Category I LOCA

estimates were determined (Section 3.5.1.2). As previously indicated in Section 3.5.1.2, BG used a topdown approach where the total LOCA Category t estimates, based on total events, were partitioned based

on the relative cracking frequencies among the LOCA sensitive systems. Bengt Lydell determined

cracking frequencies associated with the welds and degradation mechanisms for the base case system, and

then determined the system frequencies by scaling with the number of susceptible welds. The.fact that

these two approaches generally agree implies that the failure rate of the modeled degradation mechanism

for a system is similar to the relative failure rates for all degradation mechanisms associated with the

same system.

The biggest difference between the LOCA Category 1 operating-experience and DH PFM estimates is for

the BWR-I base case which modeled IGSCC in the recirculation system. Both Bengt Lydell and Bill

Galyean used the post-IGSCC mitigation cracking rates as the basis for the BWR-1 analysis. As with the

other estimates, their BWR-1 recirculation system LOCA Category 1 base case results are within

approximately a half order of magnitude. While the use of post-IGSCC mitigation rate is counter to the

original BWR-1 definition, the frequencies are more consistent with current conditions at BWR plants.

However, this confounds the comparison with the PFM estimates developed.for this base case. The

conditions analyzed by DH did consider IGSCC of a normal water chemistry environment and utilized

residual stresses to simulate the effect of a weld overlay, although the residual stress distribution was

conservative (Appendix F). Not surprisingly, then, his Category 1 LOCA frequency estimates are

significantly higher than either BL or BG who effectively modeled the frequencies associated with all the

mitigation schemes used for IGSCC.

4-5
Table 4.1 Piping Base Case Frequency Results by Participant

LOCA 25 Years 60 Years

Cat. Participant Participant

BG BL DH VC BG BL DH VC

PWR-1 Hot Leg (p er calendar year)

1 1.5E-07 7.4E-07 3.2E-08 1.8E-l 1 9.7E-07 3.2E-08

2 4.6E-08 7.6E-08 3.8E-11 1.8E-12 1.OE-07 3.8E- 11

3 1.5E-08 2.9E-08 1.1E-12 4.4E-13 3.8E-08 1.E-12

4 4.6E-09 .IE-08 l.1E-12 7.OE-15 1.4E-08 l.1E-12

5 1.5E-09 3.8E-09 8.8E-16 1.4E-15 4.9E-09 8.8E-16

6 1.3E-09 8.8E-16 1.6E-09 8.8E-16

PWR-2 Surge Line ( er calendar year)

I 1.5E-08 1.3E-07 4.8E-07 1.9E-07 1.8E-07 1.4E-05

2 4.5E-09 1.5E-08 2.2E-09 3.IE-08 2.1E-08 9.6E-08

3 1.5E-09 5.4E-09 1.5E-10 1.2E-08 7.4E-09 1.4E-08

4 4.5E-10 1.6E-09 6.3E-14 1.9E-09 2.1E-09 4.OE- 11

5 5.3E-10 3.9E-I0 7.3E-10

6 1 1 1

PWR-3 High Pressure Injection/Make-Up (HPI/MU) (per calendar year)

I 2.3E-06 1.6E-05 6.2E-05 6.7E- 12 2.OE-05 3.2E-04

2 7.0E-07 2.3E-06 6.2E-05 7.4E- 13 3.3E-06 3.2E-04

3 2.3E-07 9.2E-07 2.8E-13 9.5E-07

4

5

6

BWR-1 Recirculation System (per calendar year)

1 5.3E-05 9.5E-06 9.2E-03 1.9E-05 5.7E-04

2 1.6E-05 1.2E-06 6.8E-03 2.4E-06 5.OE-04

3 5.3E-06 4.6E-07 4.8E-03 9.2E-07 5.OE-04

4 1.6E-06 1.5E-07 3.1E-03 3.OE-07 5.OE-04

5 5.3E-07 3.OE-08 2.1E-06 6.1E-08 3.OE-06

6 _ j I

BWR-2 Feedwater Lines (per calendar year)

I 4.0E-06 2.5E-06 <1.OE-7 2.6E-06 1.OE-07

2 1.2E-06 3.4E-07 <1.3E-11 3.4E-07 1.3E- 1

3 4.OE-07 1.2E-07 <6.IE-13 1.3E-07 6.1E-13

4 1.2E-07 4.1E-08 <l.IE-17 4.1E-08 1.1E-17

5 7.3E-09 7.4E-09

6

Differences among the base case predictions are also graphically depicted in Figure 4.3 for larger

Category 3 piping base case frequencies (per calendar year) at 25 years of plant operations. The

variability among the Category 3 LOCA base case estimates (Figure 4.3) when compared with the

Category I LOCA estimates (Figure 4.2) would be expected to increase as the event frequency decreases.

Small analysis variations and differences can result in greater relative disparity among the LOCA

frequency estimates as the absolute frequencies decrease. However, for four of the five piping base cases,

the range among the LOCA Category 1 and 3 estimates are similar.

4-6
The biggest increase in variability with increasing LOCA size occurs for the BWR-2 base case which

considered thermal fatigue and FAC in feedwater piping. For this particular base case, the variability

between the base ease frequencies for the Category 1 LOCA estimates is less than 2 orders of magnitude

while the variability among the Category 3 estimates is approximately 6 orders of magnitude. This

additional variability results from the substantial LOCA Category 3 frequency decrease predicted by DH.

The reason for this large decrease is that DH did not model FAC (Section 3.5.1.2). Flow-accelerated

corrosion is a degradation mechanism that is more likely to lead to a complete pipe rupture than thermal

fatigue which often surfaces as a leak, or potentially a partial pipe failure (i.e., Category 1 LOCA).

Hence. DH predicts that the LOCA Category 3 frequency contribution due to thermal fatigue decreases

substantially. Conversely, the FAC contributions, as modeled.by BG and BL, become more important

contributors for the larger LOCA sizes.

1,OOE-01

Legend:

BG - Bill Galyean

1.OOE-02 - BLBengtLydeI I--DH _

DH - Dave Harris

VC - Vic Chapman

i.00E-03

PWR-1 PWR-2 Surge PWR-3

1.OOE-04 Hot Leg Line HPI/MU _

A DH BG

" 1.OOE-05 LL

BG

Q 0BL DHG BL

r-/ vc "10 BL

1 O0E-07 -L0 BG CH

U. DH

i 1.OOE-08

0

"-j _ _ _ _ _ _ _ BWR-1 BWR-2 1 .O0E-09

Recirc Feedwater

System Lines

1.O0E-10

VC

1OOE-11 VC

1.0OE-12

Figure 4.2 Category 1 Piping Base Case Frequencies at 25 Years

4-7
1.00E,

1.00E1 .00E,

1.OOE,

1.00E,

U

0 1.0 0 E ,

1.00E0

0 1 OOE-l 1.00E1q

0 I.OOE

1.00E-0

-.OOE--

1.0OE-1 1 .000-

01 PWR-1 PWR-2 PWR-3 BWR-1 Recirc BWR-2

Hot Leg Surge Line HPI/MU System Feedwater T DH Lines

03

Legend:

BG - Bill Galyean

BL - Bengt Lydell

DH - Dave Harris

05 VC - Vic Chapman __BG

06 BL.

BL BG BG RL

07

BL 08 '0 BG •VIC

BL

09 BG

DH

11

2 -_ < > D _ H__H 

21 VC_ AVC 3 O1H

13

Figure 4.3 Category 3 Piping Base Case Frequencies at 25 Years

The estimated PWR and BWR base case frequencies are illustrated as a function of the LOCA size

categories in Figures 4.4 and 4.5. These figures are more useful for evaluating the relative effects of

increasing LOCA size than Figures 4.2 and 4.3. The PWR base case LOCA frequency estimates are

illustrated in Figure 4.4, while the BWR base case results are shown in Figure 4.5. In both figures, the

results for each base case are connected by a vertical line to indicate the variability among the results (as

in Figures 4.2 and 4.3). The base case results have been offset slightly and the number above each

vertical symbol represents the base case number. For example the 1 above the results in Figure 4.4

indicates that these results are for the PWR-I base case while the I above the results in Figure 4.5

indicates that these results are for the BWR-1 base case. The results for each base case team member are

presented using the same symbol type in both figures. The symbol fill is also varied to indicate the base

case number.

As indicated in Figure 4.4 for the PWR base case results, both BG and BL predict that the PWR-2 (surge

line) frequencies are about an order of magnitude less than the PWR- 1 (hot leg) frequencies for all of the

LOCA categories. The DH estimates also predict a similar trend. Conversely, VC estimates that the

PWR-2 frequencies are about 4 orders of magnitude higher than the PWR- I frequencies. Note that VC,

as discussed previously, did not model PWSCC in either the PWR-1 or PWR-2 base cases. However, the

cyclic stresses in the surge line were higher than in the hot leg, which leads to VC's higher predicted

failure probability for PWR-2.

It is also apparent in Figure 4.4 (as in Figures 4.2 and 4.3) that the operating-experience-based estimates

are most similar. For all LOCA categories, BL's PWR base case predictions are less than an order of

magnitude higher than BG's predictions for all three PWR base cases. This is much closer agreement

than the PFM results, but is not surprising given the similarity-in the approaches discussed previously.

There is also (as in Figures 4.2 and 4.3) large discrepancies between the PFM estimates, which are a

reflection of modeling differences as discussed previously. However, occasionally the PFM estimates

4-8
actually become closer with increasing LOCA size (e.g., PWR-I estimates). Vic Chapman speculates that

some of the differences in the estimates may be attributed to the initial flaw distributions and how the

transition from a surface flaw to a through-wall crack was modeled. These factors determine the crack

size and therefore the leak rate when the flaw penetrates the piping outer surface. Vic Chapman believes

that these factors significantly affect smaller LOCA Category estimates but have less effect as the LOCA

size increases.

The more interesting observation in Figure 4.4 is that most of the differences among the LOCA Category

I estimates remain relatively consistent as the LOCA size increases. In fact, three of the base case team

members (BG, BL, and VC in Figure 4.4) maintain approximately a 1⁄22 to I order of magnitude decrease

in LOCA frequency with each successive LOCA size increase for all three PWR base cases. This relative

decrease in BG's estimates mirrors his assumption of approximately a half order of magnitude decrease in

the LOCA frequency for each successive LOCA category. Bill Galyean's assumption is based on an

evaluation of the propensity of though-wall fatigue flaws to lead to various LOCA break sizes [4.2].

The other operating-experience estimates, provided by Bengt Lydell, developed conditional failure

probability (CFP) relationships based on an analysis of service history trends. Therefore, these CFPs

were not assumed a priori. While Bengt Lydell's conditional probabilities vary somewhat as a function of

degradation mechanism and LOCA size, the average failure frequency decrease is approximately 1⁄22 order

of magnitude between successive LOCA categories. See Appendix D for more detail. The relative

decrease in VC's results is predicted directly by the PFM analysis.

Only DH's results tend to predict a more dramatic decrease between the Category I and bigger LOCA

sizes in the PWR-1 and PWR-2 base cases. Big decreases between his Category I and 2 predictions

occur due to the expectation that the cracking in these base cases is much more likely to result in a smaller

break size. However, beyond Category 2, the DH LOCA frequencies for the PWR-I and 2 base cases

decrease, on average, by approximately 1⁄2/ to I order of magnitude per LOCA category up through the

largest LOCA Category modeled. It should be noted that DH only performed analyses for LOCA

Categories 3 and 6 for the PWR-1 base case and then assumed that the Category 3 and 4 results were

equivalent and that the Category 5 and 6 results were equivalent. This treatise explains the step-wise

behavior apparent between LOCA Categories 3 through 6 in DH's PWR-1 results.

There are several implications which result from these trends in the relationship between the frequencies

and LOCA size.. The first is that the average conditional probability between successively increasing

LOCA categories for all these base cases generally falls between 0.1 to 0.5, which is equivalent to a 1⁄22 to

I order of magnitude decrease. Further, the conditional probability associated with each of the four base

cases results also typically falls within this range. More importantly, and consistent with previous

discussion (Figures 4.2 and 4.3), is the finding that much of the variability among the estimates is

apparent for the smallest LOCA sizes. If the LOCA Category 1 estimates were normalized to eliminate

variability, subsequent LOCA Categories would not differ as widely.

4-9
100

10-2

0~

U10-4

10.6

10.8

10-10

10-12

10-14

10-16

10-18

#00 BG

2 E 0 1 BL 123 12 00 DH

23 A .A VC

123 

12

2I

1 2 3 4 5 6

LOCA Category

Figure 4.4 PWR Piping Base Case Frequencies at 25 Years of Plant Operation

"7

0

a,

ULL

0

0

-J

100

10-2

106

10.6

10.6

10-10

10-12

10-14

1 2 1 2 1 2 1 2 *o BillGalyean

I 9 El Bengt Lydell

II 0 David Harris

12

I 0

I0

10-16

10-18

1 2 3 4 5 6

LOCA Category

Figure 4.5 BWR Piping Base Case Frequencies at 25 Years of Plant Operation

4-10
The BWR base case results (Figure 4.5) exhibit many similar trends as the PWR results in Figure 4.4).

Both BL and BG predict slightly higher frequencies for the recirculation system (BWR-1) than for the

feedwater system (BWR-2). Both operating-experience-based predictions are once again consistent but,

contrary to the PWR results, the BG predictions tend to be slightly higher. The operating-experience base

case frequencies again decrease by approximately one half order of magnitude for each successive LOCA

size increase because similar conditional failure probability relationships are used as in the PWR

analyses.

However, the DH results for BWR-1 LOCA Category I are two orders of magnitude higher than the other

estimates. This is a consequence of using the operating-experience-based TWC frequency (Figure 4.1) to

calibrate these estimates. There is also very little expected frequency decrease between the TWC

frequencies and LOCA Category 4 estimates. However, a precipitous, three orders of magnitude decrease

occurs between LOCA Categories 4 and 5. The implication is that DH predicts that through wall cracking

and LOCAs are equally likely up to a break size of 7 inches (178 mm) diameter (i.e., LOCA Category 4).

Only breaks larger than this become much less likely. As in the PWR comparisons, variability among the

three BWR-1 estimates is largely a function of the initial variability present in the Category I results.

Normalization of using the LOCA Category 1 estimates would substantially decrease the variability in

subsequent LOCA categories.

The DH BWR-2, LOCA Category 1 estimate is much closer to the operating-experience estimates than

the BWR-I base case. This result is also lower than the BG and BL estimates. Further, the DH results

for larger LOCA categories decreases precipitously compared to the other BWR-2 estimates. This

increased variability with increasing LOCA size is contrary to the other base case results. The reason for

this large decrease, as discussed previously, is that DH did not model FAC (Section 3.5.1.2). Hence, DH

predicts that the LOCA Category I frequency contribution due to thermal fatigue is high, but then

decreases substantially for each successively larger LOCA size. Conversely, the FAC contributions, as

modeled by BG and BL, become more important contributors for the larger LOCA sizes.

The base case frequency estimates, and the-corresponding variability, are not significantly affected by the

time period (i.e., 25 versus 60 years) of operation. From Table 4.1, BL predicts that the LOCA

frequencies for all base cases will increase by less than a factor of 2 between 25 and 60 years of

operation. The DH 25 and 60 year results are identical for the PWR-1 and BWR-2 base cases.

Additionally, the PWR-3 60 year estimates increase only by 1⁄22 an order of magnitude. The biggest

changes are predicted for the PWR-2 and BWR-1 estimates. There is approximately a two order of

magnitude increase between the 60 and 25 year PWR-2 estimates for each LOCA Category. The BWR-I

LOCA Category I through 4 estimates decrease by approximately one order of magnitude between 25

and 60 years. However, there is virtually no change with time between the BWR- 1 LOCA Category 5

estimates. The leak frequencies in DH's analysis are determined by averaging the cumulative LOCA

probability over the time period being evaluated, either 25 or 60 years. Instantaneous frequencies may

therefore differ somewhat from these simplified average estimates for the BWR-I and PWR-2 analyses.

The LOCA frequency increases with time predicted in the PWR-2 estimates are caused by continued

crack growth from thermal fatigue. Decreases in the BWR-1 frequencies with time are expected because

cracks in the analysis with the highest probability of leading to a LOCA have already been detected and

repaired by 25 years of operation.

The significant variability among the PPM and operating-experience base case estimates reflects the

current uncertainty in the state of knowledge in making these predictions. While reasons and trends

associated with this variability are explored in this section, the elicitation did not attempt to resolve these

discrepancies. Rather, one of the elicitation's goals was to reflect the uncertainty in the technical

community through the use of these base cases. Therefore, differences between the base case analyses

4-11
were highlighted and possible causes (as discussed in this section) were supplied to the panelists to

inform the selection of their base case results for anchoring their elicitation responses and in providing

uncertainty estimates.

The other purpose of the base case analyses (Section 3.5) was to provide anchoring points as a basis for

panel members' elicitation responses. However, decisions regarding the use and application of these base

case results were made by each individual panelist. The expectation was that each panelist's use of the

base case results would be informed by the assumptions, limitations, and approaches used to develop each

estimate; the variability among estimates; and the difference between the base cases and the actual plant

operating systems and conditions. Some panelists utilized the base case results extensively, others

considered the relative trends expressed by the base case estimates, and others chose not to utilize them at

all.

In general, those panelists using the base case estimates chose to anchor their responses to base case

results based on the operating-experience estimates instead of the PFM results. The operating-experience

estimates were most often used for anchoring the LOCA Category I responses. The PFM results were

used only by some panelists to extrapolate their assessments beyond 25 years or to LOCA sizes greater

than Category 1, where no passive-system failure data exists.

As seen in this section, the PFM assumptions are less critical for assessing relative trends for increasing

LOCA sizes than in determining absolute frequencies. Therefore, the differences in absolute frequencies

between the operating-experience base cases and the PFM base cases are not considered significant in

accessing variability among the individual elicitation responses. Additional discussion of the use of the

base case estimates is provided in Section 6 while a more complete description of anchoring philosophy

chosen by each panelist is found in Appendix K.

4.3 Pipin-g Base Case Sensitivity Studies

A number of sensitivity studies of the base case systems were conducted to evaluate the effects of various

parameters on the piping base case frequencies. These studies were also conducted by various base case

team members. They were used to evaluate the effect of certain variables and analysis choices on the

base case estimates. The sensitivity analyses and results were presented to the panelists during the base

case review meeting so that they could inform subsequent elicitation responses. One sensitivity analysis

evaluated the effect of various leak detection strategies as part of an ISI program on all of the base cases

(Section 4.3. 1). All other sensitivity analyses were conducted for a specific base case.

The BWR-1 base case analysis assumed that the model plant followed the Generic Letter 88-01

inspection technique, used weld overlay to reinforce the flawed piping, and utilized normal water

chemistry. This base case definition was chosen to best compare the operating-experience and PFM

estimates by only considering a single, less-complex mitigation strategy, i.e., weld overlay. It was well

recognized by the panelists that this base case is not representative of current BWR operating conditions

which generally employ hydrogenated water chemistry in addition to mitigation strategies other than weld

overlays (e.g., noble metal chemistry addition, mechanical stress improvement, etc.). Therefore, several

additional sensitivity analyses were conducted for this base case to determine the effect of mitigation and

applied loading variables. Specifically, the effectiveness of weld overlays (Section 4.3.3) was evaluated;

the general effectiveness of IGSCC mitigation through analysis of operating experience (Section 4.3.4)

was assessed; and the effect of variations in the applied plant loading history on the LOCA frequencies

(Section 4.3.5) was examined.

4-12
The PWR-l base case analysis considered PWSCC cracking in a hot leg bimetallic weld subject to typical

ISI inspection procedures. The sensitivity analyses for this base case included an examination of the

influence of hydro-testing and safe shutdown earthquake (SSE) seismic loading on the LOCA frequencies

(Section 4.3.6), and considered the effect of degraded material properties (Section 4.3.7). The effect of

ISI on the surge line cracking frequencies resulting from thermal fatigue was evaluated to illustrate the

effect on the PWR-2 base case (Section 4.3.2). Finally, the effect of applied loading history variations

was also studied for the high pressure injection make-up line cracking defined by the PWR-3 base case.

4.3.1 Effect of In-Service Inspection with Different Leak Detection Strategies

Bengt Lydell (BL) used a Markov model approach to investigate the sensitivity of the base case LOCA

frequencies to different ISI strategies. This approach determines the ISI effectiveness factor, /, which is

defined as the ratio of the time-dependent LOCA frequencies with inspections to the applicable

frequencies if no inspections were conducted. Three different inspection strategies were investigated: (1)

no ISI, (2) Section Xl inspection with POD equal to 0.5, and (3) risk-informed ISI with the POD equal to

0.9. See Appendix D for more details.

The effect of IS[ was evaluated for all the base case systems. Figure 4.6 illustrates the results for the

BWR-1 evaluation for Category I LOCAs. Here, baseline frequencies are depicted as a function of

operating time assuming that no active ISI is conducted. However, plant walk downs are performed with

varying periodicity to search for primary system leaks. Four different walk down periodicities are

considered: none, after the hydro test at each refueling outage, weekly, and during each shift. As can be

seen in Figure 4.6, the Category 1 LOCA frequency decreases by about one order of magnitude for each

successive periodicity increase between no walk down, refueling outage walk downs, and weekly walk

downs. However, there is little additional benefit to increasing the walk down frequency to more than a

weekly basis. Also, most of the benefits associated with increased inspection periodicity occur over the

first 25 years of service. Beyond 25 years of plant operation, the slopes of the LOCA frequency results

(Figure 4.6) are not a strong function of inspection periodicity. Specifically, the 60 year frequencies are

about 1/2 an order of magnitude greater than the 25 year estimates for the range of reported walk-down

periodicities. This increase is consistent with BL's 25 and 60 year base case results (Table 4.1).

0 4

0 6

____ ::: : _L L - :- .S- -fl Hyw~o Tesl - : - •

_ - : :L::T: -6--w ekly - - -=..

OE 07 00

0 10 20 30 40 50 60 70

Plant Age [Years]

Figure 4.6 BWR-1 LOCA Frequencies as a Function of Plant Operating Time for the Case of No

ISI with Different Leak Detection Strategies

4-13
Figure 4.7 illustrates the effect of different walk down periodicities on the BWR-1 base case Category 1

LOCA frequencies when ISI is routinely performed and the POD is 0.9. The same four leak detection

strategies as portrayed in Figure 4.6 are included. Comparing Figures 4.6 and 4.7, one can see that the ISI

program does not significantly decrease the LOCA frequency estimates. Without performing walk

downs, the LOCA frequencies after 25 years of plant operations are 9x10-5/reactor year without ISI

(Figure 4.6) and 5x10-5/reactor year with the assumed ISI (Figure 4.7). This less than a factor of two

difference is not terribly significant. Differences are even less when the POD drops to 50%. For

example, the BWR-I Category I LOCA frequency after 25 years of plant operations is 6xO1/reactor year

when the POD is 50% and no walk downs are performed. The effect of ISI is similar when other walk

down periodicities are considered. Additionally. these trends are consistent for the other base case piping

systems. This analysis therefore suggests that conducting frequent plant walk downs is a better strategy

for decreasing LOCA frequency risk than is performing ISI. More details are provided in Appendix D.

1 .0E-03

1 .OE-04

1,0E-05

1... E-06

1.01E-07

1.0 E-08

-CA- None -- D--Hydro Test,

-A- Weekly --X-- Shi ft

I

0 10 20 30 40 50 60 70

Plant Age [Years]

Figure 4.7 BWR-1 LOCA Frequencies as a Function of Plant Operating Time for the Case of ISI

with 90% POD with Different Leak Detection Strategies

4.3.2 PFM ISI Assessment

An assessment of the effect of ISI was also carried out using PFM analysis by VC for the surge line

elbow weld in the PWR-2 base case. The defect distribution and density was generated by RRPRODIGAL. The POD relationship as a function of the crack depth to component thickness ratio (a/t)

was defined by the following equation:

fPoD--ý(D C13 + c., In(a) where cl = 1.526 and c2 = 0.533 (4.1)

4-14
This POD curve is shown in Figure 4.8 and the cl and c, parameters were chosen so that the POD for

defects that are 70 percent of the component thickness is approximately 0.90. This POD is believed to

conservatively represent historical detection resolution, but for future inspections that conform to more

modem standards, this POD could be much better. This POD relationship is more complex than the

constant POD assumed in the previous ISI sensitivity analysis (Section 4.3. 1). More details are provided

in Appendix G.

The results of this ISI sensitivity study on the 60 year cumulative failure probabilities are summarized in

Table 4.2 and Figure 4.9 for various ISI intervals. The reduction factor in the table and figure is defined

as the ratio of LOCA frequency without ISI to the LOCA frequency with the assumed ISI periodicity.

Periodicity varies from no ISI to inspections conducted every 10 years. A special case assuming only a

pre-service inspection (PSI) is also considered. These results suggest that even with this rather poor

inspection capability, and for a weld with a high failure probability, reductions in the cumulative

probability of failure of a decade or more can be achieved with two or three inspections during the life of

the plant. The results also indicate that going beyond three inspections leads to little additional benefit

assuming that indications are not uncovered. In fact, if a fourth inspection is carried out at forty years and

no repairable indications are found, there is almost no additional benefit in conducting an inspection at

fifty years. It should be noted that this analysis of the thermal fatigue risk to the surge line assumes that

no new cracks are formed during service. More details are provided in Appendix G.

It is interesting to compare the two ISI sensitivity studies. Both results predict LOCA frequency

reductions of about a factor of 2 to 3 due to inspections carried out between 25 and 60 years of plant

operation on the applicable 60 year LOCA frequency estimates. However, the VC study in this section

predicts a much bigger reduction due to inspections carried out prior to 30 years of service. Conversely,

the BL study (Section 4.3.1) does not predict similar reductions due to ISI during the first 30 years of

service. However, the BL study does predict that periodic plant walk down inspections during the first 30

years of service result in the biggest LOCA frequency reductions.

4-15
0

Q

0

L.

0.

0.9 -

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0 -I.--

0 0.2 0.4 0.6 0.8 1

a/t

Figure 4.8 Probability of Detection (POD) Curve Used by Vic Chapman in ISI Sensitivity Analysis

LL

UO

-a

mt

30

25

20

15

10

5

0

0 1 2 3

Number of ISI's over 60 Years

4 5

Figure 4.9 Graphical Results from Vic Chapman's ISI Sensitivity Analysis

4-16
Table 4.2 Tabular Results from Vic Chapman's ISI Sensitivity Analysis

Cumulative Probability Factor for of Failure at 60 years General Use

No ISI 1.3 x 10-4 1

0 years (PSI) 4.2 x 10.5 3

10 years 3.8 x 10.5 3.4

10, 20 years 1.3 x 10.5 10

10, 20, 30 years 6.5 x 10.6 20

10, 20, 30, 40 years 4.8 x 10.6 27

10, 20, 30, 40, 50 years 4.7 x 10-6 28

4.3.3 Effect of Weld Overlay on IGSCC Mitigation

The BWR- I base case assumed that a weld overlay was installed at 20 years of service to mitigate the

effects of IGSCC. In order to examine the effect of this mitigation strategy on the estimated LOCA

frequencies, a sensitivity analysis was performed assuming that no weld overlay was applied. The piping

system chosen for this analysis was the 12-inch diameter section of the recirculation system defined

within the BWR-1 base case. There are two similar recirculation loops in this base case with a total of

121 field, shop, and safe end welds. The piping is assumed to be fabricated from Schedule 80 Type 304

stainless with a nominal wall thickness of 0.687 inches (17.4 mm).

Because IGSCC is the degradation mechanism being considered, the time at stress primarily influences

the cracking rate while the number of stress cycles is of secondary importance. For the baseline

condition, the default residual stress pattern in PRAISE was assumed. These default residual stresses for

intermediate size lines vary around the circumference and linearly through the thickness. The residual

stress at a given angular location at the ID is normally distributed with a mean of 1.86 ksi (12.8 MPa) and

a standard deviation of 2.89 ksi (19.9 MPa). These values were derived from benchmarking the PRAISE

results with field observations of leaks for the recirculation system. The mean residual stress at the OD is

-1.86 ksi (-12.8 MPa), and the standard deviation is the same as at the ID. For the case where the weld

overlay was applied at 20 years, an alternative residual stress field [4.3] was assumed (Figure 4.10). The

PRAISE code cannot model the nonlinear gradients illustrated in this figure, so a linear approximation

was used. The linear gradient generally underestimates the beneficial effect of the weld overlay within

the inner half of the pipe wall thickness. More details are available in Appendix F.

The cumulative probability for a Category 1 LOCA, both with and without weld overlay application, are

illustrated in Figure 4.11. The results, of course, are identical up to 20 years when the overlay is applied.

The derivative of the curves in Figure 4. 11 is the LOCA frequency as a function of time.. After 40 years,

the LOCA frequency (i.e., derivative of curve) with no overlay is about 7 times greater than the frequency

with the overlay applied. The effect of the weld overlay also leads to consistent LOCA frequency

reductions for LOCA Category 2 - 4 (not shown) at both 40 and 60 years. More detailed results are

provided in Appendix F.

4-17
.-- IA ý111101 T•.•N-SS

Figure 4.10 Through-Wall Residual Axial Stress Distribution Used in Weld Overlay Analyses

12 in recirc, overlay at 20 years

0.30

E

A

.X

cl

C-i

E)

0 (0 40 50 60

time, years

Figure 4.11 Effect of Weld Overlay Repairs on the Cumulative Category 1 LOCA Frequencies for

12-inch Diameter BWR Recirculation System Piping

4-18
4.3.4 General IGSCC Mitigation Effectiveness

Weld overlays are just one of several IGSCC mitigation strategies for BWR plants. Other effective

strategies include hydrogen and noble metal water chemistry improvements, pipe replacement with more

crack resistant materials, and post-weld heat treatment of susceptible welds. These strategies are coupled

with higher quality inspections to identify cracking before it gets severe. All of these methods have been

used by various plants for mitigating IGSCC. The passive-system failure data contains information on

IGSCC cracking events both before and after these various mitigation techniques were generally applied.

While it is more difficult to assess the effectiveness of individual mitigation techniques, it is

straightforward to examine the general effect of mitigation on the IGSCC cracking frequency. Bill

Galyean simply segregated the operating experience into events prior to 1985 and events after 1985 to

represent the pre-mitigation and post-mitigation periods, respectively. He then calculated the average

IGSCC cracking frequency for the pre-mitigation and post-mitigation time periods. This LOCA precursor

frequency is then converted into LOCA frequencies for each category using the generic conditional

LOCA probability relationship that he used to develop his base case frequency estimates. This analysis

reveals that the pre-mitigation cracking frequency is approximately 60 times higher than the postmitigation frequency for all LOCA categories (Figure 4.12). In his analysis, the cracking frequency is

assumed to be directly proportional to the LOCA frequency, so that this factor separates the premitigation and post-mitigation LOCA frequencies as well. More details are provided in Appendix E.

Bengt Lydell also considered the general effects of mitigation (e.g:, hydrogen water chemistry, weld

overlays, etc.) on post-1985 through wall IGSCC cracking frequencies through Bayesian analysis. See

Appendix D for more details. As indicated in Section 4.2, both Bengt Lydell and Bill Galyean used the

post-mitigation cracking rates as the basis for the BWR-l analysis.. This was counter to the original

BWR-1 definition (Section 3.5).

1.OE-03

1.OE-04

._,1.OE-05

w 1.OE-06

0r

Uo 1.OE-07

0-.J

1.OE-08

1.OE-09

0 1 2 3 4 5

LOCA Category

-i- Pre-Mitigation Results -t- Post-Mitigation Results

6

Figure 4.12 Pre and Post-Mitigation Recirculation LOCA Frequencies

4-19
4.3.5 Effect of Applied Loading Magnitude

The load history and applied stress magnitude significantly influence the LOCA frequencies calculated

using PFM analysis of components. David Harris (DH) conducted a sensitivity analysis for the BWR-l

base case to examine the effect of the normal operating stresses on precursor leak and LOCA frequencies.

The analysis was conducted for a 12-inch diameter recirculation system for which a weld overlay repair

was installed at 20 years of plant operations (part of the BWR- 1 base case). The PRAISE code was used

for this analysis and frequencies for a single weld joint are summarized in Figure 4.13. Obviously, the

precursor leak and LOCA frequencies for a modeled weld increase as the normal operating stresses

increase.

The predicted precursor leak frequency for the 12 ksi (83 MPa) mean normal operating stress is 6x 10-4 per

weld per reactor year. This frequency coincides quite well with the pre-mitigation cracking rates

experienced in service (see Appendices D and F). Increasing the normal operating stress from 12 to 15

ksi (83 to 103 MPa) (Figure 4.13) leads to approximately an order of magnitude increase in the

frequencies associated with all break sizes. A smaller frequency increase occurs when the applied stress

increases from 15 to 20 ksi (103 to 138 MPa).

The selection of the applied stress for the base case analysis is not as important as Figure 4.13 would

indicate because as the assumed applied stress increases, the number of weld joints in a system which will

be subject to that stress level decreases. The 20 ksi (138 MPa) applied stress was chosen for the BWR-I

base case analysis because this was more consistent with the approach used for the other systems.

However, it was assumed that this high applied stress only exists at 2 weld joints in the recirculation

system. The lower 12 ksi (83 MPa) stress is likely to exist at all 49 welds in the simulated system.

Therefore, both stresses lead to similar recirculation system leak frequencies (Table F.23 of Appendix F)

and both stresses correspond to the operating-experience-based leak frequencies (Figure 4. 1) used for

benchmarking the base case the analysis. In actual service, variability in the residual stress magnitude

and distribution also influences the LOCA susceptibility of a particular weld joint. Possible residual

stress variability has not been considered in this analysis, but higher tensile stresses at the inside pipe

surface than assumed will increase the LOCA susceptibility of that weld.

The effect of the applied loading magnitude was also evaluated for the PWR-3 (HPI/MU nozzle) base

case. Thermal fatigue is being modeled in this base case and the cyclic loading magnitude is therefore the

principal consideration. In these results, the applied cyclic stress is 25 ksi (172 MPa) in order to properly

benchmark the operating-experience cracking frequencies associated with this base case (Figure 4. 1).

However, while these stresses lead to the highest leak frequencies, the largest LOCA frequency occurs for

lower applied cyclic stresses. The reason is that higher cyclic stresses promote leak-before-break in this

component by fostering through-wall crack growth instead of surface crack growth that is associated with

higher LOCA frequencies. See Appendix F for additional details and results of this analysis.

4-20
1.0E-01

1.0E-02 25 Years of Operations

1.OE-03

06

& 1.0E-04

I.

1.OE-05

1.0E-06

8 10 12 14 16 18 20 22

Normal Operating Stress, ksi

-4*-Leak (>O gpm) -Cat 1 LOCA (ýl 00 gpm) -hCat 3 LOGA (>5.000gpm) --- ODEGB. 12' Pipe

Figure 4.13 PRAISE Analysis Showing Effect of Normal Operating Stress on Various Size

Precursor Leak and LOCA Frequencies for a Single Weld Joint in 12-inch Diameter Section of

Recirculation System

4.3.6 Effect of Seismic Loading and Hydro Test

The impact of seismic stress was not explicitly considered within the elicitation because the principal

objective of this elicitation (Section 2) is to develop LOCA frequencies under normal operating loads and

expected transients. However, it is relatively straightforward to examine the effect of a prescribed

seismic loading on the LOCA frequencies. The elicitation panel indicated that these sensitivity

calculations would be beneficial and could serve as a surrogate for the effect of other rare emergencyfaulted loads. The hydro test is conducted prior to initial plant operation. While the base case estimates

include this test. it is also straightforward to evaluate the effects that this test has on LOCA frequencies.

The role of an assumed SSE seismic loading and the hydro test was studied for hot leg (PWR- 1) failure

frequencies. However, while hot leg PWSCC cracking is the PWR-I base case (Table 3.7), only the

impact of seismic and hydro test loading on the LOCA frequencies associated with pre-existing defects

growing by fatigue was modeled. These frequencies are only one component of the total LOCA

frequencies associated with this base case. The effect of seismic loading or hydro testing on cracks

initiating and growing due to PWSCC was not considered. However, the initial hydro test does not

significantly alter the failure probabilities associated with service-induced crack initiation and growth.

The PRAISE code was used by DH for this analysis and seismic stresses up to 5 SSE were applied at

either 25, 40, or 60 years of operation. Only one seismic load application was applied in any single

analysis. The seismic magnitude for this sensitivity analysis was selected from the design seismic load

spectrum developed for a specific plant. This sensitivity analysis utilized similar inputs and assumptions

as this base case analysis (Appendix F). The effect of the hydro test is summarized in Figure 4.14 while

the effect of SSE loading is depicted in Figure 4.15. In general, the low absolute frequencies depicted in

Figures 4.14 and 4.15 imply that failure is unlikely due to the seismic and/or hydro test loading of

4-21
preexisting defects under the assumptions and conditions modeled. The main purpose of these results is

to examine the relative changes.

The initial hydro test moderately affects the LOCA frequencies (Figure 4.14). The Category 1 LOCA

frequencies attributed to thermal fatigue decrease by an order of magnitude while the Category 6

frequencies decrease by approximately 1/2 an order of magnitude with the application of the hydro test.

These trends are generally consistent even if SSE loading is not included (Appendix F). The general

decreases occur because hydro testing can identify flaws before the component is place in service that

could eventually lead to thermal fatigue failures during service. Bigger Category 1 decreases exist

because the thermal fatigue cracks modeled that grow from the pre-existing flaws, which are discovered

through hydro-static testing, more likely lead to smaller LOCAs. Hence, the hydro test results in a bigger

decrease of the Category I LOCA frequencies. Also, of note from Figure 4.14 is the fact that the effect of

the hydro test is slightly more pronounced at 60 years than it is at 25 or 40 years.

Seismic SSE loading magnitudes (Figure 4.15) have a minimal effect on the Category I and Category 6

LOCA frequencies. There is also no substantial LOCA frequency increase for 5SSE loading. This

minimal effect is attributed to the small SSE stresses that are applicable for the hot leg configuration

modeled. These stresses are plant specific and other configurations, resulting in higher stresses, could

have a greater impact on the LOCA frequencies. The effect of seismic loading appears to decrease

slightly with operating time (Figure 4.15). This trend is likely due to identification and repair of cracks in

each subsequent inspection.

However, in all these analyses, the LOCA frequencies attributed to fatigue from preexisting defects are

extremely small regardless of the application of seismic loading or the hydro test. These frequencies are

much less than the earlier PWR-1 base case estimates made by DH (Table 4.1 and Figure 4.4). It is not

known if these trends related to seismic stress and hydro test application have similar effects when

PWSCC failure is considered and the LOCA frequencies are higher. The greater crack propensity of

PSWCC due to the elevated crack initiation and growth rates may cause more significant LOCA

frequency increases due to the same applied seismic loading. Additionally, the hydro test may be less

effective in screening for PWSCC cracks than thermal fatigue cracks that initiate from pre-existing

defects because the PWSCC cracks initiate after service commences.

An additional sensitivity study was performed for seismic events for the PWR-2 base case. There are

differences in the treatment of seismic events between PWR-I and PWR-2 sensitivity analyses, because

of the level of detail of the seismic stresses that was available. For the PWR-I case, specific stresses for

the seismic events were available (as discussed in Appendix F), and the seismic stresses were applied

once - at 25, 40 or 60 years as previously detailed. For the PWR-2 case, a list of cyclic stress amplitudes

was available, which included seismic events. The stress history without seismic loading was determined

using rainflow counting with seismic stresses set to a low value. Therefore a different set of stress

amplitudes was considered to evaluate the seismic loading effects (see Tables F.8 and F.9 of Appendix F).

For the PWR-2 base case, when seismic events are considered, the seismic stresses do not occur at a

specific time. Due to the large number of high stress cycles for PWR-2, fatigue crack initiation was

considered in this sensitivity analysis. This contrasts the PWR-1 sensitivity analysis which only

evaluated the seismic and hydrotest loading impact on LOCA frequencies. The baseline LOCA

frequencies are much higher in the PWR-2 analysis than in the PWR-1 and the SSE loading does increase

the LOCA frequencies by a factor of 3. This is a larger effect than previously predicted in the PWR- I

sensitivity analysis, but the increase is still not substantial. More details are provided in Appendix F.

4-22
1.0E-16

(0

- 1.OE-17

.0R Category 1, With Hydro

.0IF

00

o1C- Q, 1.0E-18 Category 6, No Hydro -..

0 1 -"

Category 6, With Hydro

E 1.OE-19

All with SSE

1.OE-20

0 10 20 30 40 50 60 70

Time Frame, years

Figure 4.14 Effect of a Hydro Test on the Cumulative LOCA Frequency for the Hot Leg with a

SSE Load Included

1.OE-16

.0

0

E

C.)

1.OE-17

No Hydro for All Cases

With SSE

Category 1 LOCAs 1

Without SSE

With SSE

Category 6 LOCAs Without SSE

1.OE-18

0 10 20 30 40

Time Frame, years

50 60 70

Figure 4.15 Effect of a SSE Load on the Cumulative LOCA Frequency for the Hot Leg Without a

Hydro Test

4-23
4.3.7 Effect of Degraded Material Properties

Once again, the hot leg was analyzed by DH to examine the effect of degraded material properties on the

estimated LOCA frequencies. The effect of thermal aging was simulated by increasing the material flow

stress and decreasing the fracture toughness. Several different aging conditions were evaluated using the

embrittled properties consistent with a highly aged, cast austenitic stainless steel alloy (CF8M). Once

again, only thermal fatigue from pre-existing defects was modeled, using the PRAISE code with inputs

similar to the base case analysis. See Appendix F for more details.

Results for Category 1 LOCA frequency estimates are summarized in Figure 4.16. As with the hydro test

and SSE hot leg sensitivity studies, the absolute failure frequencies are extremely low because only

thermal fatigue due to pre-existing defects was modeled. The only cyclic stresses considered in this

fatigue analysis were 3 heat-up and cool-down cycles (with a 6.5 ksi (45 MPa) stress range) each year

plus the seismic stresses which were generally quite low, i.e., 1.96 ksi (13.5 MPa) for an SSE event if the

seismic event contained 200 stress cycles all of the same magnitude. These stresses result in very little

crack growth over the operating history which causes the low absolute frequencies depicted in Figure

4.16. The implication is that failure is unlikely due to this specific mechanism under the assumptions and

conditions modeled in the analysis.

The principal point of Figure 4.16 is to demonstrate the relative changes in the frequencies due to changes

in material fracture toughness for this one set of analysis conditions. As can be seen in Figure 4.16, the

LOCA frequency increases due to fracture toughness reductions are much more significant than the

effects of a hydro test or SSE seismic loading (Figures 4.14 and 4.15). In this case, the limiting aging

properties for CF8M causes a 5 order of magnitude increase in the LOCA frequencies compared with the

unaged, baseline LOCA estimates. More modest toughness decreases still result in an order of magnitude

increase in the estimated LOCA frequencies. The results of Figure 4.16 are not time dependent. Time

dependency is a function of material property degradation with continued aging. However, the PRAISE

code can only consider time-invariant material parameters. Continuing material property degradation

with aging would be expected to result in increasing LOCA frequencies with time.

It is not known if similar order of magnitude increases would result if these aging properties were used in

the complete PWR-1 base case analysis. This consideration is largely irrelevant because significant aging

toughness decreases are not expected for the hot leg weld material. However, this analysis does illustrate

the importance of the weld fracture toughness on the LOCA susceptibility when a cracking mechanism is

present. These results are consistent with relative differences described in Section 1.2 for a previous

study on the effects of material toughness [4.4].

4-24
1.0E-13

Cat 1 LOCAs 0 I Extremelv aned CFBM

1.0E-14

U)

2

=1.OE-15-

.0o

C. 1.OE-16

tO Unaged CF8M, J-T Multiplied by 0.6

0

-J 1.OE-17

Unaged CF8M

: 1.OE-18

E

1.OE-19 Base Case Material

1.OE-20

0 10 20 30 40 50 60 70

Time Frame, years

Figure 4.16 Effect of Material Properties (Fracture Toughness) on the Cumulative LOCA

Frequency for the PWR-1 Base Case

4.4 Non-Piping Base Case Results

In addition to developing base case frequencies for piping systems, base case frequencies were also

developed for a number of non-piping components. A non-piping precursor database based on a review

and analysis of LERs was developed. This database is described in more detail in Appendix H. In

addition, a series of separate studies were conducted to develop base case frequencies for a number of

non-piping components and subcomponents. These studies included: a SGTR frequency study, an

overview of the PTS re-evaluation effort, and a BWR vessel rupture and PWR CRDM ejection analyses.

More information on the details associated with each of these base cases is provided in Section 3.5.2. The

results from each of these studies are provided in this section. Also, additional details and results for the

BWR vessel rupture and PWR CRDM ejection analyses can be found in Appendix I.

4.4.1 Steam Generator Tube Rupture Base Case

The LER non-piping database was used to conduct this study as explained more fully in Section 3.5.2.2.

From 1990 to 2002 there were 15 reports of steam generator tube leaks. There is a total of 929 reactor

calendar years represented in this period, so the mean leak frequency over this period is 16x10-3 per

calendar year. The leak events plotted as a function of calendar year are summarized in Figure 4.17. Two

year increments have been selected for binning purposes. While the number of tube leaks per year is

relatively small, these events appear to be relatively constant over this time-frame. The base case SGTR

frequency was also assumed to constant over this time period under the presumption that the leaking

event frequency is directly proportional to the tube rupture frequency.

4-25
LER Events (1990 - 2002)

5

-0

I0

03 -

E

CZ

CU

0)

E

0 "1"-

90-91 92-93 94-95 96-97 98-99 00-01 '02

Figure 4.17 Steam Generator Tube Leaks as a Function of Event Date

In assessing the SGTR data, the database was queried over a 15 year time period between 1987 and 2002.

This search was expanded over the prior 1990 to 2002 study in order to capture a couple of known

SGTRs that occurred in 1987 and 1989. During this 15 year period, there were a total of 4 SGTRs with

resultant leak rates greater than 100 gpm (380 lpm) (LOCA Category 1). These 4 ruptures occurred at

North Anna in 1987, McGuire in 1989, Palo Verde in 1993, and Indian Point in 2000. This same 15 year

time period represents 1,133 calendar years of reactor operation. Therefore, the frequency of steam

generator tube Category 1 ruptures (with resultant leak rates greater than 100 gpm [380 lpm]) was 4/1,133

calendar years, or 3.5x 103 per calendar year. NUREG/CR-5750 [4. 1] conducted a similar assessment of

SGTRs, and estimated a frequency of 7x 10-3 per calendar year. These two frequencies are consistent

because there has been one additional SGTR since the completion of NUREG/CR-5750 at Indian Point.

However, the number of reactor operating calendar years is more than twice in the NUREG/CR-5750

estimate

This steam generator rupture Category I LOCA frequency of 3.5x 10-3 per calendar year was used as the

base case value for this elicitation exercise. This base case frequency estimate was provided to each

panelist. Each panelist then, as part of their elicitation, assessed the adequacy of this Category 1 LOCA

SGTR frequency estimate for both the current-day and future operating time periods. This frequency

estimate was also provided for anchoring the relative LOCA contributions from other Category 1 LOCA

sources, multiple steam generator tube failures resulting in Category 2 and higher LOCAs, and larger

LOCA (Category 2 - 6) failures in other piping systems and non-piping components per the discretion of

each panelist.

4.4.2 Pressurized Thermal Shock (PTS) Base Case

The potential LOCA contribution due to PTS of the RPV was analyzed using available data from the

NRC's PTS Re-Evaluation Project [4.5] as discussed in Section 3.5.2.3. The total PTS through-wall

4-26
cracking frequency results for 3 plants (Oconee, Beaver Valley, and Palisades) at 32 effective full-power

years (EFPY) was first obtained from current results. The EFPY estimate most closely corresponds to the

current fleet average of 25 years used to represent current-day frequencies within the elicitation. The total

PTS through-wall cracking frequencies for each plant were then adjusted to consider only the

contributions due to stuck open primary side valves, stuck open secondary side valves, and feed and bleed

operations. This adjustment removes the frequency contributions due to passive system LOCA failures so

that the resulting PTS risk contributions are independent of the LOCA frequency estimates. The total

PTS risk is proportional to the SB and MB LOCA frequencies. More information on the total PTS risk

and changes resulting from differences in the assumed initiating event SB and MB LOCA frequencies is

contained in [4.5].

The adjusted results for each plant are summarized in Table 4.3. The geometric mean (GM) of the 32

EFPY results for these three plants is also determined along with the 5 1h and 9 5 th percentile of the mean

estimates (Table 4.3). The 5 th and 9 5 th percentile are estimated by determining the values that lay two

standard deviations from the GM estimates using the results from the three plant-specific calculations.

These percentiles provide some indication of the plant specific variability of the PTS through-wall

cracking estimates. While these base case estimates are developed from the 32 EFPY results, the PTS

through-wall cracking frequencies at 60 EFPY increase by less than a factor of two for each plant.

Therefore, this calculation is not particularly sensitive to the future operating time period considered in

this elicitation. The results from this PTS assessment were provided to the panelists for benchmarking

other large piping and non-piping failures with expected low LOCA frequencies.

Table 4.3 Average and Average Plus and Minus Two Standard Deviations of the Adjusted

Through-Wall Crack (TWC) Frequency from the PTS Re-Evaluation Project

Plant Adjusted Category Geometric 9 5 th Percentile 5th Percentile

6 LOCA Mean (per calendar (per calendar

frequencies from (per calendar year) year)

PTS study year)

(per calendar year)

Oconee 3.6E- 11

Beaver Valley 7.5E-10 5.IE-10 7.OE-08 3.7E-12

Palisades 4.8E-09

4.4.3 BWR Vessel Rupture and PWR CRDM Base Cases

BWR vessel beltline weld failure was modeled to calculate failure frequencies due to normal operational

loading and LTOP events. In addition, the large, predominantly feedwater, nozzles (6 to 28 inch) were

analyzed to predict failure frequencies due to thermal fatigue. Both contributions are combined to

provide an estimate of the total BWR RPV LOCA frequency as a function of LOCA size for use as a base

case. The individual nozzle and beltline results were also available for anchoring. The PWR CRDM leak

and ejection frequencies were used to determine the associated LOCA frequencies for use as a base case.

A more complete description of this analysis is provided in Section 3.5.2.3 and in Appendix I.

Table 4.4 summarizes the base case LOCA frequencies for the BWR beltline weld and feedwater nozzle

subcomponents as well as the combined LOCA frequencies for the BWR RPV. Table 4.5 presents the

PWR base case LOCA frequencies due to CRDM ejection. LOCA frequencies are provided at 25, 40,

and 60 years of plant operation in both tables. The PWR CRDM and combined BWR vessel LOCA

frequencies are graphically summarized in Figures 4.18 and 4.19, respectively. The predicted.25-year

PWR CRDM Category I LOCA frequency (Figure 4.18) is relatively high and is consistent with cracking

4-27
experience within the plants. The CRDM LOCA frequencies decrease by less than an order of magnitude

between the LOCA Categories I and 2. Category I LOCAs represent a partial CRDM break while the

Category 2 LOCAs result from a complete ejection. The relatively small decrease between LOCA

Categories 1 and 2 is a reflection of the rapid crack growth rates that are possible due to PWSCC. The

Category 3 CRDM LOCAs are assumed in this analysis to result from simultaneous ejections of

independent nozzles. Common cause CRDM ruptures are not considered in this base case analysis.

However, the panel was asked to consider the likelihood of common cause failures in the elicitation.

These base case Category 3 LOCA estimates were available for possible anchoring during this common

cause consideration.

Periodic inspection over the next fifteen years is expected to decrease the LOCA frequencies by less than

an order of magnitude (Figure 4.18). No further reduction in LOCA frequencies is predicted in

subsequent inspections between 40 and 60 years of life. The implication is that all the cracking which

previously initiated prior to the onset of periodic inspections will have been discovered by 40 years of

service. After 40 years, steady state is reached and additional inspections are only likely to find new

cracking indications.

Not surprisingly, the predicted BWR vessel LOCA frequencies (Table 4.4 and Figure 4.19) are much

lower than the CRDM LOCA frequencies for LOCA Categories 1 and 2. This reflects the diminished

concern of BWR vessel crack initiation and growth due to active degradation mechanisms, as well as the

robustness of the vessel design and operation. However, the Category 3 base case BWR and PWR nonpiping LOCA frequencies are actually similar. This difference is simply a function of the relative

component sizes. A single PWR CRDM ejection is limited to a Category 2 LOCA while the combined

BWR feedwater and vessel ruptures can contribute to all LOCA categories.

Feedwater nozzle failures provide the dominate contribution for the BWR LOCA Categories 1 - 4, but

the nozzle size precludes contribution to larger category LOCAs (Table 4.4). Hence, the Category 5 and

6 LOCA frequencies are comprised solely by the beltline failure contribution. The beltline failure

frequencies continue to increase with time due to the additional irradiation embrittlement while the

feedwater LOCA frequencies are relatively insensitive to operating time. This time insensitivity stems

from both the positive affect of mitigation procedures employed to address this degradation and the cutoff

frequencies associated with the PFM analysis. More information is provided in Appendix I. These

subcomponent trends explain why the total BWR vessel LOCA frequencies exhibit little time sensitivity

for LOCA Categories I - 4 while the Category 5 and 6 LOCA frequencies continue to increase with

operating time because they are governed by the beltline failures. It is interesting that after 60 years of

operation, the Category 5 and 6 LOCA frequencies due to beltline failures are nearly identical to the

Category 4 feedwater nozzle frequency.

It is also interesting to note (Table 4.4) that although the beltline LOCA frequencies are smaller, they

decrease more gradually than the feedwater frequencies as a function of LOCA size. This trend reflects

the expectation that the partial failure of the large, BWR vessel is not much more likely than a complete

failure. This trend becomes even stronger after 60 years of service due to radiation embrittlement of the

vessel material. The difference between a Category I and Category 6 LOCA after 60 years is

approximately a factor of 5 (Table 4.4). Therefore, large and small LOCAs resulting for BWR vessel

failures are almost equally likely after this service time. A similar trend is predicted in DH's PFM

analysis for the BWR-I piping base case frequencies for LOCA Categories 1 - 4 (Figure 4.5). LOCA

decreases could be more gradual as both the component size increases and material brittleness increases.

Similar trends are also discussed in the elicitation results (Section 6).

4-28
Table 4.4 BWR RPV LOCA Frequencies Broken Down by Major Subcomponent (Beltline and

Feedwater Nozzles)

LOCA LOCA Frequency (per calendar year)

Category 25 Years 40 Years 60 Years

RPV Beltdine

I 8.OE-9 2.4E-8 3.7E-8

2 1.9E-9 5.OE-9 2.3E-8

3 1.OE-9 2.5E-9 1.8E-8

4 4.0E-10 1.OE-9 ].4E-8

5 1.9E-10 4.5E-10 1.IE-8

6 7.9E-1 I 1.9E-10 8.OE-9

Feedwater Nozzles

1 8.OE-7 1.2E-6 L.OE-6

2 1.6E-7 2.4E-7 2.OE-7

3 3.2E-8 4.7E-8 4.OOE-8

4 6.4E-9 9.4E-9 8.OE-9

BWR Vessel - Combined

1 8. IE-07 1.2E-06 1. OE-06

2 1.6E-07 2.4E-07 2.2E-07

3 3.3E-08 5.OE-08 5.8E-08

4 6.8E-09 L.OE-08 2.2E-08

5 1.9E-10 4.5E-10 1.1E-08

6 7.9E-11 1.9E-10 8.OE-9

Table 4.5 PWR RPV (CRDM) LOCA Frequencies

LOCA LOCA Frequencies (per calendar year)

Category 25 Years 40 Years 60 Years

RPV (CRDMs only)

1 1.OE-03 2.2E-04 2.2E-04

2 2.0E-04 4.0E-05 4.0E-05

3 3.2E-08 1.6E-09 1.6E-09

4-29
PWRs

.OE-03 - ____________ _________________________________

0,

O

u> 1.0E-05 U

0

-. 1.OE-06

.J

1.OE-07 -

1.0E-08

0 1 2

LOCA Category

3 4

-- 25 Years -E8- 40 Years -- 60 Years

Figure 4.18 PWR CRDM LOCA Frequencies at 25, 40, and 60 Years

Cu

0

U0

-j

1.OE-05

BWRs

1.0E-06

1.0E-07

1.OE-08

1.OE-09

I.OE-10

1.0E-11

0 1 2 3 4

LOCA Category

5 6 7

-4-25 Years -8- 40 Years - 60 Years

Figure 4.19 BWR Combined RPV LOCA Frequencies at 25, 40, and 60 Years

4-30
4.5 References

4.1 Poloski, J. P., et. al., "Rates of Initiating Event at U.S. Nuclear Power Plants: 1987-1995,"

NUREG/CR-5750, February 1999.

4.2 Beliczey, S., and Schulz, H., "Comments on Probabilities of Leaks and Breaks of Safety-Related

Piping in PWR Plants," International Journal of Pressure Vessel and Piping, Vol. 43, pp. 219 -

227, 1990.

4.3 T. C. Chapman, et. al., "Assessment of Remedies for Degraded Piping," Electric Power Research

Institute Report NP-588 I-LD, 1988.

4.4 Simonen, F.A., Doctor, S.R., Schuster, G.J., and Heasler, P.G., "A Generalized Procedure for

Generating Flaw-Related Inputs for FAVOR Code," NUREG/CR-6817, March 2004.

4.5 Memorandum from A.C. Thadani to S.J. Collins, Technical Basis for Revision of the Pressurized

Thermal Shock (PTS) Screening Criteria in the PTS Rule (10 CFR 50.61), December 31, 2002.

4-31

5. ANALYSIS OF ELICITATION RESPONSES

The responses for each panel member were analyzed separately and then aggregated to obtain group

estimates. Individual estimates of selected parameters of LOCA frequencies for BWR piping, PWR

piping, BWR non-piping, and PWR non-piping failures were calculated for each panelist that

provided the relevant input responses'. A unified response and analysis framework was used to

ensure consistency and commonalty in processing the panelists' inputs. The panelists' responses were

assumed to represent the median, 9 5 "h, and 5 th percentiles of their subjective uncertainty distributions

for each elicitation question and the analysis framework was based on the assumption that the

panelist's responses could be represented by split lognormal distributions.

The final output for each panelist was a set of individual estimates for the mean, median, 5" and 95'h

percentiles of the total passive LOCA frequencies for BWR and/or PWR plants. The LOCA

frequency parameter estimates for the individual panelists were then aggregated to obtain group

LOCA frequency parameter estimates. Statistical confidence bounds were calculated to provide a

measure of panel diversity for the individual LOCA frequency parameters. A number of sensitivity

analyses were also conducted to examine the robustness of this analysis procedure and to identify the

assumptions and techniques that most significantly affect the aggregated group estimates for each of

the six LOCA categories. Details on the analysis of the panelists' elicitation responses follow.

5.1 Analysis Framework

A common analysis framework was adopted to analyze the elicitation responses as depicted in Blocks

13 and 16 in Figure 3.1. The analysis framework is "common" because the same calculation

methodology is used to process the elicitation responses for each panelist. This allows the results to

be compared across panelists and permits meaningful measures of panel diversity to be developed.

The analysis framework is consistent with the structure of the elicitation questions (Section 3.8) which

were developed to assess fundamental piping and non-piping LOCA contributing factors. The

framework was also chosen to be consistent with certain principles that are subsequently discussed.

As described in Section 3.8, panel members were asked to supply three numbers for each elicitation

question: a MV, an UB, and a LB. As described in Section 3.3.2, these numbers were assumed to

correspond to the median, 9 5 th percentile, and 5 th percentile, respectively, of the panelist's subjective

uncertainty distribution. The goal was to estimate LOCA frequencies for four plant-type

combinations (BWR piping, BWR non-piping, PWR piping and PWR non-piping) and combine these

to estimate total BWR and PWR passive LOCA frequencies. These bottom-line estimates are all in

the form of selected parameters of the LOCA frequency distribution. The parameters used are the

mean, median, 9 5

rh percentile, and 5 h percentile. These parameters are considered sufficient for

regulatory applications.

There are two basic approaches to aggregating the individual panelist responses to obtain group

estimates of the bottom-line parameters. The first approach is to propagate the responses separately

for each panelist to obtain individual total LOCA frequency estimates and then combine them to

obtain group estimates of the bottom-line parameters. The second approach is to combine the

responses for each question to obtain a group estimate for each elicitation question and then propagate

these group estimates to determine the total LOCA frequency parameters. Because the propagation

and aggregation algorithms are highly nonlinear, these two approaches may lead to significantly

different results. The second approach, in effect, replaces all the panel members' responses with those

of a quasi-panelist (in this case, a 13 th panel member) who is informed by the collective panel

wisdom. However, the strategies that individual panelists used to respond to each question varied a

great deal and not all panelists answered each elicitation question. Therefore, this second approach

would result in significant inconsistencies in the quasi-panelist's responses which would make it

Not all panelists provided input responses for both BWR and PWR plants.

5-1
impractical to either interpret or apply the bottom-line estimates. In contrast, because careful attention

was paid in the elicitation sessions to eliminating inconsistencies within an individual panelist's

responses, the first approach should yield self-consistent individual results. Consequently, there are

fewer difficulties in interpreting and applying the bottom-line estimates. Therefore, the first approach

was used to aggregate the panelists' responses.

A guiding principle in the analysis framework is to make as few assumptions as are necessary to

estimate the four bottom-line LOCA frequency parameters. Although the approach adopted can be

extended to estimate the entire LOCA frequency distribution for each LOCA category, this was not

done. Such an extension would involve fitting an entire distribution to estimates of the four bottomline parameters and would require assumptions about the form of this distribution. Instead, the

approach adopted in this study was to focus on the selected bottom-line parameters and estimate each

of them separately. To go beyond this approach would necessitate assuming more structure to the

panelists' responses, and more assumptions about the LOCA frequency distributions than is necessary

or warranted.

It is important that the bottom-line LOCA frequency parameter estimates reflect both individual

uncertainty and panel diversity. Individual uncertainty stems from the uncertainties in each panel

member's responses, as embodied in the UBs and LBs for each elicited quantity. These uncertainties

are propagated to obtain estimates of the means, medians, 5rh and 9 5th percentiles of the LOCA

frequency distributions. Panel diversity stems from differences among the individual panel member

estimates. Because of the lack of data and the variety of approaches used by individual panel

members, it is not surprising that there were large differences in their responses and in the resultant

bottom-line estimates. In this sense, panel diversity is simply a reflection of the existing scientific

uncertainty about the LOCA frequencies being estimated. Confidence intervals for the estimated

bottom-line parameters were developed to reflect panel diversity.

The analysis framework is summarized in Figure 5.1 using a flowchart. The piping contribution is

outlined in the left-hand branch of Figure 5.1. The individual responses (Section 5.2) for the

anchoring frequencies (Block 1.2) and adjustment ratios (Block 1.3) are assumed to be independent

split lognormal distributions (Section 5.3.1) and are multiplied as described in Section 5.3.3 to

determine the piping system frequencies (Block 1.4). These system frequencies are then summed for

all contributing piping systems to obtain the un-normalized piping contribution (Block 1.5) as

described in Section 5.3.4. The un-normalized piping contribution is then multiplied (Section 5.3.3)

by the piping contribution factor (Block 1.6) to determine the piping contribution (Block 1.7).

The non-piping calculations (right-hand branch of Figure 5.1) are analogous except for an additional

step required to determine the non-piping component from the subcomponent frequencies. The

individual responses for the anchoring frequencies (Block 1.9) and adjustment ratios (Block 1.10) are

assumed to be independent split lognormal distributions and multiplied (Section 5.3.3) to determine

the non-piping subcomponent frequencies (Block 1. 11). These subcomponent frequencies are then

summed (Section 5.3.4) for all contributing non-piping subcomponents to obtain the un-normalized

non-piping component frequencies (Block 1.12). These frequencies are then multiplied (Section

5.3.3) by the component contribution factor (Block 1.13) to determine the non-piping component

frequencies (Block 1.14). The non-piping component frequencies are then summed (Section 5.3.4) to

calculate the un-normalized non-piping contribution (Block 1.15) which is then multiplied (Section

5.3.3) by the non-piping contribution factor (Block 1.16). The end result is the non-piping

contribution (Block 1.17). The piping and non-piping contributions are then summed (Section 5.3.4)

to develop individual estimates of the bottom-line parameters (mean, median, 9 5 th and 5"h percentiles)

of the total LOCA frequencies (Section 5.3.5; Block 1. 18). Finally, the individual estimates of the

bottom-line parameters are combined (Section 5.4) to develop the group estimates and associated

confidence intervals (Block 1.19).

Details of the analysis framework outlined in Figure 5.1 and the calculation methodology to

implement this framework are discussed in the sections below.

5-2
Piping Non-Piping

1.18 Individual Total LOCA Frequencies

(Mean, Median, 95•, 5 "h)

Figure 5.1 Flowchart of the Analysis Framework

5-3
5.2 Elicitation Responses

A common response format was provided to each panelist prior to their individual elicitation sessions

for recording their responses. However, panelists were allowed to provide responses in a format

consistent with their individual approaches. While several panelists utilized the common response

format, many panelists did not. In general, the elicitation responses and associated formats varied

significantly among the panelists. The input responses required from the panelists are indicated by

the shaded blocks in Figure 5.1.

Anchoring frequencies were initially selected by each panelist (Blocks 1.2 and 1.9 in Figure 5.1) for

anchoring subsequent responses. Often, these anchoring frequencies and conditions were adopted

from the piping and non-piping base cases (Sections 3.5 and 4). The base case anchoring frequencies

utilized in the common analysis framework pertain to each LOCA size category and are associated

with 25 years of plant operation for the variable conditions defined for each base case (Section 3.5).

However, several panelists chose not to utilize the base cases for anchoring. Some of these panelists

developed absolute LOCA frequencies for each piping system/non-piping subcomponent, and some

developed alternative anchoring conditions and frequencies than those described in Section 3.5.

Next, after choosing appropriate anchoring frequencies, each panelist chose adjustment ratios to relate

the anchoring frequencies to the associated LOCA frequencies for that piping system/non-piping

subcomponent after 25, 40, and 60 years of plant operation. Adjustment ratios were chosen for each

time period and each LOCA size category to account for differences between the base case conditions

and the assessed contributors to the LOCA frequencies (Blocks 1.3 and 1.10). Panelists that

developed absolute LOCA frequencies did not need to supply adjustment ratios.

All anchoring frequencies and adjustment ratios had associated MVs, or medians, as well as bounds to

represent 90% coverage intervals. The MVs and coverage intervals for the-adjustment ratios were

obtained directly from the elicitation responses. The anchoring frequency coverage intervals were

based on the source of the anchoring frequency estimates. A factor of three was generally used to

quantify the ratio between the coverage bounds (UB and LB) and the MVs for the piping and nonpiping base case frequency estimates (Sections 3.5 and 4) when the base case results were chosen as

anchoring frequencies. The factor of three was based on a consideration of operating experience

uncertainties, but panelists could adjust this factor if desired in their elicitation responses (Section

3.10).

It was generally not necessary to develop anchoring frequency coverage intervals for panelists who

independently developed system-based frequencies or who chose alternative anchoring frequencies.

The uncertainties for the system-based frequencies were provided directly by the panelists while the

uncertainty associated with any alternative anchoring frequencies was simply reflected in the

adjustment ratio coverage intervals. In practice, the distinction between uncertainties in the anchoring

frequencies and adjustment ratios is somewhat artificial since these uncertainties are combined as

discussed in Section 5.3.

The only other inputs supplied by the panelists were the contribution factors (Blocks 1.6, 1.13, and

1.16). As discussed in Section 3.8, the panelists were not required to provide information on all

piping system or non-piping subcomponents, but only on those whose total contributions were

thought to represent at least 80 percent of the piping and non-piping LOCA frequencies (Blocks 1.5

and 1.15). The contribution factors were used to normalize each panelist's input to determine total

LOCA frequency estimates. The piping contribution factor (Block 1.6) is the reciprocal of a

panelist's MV estimate of the total percentage contribution of the quantified piping systems to the

LOCA frequency for all piping systems. Each panelist also provided a coverage interval for the

piping contribution factor. For example, if a panelist assessed all of the piping systems, the MV

contribution factor as well as the UBs and LBs would be I (or 100 percent) and the contribution factor

coverage interval is a single point. As another example, suppose a panelist estimated that the assessed

5-4
piping systems represent 85 percent of the total piping LOCA frequency with a LB and UB of 75 and

95 percent. The piping contribution factor is then 1.18 (1/0.85) with a LB of 1.05 (1/0.95) and a UB

of 1.33 (1/0.75). The corresponding contribution factor coverage interval is (1.05, 1.33).

Similarly, panelists provided one of two non-piping contribution factors (Blocks 1.13 and 1.16). The

analysis method chosen by each panelist determined which non-piping contribution factor was

required. The two possible analysis methods are mutually exclusive. Therefore, the contribution

factor associated with the approach not selected is a single point value of I (or 100%) by definition.

If a panelist assessed each non-piping component separately (i.e., pumps, valves, pressure vessel,

etc.), then a component contribution factor (Block 1.13) was required for each non-piping component

assessed. The component contribution factor is the total percentage contribution (> 80%) of all

quantified non-piping sub-component failure frequencies to the LOCA frequency associated with that

component. For example, if the panelist assessed RPV LOCA contributions and believes that vessel

rupture contributes 90 percent of the frequency associated with RPV LOCAs, then the component

contribution factor for the RPV is 1.11 (1/0.9). Similarly, contribution factors were then required for

the remaining non-piping components (i.e., steam generator, pressurizer, pumps, valves, etc.) based

on the subcomponents assessed for each component. The panelist also provided a coverage interval

for each non-piping component contribution factor.

For panelists who considered specific failure scenarios without regard to the applicable component's

contribution to the non-piping LOCA frequency, a non-piping contribution factor (Block 1.16) was

required. Here the contribution factor is the total percentage contribution of the specific failure

scenarios that were quantified to the total non-piping LOCA frequency. For example, for Category 2

PWR LOCAs, if the panelist believes that SGTRs, CRDM ejections and pressurizer heater sleeve

failures together account for 95 percent of the total non-piping LOCA frequency, then the non-piping

contribution factor is 1.05 (1/0.95). The panelist also provided a coverage interval for each nonpiping contribution factor.

As discussed in Section 3.10, it was occasionally necessary for the facilitation team to impute missing

elicitation response inputs so that complete LOCA frequency results could be developed for each

panelist. Almost always, the missing inputs pertained to coverage intervals corresponding to MV

responses that were provided. Missing inputs were imputed in a manner consistent with the rest of the

panelist's responses. Panelists were required to either verify imputed inputs or directly provide this

information to ensure that they agreed with any imputed input values.

A spreadsheet was developed for each panelist containing the input responses for each piping system

and non-piping subcomponent LOCA frequency contribution. The spreadsheet structure uses the

common response format described earlier and allows processing using the analysis framework

(Section 5.1) with the calculation formulas (Section 5.3) to determine individual total LOCA

frequency estimates. The spreadsheets are all identical to ensure consistent processing and foster

comparisons of the panelists' responses.

There is one situation where this approach did not necessarily reflect the panelist's opinion about the

contribution of individual piping systems to the total piping LOCA frequency contributions. One

panelist, provided global LOCA piping estimates and uncertainties for PWR plants based on plantspecific studies associated with the development of risk-informed ISI methodology procedures

(Appendix K, page K-2). However, to utilize the response and analysis framework discussed in

Sections 5.1 and 5.2, this panelist's piping responses were apportioned among the LOCA-sensitive

systems based on the percentage of welds in each system. This panelist did not confirm the validity or

accuracy of this apportionment. While the final LOCA frequency estimates and uncertainties matched

his elicitation input, this analysis should not be construed to reflect his beliefs about the contribution

of individual piping system LOCA frequencies. This panelist did provide non-piping responses for

individual failure scenarios that were consistent with the existing framework. Hence, a similar

5-5
apportionment of his non-piping responses was not required. No other analysis of a panelist's

responses deviated from the analysis framework described in Sections 5.1 and 5.2.

5.3 Individual Estimates

Each panelist's responses (i.e., MVs, UBs, and LBs) were propagated to estimate the bottom-line

parameters (i.e., the means, medians, 9 5Ih and 5th percentiles) of the LOCA frequency distributions

for each of the four plant-type combinations (BWR piping, BWR non-piping, PWR piping, PWR nonpiping) that the panelist quantified. The BWR piping and non-piping estimates and the PWR piping

and non-piping estimates are then summed to obtain individual estimates of the total BWR passive

LOCA frequencies and the total PWR passive LOCA frequencies, respectively.

Figure 5.2 is a flow chart outlining the assumptions and calculations required to implement the

analysis framework presented in Figure 5.1. The assumptions and equations associated with each

specific aspect of the analysis are provided in the subsections below. Flowchart block numbers from

Figures 5.1 and 5.2 are referenced within the text as appropriate using the same numbers illustrated in

these figures. For example, a reference to Block 2.1 refers to block 2.1 in Figure 5.2.

5-6
2.1 Individual Panelist Responses

(MV, UB, LB)

2.2 Split Lognormal

Upper (MV, UB) = Median, 95th

Lower (MV, LB) = 5th

2.3 Assume Two Independent

Split Lognormals

(Base Case; Ratios) (X

Figure 5.2 Flow Chart of Required Calculations

5.3.1 Lognormal Structure

In order to propagate the elicitation responses, it is necessary to specify a probabilistic structure for

them. The assumed probabilistic structure is based on the lognormal distribution, which was chosen

to reflect the structure of the elicitation questions. Because all of the questions called for a ratio as a

response, the lognormal distribution is a natural choice. The loguniform or the logtriangular

distributions are possible alternatives. Although no sensitivity analyses were performed to evaluate

the lognormal assumption, variations in the individual LOCA frequency estimates resulting from

replacing the lognormal with other plausible distributional forms are expected to be significantly

smaller than the differences between the individual estimates. Accordingly, because of the very large

diversity in the individual estimates (see Section 7.5), it is not expected that the particular choice of

5-7
the distributional forms would result in group estimates that are significantly different than those

based on the lognormal assumption.

Initially, for each question, the piping (Blocks 1.2, 1.3, 1.6) and non-piping (Blocks 1.9, 1.10, 1.13,

1.16) responses are assumed to be the corresponding percentiles of a lognormal distribution. Thus,

the MV is assumed to be the median, the UB is assumed to be the 95 h percentile, and the LB is

assumed to be the 5t" percentile of a lognormal distribution. A complication arises if the UB and LB

are not multiplicatively symmetric about the MV, as often happens. In such cases, there is no

lognormal distribution determined by the given MV, UB and LB which satisfies the assumed

percentile assignments. Therefore, a split lognormal distribution (Block 2.2) is assumed as the

probabilistic structure to address this situation. This probabilistic structure was endorsed by a peer

reviewer as reasonable and appropriate [5.1 ].

A split lognormal distribution is a combination of two halves of separate lognormal distributions.

Because a lognormal distribution can be determined by its median and one other percentile, two

lognormal distributions can be determined from the response to each elicitation question -- one by the

MV and LB and another by the MV and UB. For values less than the median, the split lognormal is

the lower half (i.e., the part less than its median of the lognormal distribution determined by the MV

and LB). For values greater than the median, the split lognormal is the upper half (i.e., the part

greater than its median) of the lognormal distribution determined by the MV and UB. Clearly, if the

UB and LB are symmetric about the MV, then the split lognormal is identical to the lognormal

distribution determined by the MV, UB, and LB. Accordingly, the split lognormal distribution is

assumed for both the symmetric and non-symmetric cases. With respect to the three bottom-line

percentiles in Block 2.6, note that the median of a split lognormal is equal to the common median of

its upper and lower halves, the 9 5 "h percentile is equal to the 9 5 th percentile of its upper half and the 5ih

percentile is equal to the 5 "h percentile of its lower half.

The LOCA frequency for each of the four plant-type combinations is a sum of contributions from

piping systems and non-piping subcomponents (Block 1.18). Each of these contributions is a product

of an anchoring frequency from a base case (Block 1.2 or 1.9) and an adjustment ratio (Block 1.3 or

1.10). As noted above, the anchoring frequencies and adjustment ratios are each modeled by a split

lognormal distribution. Thus, the inputs to Blocks 1.4 and 1.11 are products of two split lognormal

distributions. Each panelist's bottom-line LOCA frequency estimate for the four plant-type

combinations is a normalized sum of these products over all piping systems (Block 1.7) and nonpiping components (Block 1.17). Thus the bottom-line LOCA frequencies are sums of products of the

split lognormal distributions which represent the panelist's responses.

From Figure 5.2, the calculation of the individual bottom-line parameters is carried out in two parallel

paths - one for the percentiles and the other for the means. Section 5.3.2 provides the basic lognormal

formulas, Section 5.3.3 provides formulas for the product of two independent split lognormal

distributions and Section 5.3.4 shows these results are propagated to obtain the bottom-line LOCA

frequency parameters.

5.3.2 Basic Lognormal Formulas

By definition, Y has a lognormal distribution if the distribution of ln(Y) is normal. A lognormal

distribution is denoted by LN(p, u), where ln(Y) is normal with a mean p and variance o2 (or standard

deviation a). Note that ln(Y) is sometimes referred to as the underlying normal of the lognormal Y.

Denote the median of Yby m(Y), its expected value by E(Y), its variance by V(Y), its standard

deviation by SD(Y) and its pth percentile by b,(Y). These lognormal parameters can be written as

follows [5.21.

m(Y) = exp(,u) (5.1)

g(") = exp(u + a, /2) (5.2)

5-8
V(Y) = exp( 2p + 6")6 [exp(2) - 1] (5.3)

SD(Y) = exp(p + 02/2) . [exp(2) - 1]112 (5.4)

bp(Y) = exp(,u + kpo) (5.5)

where kp is the pt, percentile of the standard normal distribution (P = 0 and o1 = 1).

Some other useful relations are the following.

E(Y) = m(Y) exp(o2 /2) (5.6)

V(Y) = [E(Y)] 4 /[m(Y )]2 - [E(Y)] 2 (5.7)

m(Y) = E(Y) [I + V(Y)/[E(y)] 2 1-1/2 (5.8)

Solving Equation 5.6 for a yields

u = [2 In { E(Y)/m(Y) } ] 2 (5.9)

A commonly used parameter is the error factor, defined as the ratio of the 95rhpercentile to the

median. Because the 5 h and 9 5th percentiles of a lognormal are multiplicatively symmetric about the

median, the error factor is also equal to the ratio of the median to the 5th percentile. Setting k95 =

1.645, it follows from Equations 5.1 and 5.5 that the error factor of Y is

EF(Y) = b95(Y)/m(Y) = m(Y)/bs(Y) = exp(l.645a) (5.10)

5.3.3 Piping System and Non-piping Subcomponent Frequencies

From Figure 5.1, each piping system frequency (Block 1.4) or non-piping subcomponent frequency

(Block]. 1.) is a product of an anchoring frequency from a base case, or a suitable alternative as

described in Section 5.2 (Block 1.2 or 1.9), and an adjustment ratio (Block 1.3 or 1.10). As indicated

in Block 2.2 and described in Section 5.3.1, the anchoring frequencies and adjustment ratios are each

modeled by a split lognormal distribution. Thus, the piping and non-piping frequencies are products

of split lognormal distributions.

In order to evaluate the product of any two distributions, it is necessary to specify their correlation

structure. The statistical characteristics of the base case anchoring frequency and adjustment ratio

distributions depend on the statistical characteristics of the uncertainty bounds provided by the

panelists. Because the anchoring frequencies and adjustment ratios are generally provided by

different panel members and because they have different structures (one is an absolute number and the

other is a ratio), their associated uncertainty bounds are assumed to be statistically independent.

Accordingly, the inputs to Blocks 1.4 and 1.11 are each assumed to be the product of two independent

split lognormal distributions (Block 2.3). The calculations of the percentiles (Block 2.4) and the

means (Block 2.9) of the product of two independent split lognormal distributions are discussed

below.

5.3.3. 1 Calculation of Percentiles - The required percentiles are calculated by the following

algorithm. First, the median of the product of two independent split lognormal distributions is

calculated by multiplying the medians of the two split lognormals. Next, the 9 5 th percentile of the

product is set equal to the 95h percentile of the product of the two lognormals which are the upper

halves of the anchoring frequency and adjustment ratio split lognormal distributions. Finally, the 5"'

percentile of the product is set equal to the 5 "h percentile of the product of the two lognormals which

are the lower halves of the split lognormals.

5-9
Although this algorithm for determining the required percentiles of the product of two independent

split lognormals is not exactly equivalent to multiplying the split lognormals as two-part distributions,

the results are a good approximation to those obtained through exact multiplication. The algorithm is

used for computational convenience, and also because the median and 95"h percentiles of each product

are largely determined by the contributions from both upper halves of the split lognormal responses.

The median and 95th percentiles are the most important percentiles in this study.

To implement the algorithm, a formula for the percentiles of the product of two independent

lognormal distributions is required. As shown below, the product of two independent lognormals is a

lognormal. Because the percentiles of a lognormal are determined by the mean and standard deviation

of its underlying normal distribution (Equation 5.5), formulas are required to calculate the mean and

standard deviation of the underlying normal of the lognormal which is the product of two independent

lognormals.

Denote the two independent lognormals by Z, and Z2 , where Z4 = LN(,u.ia) for i = 1, 2. The

lognormals Z, and Z2 represent either the upper or lower halves of the corresponding anchoring

frequency and adjustment ratio split lognormals. Recall that the MV, UB, and LB values supplied by

a panelist in response to each question are assumed to correspond to the median, 9 5th and 5th

percentiles, respectively, of a lognormal distribution. Denote the panelist responses corresponding to

Zi by MVi, UBi and LBi and denote the median of Zi by mi. Then mi = MVi and, from Equation 5.1,

pi = ln(mi) = ln(MV), i =1, 2. (5.11)

It is convenient to convert the responses supplied by a panelist into error factors of the corresponding

lognormal distributions in order to determine the o-.. Denote the error factor of Z4 by EF,. Then, for Z4

corresponding to the upper half of a split lognormal, EFi = UBIMVi and, from Equation 5.10,

ai = ln(EF,)/1.645 = ln(UBi/MV)/1.645, i = 1,2 (5.12)

Let Z = Z, Z2 be the product of the two independent lognormals, where the variable Z represents either

the piping system (Block 1.4) or non-piping subcomponent (Block 1.11) frequencies. Because the

sum of any number of normal distributions is always normal, the product of two lognormals is always

a lognormal, whether or not they are independent. Hence Z = LN(u, a) is lognormal. Because

multiplying two lognormals is equivalent to adding their underlying normal distributions, it follows

from the independence of ZI and Z2 that p =,• + ,u 2 and o = [(o,) 2 + (0a2)

2

]112 . In terms of the panelist

responses, it follows from Equations 5.11 and 5.12 that

y = ln(MVI) -+ ln(MV2) = ln[(MVI)(MV,)] (5.13)

a = [(ln(UBI/MVI)) 2 + (ln(UB2/MV)) 2

]' /1.645 (5.14)

From Equation 5.5, the median, 95th and 5'h percentiles of Z are given by

b50(Z) = exp(p) = (MV)(MV-) = m I 2 (5.15)

b 95(Z) = exp(p + 1.645a) (5.16)

b5(Z) = exp(p - l.645a) (5.17)

The formulas in Equations 5.13 through 5.17 apply when Z corresponds to the upper halves of the

split lognormals. When Z corresponds to the lower halves, the error factor of Z4 is defined by EFi =

MVi/LBi and the formulas in Equations 5.12 through 5.17 apply with UBi/MVi replaced by MViILBi

for i = 1, 2. Note that Equation 5.15 demonstrates that the median of the product of two independent

split lognormal distributions is the product of the medians of the two split lognormals, as stated above.

5-10
For reference, from Equations 5.2 and 5.3, the mean and variance of Z are given by

E(Z) = (MV1)(MV2) exp[{ (ln(UB,/MVI)) 2 + (ln(UB2/MV2))2}/5.412] (5.18)

V(Z) = [E(Z)] 2{ exp[ {(ln(UB,/MVi)) 2 + (ln(UB2IMV 2))2 }/2.706] - 1 (5.19)

5.3.3.2 Calculation of Means - The mean of the product of two independent distributions is always

equal to the product of the means of the two distributions. Accordingly, to calculate the means in

Block 2.9, a formula for the mean of a split lognormal distribution is needed. The mean of a

lognormal distribution is more strongly influenced by the values in the upper half of the distribution

as a result its skewed shape. Additionally, as can be seen from Equation 5.2, the mean of a lognormnal

distribution is proportional to an exponential function of the variance of its underlying normal. These

features imply that the mean of a lognormal is a sensitive function of the upper percentiles (i.e., >

95t) of the underlying normal distribution. Consequently, the mean of a split lognormal is also a

sensitive function of the upper percentiles of the distribution.

The upper percentiles and consequently the mean of the elicitation response largely are defined by the

ratio of the UB to the MV. For some panelists, this ratio can be as large as 1,000. In such a case, the

ratio of the mean to the median for a lognormal distribution is 6747 (see Equation 5.6). This mean to

median ratio implies that the mean corresponds to a much higher percentile than the 9 5ih percentile

which is the UB elicited from the panelist. In other words, the mean is extrapolated far beyond the

elicitation responses in situations where the error factor is large.

To remedy this problem, the split lognormals in Block 2.2 that are used to calculate the means are

replaced by split lognormals truncated at the 9 9 .9h percentile. The remaining 0.1 percent of each

distribution is concentrated at the 9 9 .9[h percentile. This truncation point was chosen to be reasonably

conservative, yet ensure that the mean is not dominated by the extreme upper tail of the distribution.

Furthermore, because the panelists provided no information beyond the 9 5 th percentile, any truncation

point beyond the 9 5th percentile is consistent with their responses. The truncated split lognormal is

used only to calculate the means because truncation at this percentile minimally affects the 5 th,

median, or 9 5

Lh percentile estimates.

The mean of a truncated split lognormal distribution, W, is given by

+ ex .- rW) ) -t)] (ID-cCt ))ex (to (5.20)

where

m(W) = median of W

PL2 = variance of lower half of ln(W)

OU2 = variance of upper half of ln(W)

M = cumulative distribution function of the standard normal distribution

t= truncation point corresponding to the p"' percentile

The means of the truncated split lognormals corresponding to the anchoring frequencies and

adjustment ratios (Blocks 1.2, 1.3, 1.9, 1.10) are multiplied to obtain the means in Blocks 1.4 and

1.11. Denote the distribution of the frequency in Block 1.4 or Block 1. 11 by Z Then

Z= UV (5.21)

where U is a truncated split lognormal corresponding to an anchoring frequency and V is a truncated

split lognormal corresponding to an adjustment ratio. Based on the assumed independence of U and V

E(Z) = E(U) E(V) (5.22)

5-11
where E(U) and E(V) are given by Equation 5.20.

5.3.4 Piping and Non-piping Contributions

Section 5.3.3 provides formulas for calculating the piping system and non-piping subcomponent

percentiles and means for the four bottom-line frequency parameters (Blocks 1.4 and 1. 11). This

section describes the methodology used to calculate the percentiles and means of the piping and nonpiping contributions (Blocks 1.7 and 1.17).

5.3.4.1 Calculation of Percentiles-From Figure 5.1, the piping and non-piping contributions

depend on sums and products of frequencies (i.e., Blocks 1.5, 1.12, 1.15 for the sums and Blocks 1.6,

1.13, 1.16 for the products).

Denote any of the sums of frequencies by X with its j'h term denoted by Zj. Then

X = Z Zj (5.23)

To calculate the percentiles of a sum, it is necessary to specify the correlation structure of its

individual terms. The Zj in Equation 5.23 express the uncertainties of the panelists about their

responses as provided by the elicited UBs and LBs. The correlation structure is determined by the

correlations between all pairs of the individual Zj terms. Although the exact correlation structure is

unknown, this study assumes that the Zj are positively correlated. This means that if one Zj is high,

then the other Zj's will tend to be high. This is a reasonable assumption because most panelists noted

that degradation mechanisms that significantly contribute to the LOCA frequencies affect many

systems similarly. Therefore, if conditions lead to higher failure probabilities in one piping systemdue to a specific degradation mechanism, then the failure probabilities of other susceptible systems

will also increase. This positive correlation also should extend to the uncertainties. In other words, if

a panelist expressed large (or small) uncertainties for one system, then other systems will also have

large (or small) uncertainties if these measures are positively correlated. It was observed in the

elicitation responses that the uncertainties were indeed positively correlated which implies that the

susceptibility of each system is also positively correlated.

For positively correlated Zj, the correlation structure is bounded by the independent case (all pair-wise

correlations are zero) and the perfect rank correlation case (all pair-wise correlations are maximal).

These correlation bounds can be translated into bounds on the variance V(X) of X. To calculate V(X),

let Pjk be the correlation coefficient of Zj and Zk and let SDj = [V(Z1 )] 1/2 be the standard deviation of Zj.

The variance of X is given by

V(X) = Y'V(Z,) + Y-j •kpjk (SDj) (SDk) (5.24)

The LB on the variance is attained when the components are independent and Pjk = 0. For this case,

the variance of the sum in Equation 5.23 is the sum of the variances, and the LB on the variance of X

is

VL(X) -YV(Zj) (5.25)

Clearly, an UB on the variance of X is given by Equation 5.24 with Pjk = 1 for all j + k (perfect

correlation) and is equal to

Vu(X) = V(Zj) + Ej k (SDj)(SDk) = [y (SDj)] 2 (5.26)

However, this UB is not typically achieved because it requires that the correlation coefficients for all

pairs (Zi, Zk) be simultaneously equal to 1. For this to happen, each pair (Z4, Zk ) has to be linearly

related or, in other words, each Zj must be a linear function of Z1. Because this constraint is almost

5-12
never satisfied, the UB on the variance is achieved by assuming a perfect rank correlation structure

where the correlation between all pairs (Zj, Zk) is maximal instead of being equal to one. This

condition is achieved if Z2 and Zk, are functionally related by some monotonically increasing function

F, i.e., Zj = F(Zk)}.

To see why this condition implies maximal correlation, consider the lognormal case. Let Zi be

lognormal with underlying parameters A. and o. Therefore, ln(Zi) is normal with mean A and standard

deviation oi. and Z, = exp(A. + o W,) where Wi is a standard normal with mean A = 0 and standard

deviation o = 1. Then Z, and Z2 will have maximal correlation if W = Wv2 = W, i.e., if the same

standard normal distribution is used in the expressions for Z, and Z2. Therefore, the sum X = Z,+ Z2 is

a monotonic function of W and-the percentiles of X correspond directly to the percentiles of W. It

follows that bp(X) = exp[,ur + ul b,(W)] + exp[u2 + U2 bp(W)] = bp(Z1 ) + bp(Z2), where bp(X) is the pth

percentile of X. In particular, the median m(X) = m(Z I) + m(Z 2), and the 95 h percentile of the sum

equals the sum of the 9 5 "h percentiles. The assumption of perfect rank correlation generalizes to the

sum of any number of components by setting Zj = exp(/j + aW). Thus, the pt percentile of the sum

of any number of lognormals with perfect rank correlation is the sum of the p'h percentiles of the

components.

This conclusion for the lognormal distribution can be generalized when summing arbitrary

distributions, provided that the distributions satisfy the perfect rank correlation assumption. From

Equation 5.23, if b,(X) is the p'h percentile of X, then

b,(X) = )Z b,,(Zj) (5.27)

The multiplication algorithm is used to calculate the percentiles of the product of two independent

split lognormals for Blocks 1.6, 1.13, and 1.16 described previously inSection 5.3.3.1. Let

Z =Z Z,) (5.28)

where Z = piping (Block 1.7) or non-piping (Block 1.17) contribution

Z= un-normalized piping (Block 1.5) or non-piping (Block 1.15) contribution

Z2= piping (Block 1.6) or non-piping (Block 1. 16)-contribution factor

The non-piping analysis requires an additional product calculation to determine the non-piping

component frequencies (Block 1. 14). If Z represents the non-piping component frequency, then Z,

and Z2 represent the non-piping un-normalized component frequencies (Block 1.12) and the nonpiping component contribution factor (Block 1.13), respectively.

There are two potential complications in applying the multiplication algorithm to Equation 5.28.

From Section 5.3.3.1, the multiplication algorithm assumes that both Z, and Z2 are split lognormals.

First, because the contribution factors all lie between 1 and 2, a split lognormal with its unbounded

range is at best an approximation to the distribution of a contribution factor. However, because the

uncertainties in the contribution factors all have very narrow ranges, the distribution of any

contribution factor and the split lognormal Z2 representing it are essentially the same constant.

Second, from Blocks 1.5 and 1.15, Z, is a sum of split lognormals, which does not have a split

lognormal distribution. However, with the multiplication algorithm, a split lognormal is fit to the

median, 95 h and 5 h percentiles ofZ1. Therefore, the median, 95"' and 5t percentiles of the split

lognormal fit to Z, all coincide with the respective percentiles of Z1 . Because multiplying Z, by Z 2 is

approximately equivalent to multiplying Z, by a constant, the percentiles of Z are not significantly

altered by the assumption that Z, is split lognormal. Thus, using the multiplication algorithm yields a

good approximation to the percentiles of Z.

5-13
As discussed previously, the actual correlation structure is expected to be much closer to perfect rank

correlation than independence. Degradation mechanisms and other LOCA contributing factors can

affect many piping systems and non-piping subcomponents similarly. Hence, there is an expectation

that a strong correlation exists. This correlation is, in fact, evident in many of the panelists'

responses. Consequently, the baseline estimates are all calculated assuming perfect rank correlation.

To implement the calculation, a perfect rank correlation structure is assumed for all the summation

blocks in Figures 5.1 and 5.2 that calculate percentiles. These include the un-normalized piping

contribution (Block 1.5), the non-piping un-normalized component frequencies (Block 1.12), and the

un-normalized non-piping contribution (Block 1.15), and the baseline estimates for the LOCA

frequency percentiles (Blocks 1.18 and 2.6). However, in a sensitivity study, the perfect rank and

independent correlation structures are compared using case studies to represent the individual

elicitation responses which are used to determine these un-normalized contributions (Sections 5.6.3

and 7.6.3). The cases were chosen to span the range of features (i.e., symmetry, error factors, etc)

characterizing the panelists' responses.

5.3.4.2 Calculation of Means - The calculation of the means (Block 2.12) is more straightforward

than the calculation of the percentiles. As noted at the beginning of Section 5.3.4.1, the piping and

non-piping contributions depend only on sums and products of frequencies. Because the contribution

factors in Blocks 1.6, t. 13 and 1.16 are structurally different than the frequencies which they

multiply, it is reasonable to assume that their distributions are independent of the frequency

distributions. Therefore, the means of the frequency contributions in Blocks 1.7 and 1.17 are

calculated by simply replacing the component frequencies by their means. Because all of these

components have split lognormal distributions, the formula in Equation 5.20 is used.

5.3.5 Total LOCA Frequencies

The analysis framework described above is used with each panelist's responses to calculate the

estimated bottom-line parameters of the LOCA frequencies for BWR and PWR piping and non-piping

systems. The total BWR and PWR LOCA frequencies (Blocks 1.18 and 2.6) are calculated by

summing the frequencies corresponding to the piping and non-piping contributions (Blocks 1.7 and

1.17, respectively) as described in Section 5.3.4. The total LOCA frequencies are calculated only for

those individual panelists who have supplied sufficient quantitative input response information.

5.4 Group Estimates and Confidence Intervals

Section 5.3 describes how to calculate the bottom-line parameters (means, medians, 9 5th, and 5 t"

percentiles) of total passive LOCA frequencies based on the responses of any one panelist. The final

step in the analysis framework is to combine the individual estimates to obtain group estimates for the

total passive BWR and PWR LOCA frequencies (Blocks 1.18, 2.7, 2.13). Once the individual

bottom-line parameters have been calculated, each parameter is combined independently of all other

parameters. In other words, the group mean is a function only of the individual means, the group

median is a function only of the individual medians, and so on. Even though the individual parameter

estimates are constrained by the split lognormal structure applied to each panelist's responses, the

group parameter estimates are not constrained by this structure. This is consistent with the guiding

principle discussed in Section 5.1 of making as few assumptions as necessary. In particular, no

assumption is made about the form of the total LOCA frequency distribution, e.g., that it is split

lognormal. Because estimated values of the four bottom-line parameters are sufficient for regulatory

applications, any additional constraints on them are not required.

5.4.1 Justification for Using Group Estimates

The justification for combining the individual estimates into group estimates for the bottom-line

parameters is the empirical observation that a group estimate of a quantity about which its individual

members have only vague information can be surprisingly close to the "truth". Here, a group estimate

is defined as a value near the center of the individual estimates. This empirical observation is

5-14
documented in [5.3], which reports on many examples of this phenomenon. Many of these

quantitative examples use "almanac-type" questions, where the answers are known but about which

the respondents can only guess.

Part of the elicitation training of the panelists consisted of an exercise in which the panelists were

asked "almanac-type" questions about chronic disease statistics (see Section 3.3.2 and Appendix C).

The training exercise questions were designed with a similar structure as the LOCA frequency

elicitation questions to make them as relevant as possible. The training exercise responses were then

combined into group estimates, and the results of the training exercise (Appendix C) supported the

empirical observation that group estimates are more accurate than individual estimates and are

relatively accurate.

A natural question which arises is the relevance of this training exercise to this study's elicitation

procedure which uses an expert panel chosen for their extensive knowledge and experience with

highly complex physical phenomena. On the surface, this elicitation would appear to be very

different from asking an arbitrary group of people --- with no direct topical expertise --- to estimate

chronic disease statistics. However, the elicitation questions asked the panelists to estimate quantities

which differ in three important ways from their relevant scientific expertise. First, virtually all of the

questions ask the panelists to extrapolate well beyond their knowledge and experience with the

LOCA-initiating phenomena. Secondly, the questions all concern the frequencies of occurrence for

LOCA-initiating phenomena, which is a very different concept than the phenomena themselves.

Finally, the questions all effectively asked the panelists to estimate fractals of their subjective

probability distributions for the LOCA frequencies. This is an abstract concept which is even further

removed from the LOCA-initiating phenomena. For all of these reasons, the elicitation questions

asked the panelists to estimate quantities about which they have no specific, direct expertise. Thus,

asking questions related to LOCA frequencies of these panelists is analogous to asking almanac-type

questions of an arbitrary group of people. In other words, the elicitation questions are analogous to

almanac-type questions, for which the improved accuracy of group compared to individual estimates

has been validated [5.3].

5.4.2 Confidence Intervals -- Lognormal Model

Let {uk }, k = 1, 2,..., n, where "n" is the number of panelists, denote a set of individual estimates of

any one bottom-line parameter. For example, { Uk } could be the set of current-day estimates for the

mean BWR Category I LOCA frequency, as calculated from the panelists' responses. A basic

premise of any expert elicitation process is that the panel responses as a whole have no significant

systematic bias. In other words, while individual responses can be highly uncertain and differ

drastically, they do not systematically over- or underestimate the quantities of interest. The goal,

based on the Uk values only, is to calculate an unbiased group estimate of each bottom-line parameter

and to also obtain a measure of panel diversity associated with the group estimate of each parameter.

The baseline approach (Section 5.5) used here simultaneously provides a group estimate and a

measure of panel diversity. The baseline approach is motivated by the observation that {U1} typically

spans several orders of magnitude (see Section 7.5) and is best described by a highly skewed

distribution.

The assumption of statistical independence applies only to each panelist's individual responses (MVs,

UBs, and LBs) to the elicitation questions and is used to derive each panelist's uncertainty distribution

from their responses. Since the panelists' individual quantitative responses were never discussed in

the group sessions, the statistical independence assumption is warranted. It should also be noted that

statistical independence of two distributions describes the variability of the distributions about their

means rather than the relation between their absolute values. Two distributions can have the same

mean and still be statistically independent. Thus, even if the MVs for a particular question were

correlated among panelists as a result of group discussions, there is no reason to believe that the

uncertainties about their responses from the individual elicitation sessions were also correlated.

5-15
To construct confidence intervals for the group estimate (Blocks 1.19, 2.7, 2.13), a lognormal

structure (Block 2.8) is assumed. Specifically, it is assumed that the {Uk} values are a sample from a

lognormal distribution, U. This assumption is made only for analytical purposes and should not be

interpreted to mean that the individual panelist estimates are all generated by the same process. An

analogy is a sample of heights from a defined population. For descriptive or statistical purposes, it is

customary to assume that the heights in the sample are normally distributed, but this assumption does

not imply that the heights are determined by the same physical process.

It is convenient to write U in the form

U = g exp(W) (5.29)

where W has a normal distribution with mean 0 and variance "r2. The interpretation of U is that the

true value of the bottom-line parameter estimated by the Uk is g and that exp(W) is a bias factor which

describes the differences between the panelists' estimates and the true value of the parameter. This

structure for W assumes no systematic bias in the panelists' estimates of the true value U. Therefore g

is the median of U and In(U) has a normal distribution with mean ln(g) and variance r-.

Define

wk =ln(uk) fork=l,2,...,n. (5.30)

From Equation 5.29, the standard estimate for ln(g) is the sample mean of the Wk individual estimates

for the bottom-line parameter of interest. Hence, the group estimate of g is the GM of the uk.

Denoting the group estimate of g by g*, it follows that

g* = [rI Uk] • (5.31)

A measure of panel diversity is a confidence interval for g*. From Section B.4.2 of Reference 5.2, a

two-sided 100(1 -a)% confidence interval (where I--a is the desired confidence level) for ln(g) is given

by

ln(g*) - tI_,2(n-l)[S/n"2] < In(g) < ln(g*) + tj_,/ 2(n-1)[S/n"2] (5.32)

where tl,92(n-l) is the 100(1-o./2) percentile of a Student's t distribution with n-I degrees of freedom,

and

S2 = Z [wk- ln(g*)]2 /(n-l) (5.33)

is the sample variance of the w k. The corresponding 100(1-ax)% confidence interval for g is obtained

from Equation 5.32 as.

g* exp{- tI.-2(n-l)[S/n 1

2j} < g < g*exp{ tl_,_(n-1l)[S/n 112]} (5.34)

For this analysis, a is chosen as 0.05 to calculate a 95% confidence interval for g.

5.5 Baseline LOCA Frequency Estimates

A baseline analysis was conducted to process the panelists' elicitation responses (Section 5.2). The

purposes of this analysis were to develop a set of LOCA frequency estimates that could be used to

investigate important LOCA frequency contributing factors (Sections 7.2 - 7.4), assess panel member

uncertainty and group variability (Section 7.5), provide a reference for comparison with subsequent

sensitivity analysis results (Sections 5.6 and 7.6), and serve as the basis for the summary elicitation

results (Sections 7.7 and 7.8). These summary results are meant to provide appropriate group LOCA

frequency estimates, taking the elicitation structure and results into account. The baseline analysis

5-16
procedure and assumptions for each analysis step have been discussed in Sections 5.3 and 5.4, but

they are summarized here for convenience.

There are six key elements of the baseline analysis framework:

(i) The MV, UB, and LB supplied by the panelists for each elicitation question were

assumed to correspond to the median, 9 5th percentile, and 5 th percentile, respectively, of a

split lognormal distribution with the mean.calculated assuming the upper tail is truncated

at the 99. 9 th percentile.

(ii) Panelist responses are not adjusted to-account for possible overconfidence in the

uncertainty estimates for each elicitation response.

(iii) Split lognormal distributions were summed by assuming perfect rank correlation among

the individual terms.

(iv) The individual estimates are the total LOCA frequency parameters (i.e., mean, median, 5 th

percentile, and 95'" percentile) determined for each panelist

(v) The group estimates of the total LOCA frequency parameters are defined as the GMs of

the individual estimates.

(vi) Panel diversity is characterized with two-sided 95% confidence intervals based on an

assumed lognormal model for the individual estimates.

These key baseline analysis elements were selected to be consistent with the analysis framework

(Section 5.1). If several consistent choices were possible, then conservative analysis choices were

made. Element (i) naturally results from the lognormal analysis framework defined in Sections 5.1

and 5.3.1. Sensitivity analyses were also conducted to examine the effects f. the truncation method

for calculating means (Section 5.6.1). Element (ii) uses the raw responses provided by the panelists.

However, panelist overconfidence is a well-known phenomenon [5.4] and possible adjustment

schemes were evaluated as sensitivity analyses (Section 5.6.2). Element (iii) assumes maximal

correlation between the contributions to the piping and non-piping frequencies for each panelist.

Sensitivity analyses were conducted to evaluate the effects of different distributional shapes on the

mean frequencies (Section 5.6.1), and when assuming an independent rank correlation structure for

sample panelist responses.

A number of natural aggregation points (element iv) exist for combining the individual panelist

responses: (1) after the total LOCA frequencies have been determined, (2) after the separate piping

and non-piping component frequencies have been calculated, and (3) at each individual elicitation

response. Aggregation point (1) was chosen for element (iv). This selection stems from the careful

attention paid during the individual elicitation sessions on eliminating inconsistencies between the

responses of each panelist. Also, the correlation between a panelist's piping and non-piping responses

is retained by this selection. The other two aggregation points lead to inconsistencies (Section 5.1)

because the strategies that individual panelist used to respond to each question varied a great deal and

not all panelists answered each elicitation question. Therefore, aggregation of the individual total

LOCA frequency estimates is the best choice, although sensitivity analysis (Section 5.6.4.2) was

conducted to evaluate the effects of aggregation responses at points 2 and 3.

The use of the GM to aggregate the individual results (element v) and confidence intervals to capture

panel diversity (element vi) results from the interpretation and structure of the individual responses.

As discussed in Section 5.4, both of these elements are a consequence of the assumed lognormal

structure of the individual bottom-line LOCA frequency estimates, although the use of the GM for

aggregation can be justified without assuming a lognormal structure (see below). In addition to being

consistent with the actual elicitation results, this approach is motivated by the fact that the estimated

LOCA frequencies are based on expert elicitation responses, which are fundamentally assumed to not

be systematically biased (see Section 3.3). The individual responses can be highly uncertain and they

can differ drastically, but no significant systematic bias is assumed. The questionnaire structure and

the elicitation training were designed to achieve this goal, and the training exercise results (Appendix

C) are consistent with this fundamental assumption.

5-17
A consequence of the assumption of no systematic bias is that the group estimate should be

somewhere in the middle of the individual estimates, especially if there are wide differences in the

results. One obvious choice would be to calculate the group estimate using the median of the

individual estimates. Other possible choices include the GM and the trimmed geometric mean (TGM)

because they are also estimates of the median. The arithmetic mean (AM) is another possible choice.

However, the AM of individual estimates is often not a good measure of the median group opinion

when the individual estimates are widely varying. In this case, the AM is dominated by the one or

two largest results and cannot be fairly described as a group estimate.

There is support for the use of the median or GM in the literature. In previous NRC applications (see

References 5.5-5.8), the median was used as a group estimate when the individual estimates varied by

several orders of magnitude. In Reference 5.4, Meyer and Booker state: "To overcome the influence

of extreme values when forming an aggregation estimate, use the median or geometric mean" (p.

310). In Reference 5.9, von Winterfeldt and Edwards recommend averaging probabilities (p. 136).

However, they conclude: "The only context in which we have any reservations about this conclusion

is that of very low probabilities ----- for such extreme numbers, we would prefer averaging log odds

to averaging probabilities." For very low probabilities, averaging log odds is equivalent to using the

GM. Taking these considerations into account, and noting that a sensitivity study showed that there is

little difference between using the median or the GM to aggregate the study results (see Section

7.6.4.1), the GM was chosen as the most appropriate group estimate which utilizes all the individual

estimates.

Sensitivity analyses were also conducted to evaluate the ramifications of assuming a lognormal

structure for the individual LOCA frequency estimates. Different aggregation schemes (element v)

are examined in Section 5.6.4, including AM aggregation (Section 5.6.4.1) and mixture-distribution

aggregation (Section 5.6.4.4). Estimatingpanel diversity through the quartiles of the distribution is

examined in Section,5.6.5.

5.6 Sensitivity Analyses

A number of sensitivity analyses were conducted to examine the effects of modifying the analysis

framework used to calculate the baseline LOCA frequency estimates outlined in Section 5.5. There

are many possible alternatives to the baseline elements which could have been chosen to estimate the

group LOCA frequency bottom-line parameters. The analyses described below were selected either to

evaluate plausible alternatives or to quantify the maximum possible LOCA frequency differences

which could result from using alternative calculation schemes. The sensitivity analyses apply to only

one element of the baseline analysis individually; combinations of alternatives were not evaluated.

5.6.1 Calculation of Means

The baseline estimates of the mean are based on a split lognormal truncated at the 99.9 th percentile.

Sensitivity analyses analyzed the effects that other distributional shapes have on the calculated means

for the individual elicitation responses. Several other plausible choices were evaluated: an

untruncated split lognormal, a lognormal corresponding to the upper half of a split lognormal (upper

tail lognormal), a split log-triangular.or a normalized split lognormal truncated at the 9 9 .9ih percentile.

For these later two distributions the mass beyond the truncation point is proportionately distributed

within the upper half of the distribution. The effects of varying the truncation point for the split

lognormal distribution were also examined. See Section 7.6.1 for the results of the sensitivity

analyses for the calculation of means.

5.6.2 Overconfidence Adjustment

The baseline estimates assume that the UB and LB supplied by the panelists for each elicitation

question correspond to the 9 5ih percentile and 5 h percentile, respectively, of a lognormal distribution.

In other words, the baseline estimates are based on the assumption that all the (LB, UB) uncertainty

intervals have 90 percent coverage, i.e., they all have a 90 percent chance of containing the true value.

5-18
However, extensive experience with such subjectively determined intervals has demonstrated that

people tend to underestimate the uncertainty in their answers, i.e., they tend to be overconfident.

Instead of having about a 90% chance of containing the true value, nominal 90% coverage intervals

have been found to only have about 30% to 70% coverage of the true value [5.4].

Accordingly, sensitivity analyses were performed to evaluate the effects of assuming that the

panelists' uncertainty intervals did not always correspond to the stated 90% coverage interval. The

MVs supplied by the panelists were not adjusted, i.e., they always correspond to the median of the

assumed lognormal distributions. Two types of overconfidence adjustments were investigated. The

first type adjusted the coverage intervals directly for each individual elicitation response. For

example, one sensitivity analysis assumed that the 90% intervals supplied by the panelists correspond

to only 50% coverage, i.e., the associated UBs and LBs were assumed to correspond to the 7 5th

percentile and 25" percentiles, respectively, of their associated lognormal distributions. The second

type of overconfidence adjustment was applied to the error factors associated with the bottom-line

LOCA frequency estimates and not the individual responses as before. Because the error factor of

any estimated LOCA frequency parameter depends on the uncertainty intervals on which the estimate

is based, increasing the error factor is equivalent to an indirect overconfidence adjustment of the

underlying uncertainty intervals. Both blanket and target overconfidence adjustments were applied.

In a blanket adjustment, all panelists were adjusted by the same amount. In a targeted adjustment,

panelists with relatively small uncertainties were adjusted more than panelists with larger

uncertainties. The rationale for a targeted adjustment is that panelists with relatively small

uncertainties are more likely to be overconfident. See Section 7.6.2 for the results of the

overconfidence adjustment analyses.

5.6.2. 1 Coverage Interval Adjustment - For any UB and LB supplied by a panelist, formulas were

developed to calculate the results of any level of overconfidence adjustment. The overconfidence

adjustment is accounted for by defining an adjusted UB and LB corresponding to the 9 5 th and 5 "h

percentiles, respectively, of the panelist's distribution. This allows the results of the overconfidence

adjustment to be calculated by simply substituting the adjusted UB and LB into the formulas

developed for the unadjusted UB and LB. As stated previously, the MV supplied by the panelist were

not altered by the overconfidence adjustment.

The formula for adjusting a UB is presented first. Let b be the value of a UB supplied by a panelist

and let p be the value of the percentile of the panelist's assumed lognormal distribution corresponding

to b. Define UBp(b) as the adjusted value of the LUB, i.e., UB,(b) corresponds to the 9 5th percentile of

the panelist's adjusted distribution. (For the case of no adjustment, p = 95 and UB95(b) = b.) Let m be

the value of the associated MV supplied by the panelist. Denote the error factor of the panelist's

adjusted lognormal distribution by r,,. From Equation 5.10,

r,, = UB,(b)/m (5.35)

Before adjustment, the error factor, r, is equal to b/rn. Hence, the overconfidence adjustment is

equivalent to changing the error factor from r to r, for each elicitation response.

Referring to Section 5.3.2, let o2 be the variance of the underlying normal of the panelist's lognormal

distribution. From Equation 5.10,

r, = exp(l.645u) (5.36)

From Equation 5.1, Equation 5.5 and the relation r = b/m,

r = exp(kou) (5.37)

It follows from Equations 5.36 and 5.37 that

5-19
rp =r> (5.38)

where c(p) = 1.645/k-.

From Equations 5.35 and 5.38, and the relation r = b/m,

UBp(b) = bc(p) i r`'.P), (5.39)

where b is the UB and rn is the MV supplied by the panelist. This is the desired formula for the

overconfidence adjustment to the UB.

Because the UB and LB are not necessarily symmetric about the MV, the LB must be adjusted

independently of the UB. As for the UB adjustment,'let b' be the value of a LB supplied by a panelist

and let p' be the value of the percentile of the panelist's assumed lognormal distribution corresponding

to b'. Define LBp{b) as the adjusted value of the LB, i.e., LB,{b) corresponds to the 5 'h percentile of

the panelist's distribution. (For the case of no adjustment, p'= 5 and LBO(b' = b'.)

The derivation of the formula for LBp(b) is analogous to the derivation of the formula for UBp(b).

The only difference is that the error factor is equal to the MV divided by the LB, instead of the UB

divided by the MV. The adjusted error factor (r',) is defined then defined as in Equation 5.35 as

ri, = m/LBp,,(b'D, (5.40)

where m is the MV supplied by the panelist. Equation 5.36 remains unchanged for rp,, but Equation

5.37 for the unadjusted error factor (rD becomes

r'= exp(-k,,u) (5.41)

where k-, is the pth percentile of the standard normal distribution associated with the LB and MV

response.

Note that, because p' is the percentile corresponding to a LB, p'< 50 and k-, is negative. From the

symmetry of the normal distribution, k,, = -kl 00-p,. Hence Equation 5.38 for the LB adjustment is

' = [r']cf(), (5.42)

where c'(p) = 1.645/klo0,.. Then Equation 5.39 becomes

LB,(b') = [b]c'(P") (5.43)

where b'is the LB and m is the MV supplied by the panelist. This is the desired formula for the

overconfidence adjustment to the LB. Equations 5.39 and 5.43 express similar forms for the adjusted

UB and LB responses.

Because an overconfidence adjustment implies that the adjusted percentile is always closer to 50%

than the nominal percentile, p < 95 and p'> 5., Hence, c(p) and c'(p') are always > 1, and the adjusted

bounds are always further away from the median than the unadjusted bounds. Therefore, the

panelist's adjusted distribution is always broader than the unadjusted distribution. In other words, an

overconfidence adjustment always increases the uncertainty in the panelist's response.

Five separate overconfidence adjustments were evaluated to examine their effects on the LOCA

frequency estimates: two blanket adjustments and three targeted adjustments. As previously stated,

the blanket adjustments adjust the responses for all panelists by the same amount. The targeted

adjustments use a two-level adjustment. Those panelists expressing larger uncertainties were adjusted

by smaller amounts, while those panelists with smaller uncertainties were adjusted by larger amounts.

5-20
The various overconfidence adjustment schemes are summarized in Table 5.1. The Blanket I (B l)

adjustment is the most severe adjustment because the responses which nominally represent 90%

coverage intervals are assumed to represent 50% coverage intervals. Each successive adjustment

scheme is less severe. Thus, the Blanket 2 (B2) adjustment assumes that the nominal 90% coverage

intervals actually have only 60% coverage. The targeted adjustment schemes are based on a division

of panelist responses into three error-factor bins: less than 10 (less uncertainty), between 10 and 50,

and greater than 50 (greater uncertainty). The Targeted I (TI) adjustment does not adjust the 3 or 4

panelists with error factors greater than 50 (coverage interval I) and adjusts the coverage intervals to

50% for the remaining panelists (coverage interval 2). The Targeted 2 (T2) adjustment uses the same

criteria to represent coverage intervals I and 2, but adjusts the more uncertain responses to 80%

coverage while the remaining panelists are adjusted to 60% coverage. The Targeted 3 (T3)

adjustment adjusts only the panelists with error factors less than 10 (coverage interval 2) from 90% to

60% coverage. Results of this analysis are presented in Section 7.6.2. 1.

Table 5.1 Elicitation Response Adjustment Schemes

Overconfidence Coverage Population Coverage Population

Adjustment Interval 1 Interval 2

Blanket 1 50% All NA NA

Blanket 2 60% All NA NA

Targeted 1 90% 4 50% remaining

Targeted 2 80% 4 60% remaining

Targeted 3 90% 4 60% remaining

5.6.2.2 Error-Factor Adjustment - The error-factor adjustment is applied to the one-sided error factors

associated with a panelist's 9 51h and 5th percentile estimates. For the present discussion, a one-sided

error factor is defined as either the ratio of the 9 5(h percentile to the median or the ratio of the median

to the 5 th percentile. The median is unadjusted in this analysis. Therefore, the error-factor adjustment

is equivalent to adjusting the 9 5 th or 5(h percentile of a panelist's LOCA frequency distribution. The

adjustment can be performed by either changing the value of the 9 5th or 5th percentile or by changing

the value of the error factor. Changing the percentile value is similar to the confidence level

adjustment in the previous section except that it applies to the bottom-line 9 5th or 5 th percentile, rather

than the LB and UB elicitation responses.

As for the coverage interval adjustment (Section 5.6.2.1), the effective coverage interval of the

original responses can be determined to quantify the magnitude of the overconfidence adjustment.

For any one-sided error factor which is adjusted, let EFt be its original value and let EFa be its

adjusted value. Given EF, and EFt, define p, as the percentile in the adjusted distribution

corresponding to the original error factor. From Equation 5.10, the underlying standard deviation of

the adjusted lognormal distribution (a.) is given by

a, = In(EF)/I .645 (5.44)

since EF, now is assumed to represent the true percentile.

From Equation 5.5,

EFo = exp(k,, a,), (5.45)

where = k. is the p. percentile of the standard normal distribution. Solving Equation 5.45 for k- and

using Equation 5.44 yields

5-21
ký = 1.645 [ln(EFo)/ln(EF)]( (5.46)

Then k. can be calculated from Equation 5.46 and p. can be determined from tables of the normal

distribution. For the 9 5rh percentile, EF,, > EFo, k,, < 1.645 and therefore p,, < 95. This result is to be

expected, because increasing the error factor broadens the distribution. A similar result holds for the

5th percentile.

The error-factor adjustment described above is applied to the error factors associated with the 9 5th and

5rh percentiles of the panelists' various total LOCA frequency estimates (Section 7.6.2). Separately

for each percentile and LOCA estimate, each panelist's error factor is compared with the GM of all

the panelists' error factors. Error factors falling below the GM are adjusted up to the GM. Error

factors above the GM are not adjusted. For each panelist, the 95 h and 5 h percentiles are recalculated

using the formulas in Section 5.3.2 with the adjusted error factors, assuming the median remains

unchanged. The mean is calculated using the adjusted error factors for the 95"' percentile. This

results in a variable adjustment as a function of plant type, LOCA category, and operating time. See

Section 7.6.2.2 for additional information and the results of this analysis.

5.6.3 Correlation Structure

The baseline estimates of the percentiles (Section 5.5) are calculated assuming perfect rank correlation

between the summed component distributions (Block 2.6). As discussed in Section 5.3.4, this

baseline assumption maximizes the pairwise correlations between the distributions. Because the

correlations are assumed to be positive, they are minimized by assuming the distributions are

independent such that all pairwise correlations are equal to zero. These bounding assumptions lead to

the largest differences in the summed component distributions attributable to the correlation structure.

Accordingly, as a sensitivity analysis, the effect of assuming that the summed distributions are

independent is evaluated.

An evaluation of the effects of an assumed independent correlation structure is only possible through

Monte Carlo simulation. Because of the very large number of individual distributions that comprise

the elicitation responses, the bottom line estimates were calculated only for selected panelist

responses. Accordingly, ten simulation trials were selected to span several important variables as

summarized in Table 5.2. First, both the panelists and the range of LOCA plant type combinations

(i.e., PWR piping, PWR non-piping, BWR piping, and BWR non-piping) and time periods were

sampled. Second, a number of distributions representing contributing piping components (or nonpiping subcomponents) which must be summed to develop the bottom-line estimates were used.

Third, and most important, the Monte Carlo trials were selected to span the range of distinguishing

characteristics representative of the elicitation responses. These distinguishing characteristics (last

column in Table 5.2) indicate whether the elicitation responses are generally symmetric (S in Table

5.2) or asymmetric (U in Table 5.2), and indicate the relative magnitude (small, moderate, large) of

the upper or lower error factors (UEF and LEF, respectively) in Table 5.2. The characteristics are

representative of the variability among the entire population of elicitation responses. Additional

details concerning the Monte Carlo simulation and the results are contained in Section 7.6.3.

5-22
Table 5.2 Summary of Monte Carlo Trials

Trial Number of Panelist LOCA Plant Type/Time Distinguishing

Number Distributions Period Characteristics

1 12 A BWR-I Piping @ 25 yrs S, small LEF/UEF

2 12 A BWR-2 Piping @ 25 yrs U, small UEF

3 2 C PWR-6 Piping @ 25 yrs U, large UEF

4 4 C BWR-3 Piping @ 25 yrs S, moderate LEF[UEF

5 14 G PWR-5 Non-Piping @ 60 yrs U, large LEF

6 5 C BWR-3 Non-Piping @ 25 yrs U, large LEF

7 8 J PWR-5 Non-Piping @ 25 yrs S, large LEF/UEF

8 7 1 PWR-4 Piping @ 25 yrs S, moderate LEF/UEF

9 4 E BWR-4 Non-Piping @ 25 yrs U, large LEF

10 9 B PWR-3 Non-Piping @ 25 yrs S, small LEF/UEF

5.6.4 Aggregation

As discussed in Section 5.4, the individual total LOCA frequencies were combined to obtain group

estimates of the total BWR and PWR LOCA frequencies. The baseline estimates were calculated

using the GM of the individual estimates (see Equation 5.31). However, as discussed in Section 5.4),

there are several other ways to construct group estimates. A number of sensitivity analyses were

performed to examine other methods for aggregating the panelists' responses. Many different

methods were considered which use various measures of.group opinion, combine panelist responses at

different stages of the analysis, and employ different methods of calculating the bottom-line LOCA

frequency parameters.

5.6.4.1 Group Estimate - As discussed in Section 5.4, the principal requirement for a group

estimate based on expert opinion is that it be somewhere in the middle of the group. Consequently, a

group estimate should not be overly dependent on outliers or extreme values. This requirement

implies that a group estimate should be a measure of central tendency, and the GM is a plausible

candidate (Section 5.4), especially since the estimated frequencies often differ by several orders of

magnitude. However, because the individual estimates based on the panelists' responses were usually

highly variable, the GM could conceivably be driven by one or more outliers. An alternative group

estimate is the TGM, which is the GM of the values remaining after the largest and smallest values

have been omitted from the set. Still another alternative is to use the median of the distribution of

individual estimates. While the median is potentially much less dependent on outliers than the GM or

TGM, it literally depends on only the center of the distribution and does not reflect any other aspect of

the distribution. It is of interest to note that the median has been used as a group estimate in previous

NRC applications (see References 5.5 - 5.8).

Another measure which could potentially be used as a group estimate is the AM of the panelists'

bottom-line parameter estimates. The AM is always larger than the GM, except in the trivial case

when the distribution is a constant. Therefore AM aggregation always yields more conservative

LOCA frequency estimates. However, when the individual estimates span one or more orders of

magnitude, as they do in this study, the AM largely reflects the highest estimates and contributions

from estimates below the median value are negligible. Therefore, for the results of this study, the AM

is not a good measure of central group opinion.

This study uses the GM to aggregate the individual estimates to obtain the baseline group estimates

(Section 5.5). Sensitivity analyses were conducted to evaluate the differences resulting from using the

TGM, median, or AM as aggregation schemes. Results of this analysis are contained in Section

7.6.4.1. Section 7.6.4.1 also includes a more in-depth comparison of the AM to the GM as a group

estimate for the results in this study.

5-23
5.6.4.2 Aggregation Point- The aggregation point is the point in the analysis framework where the

individual responses are combined to construct a group response (Sections 5.4 and 5.5). The baseline

analysis used the last possible aggregation point and combined the individual total LOCA frequencies.

This choice is most consistent with the elicitation structure and responses as discussed in Section 5.5.

However, one alternative is to combine the individual piping and non-piping LOCA frequencies to

construct group piping and non-piping LOCA frequencies, respectively. The two group estimates are

then added as described in Section 5.3.4 to construct group estimates of total LOCA frequencies.

A second alternative is to combine at the earliest possible aggregation point, i.e., to combine the

individual responses to each of the elicitation questions. Both MV and bounding responses are

developed for the responses, and the responses are analyzed using the calculation procedure in Section

5.3 to determine total LOCA frequency estimates for BWR and PWR plant-types. This is equivalent

to synthesizing responses for a 13 th panelist from the responses of the other 12 panelists (Section 5.2).

Section 7.6.4.2 provides more information on these calculations and summarizes the differences in the

baseline results attributable to these two alternative aggregation points.

5.6.4.3 Aggregation Parameters - In the baseline methodology, estimates for the mean and other

bottom-line parameters (i.e., median, 5"h and 9 5 th percentiles) were aggregated directly from the

individual panelist responses (Section 5.5). An alternative approach is to calculate the bottom-line

mean estimates from the aggregated percentile estimates by assuming a distributional relationship for

the percentile estimates. The method used for this.sensitivity analysis (called the MEF method)

calculates the group 5th and 9 5 th percentile estimates from the aggregated median and error factors

instead of directly aggregating each percentile as in the baseline methodology. The bottom-line mean

is then calculated from the aggregated median, 5 'h and 9 5th percentile values by assuming that the

underlying distribution is a split lognormal which is truncated at the 9 9 .9h percentile. This is the

same distributional form assumed for calculating the mean estimates from the individual elicitation

responses in the baseline methodology (Section 5.5)

The 5 h and 9 5th percentile estimates determined by the MEF approach are identical, by definition, to

the estimates determined by direct aggregation since all the individual panelist estimates can be

equivalently represented by the 5"', 5 0 th, or 9 5 th percentiles (baseline methodology) or by the median,

UEF and LEF (MEF approach). However, the means can be determined at several points in the

analysis and differences do result from this choice. The baseline approach calculates the means at the

earliest possible point in the analysis from the individual distributions while the MEF approach

calculates the means as the last step in the analysis after aggregating all individual responses. The

means could also be calculated after the bottom-line percentile estimates for each individual panelist

are determined. It is postulated, however, that the means from the MEF approach will differ most

from the baseline approach because the characteristics of the aggregated distributions are likely to be

the most different from the individual elicitation response distributions due to the intermediate

processing steps (Section 5). It should be noted that calculation of the group mean in this way also

requires that the distributional form of group LOCA frequency estimates be assumed, which is

counter to the objective (Section 5.4) of minimizing assumptions related to the bottom line

parameters. Additional details and results of this sensitivity analysis are provided in Section 7.6.4.3.

5.6.4.4 Mixture-Distribution Aggregation -This is a method of expert opinion aggregation for

probability distributions used in the NUREG- 1150 study [5.10] which uses the arithmetic average of

the panelists' probability distributions as the aggregate probability distribution. The method is

described briefly on pages 3-6 of Reference 5.11. It is stated there that one of the principles used in

the expert judgment process was that the aggregation of judgments from various experts should

preserve the uncertainty that exists among alternative points of view.

The mixture-distribution approach assumes that the expert panel is a random sample from the

population of all experts and that the goal is to obtain an unbiased estimate of the aggregate

distribution function of the LOCA frequencies averaged over the population of all experts. For a

5-24
panel of N experts, denote the cumulative distribution function for expert i by Fi(x) and the group

cumulative distribution function for the LOCA frequency by G(x). Then

I 'V

GGO)=-1 Y F,.(xC) :(5.47) Ni=

Equation 5.47 has the form of a mixture distribution where Fi(x) is the probability that expert i would

assign to the statement that the LOCA frequency X is less than x. G(x) is the group estimate of the

probability that the LOCA frequency X is less than x. The corresponding density functions are

obtained by differentiating Equation 5.47 to obtain

N1

Integrating Equation 5.48, the mean LOCA frequency for a given set of conditions (e.g., current-day,

BWR, Category 1 LOCA frequency) is given by

SNI

x=fxg(x)dx= - J xf,.(x)dx=--Zxi (5.49) N j=1 N

As noted above, the panel of experts is considered a random sample of experts from the population of

experts. Denoting the number of experts in the population by Na11, the population aggregate

distribution is given by

I N.1

GNM,( WtYFJ(x) (5.50)

If the panel of N experts is a random sample from the population of Na1i experts, G(x) from Equation

5.47 is an unbiased estimate of Gpa,(x).

Note that no matter how large a sample is taken, there will always be large variability in the expert

opinions. Mixture-distribution aggregation is a technique that attempts to capture the uncertainty

expressed by this variability (see page 9.7 of Reference 5.12).

The mixture-distribution aggregation scheme will always result in higher mean and 95'h percentile

estimates and lower 5th percentile estimates than the other aggregation schemes (see Section 7.6.4.4).

Consequently, the mixture-distribution scheme exhibits the greatest difference between the 5th and

95th percentiles. However, the mean and 9 5 th percentile estimates are often dominated by the

maximum individual estimates while the 5ih percentile is often dominated by the minimum estimate.

These characteristics imply th;at the extreme individual estimates will often dominate the mean, 5th,

and 9 5th percentile estimates. Therefore, the mixture distribution may not represent central estimates

of group opinion when the differences among the individual estimates are large.

Obtaining central estimates of group opinion was a principal objective of this elicitation (Section 2).

Even if conservative central estimates of LOCA frequencies are required, the mixture-distribution

scheme may not be appropriate. In such cases, either high confidence bounds (Section 5.4) or the

quartile method (Section 5.6.5) could be used to provide conservative estimates of group opinion.

5.6.5 Panel Diversity

The baseline approach (Section 5.5) for quantifying panel diversity is to construct two-sided

confidence intervals for the group estimates of the bottom-line LOCA frequencies parameters. As

discussed in Section 5.4. this calculation assumes that the individual estimates are characterized by a

lognormal structure. The baseline measures of panel diversity are 90% confidence intervals centered

on the GM of the individual estimates.

5-25
An alternative approach is a non-parametric one, where no assumptions are made about the

probabilistic structure of the individual estimates. For each set of individual panelist estimates, a

quartile interval is constructed from the upper and lower quartiles of the individual estimates. The

lower and upper quartiles represent the 25th and 751h percentiles, respectively, of the individual

panelist estimates. In this quartile method, the median (or 50h percentile estimate) is an obvious

measure of central group opinion. A sensitivity analysis comparing the confidence interval and

quartile method is presented in Section 7.6.5. In Section 7.6.4.1, it is shown that the GM and median

provide similar measures of central group opinion. Thus, for these elicitation results, a quartile

interval is a measure of group diversity about the median just as the confidence interval measures

diversity about the GM for the assumed lognormal structure.

5.7 References

5.1 Atwood, C. L., "Review of Draft Report on LOCA Frequiency Estimates by Expert

Elicitation," NRC ADAMS Accession No. ML051430327, May 2005

5.2 Atwood, C.L., LaChance, J.L., Martz, H.F., Anderson, D.J., Englehardt, M., Whitehead, D.,

and Wheeler, T., "Handbook of Parameter Estimation for Probabilistic Risk Assessment,"

NUREG/CR-6823, U.S. Nuclear Regulatory Commission, Washington, DC, September 2003.

5.3 Surowiecki, J., "The Wisdom of Crowds", Doubleday, New York, NY, 2004.

5.4 Meyer, M.A. and Booker, J.M., "Eliciting and Analyzing Expert Judgment: A Practical

Guide," NUREG/CR-5424, U.S. Nuclear Regulatory Commission, January 1990.

5.5 Vo, T.V., Heasler, P.G., Doctor, S.R., Simenon, F.A., and Gore, B.F., "Estimates of Rupture

Probabilities for Nuclear Power Plant Components: Expert Judgment Elicitation," Nuclear

Technology, Vol. 96, pp. 259-271, 1991.

5.6 Vo, T.V., Simenon, F.A., Gore, B.F., and Livingston, J.V., "Expert Judgment Elicitation on

Component Rupture Probabilities for Five PWR Systems," Reliability and Risk in Pressure

Vessels and Piping, PVP-Vol. 251, pp. 127-140, American Society of Mechanical Engineers,

1993.

5.7 USNRC, "A Pilot-Application of Risk-Informed Methods to Establish Inservice Inspection

Priorities for Nuclear Components at Surry Unit I Nuclear Power Station ," NUREG/CR1681, Rev. 1, PNNL-9020, Rev.], U.S. Nuclear Regulatory Commission, February 1997.

5.8 USNRC, "Technical Elements of Risk-Informed Inservice Inspection Programs for Piping,"

Draft Report, NUREG-1661, U.S. Nuclear Regulatory Commission, January 1999.

5.9 Von Winterfeldt, D. and Edwards. W., "Decision Analysis and Behavioral Research,"

Cambridge University Press, New York, NY, 1986.

5.10 "Severe Accident Risks: An Assessment for Five U.S. Nuclear Power Plants," NUREG- 1150,

U.S. Nuclear Regulatory Commission, December 1990.

5.11 USNRC, "Analysis of Core Damage Frequency from Internal Events: Expert Judgment

Elicitation," NUREG/CR-4550, Vol. 2, U.S. Nuclear Regulatory Commission, April 1989.

5.12 Gorman, E.D., Breeding, R.J., Helton, J.C., Brown, T.D., Muffin, W.B., Harper, F.T., and

Hora, S.C., "Evaluation of Severe Accident Risks: Methodology for the Containment, Source

Term, Consequence, and Risk Integration Analyses," NUREG/CR-455 1, Vol. 1, Rev. 1, U.S.

Nuclear Regulatory Commission, December 1993.

5-26
6. QUALITATIVE RESULTS AND DISCUSSION

The detailed results from the elicitation exercise are presented in the next two sections of this report. In

Section 6, the qualitative results (i.e., rationale and insights) are presented. In Section 7 the quantitative

(i.e., numerical LOCA frequency estimates) are presented. The qualitative results presented herein focus

on recurring insights for important technical issues which contribute to passive system LOCAs. These

insights usually summarize individual responses, and do not necessarily represent a panel consensus or

majority opinion. However, most insights were generally raised by more than two panel members, unless

explicitly noted. Individual insights expressed by less than two of the participants are typically not

presented. However, some of the more interesting individual opinions are presented with their associated

quantitative results in Appendix L, where the detailed results are discussed. The bulk of the qualitative

rationale contained in this section was obtained during the individual elicitations, although insights

obtained from the three group panel meetings are also included. The insights from the individual

elicitation sessions have been mined from the associated minutes, handouts, and written responses.

Minutes were recorded during each interview session. In addition, the participants often provided

handouts with their preliminary responses prior to their elicitations. Then, after each session, most

participants provided more complete written rationale to accompany their final elicitation responses.

6.1 Base Case Evaluations

There were two basic approaches conducted to evaluate the base case conditions: review and assessment

of operating experience and PFManalyses. Some broad opinions surfaced regarding each approach. The

advantages of using an operating-experience-based approach are that it naturally captures the effects of all

observed degradation mechanisms as well as including the actual mitigation, loading, and plant operating

conditions experienced in the plants. As such, operating-experience analysis provides an accurate

characterization of past precursor events (e.g., leaks). The perceived difficulties include the fact that it is

difficult to extrapolate precursor event data to predictions of LOCA frequencies, especially for future

operating periods.

A number of the panelists commented that they believe that the surface history predictions are accurate

for assessing LOCA frequencies over the first 25 years of plant operation, but are somewhat skeptical of

future operating period estimates. Future predictions can be difficult using operating experience because

emerging and future degradation mechanisms are not contained within the passive-system failure data.

Also, the use of operating experience can bias the predictions if data related to past problems which have

already been successfully mitigated (e.g., IGSCC in BWRs) and infant mortality rates have not been

adequately screened. The data must be carefully evaluated to avoid these types of analysis pitfalls. At

least one panelist also commented that operating experience is not effective for inferring larger LOCA

sizes since the events largely consists of partial cracking and low-leak rate events. Finally, a number of

the panelists commented that only the reporting of actual leaking cracks is actually comprehensive within

the database. The reporting of the non-leaking precursor events is likely underreported.

There are several perceived advantages of the PFM approaches. First, they are capable of predicting the

future damage for both currently existing and newly occurring degradation mechanisms that have been

adequately modeled. This is valuable in assessing the required action necessary to mitigate emerging

mechanisms before their impact is fully evident in the passive-system failure data. Also, PFM is capable

of predicting the possible evolution of leaking and non-leaking cracks to a larger LOCA sizes.

There are several disadvantages that panelists expressed when using PFM to predict LOCA frequencies.

At least one panelist indicated that only a few degradation mechanisms are realistically modeled.

Specifically, the acciiracy of the models for predicting crack initiation and crack linking from multiple

6-1
initiated cracks was questioned for SCC-type mechanisms. This potential limitation can lead to

underestimates of service cracking frequency and predicted crack lengths and result in unconservative

LOCA frequency estimates. However, several panelists countered that conservative design stresses and

transients are usually applied in the PFM analyses that are not representative of actual service stress

values. These stresses result in conservative LOCA frequency estimates. Finally, it was felt by some

panelists that the existing PFM models are too simplistic. The input variables, assumptions, and models

are usually idealizations whereas the variables which are most important to LOCA susceptibility are often

very complex. While the intent is to make conservative idealizations in the analysis, this contention is

sometimes difficult to prove.

As a result of these beliefs, most panelists chose to anchor their elicitation responses to one or both of the

operating-experience-based base case analyses (Sections 3.5 and 4) for making current-day (i.e., 25 year)

LOCA frequency estimates. For future year projections (i.e., 40 or 60 year estimates), a number of the

participants augmented the operating-experience-based assessments with results from the PFM studies

and associated sensitivity analysis to develop their responses. Several panelists also relied primarily on

the results from PFM analysis for all their predictions, but benchmarked them in some way by the

passive-system failure data. A more complete description of anchoring philosophy chosen by each

panelist is found in Appendix K.

6.2 Safety Culture Effects

There were two basic principals underlying the panelists' safety culture evaluations. First, most panelists

assumed and expected that regulatory oversight policies and procedures will continue to be used to

identify and mitigate risk associated with plants with deficient safety practices. It was also anticipated

that current regulatory oversight practices would continue to evaluate aging management and mitigation

strategies with the objective of reasonably assuring that future plant operation and maintenance is

sufficient.

As discussed in Section 3.4.2, there are several competing issues that can either positively or negatively

affect the future utility and regulatory safety cultures that were considered. The safety culture elicitation

question was structured so that the change in the future safety culture resulting from each issue was

quantitatively assessed relative to the existing safety culture. Panelists were also asked to qualitatively

identify the strongest factors. Many panelists also provided qualitative opinions on the existing US safety

culture. The panelists agreed that the utility and regulatory safety cultures are highly correlated and that

there are no real distinctions between regulatory and utility safety culture. The panel members also

generally believe that the future overall safety culture will not differ dramatically from the current culture.

In fact, most participants expected a small improvement due primarily to continued nuclear experience

and technology advances. Some of the qualitative rationale supporting this weak trend follows.

There are several positive safety culture influences that are expected. Several panelists believe that

deregulation will have a positive effect on safety culture by requiring the industry to adopt an asset

management strategy to optimize continued plant operation. These panelists believe that favorable

economic incentives are in place today that bode well for the future of nuclear power. Plants are therefore

being viewed as assets that need to be maintained to maximize their investment potential. As a result,

management realizes that proactive and periodic maintenance is preferable to the risk of an extended

outage. Another positive factor for improving future safety is the large experience and knowledge base

developed during the previous 25 years of operation. This information allows plant operators to focus on

actual problems, and not just perceived or postulated problems. Utilities and regulators also now have

risk-informed methods to use as a basis for identifying important areas of concern. However, one panelist

was disturbed by his perception that some utilities do not want to invoke risk-informed practices unless

there is an immediate economic payback. However, this was not a majority opinion.

6-2
Some panelists also expect the safety culture to improve over the next 15 years due to the increased

awareness of the consequences of passive system degradation. Recent high-profile examples of vessel

head degradation at Davis-Besse and CRDM cracking at a number of PWR plants have~illustrated the

potential economic ramifications associated with extended plant outage. Also, there is a realization that

problems at one plant can affect the public's perception of the entire nuclear industry. One panelist

commented that another high visibility event, similar to the Three Mile Island (TMI) accident, could

result in a loss of public trust that would be difficult to restore.

There are also several negative safety culture influences that may temper these predicted improvements.

Several panelists cautioned that focusing on economic performance can also be detrimental. Economic

pressures to keep plants operating, especially at high capacity factors, may create a poor safety

environment. Utilities could be tempted to forego maintenance and inspections unless there are near-term

economic incentives. Another potential negative safety culture factor is the inevitable decommissioning

of plants. As plants approach their decommissioning date, safety may suffer as the cost benefit for plant

maintenance decreases. There are also a number of personnel issues associated with decommissioning

that could negatively affect safety culture. The morale of the plant operating personnel may suffer due to

job uncertainty. Furthermore, the most competent plant personnel may migrate to other industries first

and new workers may be difficult to entice to the field. The resultant workforce may then be less

competent, overworked, and unmotivated during the latter years of plant operations.

A number of panel members raised concerns about the currently aging nuclear workforce. Obviously,

there is a need to attract young, energized new staff into the field. However, a potential stumbling block

to satisfying this goal is the fact that the number of the universities with nuclear engineering programs has

decreased. Another negative influence mentioned by several panelists is complacency. With a few

notable exceptions, the nuclear industry has an excellent safety record over the past 25 years.

Unfortunately, this good record may breed a false sense of security. These panelists are concerned that

complacency could lead to an overall erosion of safety unless proper vigilance is maintained.

Some safety culture concerns mentioned during the elicitations are due to the influence of politics and

economics on the regulatory environment. Some panelists commented that the safety culture could be

adversely affected by the country's energy demands. If demand continues to increase, and supplies

become stretched, there may be pressure to continue plant operations at the detriment of the safety

culture. A few panelists also expressed concerns that political pressure may be applied to keep plants

operating and people employed at the risk of safety. Research and plant oversight can also be affected by

future budget cuts at-the NRC.

In addition to these positive and negative safety culture influences, the panelists believe that some factors

exhibit compensating features. One example is the role of industry in ASME code activities. A few

panelists believe that strong industry participation benefits safety culture outright. However, others

believe that this participation can be detrimental if regulatory opinions are underrepresented on important

issues. This possible weakness is compensated by the NRC's ability to not endorse aspects of the code

where important disagreements exist. The.design and construction of new plants also has compensating

factors. As new plants, with improved designs and materials are developed, the knowledge gained could

benefit maintenance, repair, and replacement activities in the existing fleet. Conversely, resources could

shift to the newer, more efficient plants, which could harm safety at the older, less efficient plants.

Multiple versus single plant sites are also associated with compensating factors. The detriment of a

multiple plant utility is that there is typically less oversight per plant. However, some potential

advantages that the multiple plant utility have is that there is often more people reviewing important

decisions, and there is an increased opportunity for reassignment of personnel as plants are

6-3
decommissioned. This somewhat offsets the previously discussed concerns about deteriorating morale as

a result of decommissioning.

Many panelists discussed issues related to current safety culture differences among plants as a function of

design-type, U.S. attitudes, and plant management philosophy. For instance, a number of panelists expect

that the plant-type will influences safety culture. Specifically, the BWR plant inspectors and operators

are believed to be more experienced in identifying degradation and developing successful mitigation

strategies than their PWR counterparts. This opinion results from the BWR experience in mitigating

IGSCC in the 1980's. The implication is that the current BWR safety culture may be better than the PWR

safety culture. However, many of the same panelists also believe that the PWR experience with emerging

PSWCC issues may equalize the future safety culture.

Additionally, panelists with the most extensive international experience tend to view the current U.S.

safety culture less favorably than the other panelists. Specifically, a number of these panelists believe that

the current U.S. safety culture is weaker than in other countries. A concern is that common U.S. practice

strives for the most expedient solution without addressing the root cause of the problem. A cited example

of this practice was the U.S. industry's perceived delay in adopting the lessons-learned from earlier

French CRDM cracking experience. These and other possible, absolute differences among the panelists

are not reflected in the safety culture results since the elicitation questions assessed the future safety

culture relative to the current safety culture. Hence, the elicitation results normalize any initial panelist

differences. However, the effects of the current-day safety culture were both implicitly and explicitly

assessed by the panelists to develop the current-day LOCA frequency estimates.

Many panelists also expressed the opinion that safety culture is a highly plant-specific factor. Most

panelists believe that the industry is generally acting in a consistent, safety conscious manner. However,

individual plants can deviate markedly from general industry philosophies and practices. The vessel head

degradation at Davis-Besse was a commonly cited example. Some panelists argued that the LOCA

frequencies at these less safety conscious plants could be greatly elevated (i.e., as much as 1 to 2 orders of

magnitude higher for plants with particularly egregious safety culture) compared with the remaining

population. However, a possible poor safety culture at a few plants would not affect either the median

LOCA frequency or safety culture estimates, but it could affect the UBs of both distributions. Some

panelists increased their UB estimates on the effect of safety culture on LOCA frequencies (Section 7.1)

to account for this effect. It should be noted however that this concern is an indication of the possible

effects and it does not imply that these conditions are in existence. Panelists provided no particular

examples of existing egregious safety cultures. There is also an expectation, as mentioned previously,

that regulatory actions using existing enforcement measures would diminish both the possibility and

impact of safety culture deficiencies at particular plants. These UB safety culture estimates therefore

serve as more of a warning for the agency to remain vigilant in addressing plant safety culture

deficiencies.

A few panelists also commented that safety culture has a cyclical component as well. In their opinion,

when passive system degradation is first identified, the safety culture improves somewhat as the industry

identifies and then develops mitigation strategies. Then, once effective mitigation has been employed, the

focus on safety wanes. None of the panelists expressing this believe were able to quantify the cyclic

variations about the median safety culture. They also expressed that this effect is much smaller than the

variability associated with the safety culture at individual plants.

The panelists were also asked to comment on the relationship between safety culture and LOCA size

category. Generally, the panel members expressed the opinion that safety culture is only very weakly

correlated with LOCA size, if at all. Small leaks tend to impact plant availability, so the plants have an

6-4
economic incentive to guard against such events. However, a few panel members believe that plant safety

is focused on preventing failure in the largest pipes due to their increased risk significance.

6.3 Passive System Failure Insights

In the sections that follow, some broad passive system failure insights are discussed. The insights are first

segregated into issues associated uniquely with piping and non-piping LOCA frequency contributions.

Then, insights related to general issues that contribute to the underlying LOCA frequencies are presented

including aging mechanisms; the effect of component size; mitigation and maintenance; the effect of

operating time; and estimation uncertainties.

6.3.1 Piping and Non-Piping LOCA Frequency Contributions

Table 6.1 indicates the major piping systems and non-piping subcomponents that provide the greatest

LOCA frequency contribution for each LOCA size category. A LOCA of a given size can occur by either

a complete break of the smallest component supporting that LOCA size, or a partial break of a larger

component. Most panelists believe that complete failure of a smaller component is more likely than

partial failure of a bigger component. Therefore most panelists expect that the smallest diameter piping

system or subcomponent that could support a particular LOCA size or category is the dominant LOCA

frequency contributor. This belief is usually apparent in Table 6.1.

The major BWR and PWR piping Category I and 2 LOCA contributors are the smaller diameter

instrument and drain lines (Table 6. 1). These lines are susceptible to a number of failure scenarios that

the larger diameter lines are not susceptible to, e.g., mechanical fatigue of socket weld fittings.. In

addition, a flaw of a given size in the smaller diameter pipe is a larger percentage of the pipe

circumference than an equivalent size flaw in a larger diameter pipe. Therefore, this flaw is closer to the

failure flaw size in the smaller diameter pipe. Finally, these smaller diameter pipes are not subject to the

same level of ISI as their larger counterparts, if they are inspected at all.

6-5
Table 6.1 Major Piping and Non-Piping Contributors to the Various Size LOCA Categories

LOCA BWRs PWRs

Category Major Piping Major Non-Piping Major Piping Major Non-Piping

Contributors Contributors Contributors Contributors

1 Small diameter CRDM (i.e. stub Small diameter Steam generator

instrument and tube) penetrations instrument and tubes, pressurizer

drain lines drain lines heater sleeves,

CRDM penetrations

2 Small diameter CRDM (i.e. stub Small diameter CRDM penetrations

instrument and tube) penetrations instrument and

drain lines drain lines

3 Primary: RPV nozzles, pump CVCS, RHR, SIS Nozzles and

Recirculation, and valve bodies (DVI), PSL, surge component bodies

Secondary: line

feedwater, SRV,

RWCU, RHR,

Core Spray

4 Primary: RPV nozzles, pump Surge Line, SIS Nozzles and

Recirculation, and valve bodies (Accumulator), component bodies

Secondary: RHR, Hot leg,

feedwater, SRV, SRV

RWCU, RHR,

Core Spray

5 Recirculation, RPV, pump, and Hot Leg, Surge Manways and

RHR valve bodies line, RHR component bodies

6 N/A RPV body Hot Leg Component bodies

CRDM = Control rod drive mechanism

RPV = Reactor pressure vessel

SRV = Safety relief valve line

RWCU Reactor water cleanup system

CVCS = Chemical volume control system

RHR = Residual heat removal

SIS (DVI) z Safety injection system (direct volume injection)

SIS (Accumulator) = Safety injection system (accumulator lines)

PSL = Pressurizer spray lines

The important non-piping contributors for Category I and 2 LOCAs are CRDM penetrations in both

PWR and BWR plants. Additionally, in PWR plants, steam generator tubes and pressurizer heater sleeves

are important contributors. The pressurizer and CRDM concerns stem from relatively recent emergence

of SCC in Alloy 600 base and associated weld materials. This concern is currently at a peak because the

mechanism is prevalent, yet mitigation strategies have yet to be fully implemented. The impact of

PWSCC for PWR small-diameter non-piping components is expected to decrease over the next 10 to 15

years as effective mitigation strategies are developed and implemented. Steam generator tube failure is

also an expected dominant contributor based on the historically high failure rates and the decreased

degradation tolerance associated with these components since the design safety factors for these tubes are

less than for small bore piping. However, many panelists believe that future failure rates for steam

generator tubes will decrease due to improved inspection programs, steam generator replacement

initiatives, and improved secondary side water chemistry.

6-6
There is relatively good agreement that the recirculation lines for Category 3 and 4 LOCAs in BWR

piping are the dominant LOCA frequency contributor. There are lingering concerns about IGSCC in

these larger diameter recirculation system lines, especially for those lines which have not been replaced.

This selection is counter to the general trend that the smallest lines supporting a LOCA category are the

biggest contributors. The safety relief valve (SRV) lines, reactor water cleanup (RWCU) systems,

residual heat removal (RHR) lines, and core spray lines all are smaller than the recirculation lines, but

they were considered to be of secondary importance for this BWR LOCA category. The feedwater line is

relatively large as well, and its inclusion was driven by FAC concerns.

In contrast to the BWR piping assessment, there was no clear consensus expressed about the major

contributing PWR piping systems and non-piping subcomponents for Category 3 and 4 LOCAs. This

uncertainty stems from the large number of piping systems and non-piping subcomponents that contribute

to these LOCAs, and the lack of clear operational differences and data suggesting that any one system or

subcomponent could dominate. The nozzle and component body references in Table 6.1 refer to all

nozzles (RPV, steam generator, and pressurizer nozzles) and/or all component bodies (RPV, steam

generator, pressurizer, pumps, and valve bodies). Hence, the dominant non-piping contributors cannot be

additionally refined.

Nozzle failures are a concern because system and transient stresses can be highest at these locations.

Additionally, past degradation has been experienced in these locations. Valve and pump component

bodies are a concern because they typically are fabricated from cast stainless steel materials which are

notoriously difficult to inspect. These materials are also subject to thermal aging which reduces the

fracture toughness of the material. Fortunately, no known cracking mechanism exists for these materials

as of this date. Reactor pressure vessel concerns were due to either PTS for PWR plants or LTOP in

BWR plants.

There are only a few BWR and PWR piping systems that can support Category 5 and 6 LOCAs, and there

is subsequently greater consensus about the dominant systems. The surge line and RHR line are thought

to be important based on past degradation and the relatively large transients that are possible during

normal operation. The hot leg was also typically a greater concern than the cold leg for Category 6

LOCAs because of its higher operating temperatures. For Category 5 and 6 PWR non-piping

contributors, there remains little agreement on the dominant subcomponent failure mechanisms. There is

also no consensus about Category 5 BWR subcomponent failures and only BWR vessel failures lead to

Category 6 LOCAs. Hence, by default, BWR vessel failures are the dominant Category 6 LOCA

contributor.

It was almost universally expressed that the contribution to the overall LOCA frequencies is greater for

the non-piping components than for piping for the smaller category LOCAs in PWR plants. Specifically,

steam generator tube, CRDM, and pressurizer heater sleeve failures are expected to be the most important

Category 1 and 2 total LOCA frequency contributors. These expected higher failure rates also result in

the expectation that PWR LOCA frequencies are higher than BWR LOCA frequencies for Category 1 and

2 LOCAs. However, the non-piping component contributions to the larger (Category 3 - 5) LOCA

frequencies decreases dramatically due to the robustness (e.g., increased design margin) of these nonpiping components compared to piping. Several panelists expect that non-piping contributions become

significant again for the Category 6 LOCAs because the operating margins are similar to the largest

piping systems.

Most panelists also believe that the quantitative non-piping LOCA frequency assessments are more

challenging than the piping assessments. For non-piping, there are multiple components to consider, each

with different operating requirements and characteristics. The associated design margins, materials, and

6-7
inspection considerations also vary widely. Furthermore, there is little precursor data available on nonpiping component degradation due to the historical focus on piping reliability.

6;3.2 Important Aging Mechanisms

Most panelists believe that precursor events (e.g., cracks and leaks) are a good barometer of LOCA

susceptibility even if it is not straightforward to extrapolate precursor events to larger LOCA sizes.

Operational experience provides an indication of the historical precursor frequency associated with

degradation mechanisms in the operating fleet. Therefore, almost all panelists anchored their responses

against available passive-system failure data. In addition, a number of the participants used the weld

census data to determine the relative LOCA contributions for piping systems with similar operating

characteristics. In this approach, the LOCA frequency ratio of two systems is identical to their ratio of

LOCA-susceptible welds. For example, assume that a panelist believes that PWSCC is an important

degradation mechanism and PWSCC susceptibility is equivalent for both the hot leg and surge line. Also

assume that the surge line has a single PWSCC-susceptible weld while the hot leg has 7 PWSCCsusceptible welds. The hot leg PWSCC contribution is therefore a factor 7 higher than the surge line

PWSCC contribution. This application of weld census information is a natural approach because many

mechanisms, including fatigue and SCC, preferentiallyattack welds instead of the base metal. Reasons

for this preferential attack are related to the weld metallurgy and chemistry, the relatively high residual

stresses in the weld vicinity, and the higher weld defect density.

A discussion of the most significant aging mechanism identified by the panelists in BWR and PWR plants

follows. Most participants identified thermal fatigue, FAC, IGSCC, and mechanical fatigue as the

important degradation mechanisms to consider in BWR piping. The BWR plants are expected to be more

prone to thermal fatigue problems compared with the primary side of PWR plants because they

experience greater temperature fluctuations during the normal operating cycle. In BWR plants, thermal

fatigue remains an important contributor for the feedwater lines and the RHR system. There was a rash of

feedwater nozzle cracks reported in the 1970 to early 1980 time period in BWRs. Plant and system

modifications were implemented after a detailed study of the problem and augmented inspections are

being conducted based on NUREG-0619 [6.1 ] requirements. These mitigation measures have proven

effective as no new thermal fatigue cracks have been discovered in these BWR feedwater nozzles over the

last 20 years. Additionally, the U.S. BWR plants have been less susceptible to thermal fatigue damage

than some of the foreign designs. However, thermal fatigue is an aging mechanism that could lead to a

large LOCA because it does not manifest itself as a single crack,-but as a family of cracks over a wide

area. Thermal fatigue cracks also tend to propagate rapidly, and since it is not material sensitive (i.e., it

can attack a number of materials), it is difficult to prioritize critical areas for inspections. These reasons

explain why thermal fatigue is still regarded as an important LOCA contributor by many panelists.

The carbon-steel feedwater piping system in BWRs is the most susceptible to FAC of all the primary side

(i.e., LOCA-sensitive) components (Section 3.4.5). The main steam line is the other major carbon-steel

primary side system which experiences constant fluid flow. However, it is not as susceptible to FAC as

the feedwater system because the erosion rates associated with two-phase flow are much less. While

FAC caused a serious accident in the secondary side piping at Surry some 15 years ago, most panel

members believe that the industry has inspection programs in place today to prevent the reoccurrence of

such an event, especially in the primary side piping systems. However, one panel member expressed the

concern that the water chemistry improvements (hydrogen water chemistry - HWC) that mitigate IGSCC

could lead to FAC in unanticipated locations that are not monitored as part of these inspection programs.

Most BWRs that have implemented HWC routinely inject oxygen into the feedwater line to mitigate this

possibility. In fact there has been virtually no FAC in BWR feedwater piping since the advent of the

feedwater chemistry specifications with elevated oxygen levels. FAC and elevated oxygen do not coexist

- the piping maintains a protective oxide coating, thus protecting the steel below. Therefore, FAC

6-8
degradation can only result from interruptions in oxygen injection or excessive hydrogen injection could

potentially result in the onset of FAC. Current U.S. instances of FAC in BWR piping have been confined

to Class 3 service water systems and do not directly affect the LOCA frequencies calculated herein.

Thus, many panelists believe that a properly designed and monitored HWC program should not result in

higher FAC susceptibility, and their LOCA concerns due to FAC are minimal. Only one panelist remains

concerned about the LOCA susceptibility due to FAC degradation.

The panel consensus is that the susceptibility of BWR piping systems to IGSCC is greatly reduced

compared to what it was in the past. Measures such as improved hydrogen water chemistry, weld overlay

repairs, stress relief, and pipe replacement with more crack resistant materials have led to this reduction.

Inspection quality has also improved such that the probability of crack detection is much better than in the

past. However, as indicated earlier, there remains concern about the failure likelihood of the large

recirculation piping and the RHR lines that have not been replaced. The original piping materials are

much more susceptible to IGSCC and many lines retain preexisting cracks that initiated and grew before

HWC was adopted. Furthermore, at least one panelist is also concerned that the more IGSCC-resistant

replacement piping materials may still crack under service conditions. This panelist cited the German

plant experience with cracking in Type 347 stainless steel. Also, it is possible that cold work (e.g. due to

grinding) could increase the IGSCC susceptibility of the low carbon (L grade) stainless steel that has been

used as a replacement material in many U.S. plants. However, the U.S. BWR experience with L grade

stainless steel piping has been very good thus far. For these reasons, many panelists believe that

continued vigilance is required through the augmented inspection requirements in Generic Letter 88-01

[6.2] and NUREG-0313 [6.3].

Another aging mechanism of concern in BWR plants is mechanical fatigue. This is primarily a problem

in smaller diameter piping, especially those with socket welds, and is caused by an adjacent vibration

source. It was noted that locations susceptible to mechanical fatigue damage are not always obvious and

that it is impossible to eliminate all plant vibrations. Another concern is that plant configuration changes

can result in newly susceptible areas. This mechanism is also a prevalent root failure cause of small

diameter piping in the passive-system failure data.

Lastly, it was noted by some panelists that certain BWR piping systems (e.g., relief valve lines) may have

an increased likelihood of operating transients (e.g., water hammer) compared with similar PWR systems.

This is primarily due to increased valve openings during plant operation. These transients, combined with

existing degradation, could make the BWR plants more susceptible to LOCAs. However, this is not a

universal sentiment. At least one panelist strongly disagrees with this contention and believes that water

hammer is a design-specific issue. This panelist believes that while some BWR RPV head cooling layout

designs could be susceptible to water hammer, it is primarily a concern with secondary side systems and

some secondary side standby safety systems. Furthermore, this panelist argues that even though relief

valve lines in the main steam systems do get exposed to transient loads, they are designed for these loads.

Most participants identified thermal fatigue, mechanical fatigue, and PWSCC, as the important

degradation mechanisms to consider in PWR piping. The concerns associated with thermal and

mechanical fatigue in PWR plants are similar to those previously discussed for BWR plants. The

locations susceptible to thermal fatigue in PWR plants include the surge line which is subject to cyclic

thermal stratification stresses. Also, as identifiedin Table 6.1, concerns were expressed about the DVI

and chemical volume and control system (CVCS) lines due to periodic testing of these lines which

imposes additional thermal cycles. The DVI and accumulator safety injection system (SIS) lines have

also experienced some thermal fatigue cracking due to cold water leakage past the check valves.

Stress corrosion cracking of nickel-based alloys is a relatively new mechanism that has manifested itself

in U.S. plants over the last 5 years. It is a temperature dependent mechanism that attacks Alloy 600 type

6-9
base materials and Inconel 82/182 welds. It has many similar characteristics to previous IGSCC cracking

in sensitized stainless steels in BWR reactors. Many panel members believe that the PWSCC problems in

PWRs will be resolved (i.e., mitigated) over the next 15 years. Therefore, its contribution to the overall

LOCA frequencies may peak sometime over the next several years, but then decrease after that. To date,

instances of PWSCC in piping systems have been observed in surge lines at the surge line-to-pressurizer

weld in the United States at Three Mile Island, as well as in plants in Belgium and Japan. The hot legs

have also experienced PWSCC at the hot leg-to-reactor pressure vessel weld in the United States at, the

V.C. Summer plant and in Sweden at the Ringhals plant. Other piping systems where PWSCC could

surface in the future are the cold leg and the pressurizer spray lines in PWRs. However, since the cold

leg, and many of the BWR systems, operate at slightly lower temperatures than the hot legs and surge

lines in PWRs. Problems in these lower-temperature systems'may not materialize until later in the

operating life of the plant.

Many of the same degradation mechanisms existing for piping are important for non-piping components

as well. For example, SCC of nickel-based alloys, as described above for piping systems, is prevalent in

smaller Alloy 600 components, such as steam generator tubes, CRDMs and other penetrations. Similarly,

thermal fatigue is an important consideration at nozzle inlets and other locations which experience

thermal stratification. However, there are also several degradation mechanisms that are only a concern in

certain non-piping components. These include radiation embrittlement which reduces the fracture

toughness of the RPV, especially in PWRs where there is less shielding. In addition, steam generator

tubes, which were generally cited as a major small break LOCA contributor in PWRs, are susceptible to a

variety of unique degradation mechanisms, including fretting and wear and denting from secondary side

contamination.

There was also more concern expressed about the possibility of common cause failures in non-piping

components. Multiple steam generator tube or CRDM failures could result from the failure of a single

component due to the proximity of other components. Multiple steam generator tubes can also fail due to

a sudden secondary side pressure drop if multiple tubes are sufficiently degraded. Also, bolting failures

are only expected to lead to a LOCA if multiple bolts fail due to common causes, such as improper

installation and inspection, or the emergence of degradation mechanisms such as steam cutting or boric

acid corrosion which affect multiple bolts.

The panelists were also asked to assess the impact of future degradation mechanisms that may not have

been apparent in the operating experience. A number of the panelists indicated that if a new degradation

mechanism arises in the future, the LOCA frequency may temporarily increase until the industry has

developed an effective mitigation strategy. After this mitigation strategy has been fully implemented, the

LOCA frequencies will return to values applicable before the manifestation of the new degradation

mechanism. The severity of any increase, and the time necessary to implement effective mitigation was

often benchmarked by the panelists against experience with known degradation mechanisms (e.g., IGSCC

or FAC) using either passive-system failure data, PFM modeling, or both.

6.3.3 Effect of Component Size on LOCA Frequencies

The panelists generally believe that smaller LOCAs are more likely than larger LOCAs in piping because

small piping has a greater failure propensity. As previously mentioned (Section 6.3.1), the panelists

generally believe that complete rupture of a smaller pipe is more likely than an equivalent size opening in

a larger pipe. A given size crack is a larger percentage of the pipe circumference in the smaller diameter

line, but cracking length and likelihood is not expected to vary with piping size. Smaller piping is also

often subject to fabrication flaws which exacerbates this decreased failure margin. Additionally, smaller

diameter lines are often fabricated from socket welded pipe which has a history of mechanical fatigue

damage from plant vibrations and is also susceptible to external failure mechanisms arising from human

error (e.g., damage from equipment). Finally, small piping is typically more difficult to inspect and ISI is

6-10
not routinely performed on these lines. In contrast, the larger diameter lines are inspected more

rigorously and routinely and quality control/quality assurance programs are more stringent as piping size

increases. One outcome of the increased failure propensity of smaller diameter lines is that the industry is

experienced in dealing with failures, repair, and replacement of the smallest diameter lines such that the

mitigation of small pipe damage is relatively routine.

A number of the panelists expressed the similar opinions that the smallest applicable non-piping

component will contribute the greatest risk to each LOCA category and that smaller non-piping LOCAs

are much more likely. As discussed previously, there are a number of LOCA-sensitive small diameter

non-piping PWR components, i.e., steam generator tubes, pressurizer heater sleeves, and CRDMs, that

have all experienced either damage or failure in service. Therefore, these components should provide the

largest PWR Category I and 2 LOCA frequency contributions. In contrast, some larger non-piping

components (e.g., vessels) are fabricated with more stringent controls while other larger non-piping

components (e.g., valve and pump bodies) have bigger design margins than pipes. Hence non-piping

failures are generally expected to be less likely than piping failures for the larger LOCA Categories. The

main residual concern with large non-piping components is that inspections are difficult and not usually

performed on these components. Therefore, there is more uncertainty about the level of degradation in

these components.

A number of panelists believe that aging degradation may have the greatest effect on intermediate

diameter (6 to 14-inch diameter) piping systems because of the small and large component trends

previously discussed. As discussed, the industry has a greater tendency to replace the smallest diameter

piping as problems arise and the failure rates associated with these components are likely to be unchanged

in the future. Conversely, the largest piping has increased leak-before-break margin, and is subject to the

highest quality inspection programs due to the high failure consequences associated with this piping.

Furthermore, there is a large number of piping systems containing intermediate size piping with an

associated large number of degradation-sensitive welds. Therefore, the greater future LOCA concerns are

often associated with intermediate diameter piping.

6.3.4 Influence of Mitigation and Maintenance Actions

Timely and proper maintenance and mitigation practices were almost always considered by the panelists

to result in decreased LOCA susceptibility. Practices such as improved ISI techniques (e.g., eddy current

inspection programs for steam generator tubes), greater use of risk-informed IST, new materials, and pipe

and component replacement programs were all mentioned as having a positive influence on the estimated

LOCA frequencies. Both steam generator and RPV head replacement programs were cited as effective

strategies that the industry is undertaking for reducing LOCA susceptibility. Also, secondary side water

chemistry improvement programs were mentioned as positive measures for decreasing steam generator

tube degradation.

However, there are several potential detrimental aspects associated with any mitigation or maintenance

practices. For one, the frequent opening and closing of systems for inspections can cause problems.

There is also the increased likelihood for human error if components are:reassembled improperly or

artifacts (e.g., tools and loose debris) are left within the component after servicing. Also improper

maintenance activities associated with active system components (such as a valve) could cause increased

loading in a passive system component, and thus greater failure likelihood.

Although ISI programs are clearly beneficial, a number of participants expressed reservations about the

viability of ISI programs for small diameter piping systems, for piping systems and components

fabricated from cast stainless steels, and for welds with limited accessibility for inspections. Inspection of

smaller piping may not be as viable because degradation can initiate and lead to failure in some instances

even within inspection cycles. The concern with cast stainless steel components (i.e., pump and valve

6-11
bodies) is that there still is no proven technique for inspecting these materials. In addition, these materials

are subject to thermal aging which can reduce the fracture toughness. Fortunately, no service-induced

crack initiation mechanism has been identified for these materials or else this -issue would become much

more serious. Limited accessibility locations are a concern because inspection quality may suffer or an

inspection may not even be possible. An example of a limited accessibility location is the surge line-topressurizer bimetallic weld in Westinghouse PWRs. This particular weld also has experienced service

cracking and is particularly susceptible to PWSCC due to the high operating temperature of this location.

As the industry moves toward greater risk-informed inspection (RI-ISI) criteria, many panelists expressed

caution with this approach. The philosophy of inspecting risk-important locations is generally regarded to

be sound. However, inspection locations are largely based on experience. As a result, it is imperative

that early signs of degradation in new locations are aggressively addressed and incorporated into RI-ISI

programs. Some panelists are concerned about relying too heavily on risk-informed regulation and

eschewing good engineering practices (i.e., proactive maintenance, inspection, and material replacement

programs) if the associated risk are expected to be small.

There were a few maintenance and mitigation issues discussed that are unique to non-piping components.

There was some concern expressed that maintenance and inspection programs of the large component

bodies (pressurizer, steam generator, RPV) is not as rigorous as for piping systems. For smaller nonpiping components such as steam generator tubes and CRDMs, improved inspection methods and

mitigation programs are expected to reduce future failure frequencies. For steam generators, improved

eddy current techniques have been incorporated in inspection programs and more degradation resistant

materials are replacing older materials. Also, as mentioned above, better water chemistry control is

possible for the secondary side of the steam generator. Similarly for CRDMs, ongoing head replacement

programs with more resistant Alloy 690 materials and better and periodic inspection programs are being

followed to minimize the occurrence of further PWSCC cracking.

Mitigation and maintenance practices are also generally expected to-help uncover the emergence of new

degradation mechanisms before the risk impact becomes severe. Additionally, as mentioned in Section

6.3.2, a number of the panelists expect that targeted mitigation strategies will be developed and

implemented once the mechanism is identified. The LOCA frequencies may be higher than historical

values in the time period between the discovery and mitigation of the degradation mechanism. After

mitigation is fully implemented, the same panelists expect the frequencies to return to values applicable

before the manifestation of the new degradation mechanism.

6.3.5 Effect of Operating Time on LOCA Frequencies

Unabated aging mechanisms typically lead LOCA frequency increases with continued operating time.

However, nearly all panelists expect that the NRC and industry will aggressively respond to emerging

mechanisms. Some historical examples cited to support this contention include the IGSCC cracking issue

in BWR plants in the late 1970s and early 1980s and the PWSCC issue currently being addressed in PWR

plants. Overall, the participants believe that maintenance and mitigation will offset the natural tendency

for LOCA frequencies to increase due to aging. Most of the participants think that the various

compensating factors will balance such that the LOCA frequencies will remain relatively constant over

the future operating period, up to 35 years from today.

Furthermore, there are several factors which will promote LOCA frequency decreases in the future. A

number of the panelists expect that inspection techniques will continue to improve and result in greater

knowledge of existing plant degradation and allow degradation evolution to be more accurately

monitored. This will allow the effectiveness of mitigation strategies to be more effectively evaluated.

Other mitigation strategies are expected to improve and mature as well. In addition, the infant mortality

6-12
rates typically associated with new systems and technologies have already occurred. Thus, the

fabrication-related defect failure rates should continually decrease in the future.

Conversely, the panelists identified several factors associated with degradation mechanisms which

promote future LOCA frequency increases. Thermal fatigue, SCC, and FAC challenges are all expected

to increase. Thermal fatigue issues will increase near the end of the plant license-renewal period from the

high usage factors at many plant locations. One panelist expressed concern about future IGSCC failure

rates from potential sulfate increases resulting from infrequent ion-exchange filter replacement. Also,

over the next 10 to 15 years, a number of the participants believe that PWR LOCA frequencies will

increase until appropriate PWSCC mitigation strategies have been developed and implemented. Once

these strategies are in place, then the associated LOCA frequencies would naturally diminish. Panelists

also tempered their expectations about possible future LOCA decreases based on the possibility of new

degradation mechanisms emerging. While no specific mechanisms were identified, operating experience

has shown that previously unseen mechanisms have periodically surfaced in the fleet. The expectation of

many panelists is that new mechanisms will continue to arise periodically and present new LOCA

challenges.

6.3.6 Uncertainty in LOCA Frequency Estimates

The panel members generally expressed greater uncertainty in their predictions as the LOCA size (or

category) increases and as the future operating time period increases. These trends are natural because

assessment of larger and more distant LOCAs requires greater extrapolation of existing passive-system

failure data. There was also generally more uncertainty associated with the PWR LOCA frequency

assessments. One reason is that a number of panelists believe that the uncertainty associated with BWR

piping is less than with PWR piping. The important BWR LOCA issues largely remain IGSCC in the

future, but the BWR plants have more experience mitigating these degradation effects. Similar

experience does not exist for PWR plants, which leads to greater uncertainty about the possible effects of

degradation. However, there is also an expectation that PWR plants will gain experience from addressing

current PWSCC piping concerns. At this point in time, the associated PWR and BWR uncertainties may

again be similar. Another reason for increased PWR uncertainty is that PWR plants contain more LOCAsensitive non-piping components. Therefore, the uncertainty associated with assessing their LOCA

frequency contributions is naturally greater.

6.4 References

6.1 Snaider, R., "BWR Feedwater Nozzle and Control Rod Drive Return Line Nozzle Cracking,"

NUREG-0619, April 1980.

6.2 Generic Letter 88-01, "NRC Position on IGSCC in BWR Austenitic Stainless Steel Piping," January

25, 1988.

6.3 Hazelton, W. S., "Technical Report on Material Selection and Processing Guidelines for BWR

Cooling Pressure Boundary Piping," NUREG-0313, Rev. 2, June 1986.

6-13

7. QUANTITATIVE RESULTS

Each panelist provided quantitative answers to the elicitation questions using the framework described in

Section 3. The general approach and philosophy used by each panelist in developing their responses is

provided in Appendix K (General Approach and Philosophy of Each Panelist). Each panelist's

quantitative elicitation responses can be found through the "Electronic Reading Room" link on the NRC's

public website (http://www.nrc.gov/) using the Agencywide Documents Access and Management System

(ADAMS). The document is found in ADAMS using the following accession number: ML080560005.

The analysis procedures of Section 5 were then applied to process the results from each panelist and

aggregate the individual results to generate LOCA frequency estimates which reflect the estimates of the

entire panel. As described in Section 5.3.1, each panelist's input (Section 5.2) is processed using the

assumed split lognormal structure of each response to obtain frequency estimates for each contributing

piping system and non-piping sub-component. These individual contributors are then summed,(Section

5.3.5) to obtain total LOCA estimates for each panelist corresponding to BWR and/or PWR plant types as

appropriate. The individual estimates are then aggregated as described in Section 5.4 to estimate central

group opinion and evaluate panel diversity. This analysis produces the baseline LOCA frequency

estimates as described in Section 5.5. A number of sensitivity analyses are then performed as described

in Section 5.6 to examine the robustness of these results.

This section provides the principal LOCA frequency estimates resulting from the expert elicitation

process and the analysis procedures described in Section 5. Appendix L (Detailed Results) contains

additional detailed results and discussion, including the LOCA frequency estimates for each panelist.

These estimates are also supported by the qualitative technical insights in Section 6. These qualitative

insights are generally not replicated in this section, although it is necessary to understand them to evaluate

the quantitative results and trends. For example, the underlying damage mechanisms (Section 6.3.2) are

fundamental to the quantitative BWR and PWR LOCA frequencies. The locations of other supporting

qualitative rationales in Section 6 are referenced whenever possible in this section.

The results have been partitioned to examine the effects of key variables in various subsections. The

effects of safety culture on the LOCA frequencies are described in Section 7.1. The total LOCA

frequencies are documented along with-the relative piping and non-piping contributions and the effect of

operating time in Sections 7.2 to 7.4. While the elicitation structure defined the LOCA sizes by leak rate

categories, the LOCA categories are correlated to break sizes as described in Section 3.7 and the results

are presented with respect to break sizes in Section 7.2. Correlating the LOCA flow rate categories to

physically equivalent break diameters is helpful for identifying major system and component contributors.

Nevertheless, the results for each applicable LOCA category include contributions for all the variously-.

sized systems and components. Estimates of SGTR frequencies are provided in Section 7.8 and

compared with results from prior studies.

Panelist uncertainties and variability among the panelists' responses are discussed in Section 7.5. In

addition, numerous sensitivity analyses were performed to examine the robustness of the analysis

techniques and to understand the impact of various analysis choices on the calculated LOCA frequencies.

The results of these analyses are presented in Section 7.6. Summary results that are consistent with the

elicitation objectives and structure and are reasonably representative of the panelists' quantitative

judgments are presented both for the total LOCA frequencies (Section 7.7) and the PWR SB LOCA

frequencies without SGTR contributions (Section 7.8). The summary results are compared to the LOCA

frequency estimates developed in previous studies (Section 7.9) and the SB LOCA estimates are

compared to operating-experience estimates in Section 7.10.

7-1
Note that many figures in this section use straight lines to connect the plotted points associated with

adjacent LOCA categories. This graphical aid was added merely for visual clarity and not to imply a

linear relationship between adjacent LOCA categories. The elicitation estimated LOCA frequencies only

at each of the six defined LOCA categories (Section 3.4.1). Interpolation of frequencies between LOCA

category sizes can be done at the user's discretion depending on the conservatism required by the

application. Some common interpolation schemes are linear, multi-point nonlinear and cubic spline. A

step-wise or stair-step interpolation between two categories where the frequency for the lower category

size is used for all flow rates or corresponding break sizes between the two categories provides the most

conservative interpolation scheme. Note that any interpolation scheme does not reflect the uncertainty in

the interpolated frequencies.

7.1 Safety Culture

There were two basic tenets that guided the panelists' safety culture quantitative evaluations. First, most

panelists assumed and expected that regulatory oversight policies and procedures will continue to be used

to identify and mitigate risk associated with plants having deficient safety practices. It was also

anticipated that current regulatory oversight practices would continue to evaluate aging management and

mitigation strategies with the objective of assuring that future plant operation and maintenance are

sufficient. Therefore, the analysis and evaluation of safety culture required the assessment of the overall,

future effectiveness of regulatory practices in determining MV estimates of safety culture trends. The UB

estimates typically represent the effect of inadequate safety culture and regulatory oversight at a few (or

possibly one) specific plants.

Figures 7.1 and 7.2 illustrate the effects of the industry and regulatory safety cultures, respectively, on the

ratios of the LOCA frequency in the future to the LOCA frequency at 25 years (i.e., LOCA Ratio). The

Category I LOCA ratio results are shown using box and whisker plots'. Similarly, Figure 7.3 shows the

effect of the industry safety culture on the LOCA ratio for Category 4 LOCAs. Only the Category 4

LOCA results are shown because they are representative of the results for all larger LOCA categories.

Qualitative information provided by the panelists supporting the results in these figures is provided in

Section 6.2.

In Figures 7.1 to 7.3, LOCA ratios less than 1.0 indicate an expected LOCA frequency reduction as a

result of safety culture improvements. The median ratios in all three figures are equal to 1.0 and the

interquartile ranges (represented by the shaded boxes) are all bounded by 0.5 and approximately 1.0. This

result implies that the panel members generally expect that the safety culture will either improve slightly,

or remain essentially constant up to the end of the plant license-renewal period. Also, the majority of the

panelists expect that any future LOCA frequency reduction will be less than a factor of 2. Only some

panelists expect that the smaller LOCA categories will significantly benefit from an improved safety

culture in the future. However, there is nearly universal agreement that larger LOCA categories will not

be significantly affected (within a factor of 2) by future safety culture changes. Nearly all panelists also

expressed the opinion (Section 6.2) that the industry and regulatory safety cultures are highly positively

correlated and will remain this way in the future.

For a discussion of how to interpret the box and whisker plots in Figures 7.1 through 7.3, the reader is referred to Appendix L.

7-2
C

60/25 year ratios

1 1

40/25 year ratios

C, G

S Median! 1 values 0.0.5. 1.0 for both 40/25 and 60/25 year ratios

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

LOCA Ratio: Future/Current

Figure 7.1 Effect of Industry Safety Culture on Category 1 LOCAs

CI

60/25 year ratios

40/25 year ratios

K

Median values 1.0 for both 40/25 and 60/25 year ratios

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

LOCA Ratio: Future/Current

Figure 7.2 Effect of Regulatory Safety Culture on Category 1 LOCAs

7-3
60/25 year ratios

40/25 year ratios

D [ E,G

Median values1l.0 for both 40/25 and 60/25 year ratios

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

LOCA Ratio: Future/Current

Figure 7.3 Effect of Industry Safety Culture on Category 4 LOCAs

There are several positive and negative factors that counteract to result in the expected stability in the

regulatory and industry safety cultures. Factors that the panelists generally believe improve safety culture

include deregulation; an increased awareness of the consequences of passive system degradation;

additional industry nuclear experience; and improved technology. The positive factors are largely

balanced by countervailing panelist concerns that could increase the LOCA frequencies. These concerns

include a possible focus on economic performance instead of plant safety; the eventual decommissioning

of plants; the aging nuclear workforce; complacency resulting from the generally good nuclear safety

record; and economic and political pressures to maintain operations. Other issues evaluated have both

positive and negative aspects that tend to cancel. More discussion on these issues is contained in Section

6.2. While the median group opinion was that safety culture would slightly improve, some panelists were

more pessimistic and predicted higher LOCA ratios near the end of the plant license-renewal period (i.e.,

the 60/25 year ratios) when compared with the 40/25 year ratios in Figures 7.1 through 7.3.

The most serious general concern is that LOCA frequencies at a safety-deficient plant could be

significantly increased by its operating philosophy (Section 6.2). The vessel head degradation that

occurred at the Davis-Besse plant was frequently cited as an example of this effect (Section 6.2). Several

panelists expressed the view that safety culture deficiencies at a plant could increase the LOCA

frequencies at that plant by a factor of 10 or more. Such safety culture deficiencies at only a small

number of plants would not affect the median, or generic, LOCA frequency estimates but could affect the

upper tails of the distributions. The UB safety culture effects provided by a few panelists did reflect this

possible increase. As discussed in Section 6.2, there is an expectation that regulatory actions using

existing enforcement measures would lessen both the possibility and the impact of safety culture

7-4
deficiencies at particular plants. However, the panelists recommended continued vigilance to ensure that

deficient plant safety cultures do not develop.

[n summary, the three principal conclusions from the safety culture elicitation questions are that (1) safety

culture effects on the future generic LOCA frequencies are expected to be minimal; (2) industry and

regulatory safety cultures are highly correlated; and (3) plant-specific safety culture can significantly

affect that plant's LOCA frequencies and therefore vigilant regulatory oversight should be maintained.

Because of these findings, no modification or adjustment was applied when calculating the generic

LOCA frequencies presented in this study. This decision was endorsed by the elicitation panel.

However, it is recognized that these generic results are only applicable provided the plants continue to

operate within the context of existing regulations and their licensing basis.

7.2 Total BWR and PWR LOCA Frequencies (Baseline Estimates)

The total (piping plus non-piping) BWR and PWR passive system LOCA frequencies are provided in

Table 7. 1. These frequencies are calculated using the baseline analysis framework described in Section

5.5. The medians, means, 5 h (5"' Per.) and 9 5th percentiles (9 5th Per.) are the GMs of the panel members'

individual total BWR and PWR LOCA frequency estimates as described in Section 5.4. Estimates are

provided for the current-day frequencies (25 year average plant life) and end-of-plant-license frequencies

(40 year average plant life). Generally, the 9 5 h percentiles for both the BWR and PWR plants are

between a factor of 2 to 4 higher than the mean values. The LOCA flow rate threshold associated with

each LOCA category is also provided in Table 7. l.Recall that, as described in Section 3.4.1, the LOCA

categories are defined in terms of cumulative thresholds rather than binned intervals because the

thresholds are more consistent with the various PFM and operating-experience analyses that the panelists

performed.

Figure 7.4 depicts the mean and 9 5th percentile, current-day (25 years of plant operations) BWR and PWR

LOCA frequency estimates from Table 7.1. As illustrated, the current-day PWR Category I results are

approximately an order of magnitude higher than the BWR Category I results. This reflects the

contribution of the SGTRs which is the dominant LOCA contributor for PWR Category 1 LOCAs

(Section 6.3.1). Conversely, for the current-day Category 5 LOCAs, the BWR results are approximately

an order of magnitude higher than the PWR results. This reflects the continued risk associated with

IGSCC for the BWR recirculation system (Section 6.3.2). The BWR frequencies decrease precipitously

for the Category 6 LOCAs because only a catastrophic rupture of the RPV in BWR plants can result in a

primary LOCA of this size 2 For the other LOCA categories, the total LOCA BWR and PWR frequencies

are generally comparable and differ by a factor of 5 or less.

2As noted in Section 3, breaks can occur in either LOCA sensitivity piping or non-piping systems and components. No single

failure within a BWR primary piping system could cause a Category 6 LOCA.

7-5
Table 7.1 Total BWR and PWR LOCA Frequencies

Current-Day Estimate End-of-Plant-License Estimate

Eff (per cal. year) (per cal. year)

LOCA Break (25 years fleet average operation) (40 years fleet average operation)

Plant Size Size

Type (gpm) (inch) 5"h Per. Median Mean 951h Per. 5'h Per. Median Mean 95 h Per.

>100 1/2 4.3E-05 3.OE-04 5.6E-04 1.7E-03 3.5E-05 26E-04 5.3E-04 1.7E-03

>1,500 1 7/8 4.0E-06 5.0E-05 1.1E-04 3.7E-04 3.3E-06 4.5E-05 L.E-04 3.6E-04

BWR >5.000 3 /4 8.0E-07 9.7E-06 2.4E-05 8.5E-05 7.3E-07 9.8E-06 2.7E-05 9.5E-05

>25K 7 1.3E-07 2.2E-06 6.2E-06 2.2E-05 1.2E-07 2.3E-06 7.6E-06 2.7E-05

>100K 18 1.3E-08 2.9E-07 1.1E-06 3.9E-06 1.2E-08 3.I E-07 1.5E-06 5.OE-06

>500K 41 1.1E-II 2.9E-10 3.3E-09 8.5E-09 1.3E- 1 4.OE-10 5.0E-09 1.3E-08

>100 '/2 9.1E-04 3.9E-03 6.7E-03 1.8E-02 5.5E-04 2.6E-03 4.9E-03 .4E-02

>1.500 1 5/8 1.1E-05 I.4E-04 4.7 E-04 1.5E-03 1.1E-05 1.6E-04 5.8E-04 1.9E-03

PWR >5,000 3 2.7E-07 3.4E-06 1.3E-05 4.4E-05 6.2E-07 7.6E-06 2.8E-05 9.8E-05

>25K 7 2.OE-08 3.1E-07 1,2E-06 4.1E-06 4,OE-08 6.6E-07 2.8E-06 9.5E-06

>100K 14 5.7E- 10 1.2E-08 1.1E-07 3.OE-07 1.4E-09 2.8E-08 2,7E-07 7.4E-07

>500K 31 4.7E- I I 1.2E-09 I1E-08 4.OE-08 1.2E- 10 2.9E-09 3.9E-08 9.9E-08

7

0

C,

Cr

LL

0

0_-J

101

10-2

103

10-4

10-5

10-6

10-7

10-8

10-9

-0- PWR - Mean

- PW R - 95'h

- - BWR - Mean

-- --- BWR - 95th

1 2 3 4 5 6

LOCA Category

Figure 7.4 Total BWR and PWR LOCA Frequencies (Means and 95h Percentile Values) as a

Function of LOCA Category at 25 Years of Plant Operations

Table 7.1 also lists the minimum effective break size corresponding to each LOCA flow rate category

using the correlations described in Section 3.7. As described in Section 3.7, the break size is the

equivalent diameter of the associated break area required for each of the six threshold flow rates.

However, these break sizes do not represent actual pipe diameters. Specifically, there is no 41 inch

diameter (Table 7.1) piping in BWR plants. Only the PWR and BWR liquid correlations (Section 3.7)

were used to develop the Table 7.1 break size equivalents because the major BWR contributions come

7-6
from the liquid lines. The main steam line is generally not expected to be a significant contributor to the

overall LOCA frequencies. It is worth stressing that most panel members did utilize the effective break

size to assess failure rates for each LOCA category instead of the flow rate definitions. The LOCA

frequencies are plotted as a function of threshold break diameter in Figure 7.5 for both the PWR and

BWR mean and 95'h percentile estimates. The break diameter and LOCA category relationship is

approximately logarithmic and there is approximately a factor of 2 difference in each successive break

size. These relationships explain why Figures 7.4 and 7.5 appear to be nearly identical.

10-1

10-2

-PWR - Mean

-- - PWR - 95h

- - BWR - Mean

- BWR - 95'h

C0

U

IL

C-)

0

-J

10 1,

10-4

1 0 -5 V

10.6

10-7

10.8

10-9

0.1 1 10 100

Threshold Break Diameter (in)

Figure 7.5 Total BWR and PWR LOCA Frequencies (Means and 9 5th Percentile Values) as a

Function of Threshold Break Diameter at 25 Years of Plant Operations

It is worth reiterating that the elicitation focused solely on determining event frequencies that initiate by

unisolable primary system side failures that can be exacerbated by material degradation with age.

Therefore, active system failures (e.g., stuck open valve, pump seals, interfacing system LOCAs),

consequential primary pressure boundary failures due to either secondary side failures, or failures of other

plant structures (e.g., crane drops) are not reflected in the LOCA frequency estimates in Table 7.1. In

addition, only normal plant operational cycles and loading histories that occur over the extended licensing

period (i.e., frequency approximately 0.01 or greater per calendar year) were explicitly addressed. Rare

event loading from seismic, severe water hammer, and other sources was not considered in this generic

evaluation because of their strong dependence on plant specific factors. As with other consequential

LOCAs, the LOCA frequency contribution from rare event loading may be an important consideration

when evaluating total plant risk and determining total LOCA risk contributions.

As indicated in Section 2, a separate program was conducted to assess seismic LOCA risk [7.1 ]. Both

unflawed and flawed seismic piping analyses were conducted in order to ascertain the magnitude of any

potential adjustments to the LOCA frequency estimates provided in this section. The unflawed piping

7-7
analyses showed that the seismic-induced failure probabilities for unflawed piping are significantly lower

than the passive LOCA frequencies (Table 7.1) for large LOCAs (i.e., initiating event frequencies greater

than 1.OE-5 per calendar year).

The flawed piping seismic analysis demonstrated that even for very long circumferential cracks, the

surface-flaw depth must be greater than 40 percent of the wall thickness in order to lead to failure for a

seismic event with a 1.0E-5 annual probability of exceedance. A flaw this size is large enough so that

periodic nondestructive ISI coupled with required leak detection should ensure that such a flaw would be

detected well in advance of failure in service. Hence, the failure frequencies associated with large

LOCAs (> 1.0E-5 per calendar year) due to seismic loading of flawed piping will be significantly less

than the passive system seismic LOCA frequencies if appropriate ISI programs are in place for large

primary system piping and non-piping components (i.e., those that can lead to a LOCA size

corresponding to a failure frequency greater than I.0E-5 per calendar year).

7.3 Comparison of Pipingq and Non-Piping Contributions to Total LOCA

Frequencies

The piping and non-piping current-day mean LOCA frequency estimates are compared in Figures 7.6 and

7.7 for BWR and PWR plants, respectively. The quantitative differences between the piping and nonpiping contributions are summarized in Table 7.2. For the BWR plants, the piping contributions to the

Category 1 and 2 LOCA frequencies are a factor of 2 to 4 higher than the contribution from the nonpiping components. The BWR primary piping system contributors to these smaller category LOCAs are

the small diameter instrument and drain lines (Section 6.3. 1). The main BWR non-piping contributor to

these smaller category LOCAs is from vessel penetrations, especially the lower head control rod drive

(CRD) housings (Section 6.3.1), due to current PWSCC concerns (Section 6.3.2).

10-3

10.4

C- 10-5

C.,

(1 10-6

LL

< 10-7

0 1

0

-J 10-8

10-9

---- Piping

-0- Non-Piping

1 2 3 4 5 6

LOCA Category

Figure 7.6 BWR Piping and Non-Piping LOCA Frequencies (Current-day Estimates)
10.2

10-3 10~ 4-- Non-Piping

10-

10-5

CD 10-6

LL

0

-J

10-8 109

10.9 I I I

1 2 3 4 5 6

LOCA Category

Figure 7.7 PWR Piping and Non-Piping LOCA Frequencies (Current-day Estimates)

Table 7.2 Ratio of BWR and PWR Non-Piping Contribution to Piping Contribution

LOCA Ratio of BWR Non-Piping Ratio of PWR Non-Piping Contribution

Category Contribution to Piping Contribution to Piping Contribution

1 0.24 32

2 0.40 6.7

3 o.l 0.36

4 0.06 1.1

5 0.07 0.50

6 NA 1.4

The principal BWR piping system contributor to LOCA Categories 3 to 5 is the recirculation system

piping (Section 6.3.1) where IGSCC concerns remain (Section 6.3.2). The panel consensus is that the

susceptibility to IGSCC is greatly reduced compared to the past (Section 6.3.2). Mitigation measures

such as improved HWC, weld overlay repairs, post-weld heat treatment (PWHT), and pipe replacement

using more resistant materials have effectively reduced the likelihood of IGSCC. However, there is still

residual concern about the failure likelihood of the large recirculation piping material that has not been

replaced. The main BWR non-piping contributors to LOCA Categories 3 to 5 are the RPV nozzles and

the component bodies (Section 6.3.1). However, the BWR non-piping contributions to the total LOCA

frequencies are substantially less than the piping contributions for these LOCA sizes due to the

comparative robustness of the non-piping components. The relative non-piping contribution for all the

LOCA categories is generally expected to remain relatively constant in the future, although mitigation of

vessel penetration cracking may decrease this specific contribution over the next fifteen years (Section

6.3.4).

7-9
The PWR LOCA frequencies (Figure 7.7 and Table 7.2) are dominated by the non-piping contributions

for LOCA Categories 1 and 2. The mean non-piping LOCA frequencies are 32 and 6.7 times greater than

the piping contribution for Category I and 2 LOCAs, respectively. The major piping contributors for

PWR Category I and 2 LOCAs are, as was the case for the BWR plants, the instrument and drain lines.

The concerns associated with these PWR systems are analogous to those for BWR plants (Section 6.3.1).

However, the relative non-piping contributions are significantly higher for PWR plants because of plant

design differences and the increased population of non-piping primary pressure boundary components.

The PWR plants operate at higher temperatures and several non-piping components (e.g. pressurizer,

steam generator) have experienced service degradation due to PWSCC or actual rupture (e.g. steam

generator tubes). For LOCA Category 1, the large non-piping contribution is provided by steam

generator tubes, CRDM penetrations, and pressurizer heater sleeves (Section 6.3.1). For LOCA Category

2, the principal contribution is from the CRDM penetrations (Section 6.3. 1). The non-piping

contributions for LOCA Categories I and 2 are expected to decrease somewhat in the future as PWSCC

degradation is mitigated and as steam generator replacement programs proceed.

The relative non-piping contribution for PWR plants drops precipitously after LOCA Category 2, and the

piping and non-piping contributions are nearly the same (less than a factor of 3 difference) for LOCA

Categories 3 to 6. The steam generator tube, CRDM, and pressurizer heater sleeve components do not

substantially contribute anymore due to size limitations. The major PWR piping system contributions

include various auxiliary piping systems for LOCA Category 4; the hot leg, surge line and RHR lines for

Category 5; and the hot leg for Category 6 (Section 6.3.1). The major PWR non-piping contributors are

nozzles and component bodies for LOCA Categories 3 and 4; the manways and component bodies for

Category 5; and the component bodies for Category 6.

7.4 Effect of Plant Operating Time on Total LOCA Frequencies

The future LOCA initiating event frequencies after 40 years of operation are also provided in Table 7.1.

As indicated in Section 2, the elicitation responses did not explicitly require the determination of either

average frequencies over the time period or instantaneous frequencies at the end of this time period. It is

reasonable to assume, however, that most of the panelists did provide average values. Note that these

estimates include failure contributions from continued degradation due to mechanisms identified in the

first 25 years of operating experience as well as possible new mechanisms that could surface over the next

15 years. Current operating experience contains information about the response rate and the effectiveness

of mitigation measures in addressing several known degradation mechanisms (e.g., IGSCC and FAC).

Several panelists used this operating experience as a basis for assessing the impact of possible new

degradation mechanisms. Alternatively, PFM models can be used to assess the impact of new

degradation mechanisms by comparison with the failure frequencies associated with known degradation

mechanisms. Other panelists chose this approach.

Despite these considerations and the additional uncertainty associated with predicting future events, the

differences between the 25 and 40 year estimates (Table 7.1) are not large. The 40-year PWR median and

mean estimates are less than a factor of 3 greater than the 25-year estimates for all LOCA categories. The

LOCA Category 1 mean frequencies are expected to actually decrease slightly (by -30%) due to expected

improved mitigation practices for SGTRs and CRDM cracking (Section 6.3.4). The modest PWR LOCA

frequency increases are largely attributed to continuing PWSCC concerns in other components. The 40-

year BWR LOCA mean frequencies are even more stable, and differ by less than 35% from the 25 year

estimates for LOCA Categories I to 5. This stability reflects the opinion that IGSCC (Section 6.3.2)

susceptibility is expected to remain constant in the near term. The BWR mean Category 6 LOCA

frequency only increases by 50% over the next fifteen years. This minor increase results from an

7-10
increased RPV failure rupture frequency due to continued radiation embrittlement (Section 6.3.1).

Because of the predicted stability in these estimates over the near-term, it is recommended that the 25-

year results be used to estimate the average LOCA frequencies over the next 15 years of fleet operation.

The panelists expect somewhat larger LOCA frequency differences between the 25 and 60 year estimates.

The 25 and 60 year mean frequency estimates are graphed in Figure 7.8 for both BWR and PWR plants.

Except for LOCA Category 6, the increases in the BWR estimates are relatively modest. In fact, for

LOCA Categories I to 5, the mean BWR LOCA frequency estimates for 25 and 60 years differ by less

than a factor of 3. The BWR mean frequency increases are largely due to uncertainty about the future,

which is reflected in general increases in the 9 5 1h percentile estimates (not shown in Figure 7.8),and the

concern that new mechanisms could arise in the operating fleet (Section 6.3.4). The only significant

mean frequency increase is predicted to result from continued RPV aging and only affects the BWR

LOCA Category 6 frequency (Section 6.3. 1). However, even this increase is less than a factor of 10 over

the current-day Category 6 LOCA estimate.

From Figure 7.8, the increases in the PWR estimates in the next 35 years are typically much larger than

the BWR increases. The PWR mean frequencies increase by a factor of less than 3 for LOCA Categories

I and 2, factors of 8 and 10 for LOCA Categories 3 and 4, and factors of 10 and 18 for LOCA Categories

5 and 6, respectively. The larger increases in-the higher LOCA categories are driven by the expectation

that they will be impacted more severely by possible future degradation mechanisms. Because, as

discussed above, the differences between the 25 and 40 year estimates are not significant, the PWR

frequencies increase more than the BWR frequencies after 40 years of operation. This increase sterns

from uncertainty about the long-term mitigation effectiveness of PWSCC and the effect of future

degradation mechanisms on PWR LOCA frequencies. While no specific new degradation mechanism has

been identified for PWR plants, many panelists expect that they are more susceptible to future

degradation than BWR plants. The net effect of the relative changes after 40 years of operation is that at

60 years, the PWR frequencies are expected to be greater than the BWR frequencies for LOCA

Categories 1, 2, 3, and 6 and just slightly less than the BWR frequencies for LOCA Categories 4 and 5.

7-11
Q

0

L. Cr

LL

0

-J

10-1

10-2

10-3

10.4

10-5

10-6

10-7

10.8

10-9

-PWR: 25 years

- -- PWR: 60 years

--- BWR: 25 years

- --3- BWR: 60 years

1 2 3 4 5 6

LOCA Category

Figure 7.8 BWR and PWR Mean Total LOCA Frequencies at 25 and 60 Years

7.5 Individual Uncertainty and Panel Diversity

7.5.1 Individual Uncertainty

The BWR Category 1 and Category 3 LOCA frequency percentiles are plotted in Figures 7.9 and 7.10,

respectively, for the various panelists who answered the BWR elicitation questions. Figures 7. 11 and

7.12 are similar plots for the PWR Category I and Category 6 LOCA frequency percentiles, respectively.

In each of these plots, the panelists' medians and 5 'h and 9 5 th percentile estimates are shown. The letter

designators for the horizontal lines in these plots are the reference letters which identify each panelist.

For the BWR plants (Figures 7.9 and 7.10), the uncertainties associated with most of the panelists'

responses (i.e., the differences between their 5 h and 9 5 th percentiles) are comparable for their Category I

and Category 3 responses. The exceptions to this are Panelists A, E, and F, who expressed almost twice

as much uncertainty in their Category 3 responses as they did in their Category I responses. The

uncertainties in the larger LOCAs, Categories 4 to 6, increase with successively larger LOCA sizes. This

increase is reflected by the individual UEFs which are defined as the UB/MV responses (Sections 5.6.2.2

and 7.6.2.2) and is another measure of panelist uncertainty. The GM of the individual UEFs also

increases from a factor of about 7 for LOCA Category I to 28 for Category 6. However, the differences

in the relative uncertainties of the panelists are consistent from LOCA Categories 3 to 6. That is, the

ratios of any two panelists' UEFs (e.g., see Figure 7. 10 for BWR LOCA Category 3) are relatively

constant for LOCA Categories 3 to 6.

The PWR LOCA frequency estimates (Figures 7.11 and 7.12) exhibit many similar features to the BWR

estimates. As above, the panelists express more uncertainty as the LOCA size increases. For the PWR

Category I responses (Figure 7.11), the panelists' uncertainties varied from less than one order of

7-12
magnitude for Panelists A, B, and L to two orders of magnitude for Panelist H. This relatively low level

of uncertainty for LOCA Category I for PWRs reflects the fact that steam generator rupture is a

significant contributor to this LOCA category and there is actual rupture data to support the estimates.

Conversely, for the PWR Category 6 LOCAs, the level of uncertainty ranges from approximately two

orders of magnitude for Panelists A, B, H, and L to greater than four orders of magnitude for Panelists C,

E, and J. This level of uncertainty is a reflection of the fact that more uncertainty is naturally related to

very low frequency events, such as a Category 6 LOCA (Section 6.3.6). The UEFs vary from

approximately 5 to 30 for Category 1 and Category 6 respectively (see Section 7.6.2.2). This is

comparable to the increase for the BWR results. The largest increases are for Panelists C and J, who

expressed three orders of magnitude more uncertainty in their Category 6 responses than they did in their

Category 1 responses.

It is of interest to note that almost all of the coverage intervals in Figures 7.9 to 7.12 are quite symmetric

about the medians. Because the frequency scale in the figures is logarithmic, this reflects the general

logarithmic symmetry in the panelists' responses to the elicitation questions. However, a few of the

panelists' responses exhibit significant asymmetries for the higher LOCA categories. For instance, the

median estimates for Panelists A and E are relatively closer to their 9 5 "h percentiles for both BWR

Category 3 (Figure 7.10) and PWR Category 6 (Figure 7.12) LOCAs. This implies that these panelists

expect that the actual LOCA frequencies could only be a little higher, but significantly lower then their

median estimates. Conversely, the Category 6 PWR LOCA responses for Participants C and J have

median values closer to the 5t" percentile and substantial LOCA frequency increases above the medians

would not be surprising to these panelists.

Solid Diamonds are 5t, 50", and 951h Percentiles

A

B * .

C p -

D -

E -4"-

F ---

H

1.OOE-11 1.OOE-09 1.OOE-07 1.OOE-05 1.OOE-03 11.00E-01

LOCA Frequency (CY 1

)

Figure 7.9 Individual Estimates of BWR Category I LOCA Frequency Percentiles

7-13
Solid Diamonds are 5", 50h, and 95th Percentiles

c 0

F 4

G* *

Hp

1.00E-11 1.OOE-09 1.OOE-07 1.OOE-05

LOCA Frequency (CY 1)

1.00E-03 1.OOE-01

Figure 7.10 Individual Estimates of BWR Category 3 LOCA Frequency Percentiles

Solid diamonds are 5h, 50", and 951h Percentiles

A

B

H 4- "-------

I * 4 9

L 4-4-4

1.OOE-15 1.OOE-13 1.OOE-11 1.00E-09 1.OOE-07 1.OOE-05

LOCA Frequency (CY-)

1.OOE-03 1.OOE-01

Figure 7.11 Individual Estimates of PWR Category 1 LOCA Frequency Percentiles

7-14
Solid diamonds are 5", 50", and 951h Percentiles

B

*1 E G ---

H

j•

L

1.00E-1 5 1.00E-1 3 1.00E-1 1 1.OOE-09 1.OOE-07 1.OOE-05 1.60E-03 1.OOE-01

LOCA Frequency (CY")

Figure 7.12 Individual Estimates of PWR Category 6 LOCA Frequency Percentiles

7.5.2 Panel Diversity

There is considerable diversity in the panelists' opinions. For example, there is approximately a factor of

30 difference between the lowest and highest BWR Category I median frequency estimates (Panelists G

and C in Figure 7.9). This difference is relatively small compared to the factor of 600 difference between

the medians for these two panelists for BWR LOCA Category 3 (Figure 7.10). Panelist C also provides

the highest BWR estimates for LOCA Categories 2 and 4. Furthermore, Panelist C's estimates

increasingly diverge from the rest of the panel for each successively higher LOCA category. For LOCA

Category 4, the difference between Panelist C and the minimum estimate (Panelist G) is a factor of over

1,700. Additionally, Panelist C's median estimate is higher than many of the other panelists' 95th

percentile estimates. The reason for Panelist C's disparity stems from his expectation that IGSCC is

nearly as likely to result in a Category 4 LOCA as it is in a Category I LOCA in the primary recirculation

piping. While the other panelists largely agree that IGSCC of the main recirculation piping is important

(Section 6.3.1), the expectation that Category I and 4 LOCAs are nearly as Likely is not shared by the

other panelists.

There is also more diversity in the 9 5 th percentile estimates than in the medians for LOCA Categories I to

4. For instance, while the minimum and maximum Category 1 median estimates differ by a factor of 30,

the 9 5"h percentile estimates differ by a factor of 80. For LOCA Categories 5 and 6, there is less diversity

in the 9 5th percentile estimates than either the median or 5 th percentile estimates. This occurs because,

while all panelists expect much lower frequencies for the largest piping and non-piping failures, there is

simply more disagreement about how low these frequencies could be. Conversely, the 5th percentile

estimates exhibit the least diversity among the percentiles for LOCA Categories I to 4 and the greatest

diversity for Categories 5 and 6.

7-15
The PWR results also generally exhibit increasing diversity as-the LOCA size increases. The minimum

and maximum median Category 1 estimates differ by only a factor of 8 (Figure 7. 11). This relatively

good quantitative agreement occurs because nearly all panelists expect SGTR to be the dominant

contributor (Section 6.3.1). However, differences between the minimum and maximum median estimates

increase to between 3 and 4 orders of magnitude for LOCA Categories 2 to 4. While these differences are

comparable to the BWR differences, the overall diversity associated with the PWR estimates is greater.

While only Panelist C's BWR estimates differ significantly from the rest of the panelists, the PWR

estimates are more uniformly spread over their range. This greater diversity reflects more disagreement

about the contributing factors to PWR LOCAs of these sizes (Sections 6.3.1 and 6.3.2).

For Category 5 and Category 6 LOCAs (Figure 7.12), the differences between the minimum and

maximum median estimates are about 5 and 7 orders of magnitude, respectively. This primarily reflects

the low frequencies expected by Panelist J. In fact, Panelist J's 9 5th percentile estimate for LOCA

Category 6 is less than the 5th percentile estimates for all of the other panelists. If Panelist J's estimates

are ignored, the differences between the minimum and maximum median estimates for the remaining

panelists decreases significantly to factors of 750 and 4,000 for LOCA Categories 5 and 6, respectively.

This reduced LOCA Category 5 range also becomes consistent with the ranges for LOCA Categories 2 to

4 while the LOCA Category 6 range is not significantly larger. Panelist A's LOCA Category 6 median

value also appears to be an outlier as it is greater than the 9 5th percentiles of the other panelists. Ignoring

Panelist A's estimate decreases the range of median estimates to a factor of only 100. For LOCA

Categories 5 and 6, the differences among the panelists mainly reflect the differences of opinion

associated with estimating rare events (Section 6.3.6) and not from any significant disagreement about the

contributing factors (Sections 6.3.1 and 6.3.2).

If Panelist J's estimates are ignored, the diversity in the PWR 5"h and 9 5th percentile estimates is similar to

the diversity in the BWR estimates. The 9 5th percentile estimates have the largest ranges for LOCA

Categories 1 and 2, while the 5th percentile and median ranges are much smaller. However, for LOCA

Categories 5 and 6, the 5' percentile and median ranges are larger than the 9 5 'h percentile ranges. The

ranges for the three percentiles are similar for LOCA Categories 3 and 4. If Panelist J's estimates are

included, the 5"h percentile estimates exhibit the greatest diversity for LOCA Categories 2 to 6.

7.5.3 Confidence Intervals

Another measure of panel diversity is the set of statistical confidence intervals associated with the

estimates of the total LOCA frequency bottom-line parameters presented in Table 7.1 and Figures 7.4 and

7.5. Panel diversity as measured by the confidence intervals is distinct from the uncertainty expressed by

each panelist, which is reflected in the differences between the medians and 9 5"t percentiles as discussed

in Sections 5.1 and 7.5.1. Confidence intervals for the medians, means, and 9 5 'h percentiles as a function

of threshold break diameter are presented in Figures 7.13 and 7.14 (slightly offset for visual clarity) for

BWR and PWR plants, respectively. The confidence intervals have been calculated as described in

Section 5.4 and are illustrated by error bars about the estimated parameters. Based on the assumed

lognormal model for the individual estimates, each confidence interval has a 95% chance of covering the

median of the assumed lognormal distribution for each bottom-line parameter.

Each confidence interval in Figures 7.13 and 7.14 is symmetric about its associated bottom-line parameter

(i.e., symbols in Figure 7.13 and 7.14) as a consequence of the assumed lognormal distribution of the

panelists' estimates (Section 5.4). From the figures, the confidence intervals generally increase with

increasing break size, as would be expected given the increasing difficulty in estimating increasingly

infrequent events. This trend is consistent with the observed panel diversity as discussed in Section 7.5.2.

The confidence intervals span less than one order of magnitude for the BWR and PWR Category 1 LOCA

estimates. 'The BWR confidence interval differences then increase to a factor of approximately 10 for

7-16
LOCA Categories 2, 3, and 5, a factor of 30 for LOCA Category 4, and over two orders of magnitude for

LOCA Category 6. The PWR confidence interval differences increase to a factor of approximately 20 for

LOCA Categories 2 to 4, and over two orders of magnitude for LOCA Categories 5 and 6. The

differences between the BWR and PWR confidence intervals and the trends with increasing break size are

similar to the previously discussed trends in panel diversity (Section 7.5.2).

The group estimates in Figures 7.13 and 7.14 (i.e., the symbols) also illustrate the trends in the panelists'

uncertainties discussed in Section 7.5. 1. For example, the differences between the estimated medians and

9 5 th percentiles increase with break size. Conversely, the differences between the estimated means and

9 5 th percentiles decrease with break size. This trend implies that the skewness of the underlying

distribution increases with LOCA size so that the mean becomes a higher percentile as LOCA size

increases.

BWR: Baseline Results

10-1

0

0)

U- 0_

0.1 1 10 100

Threshold Break Diameter (in)

Figure 7.13 95% Confidence Intervals for Total BWR LOCA Frequencies

at 25 Years of Plant Operations

7-17
PWR: Baseline Results

10-1

10-2

07

IL

0

-J

10-3

10-5

10-6

1 0-7

10-8

10-9

10-

0.1 1 10 100

Threshold Break Diameter (in)

Figure 7.14 95% Confidence Intervals for Total PWR LOCA Frequencies

at 25 Years of Plant Operations

7.6 Sensitivity Analyses

Several types of sensitivity analyses were conducted to examine the assumptions, structure, and

techniques used to process the elicitation responses to develop the baseline LOCA frequency estimates.

The elements of the baseline analysis framework were defined in Section 5.5, but they are summarized

here for convenience. The framework has six key elements:

(i) The MV, UB and LB supplied by the panelists for each elicitation question were assumed

to correspond to the median, 9 5th percentile, and 5 h percentile, respectively, of a split

lognormal distribution with the mean calculated assuming the upper tail is truncated at the

9 9 .9 th percentile.

(ii) Panelist responses are not adjusted to account for possible overconfidence in the

uncertainty estimates for each elicitation response.

(iii) Split lognormal distributions are summed by assuming perfect rank correlation among the

individual terms.

(iv) The individual estimates are the total LOCA frequency parameters (i.e., mean, median, 5th

percentile, and 9 5th percentile) determined for each panelist.

(v) The group estimates of the total LOCA frequency parameters were determined using the

GMs of the individual estimates.

(vi) Panel diversity is characterized with two-sided 95% confidence intervals based on an

assumed lognormal model for the individual estimates.

The baseline results have been presented and discussed in Sections 7.2 to 7.5.

7-18
This section quantifies the effects of other assumptions or methods of calculation on the baseline

frequency estimates. The sensitivity analyses fall into one of the following five general categories: mean

determination, panelist overconfidence adjustment, correlation structure, individual response aggregation,

and panel diversity. In each case, a single change in the baseline analysis procedure is made and the

effect is quantified. While many sensitivity analyses could be performed, only a few representative or

important sensitivity analyses were conducted for each of the five general categories. A summary of the

sensitivity analyses conducted is provided in Table 7.3, along with the corresponding sections containing

the approaches and results. More details on each sensitivity analysis are contained in Section 5.6.

Table 7.3 Sensitivity Analysis Summary Matrix

Corresponding

Analysis Category Analysis Type Section

Mean Determination Effect of Distribution Shape on Mean 7.6.1

Overconfidence Blanket Overconfidence Adjustment 7.6.2.1

Adjustment Targeted Overconfidence Adjustment 7.6.2.1

Error Factor Adjustment 7.6.2.2

Correlation Strre 

Structure Perfect Rank Correlation vs. Independence 7.6.3

Measures of Group Opinion 7.6.4.1

Response Aggregation Point 7.6.4.2

Aggregation Aggregation Parameters 7.6.4.3

Mixture Distribution 7.6.4.4

Panel Diversity Confidence Bound and Quartile Comparison 7.6.5

7.6.1 Mean Determination

Each panel member provided three responses for each elicitation question: a MV, a LB, and an UB

(Section 3.8.5). These numbers are assumed to correspond to the median, 5ih percentile, and 95"

percentile, respectively, of the panelist's subjective uncertainty distribution (Section 5.1). However, in

order to combine distributions and calculate mean values, a distribution shape must also be assumed. The

multiplicative structure of the elicitation (Section 3) and the character of the elicitation responses suggest

that the distributions should have a split lognormal form (Section 5.3. 1).

However, to avoid the mean being dominated by an extreme upper tail of the distribution, the baseline

analysis calculates the means for the individual elicitation responses by replacing the corresponding split

lognormals by split lognormals truncated at the 9 9 .9'h percentile (Section 5.3.3.2). The remaining 0.1

percent of each distribution is concentrated at the 9 9 .9 th percentile. This truncation point was chosen for

the baseline analysis to be reasonably conservative. Furthermore, because the panel members provided

no information beyond the 9 5 'h percentile, any truncation point beyond the 9 5 th percentile is consistent

with their responses. Note that the truncated split lognormal is used only to calculate the means. The

other parameters of the LOCA frequencies (i.e., median and the 5 th and 9 5:h percentiles).are calculated by

assuming untruncated split lognormal distributions.

This sensitivity analysis evaluates the effects of other distributional shapes on the calculated means for

the individual elicitation responses. There are several other plausible choices as outlined in Section 5.6.1:

a split log-triangular, an untruncated split lognormal, a lognormal corresponding to the upper half of a

split lognormal (upper tail lognormal), or a normalized split lognormal truncated at the 99. 9th percentile

(the probability density function below the 9 9 .9th percentile is renormalized so that the total area remains

one). The effect of the truncation point for the unnormalized split lognormal is also examined by

truncating at the 95", 96t', 971h, 9811, 99th, and 99.99" percentiles instead of the 9 9 .9 th percentile used in

the baseline methodology.

7-19
The mean is calculated for each of these alternative distributional shapes using the assumed distributions

with a nominal median of 1, a nominal LEF of 1,000, and UEFs ranging from 10 to 1,200. This UEF

range encompasses the full range of actual elicitation responses. The nominal LEF was fixed at one value

because it has very little effect on the mean. Any elicitation response can be modeled using these

nominal parameters.

The means calculated for selected distributional shapes are plotted as a function of UEF in Figure 7.15 for

UEFs up to 100 and in Figure 7.16 for larger error factors up to 1,2003. The results for the split

lognormal, upper tail lognormal, and split log-triangular distributions are depicted in these figures

because they encompass a range of plausible distributions for calculating the means. The split lognormal

truncated at the 9 8 th percentile (Truncated Split @ 98 in legends) is displayed because the calculated

means are similar to those using the split log-triangular distribution. The normalized split lognormal

distribution truncated at the 99.9th percentile (Normal Truncated Split @ 99.9 in legends) is used for

comparison with the baseline truncated distribution, which concentrates the highest 0. 1 percent of the

distribution at the 9 9 .9 th percentile (Truncated Split @ 99,9 in legends). These figures do not show

distributions that are truncated at less than the 9 8 th percentile because such distributions do not

appropriately estimate the means.

Some general trends are readily apparent in Figures 7.15 and 7.16. The means calculated using either a

split lognormal distribution or an upper tail lognormal are virtually identical. (Their plotted curves

overlap in both figures.) This verifies that the lower halves of these distributions have minimal effects on

the means. This finding was also verified for larger LEFs than the nominal value of 1,000 used in Figures

7.15 and 7.16. As expected, the means of the untruncated lognormal distributions also provide UBs for

the means of the truncated distributions. The effect of normalizing the truncated distribution instead of

concentrating the values beyond the truncation point at the truncation point (baseline method) increases

with the UEF, but it remains insignificant. The difference between these two truncation schemes is less

than 35% for UEFs up to 1,200. As expected, the baseline truncation method results in slightly higher

calculated means than does normalization. It is also of interest that the means calculated using the split

lognormal truncated at the 98th percentile are very similar to the means calculated using the split logtriangular distribution. These two distributions provide the most appropriate LB means in this sensitivity

study.

The range of plausible means for the elicitation responses can be determined from these figures by the

difference between the upper tail or split lognormal and the split lognormal truncated at the 98th

percentile. This range increases as the UEF increases. The calculated means differ by less than 30% for

the distributions illustrated in Figure 7.15 for UEFs less than 20. The differences remain less than a factor

of two for UEFs less than 50. Note that an UEF of 20 bounds almost all of the elicitation responses for

LOCA Categories 1 to 3 while an UEF of 50 bounds most of the responses for Categories 4 to 6. The

responses for only a few panel members have UEFs above 50, and these large error factors are almost

always associated with LOCA Categories 5 and 6, which represent the most severe and rarest LOCA

events. However, some UEFs are as large as 1;000 for the rarest Category 6 events.

3 The plotted mean values can be used to determine the mean for any elicitation response by multiplying the MV of the elicitation

response by the mean value for an assumed distributional shape corresponding to the UEF of the response (i.e., ratio of UB to

MV).

7-20
60l ý I I i I I . i . . : I I ý . .. ..

60

0 Upper Tail Lognormal

50 0. ... .... Split Lognormal

--------- Truncated Split @ 99.9

----- V ... Normal Truncated Split @ 99.9

-! -E- Split Log-Triangular

40 - D---- Truncated Split @ 98

30 -

20

10

0 20 40 60 80 100

Upper Error Factor

Figure 7.15 Mean Values for Selected Distributions by UEF < 100

(Median = 1 and LEF = 1000)

(Baseline Distribution is Truncated Split at 99.9%)

The effect of the distribution shape on the mean becomes significant for UEFs greater than 100 (Figure

7.16). Starting from a factor of 2.5 for an UEF of 100, the range increases to a factor of over 30 for an

UEF of 1200. For example, for an UEF of 1,000, the difference between the split lognormal and split logtriangular means is a factor of 28. For this same UEF, the mean of the lognormal truncated at the 9 9 .9'h

percentile is a factor of 5 less than the mean of the split lognormal, and is therefore slightly closer to this

distribution than the split log-triangular. The choice of the truncation point is also significant for large

UEFs. For example, the difference in the means between truncating at the 98t" or 9 9 .9 th percentile when

the UEF is 1000 is approximately a factor of 5.5. However, the difference when truncating at the 95'h or

the 9 9 9 9th percentile (not shown) is a factor of 30. The difference between truncating at the 9 5th

percentile and the 9 9 .9 th percentile (baseline methodology) is a factor of 15 for this IJEF.

Based on the sensitivity analysis, the baseline analysis uses a split lognormal truncated at the 9 9 .9'h

percentile to calculate the means. This distributional shape was chosen as one that reflects the panelists'

responses and also results in reasonably conservative values for the means. Because the mean can be a

sensitive function of the tail of a distribution, the tail of the chosen distribution should not unduly

influence the mean. Consequently, non-truncated split lognormal distributions should not be used

because their means are overly dependent on the area beyond the 95h percentile for large error factors.

However, the truncation point should be well beyond the 95"' percentile, because truncating the

distributions just past the 95 h percentile effectively treats the UB estimates as absolute bounds. This

would be counter to the instructions given to the panelists, which asked them to effectively provide 9 5th

7-21
percentiles rather than absolute UBs (Section 3.8.5). Truncating at the 9 9 .9"' percentile only affects the

top 0.1% percent of the distribution. Because this truncation point is well beyond the information

provided by the panelists, the split lognormal distribution truncated at the 99.91h percentile provides a

reasonable UB for the mean.

Based on the sensitivity analysis, either the split log-triangular or the split lognormal truncated at the 98t'

percentile provides a reasonable LB for the mean. The difference in the means between the baseline

distribution and either of these two distribution shapes is only a factor of 5.5 when the UEF is 1000. For

UEFs below 120, the difference is less than a factor of 2. Therefore, for the bulk of the elicitation

responses, the mean is not very sensitive to the selection of an alternative distribution between these

bounds. Only when the UEF is large do the differences become significant. However, the baseline

distribution mean remains reasonably conservative for large error factors, which is a key reason for its

selection. For simplicity and consistency, the means for all elicitation responses were calculated using

this distributional shape.

J&~

103

0000

6 0o0

00

0 0 0 0 V • V v vvvv v V v V

0 V

0V

0V V

IF

Cu(1,

0

V

V V

102

0 n 0 0 0 n 0

101

100

* Upper Tail Lognormal

0 Split Lognormal

v Truncated Split @ 99.9

v Normal Truncated Split @ 99.9

E Split Log-Triangular

ol Truncated Split @ 98

0 200 400 600 800 1000 1200

Upper Error Factor

Figure 7.16 Mean Values for Selected Distributions by UEF < 1200

(Median = 1 and LEF = 1000)

(Baseline Distribution is Truncated Split at 99.9%)

7.6.2 Overconfidence Adjustment

Sensitivity analyses were conducted to investigate the effect of assuming that the panelists provided

uncertainty intervals with less coverage than the nominal 90% (i.e., the difference between the 5 h and 9 5 th

percentiles) requested in the elicitation responses. As noted in Section 5.6.2, nominal 90% coverage

intervals provided in many applications have been typically shown to have actual coverage between 30%

and 70% [7.2]. In order to correct for the typical overconfidence bias, two different types of adjustment

schemes were evaluated. The first type adjusted the coverage of the intervals supplied for each individual

7-22
elicitation question. This is called a coverage interval adjustment. The second type adjusted the error

factors of the total LOCA frequency estimates calculated for each panelist. This is called an error-factor

adjustment. Section 5.6.2 has more details on the approach of each type of adjustment. A summary of

the approaches and the results are presented below.

7.6.2. 1 Coverage Interval Adjustments - These overconfidence adjustments modify the coverage

interval ranges associated with each panelist's elicitation responses. Each elicitation question required a

MV, an UB, and a LB response. Consistent with the instructions provided to the panel, the baseline

analysis assumed that the UB and LB responses correspond to the 95h and 5 1h percentiles, respectively, of

a panelist's uncertainty distribution. As described in Section 5.6.2.1, the coverage interval adjustments

associated the UB and LB responses with adjusted percentiles resulting in coverage intervals having less

than the nominal 90% coverage. The MV responses were not adjusted.

This sensitivity analysis examined five different coverage interval adjustment schemes: two blanket

adjustments and three targeted adjustments. The blanket adjustments adjusted all panelists by the same

amount. The targeted adjustments used a two-level adjustment. Those panelists expressing larger

uncertainty were adjusted by a lesser amount, while those panelists with smaller uncertainty were

adjusted by a greater amount. The justification for considering a two-level adjustment is that the

elicitation training and instructions provided to the panelists may have sensitized them to the expectation

that uncertainties are typically underestimated in elicitations. Consequently, it is possible that those

panelists expressing larger uncertainty adjusted their responses accordingly to guard against

underestimation.

The various overconfidence adjustment schemes are summarized in Table 7.4. The Blanket I (B1)

adjustment is the most severe adjustment scheme evaluated because the nominal 90% coverage intervals

are reduced to 50% coverage. The Blanket 2 (B2) adjustment provides a smaller correction by reducing

the coverage to 60%. The targeted adjustment schemes first used an UEF (ratio of UB to median) of 20

for LOCA Category 6 to bin the panelists responses into two groups. The most severe targeted

adjustment (T I) does not adjust the coverage of the panelists having UEFs greater than 20 (more

uncertain panelists) and adjusts the coverage to 50% for the panelists with UEFs less than or equal to 20

(less uncertain panelists). The second targeted adjustment (T2) adjusts the more uncertain panelists'

coverage to 80% while the less uncertain panelists are adjusted to 60% coverage. The third targeted

adjustment (T3) is the least severe. The less uncertain panelists are adjusted from 90% to 60% coverage

while the more uncertain panelists are not adjusted.

Table 7.4 Overconfidence Adjustment Schemes

Overconfidence Coverage Panelists Coverage Panelists

Adjustment Interval 1 Interval 2

Blanket 1 50% All N/A N/A

Blanket 2 60% All N/A N/A

Targeted 1 90% 4 50% remaining

Targeted 2 80% 4 60% remaining

Targeted 3 90% 4 60% remaining

Adjusted and unadjusted individual estimates of the means and 95 percentiles for PWR Category 3

LOCAs are compared using box plots (Figure 7.17) for the Blanket 1 (B I) and Targeted 1 (TI)

adjustments. Several features of the B I and TI adjustments are apparent. Because the distribution shape

is assumed to be a lognormal, these adjustments affect the means more than the 9 5 th percentiles. This is

evident by comparing the interquartile ranges and extremes of the adjusted and unadjusted means with the

9 5 th percentiles. For both adjustments, the difference between the adjusted and unadjusted means is

7-23
greater than the difference between the adjusted and unadjusted 9 5 th percentiles. The interquartile range

of the adjusted mean is also much larger than the adjusted 95h percentile interquartile range even though

the unadjusted mean and 9 5ih percentiles have similar ranges. Furthermore, the adjustments can

significantly skew the distributions. While the unadjusted means are about half an order of magnitude

smaller than the corresponding unadjusted 9 5th percentiles, the TI means are closer to their 9 5 th

percentiles while some B I means are even larger than their 9 5ih percentiles. This increased skewness is

not supported by the elicitation responses. The B 1 and TI adjustments for the other PWR and BWR

LOCA categories exhibit similar trends to those apparent in Figure 7.17.

The blanket (B I and B2) , TI and T2 adjustment schemes are also not supported by either the passivesystem failure data for the smaller LOCA categories or the base case results (Sections 4.2 and 4.3). For

instance, six of the nine individual PWR Category 3 means in Figure 7.17 are greater than 10-3 per

calendar year and three of them are greater than 10.2 per calendar year for the B I adjustment. These

frequencies are substantially higher than either present operating-experience-based estimates or the

associated base case frequencies. However, the affected panelists provided no rationale to support these

high estimates. Other similar unsupportable results are apparent for LOCA Categories I and 2. The B2

and T2 adjustments are less severe, but even a modest adjustment of the coverage interval for those

panelists with high uncertainty leads to results that are not supported by either operating experience or the

appropriate base case frequencies.

25 Year Estimates

PWR: Category 3 LOCAs

th

95h T1

95 B1 t

9 5 1h Unadjusted - T

Mean: Ti l

Mean: B1 - . 1, 

Mean: Unadjusted

10-7 10-6 10-5 10-4 10-3 10-2 10-1 100 101

Frequency (CY-1)

Figure 7.17 Blanket I and Targeted 1 Coverage Interval Adjustments

For the Mean and 9 5 th Percentile

The results of the T3 coverage interval adjustment of the individual estimates are provided in Figure 7. 8

for PWR Category 3 LOCAs. The T3 adjustment results in only modest increases in the unadjusted

frequencies for the box plot median of the individual mean and 9 5 th percentile estimates. Also, the

7-24
interquartile ranges for the means and 9 5th percentiles do not significantly increase after the adjustment

even though many individual estimates are increased. Surprisingly, the minimum and maximum

estimates are unaffected by the adjustment. The reason is that the panelists at the extremes had more

uncertainty and hence their responses were not adjusted. Because the extreme values are unchanged

while many other estimates are increased, the unadjusted mean and 9 5th percentile interquartile regions

are more symmetrically located between the minimum and maximum estimates than are the adjusted

regions. Of course, the estimated medians are unchanged by the adjustment.

25 Year Estimates

PWR: Category 3 LOCAs

95": T3 - T

95 h5 Unadjusted -

Mean: T3 - --

Mean:Unadjusted U n

Median: T3 - tl•- , -, -, - l

Median: Unadjusted - " ,-7&77'7-* *I

10-8 10-7 10-6 10"5 10-4 10-3 10-2

Frequency (CY-')

Figure 7.18 Targeted 3 Coverage Interval Adjustments

For the Mean, Median, and 951h Percentile

As illustrated in Figure 7.18, the least severe coverage adjustment (T3) generally appears to be

supported by operating experience, the various predicted base case frequencies, and the elicitation results.

With this adjustment, only the coverage intervals for the four panelists with less uncertainty (UEF < 20)

are moderately adjusted from 90% to 60% (Table 7.4). Some adjustment of these responses is justified

because their uncertainties are lower than the remainder of the group. While the proper adjustment

magnitude cannot be rigorously determined for this scheme, the 60% coverage adjustment is within the

wide range that is supported by research [7.2]. The rationale for not adjusting the remaining panelists is

that their uncertainties are already large, and may not be underestimated.

The total BWR and PWR LOCA frequencies resulting from the T3 adjustment are plotted along with the

associated confidence intervals in Figures 7.19 and 7.20 as a function of threshold break diameter,

respectively. Table 7.5 compares the adjusted and unadjusted (baseline) results using ratios of the

adjusted to the unadjusted means and 95h percentiles. Because they are not adjusted, the median

frequencies do not change. In summary, the T3 overconfidence adjustment results in relatively small

increases of about a half order of magnitude or less for both the BWR and PWR estimates. In fact, except

for the PWR mean for LOCA Category 6, the BWR and PWR means and 9 5th percentiles are only

7-25
increased by factors of between 2 and 4 times the unadjusted results. The mean value is generally

increased as much or more than the 9 5 th percentile estimate due to the skewness of the underlying

distributions. Except for LOCA Category 1, the PWR adjustment ratios are larger than the BWR ratios.

This trend occurs because more PWR than BWR panelists are adjusted (five vs. four) and because the

unadjusted PWR UEFs are slightly higher than the unadjusted BWR UEFs,

BWR: T3 Adjusted

0

0

a)

Cr

LL

0

0

-J

10-1

10.2

10-3

10-5

10.6

10.7

1 0.8

10-9

1

0.1 1 10 100

Threshold Break Diameter (in)

Figure 7.19 BWR LOCA Frequencies with T3 Adjustment

7-26
PWR: T3 Adjusted

10-1

10.2

10-3

C-)

10-

104

g) 10-6

I1o

0 10-8

_J

10-9

10-

0.1 1 10 100

Threshold Break Diameter (in)

Figure 7.20 PWR LOCA Frequencies with T3 Adjustment

Table 7.5 Targeted (T3) Overconfidence Adjustment Compared to Unadjusted Results (Baseline)

BWR: Current-Day PWR: Current-Day 9 5 th 95th

LOCA Mean Percentile LOCA Mean Percentile

Category Ratio Ratio Category Ratio Ratio

1 1.9 2.1 1 2.1 2.1

2 2.0 2.1 2 3.3 2.9

3 2.2 2.2 3 3.8 3.0

4 2.9 2.5 4 3.8 3.0

5 2.8 2.4 5 3.8 3.0

6 2.3 2.3 6 5.3 3.3

Past studies have found that overconfidence is a typical phenomenon in elicitation exercises, which

suggests that the uncertainty of the panelist responses should be adjusted at least to some degree.

However, the overconfidence adjustment schemes evaluated in this section have a variety of deficiencies

that limit their practical application. As noted in this section only the T3 adjustment scheme appears to be

supported by operating experience, the corresponding base case frequencies, and the elicitation responses.

The blanket (B I and B2) and other targeted (TI and T2) adjustment schemes are not consistent with this

other information. Furthermore, none of the adjustment schemes uses a formal or rigorous method to

determine the appropriate adjustment level, and the choice of panelist responses to adjust in the targeted

adjustment schemes is also somewhat arbitrary.

Another deficiency of the adjustment schemes considered is that the adjustment level for each panelist is

the same regardless of the LOCA category and the magnitude of his uncertainty relative to the other

panelists. For instance, uncertainty is expected to increase with LOCA size because greater extrapolation

7-27
from the passive-system failure data is required. Therefore, it may be appropriate to have a larger

adjustment for larger LOCAs. Also, it may be appropriate to adjust a panelist's responses to a greater

extent if he is very overconfident. This situation can be inferred from the panelist's results if his

uncertainty is much less than the other panelists.

7.6.2.2 Error-Factor Adjustment -The error-factor adjustment scheme was developed to address

several of the deficiencies associated with the coverage interval adjustment schemes (Section 7.6.2.1). As

described in Section 5.6.2.2, this scheme adjusts the one-sided error factors associated with each

panelist's 5rh and 9 5 th LOCA frequency estimates instead of the coverage intervals associated with the

individual elicitation responses. Using the unadjusted median and the adjusted error factors, an adjusted

mean and 5 th and 9 5 th percentiles are calculated for each panelist.

To implement the error-factor adjustment scheme, the GM of all the individual unadjusted error factors is

first determined as a group estimate of the error factor for each plant type, LOCA category, and operating

time period evaluated. Next, error factors less than the GM for a given estimate are adjusted up to the

GM. Error factors above the GM are not adjusted. This scheme leads to a variable adjustment as a

function of plant type, LOCA category, and operating time. Furthermore, the magnitude of the

adjustment of the error factors less than the GM increases with the difference between the unadjusted

error factor and the GM.

The GMs of the unadjusted error factors for the current-day estimates are tabulated in Table 7.6. As

before, the UEF is the ratio of the 9 5 th percentile to the median while the LEF is the ratio of the median to

the 5"h percentile. The LEF GM tends to be larger than the UEF GM, except for the larger category

LOCAs, i.e., BWR LOCA Category 6 and PWR LOCA Categories 5 and 6. This reflects the panel's

opinion that there is more uncertainty associated with how low these frequencies may be compared to

how large they may be. The LEF GMs are similar for the BWR and PWR plant types. However, the

PWR UEF GMs are somewhat larger than the BWR UEF GMs, reflecting more uncertainty about the

effects of PWSCC (Section 6.3.2).

Table 7.6 Geometric Means of the Panelists' Error Factors

Current-day Estimates

LOCA BWR Plants PWR Plants

Category

LEF UEF LEF UEF

I 7 6 4 5

2 12 7 13 11

3 12 9 12 13

4 17 10 15 13

5 23 14. 20 26

6 27 29 25 33

The median, mean, 5 th, and 9 5 "h percentile BWR and PWR LOCA frequency values adjusted using the

error-factor scheme are tabulated in Table 7.7 for both the current-day and end-of-plant-license periods.

The current-day median, mean, and 9 5 th percentile estimates and their associated 97.5% upper confidence

bounds (Section 5.4.2) are tabulated in Table 7.8 and plotted in Figures 7.21 and 7.22, for BWR and PWR

plants, respectively. Table 7.9 tabulates the ratios of the adjusted error-factor mean and 9 5th percentiles

along with their associated 97.5% upper confidence bounds compared to the baseline results. Figure 7.23

illustrates the differences between the unadjusted means and means adjusted using the error-factor

scheme for the current-day PWR estimates. It is important to note in these results, that the medians are

identical to those indicated in Table 7. 1, because they are not adjusted by the error-factor scheme.

7-28
As seen in Table 7.9 and Figure 7.23, the adjusted error-factor and baseline results are not markedly

different for either BWR or PWR plants. The BWR adjustment increases (Table 7.9) both the mean and

95t percentile estimates by less than a factor of 1.5 for LOCA Categories 1 to 5 and approximately a

factor of 2 for LOCA Category 6. Similarly, the PWR adjustment increases the mean and 9 5 th percentile

estimates by no more than a factor of 2 for all LOCA categories. The 95h percentile adjustments are just

slightly larger than the mean estimates. Also, except for the largest LOCAs, the adjustments to upper

confidence bounds are comparable to the corresponding adjustments to the mean and 95h percentiles.

More importantly, the differences between the adjusted and baseline estimates do increase with increasing

LOCA category as the panelists' uncertainties increase.

Table 7.7 Total BWR and PWR LOCA Frequencies

(After Overconfidence Adjustment using Error-Factor Scheme)

Current-Day Estimate End-of-Plant-License Estimate

Eff. (per cal. year) (per cal. year)

LOCA Break (25 years fleet average operation) (40 ,ears fleet average operation)

Plant Size Size

Type (gprn) (inch) 5 Ih Per. Median Mean 9 5 th Per. 5 'h Per. Median Mean 9 5 th Per.

>100 /2 3.3E-05 3.0E-04 6.5E-04 2.3E-03 2.8E-05 2.6E-04 6.2E-04 2.2E-03

>1,500 1 7/8 3.OE-06 5.0E-05 1.3E-04 4.8E-04 2.5E-06 4.5E-05 1.2E-04 4.8E-04

BWR >5,000 31⁄4/4 6.OE-07 9.7E-06 2.9E-05 1.1E-04 5.4E-07 9.8E-06 3.2E-05 1.31-04

>25K 7 8.6E-08 2.2E-06 7.3E-06 2.9E-05 7.8E-08 2.3E-06 9.4E-06 3.7E-05

>100K 18 7.7E-09 2.9E-07 1.5E-06 5.9E-06 6.8E-09 3.1E-07 2. 1 E-06 7.9E-06

>500K 41 6.3E-12 2.9E-10 6.3E-09 1.8E-08 7.5E-12 4.OE-10 L.OE-08 2.8E-08

>100 V2 6.9E-04 3.9E-03 7.3E-03 2.3E-02 4.OE-04 2.6E-03 5.2E-03 1.8E-02

>1,500 I 5/8 7.6E-06 1.4E-04 6.4E-04 2.4E-03 8.3E-06 1.6E-04 7.8E-04 2.9E-03

PWR >5,000 3 2.1E-07 3.4E-06 1.6E-05 6.1E-05 4.8E-07 7.6E-06 3.6E-05 1.4E-04

>25K 7 1.4E-08 3.1E-07 1.6E-06 6.1E-06 2.8E-08 6.6E-07 3.6E-06 1.4E-05

>100K 14 4.1E-10 1.2E-08 2.0E-07 5.8E-07 1.0E-09 2.8E-08 4.8E-07 1.40-06

>500K 31 3.5E-1I 1.2E-09 2.9E-08 8.1E-08 8.7E-11 2.9E-09 7.5E-08 2.1E-07

Table 7.8 Current-Day Total BWR and PWR LOCA Frequencies and Upper Confidence Bounds

(After Overconfidence Adjustment using Error-Factor Scheme)

Eff. Median - Mean - 95" Per. -

LOCA Break Median 97.5% Mean 97.5% 9 5 'h Per. 97.5%

Phat Size Size conf. conf. conf.

Type (gpm) (inch)

>100. '/2 3.0E-04 8.0E-04 6.5E-04 2.1 E-03 2.3E-03 7.9E-03

>1,500 1 7/8 5.0E-05 1.81-04 1.3E-04 5.3E-04 4.8E-04 2.1E-03

BWR >5,000 3 1A 9.7E-06 4.60-05 2.9E-05 1.6E-04 1.1E-04 6.4E-04

>25K 7 2.2E-06 1.5E-05 7.3E-06 6.0E-05 2.9E-05 2.4E-04

>100K 18 2.9E-07 1.2E-06 1.5E-06 6.9E-06 5.9E-06 2.6E-05

>500K 41 2.9E-10 8.01-09 6.3E-09 1.4E-07 1.8E-08 3.9E-07

>100 1⁄2/ 3.9E-03 7. 1E-03 7.3E-03 1.5E-02 2.3E-02 5.2E-02

PWR

7>1,500 1 5/8 1.4E-04 8.9E-04 6.4E-04 3.0E-03 2.4E-03 1.2E-02

>5,000 3 3.4E-06 2.0E-05 1.6E-05 8.9E-05 6. 1 E-05 3.4E-04

>25K 7 3.1E-07 1.7E-06 1.6E-06 1.0E-05 6.1E-06 3.6E-05

>100K 14 1.2E-08 2.4E-07 2.0E-07 2.6E-06 5.8E-07 7.9E-06

>500K 31 1.2E-09 4.3E-08 2.9E-08 6.7E-07 8.1E-08 2.0E-06

7-29
Table 7.9 Ratio of Adjusted Error Factor to Baseline Results

95th

percentile,

LOCA Mean: 97.5 % 97.5%

Category Mean confidence 9 5 th Percentile confidence

1 1.2 1,1 1.3 1.1

2 1.2 1.0 1.3 1.1

3 1.2 1.1 1.3 1.2

4 1.2 1A1 1.3 1.2

5 1.3 1.2 1.5 1.3

6 1.9 2.2 2.2 2.7

1 1.1 1.0 1.3 1.1

2 1.4 1.2 1.6 1.4

PWR 3 1.3 1.3 1.4 1.4

4 1.3 1.5 1.5 1.7

5 1.8 2.3 2.0 2.7

6 1.9 2.4 2.0 2.7

BWR: Error Factor Adjustment

0

0

a,

0

-J

10-1

10-2

10-3

10.4

10.5

10-5

10.6

10-8

10.9

I0-1c

0.1 1 10 100

Threshold Break Diameter (in)

Figure 7.21 BWR LOCA Frequencies with Error-Factor Adjustment

7-30

]
PWR: Error Factor Adjustment

10-1-

10-2

10-3

W- 10-6

CT

LL

10-8

10-9

10-101 -

0.1 1 10 100

Threshold Break Diameter (in)

Figure 7.22 PWR LOCA Frequencies with Error-Factor Adjustment

PWR: Effect of Adjustment

C

a,

M

a)

U'-

0

J_

10-1

10-2

10-3

10-4

10-1

10-6

10-7

10-8

* Baseline: mean

Error Factor: mean

N

10-9

10-1

Results offset slightly for clarity

0.1 1 10 100

Threshold Break Diameter (in)

Figure 7.23 Comparison of Baseline and Error-Factor Adjustment Results

7-31
The results from the error-factor adjustment and the targeted (T3) adjustment (Section 7.6.2. 1) are also

reasonably comparable. The mean and 9 51h percentile estimates from these two adjustment schemes

differ by less than a factor of 5 for all LOCA categories and plant types. The differences are not

obviously correlated with LOCA category. However, the error-factor adjustments are consistently

smaller than the T3 adjustments. The error-factor adjustment is less because it modifies the nominal 90%

coverage intervals of the bottom-line estimates so that they effectively remain greater than 60% (the value

for the T3 adjustment) for the adjusted panelists. It might be possible to determine a coverage interval for

the adjusted panelists which would lead to a more accurate comparison of the differences between the T3

and error-factor schemes. However, a true comparison requires that the effective coverage intervals for

individual elicitation responses be determined, and this is not possible using the error-factor adjustment

scheme.

The error-factor adjustment scheme is preferable to the coverage interval adjustment schemes evaluated

in Section 7.6.2.1 because the error-factor adjustment varies as a function of the difference between the

individual and group (GM) estimates for each parameter. Furthermore, the error-factor scheme requires

no arbitrary determination of the panelists to adjust and the adjustment level, as is the case for the

coverage interval schemes. Finally, the error-factor adjustment increases with LOCA size, as does group

uncertainty. This reflects the expectation that panelist overconfidence will increase with increasing

LOCA size as the extrapolation from the passive-system failure data increases. For these reasons, the

error-factor scheme is used to adjust for overconfidence to calculate the summary estimates for this study.

7.6.3 Correlation Structure

The percentiles of the total BWR and PWR LOCA frequency distributions are a function of the

correlation structure of the distributions associated with the various LOCA-sensitive piping systems and

non-piping subcomponents and which are summed to obtain the total frequency distributions (Sections

5.3.4 and 5.6.3). While it is plausible to assume that the components of the sums are positively

correlated, the correlation structure is otherwise unknown. However, the correlation structure can be

bounded by assuming that the piping systems and non-piping subcomponents are either independent (zero

correlation) or have perfect rank correlation (maximum achievable correlation). The baseline analysis

(Section 5.5) assumes perfect rank correlation to calculate percentiles during any step which requires the

summation of distributions. This sensitivity analysis examines the effect of assuming independence

when summing distributions.

Monte Carlo simulation was used to evaluate the differences between the perfect rank correlation (PRC)

and independent correlation (IC) structures. Because of the large number of distributions that represent

the individual elicitation responses, Monte Carlo trials were used to calculate the bottom line estimates

only for selected panelist responses. Ten simulation trials were selected to span several important

variables as listed in Table 7.10. A number of criteria were considered in selecting the trials. First, a

representative sample of the panelists and the range of LOCA size, location, plant type, and time period

combinations 4 were chosen. Second, the number of distributions representing contributing piping

components (or non-piping subcomponents) which are summed to develop the bottom-line estimates was

varied. Third and most important, the Monte Carlo trials were selected to span the range of distinguishing

characteristics of the distributions representing the elicitation responses. These distinguishing

characteristics include the general shape [symmetric (S in Table 7.10) or asymmetric (U in Table 7.10)]

and the relative magnitudes (small, moderate, large) of the larger of the LEF or UEF in Table 7.10). The

range of distinguishing characteristics reflects the variability of the elicitation responses.

For example, BWR- I Piping @ 25 years refers to a Category I LOCA in a BWR piping system after 25 years of operation.

7-32
Table 7.10 Summary of Monte Carlo Trials

Trial Number of Panelist LOCA Size, Location. Plant Distinguishing

Number Summed Type and Time Period Characteristics

Distributions

1 12 A BWR-I Piping @ 25 yrs S, small LEF/UEF

2 12 A BWR-2 Piping @ 25 yrs U, moderate LEF

3 2 C PWR-6 Piping @ 25 yrs U, large UEF

4 4 C BWR-3 Piping @ 25 yrs S, moderate LEF/UEF

5 14 G PWR-5 Non-Piping @ 60 yrs S, large LEF/UEF

6 5 C BWR-3 Non-Piping @ 25 yrs S, large LEF/UEF

7 8 J PWR-5 Non-Piping @ 25 yrs U, large UEF

8 7 I PWR-4 Piping @ 25 yrs S, moderate LEF/UEF

9 4 E BWR-4 Non-Piping @ 25 yrs U, large LEF

10 9 B PWR-3 Non-Piping @ 25 yrs U, moderate UEF

Each of the individual piping component (or non-piping subcomponent) distributions is the product of

two independent elicitation response distributions: (1) the anchoring or base case frequencies (Blocks 1.2

or 1.9 in Figure 5.1) and (2) the corresponding adjustment ratios (Blocks 1.3 or 1.10 in Figure 5.1). These

distributions were simulated in the Monte Carlo trials by split lognormal distributions truncated at the

99.9t, percentile (Section 5.3.3). The products of the distributions were determined by generating 10,000

independent pairs of the anchoring frequency and adjustment ratio distributions, multiplying the paired

values and sorting them into ascending order. The distribution of the sum, S, of any number, N (column 2

in Table 7.10), of such products was then determined using a PRC structure in the following manner. The

rank I values of the N product distributions were summed to obtain the rank I value of S, the rank 2

values of the N product distributions were summed to obtain the rank 2 value of S, and so on. The

resulting 10,000 ordered values of S determine the distribution of S under the assumption of a perfect

rank correlation structure. The required percentiles (median, 5Ih and 9 5 th) were then determined directly

from the distribution for S.

The results obtained by this Monte Carlo procedure are somewhat different than those obtained by the

baseline calculation methodology described in Section 5.3.5 because the baseline calculation

methodology uses an approximation to the assumed truncated split lognormal distributions to calculate

the percentiles. Figure 7.24 compares the 5th, 50"', and 95'h percentiles calculated using the baseline

calculation methodology with the Monte Carlo simulations using the assumed PRC structure. This figure

plots the ratios of the Monte Carlo results to the baseline results for each Monte Carlo trial in Table 7.10.

The 5th and 9 5 t' baseline percentiles differ by less than 10% from the Monte Carlo percentiles for all the

trials. The medians also differ by less than 10%, except for trials 2 and 9 where the baseline medians are

approximately 30% and 20% higher, respectively, than the Monte Carlo medians.

It is somewhat surprising that the two largest differences are for the medians, because the differences

between the medians in the other eight trials are generally smaller than the differences for the 5Ih and 95"h

percentiles. However, it is interesting to note that the distributions in trials 2 and 9 are grossly

asymmetric and the LEF is at least a factor of 3 larger than the UEF. As expected, the means determined

by the Monte Carlo and baseline methods are approximately equal (not illustrated in Figure 7T24) since

they assume identical distributions for calculating the mean.

The conclusion is that the differences between the baseline calculation methodology and the Monte Carlo

procedure are generally insignificant for a PRC structure. The largest differences are expected for the

median estimates for distributions which are asymmetric and have LEFs at least 3 times larger than the

UEFs. In these cases, the approximate median estimates determined by the baseline method will be

marginally conservative, up to 30%. However, there are relatively few elicitation responses having these

7-33
characteristics. Therefore, any differences in the individual bottom line estimates will be further reduced

upon aggregating the individual responses to determine the group estimates.

As previously discussed, Monte Carlo simulation was used to evaluate the differences between the PRC

and IC structures using the previously selected distributions (Table 7.10). This approach was necessary

because no closed-form formulas exist for summing distributions using an IC structure so that individual

estimates (Block 1.18 in Figure 5.1) calculated using an IC and PRC (i.e., baseline method) structure

could be directly compared.

Therefore, summations of the distributions in Table 7.10 were determined using an IC structure as

follows. First, as described previously for the PRC simulation, the products of the independent anchoring

frequency and adjustment ratio distributions for the selected piping components (or non-piping

subcomponents) were determined using Monte Carlo simulation. Next, the product distributions for each

term in the sum, S, were independently sampled and then added for the number of required distributions,

N, to determine the total piping (or non piping) distribution. This calculation was then repeated to obtain

10,000 samples for S. These 10,000 samples were then ordered to determine the IC cumulative

distribution function of S for comparison with the PRC cumulative distribution function of S.

1.5

1.4

a,

0,

0

C-)

0

1.3

1.2

1.1

1.0

O 5th Percentile

v Median

o 95th Percentile

00

0 0 0

0

V

V

0.9

0.8

0.7

0.6

0.5

1 2 3 4 5 6 7 8 9 10

Monte Carlo Trial Number

Figure 7.24 Ratio of Monte Carlo to Baseline Results Assuming

a Perfect Rank Correlation Structure

Figure 7.25 plots the ratios of the median, 5th and 9 5"h percentile estimates determined by assuming a PRC

structure to those calculated assuming an IC structure as a function of the trial number in Table 7. 10. The

mean is not illustrated because it is unaffected by the correlation structure. For these 10 trials, the median

and 5th percentile estimates based on the independent correlation structure are consistently larger than

those based on the PRC structure. However, the PRC estimates of the 95 h percentile are usually larger

than the independent estimates with the exception of trials 3 and 7. Therefore, based on these results, the

7-34
PRC structure generally results in increased differences between the 5th and 9 5 1h percentiles compared

with the IC structure. This is not surprising because the PRC structure leads to maximum correlation

(Section 5.3.4.1 ) between the terms in the sum and this broadens the distribution of the sum, S.

It is also apparent from Figure 7.25 that differences resulting from the correlation structure decrease as

the percentiles of S, increase. Differences in the 5th percentile can be nearly two orders of magnitude.

The largest differences in the 5 'h percentile are greater than one order of magnitude and occur for trials 5,

7, and 9. The distributions in these trials all have relatively large LEFs. Differences in the medians are

less than a factor of two for all the Monte Carlo trials except 3, 5, and 7, which have distributions with the

largest error factors. The largest difference in the median estimates is more than an order of magnitude

and occurs for trial 7, which is determined by summing several (8) distributions with large UEFs of z

300. Differences in the 95 h percentiles are generally less than a factor of two. The largest differences

occur for trials 3 and 7, which consist of asymmetric distributions characterized by large error factors (up

to 1,000).

10

0 5th Percentile

- Median

--U-- 95th Percentile

0

6O

0r

0.1

0.01

1 2 3 4 5 6 7 8 9 10

Monte Carlo Trial Number

Figure 7.25 Ratio of PRC to IC Structure Results

In summary, the correlation structure affects the lower percentiles more significantly than the 9 5th

percentile. The PRC structure leads to significant decreases in the medians and especially the 5 th

percentiles. Differences between the correlation structures increase as both the error factor and the

number of distributions summed increase. This point should be considered when interpreting the baseline

results for the median or 5rh percentile. The 9 5 'h percentiles are not particularly sensitive to the correlation

structure with the PRC structure leading to higher 9 5rh percentile estimates except for extremely

asymmetric distributions with large UEFs (> 100). Of course, neither correlation structure affects the

means of the summed distributions.

7-35
It should be emphasized that the distributions chosen for the Monte Carlo trials spanned the full range of

characteristics of the elicitation responses. Consequently, the largest differences exhibited in Figure 7.25

are not representative of the majority of the elicitation distributions. Therefore, when the individual

results are aggregated to form group estimates, any differences between the lower percentiles due to the

correlation structure will be reduced.

The assumption of a PRC structure for the baseline calculation method is justified by the observation that

the elicitation responses tend to be strongly correlated. Most panelists noted that degradation mechanisms

that significantly contribute to the LOCA frequencies affect many systems similarly. That is, if

conditions lead to higher failure probabilities in one piping system due to a specific degradation

mechanism, the failure probabilities of other susceptible systems will also increase. Furthermore, the

choice of correlation structure has little effect on the estimated means and 95th percentiles of the total

LOCA frequencies (Block 1.18 in Figure 5.1), which are the most important parameters for most

applications.

7.6.4 Aggregation

Several analyses were performed to examine the sensitivity of the baseline results to other possible

aggregation schemes for combining the panelists' individual estimates to form group estimates.

Sensitivity studies evaluated the effect of using different measures of group opinion other than the GM

(Section 5.6.4.1), the effect of aggregating panelist responses at different stages of the analysis (Section

5.6.4.2), and the sensitivity of the results to the method of calculating parameters using the lognormal

parameterization (Section 5.6.4.3). Aggregation by averaging the panelists' distributions to create a

mixture distribution was also evaluated (Section 5.6.4.4). More details on each of these analyses are

available in Section 5.6.4.

7.6.4. 1 Measures of Group Opinion - The objective of the elicitation (Section 2) is to determine a

group estimate of LOCA frequency. The technique used to determine group opinion can have a

significant effect on the group estimates. Several different aggregation schemes were used to determine

group estimates from the individual total LOCA frequency estimates: GM, TGM, median, and AM. The

baseline LOCA frequency estimates use the GM (Section 5.5).

There are no significant differences between the median, GM, and TGM group estimates of the mean

PWR LOCA frequency estimates at 25 years of plant operation (Figure 7.26). In general, the differences

among these three measures are typically less than a factor of 2 for all the bottom line parameters (i.e.,

mean, median, 5 th, and 9 5 th percentiles). There was some initial concern that the very low estimates

provided by Panelist J for the PWR Category 5 and 6 LOCA frequencies might significantly affect the

GM estimates. However, from Figure 7.26, this is not the case.

Differences among these measures are also slightly less than a factor of 2 for the BWR LOCA frequency

estimates (Figure 7.27) except for LOCA Category 6, where the median group estimate is approximately

a factor of five higher than the GM estimate. In this particular case, there were two panelist's responses

for the mean and 9 5 th percentile estimates that were approximately 2 orders of magnitude less than the

remaining estimates. These low individual estimates cause the GM group estimates to fall below the

median.

7-36
PWR: Group Estimates of Mean

>1;

C-,

0

-j

10-1

10-2

10-3

10-6

10-8

10-9

C-

>

C

C

CC

1 2 3 4 5 6

LOCA Category

Figure 7.26 Group Estimates of Mean PWR LOCA Frequencies at 25 Years

BWR: Group Estimates of Mean

10-2

10-3

S10-4

10-s

) 10-6

10-7

) -- Geometric Mean

10 - Trimmed Geometric Mean

--- A-- Median

10-9 .

1 2 3 4 5 6

LOCA Category

Figure 7.27 Group Estimates of Mean BWR LOCA Frequencies at 25 Years

7-37
The AM and GM group estimates of the BWR and PWR LOCA mean frequencies at 25 years are plotted

in Figures 7.28 and 7.29, respectively. Because the AM of any data set is always larger than its GM, the

AM LOCA frequency estimates will always be larger than the GM estimates. However, these figures

indicate that the AM mean LOCA frequency estimates are significantly higher than the GM estimates.

The difference between the estimates can be as large as two orders of magnitude for BWRs and one and a

half orders of magnitude for PWRs.

10-1

102 k

10-

>% 10-4

0

C:

0_

L-J

10.8

U--.-

U-.- -

-- ~--- N __ -4ý , __ __ -*'--

N

N

N

\

'S

V

- -0 - Geometric Mean

-- U---- Mean of Mixture Distribution

1i09

1 2 3 4 5 6

LOCA Category

Figure 7.28 Arithmetic-Mean and Geometric-Mean Group Estimates of

Mean BWR LOCA Frequencies at 25 Years

7-38
10-1

-- 4- Geometric Mean

10.2 ,..., -- m---- Mean of Mixture Distribution

>- 10.3 N"

10-3

C)

>% 10-4"UK

Cl

S10-6

L_,L 10-6•

O \ .-.

-J

: 10.58"

10.8

1 2 3 4 5 6

LOCA Category

Figure 7.29 Arithmetic-Mean and Geometric-Mean Group Estimates of

Mean PWR LOCA Frequencies at 25 Years

The LOCA frequencies determined using the AM aggregation method at 25 and 40 years of plant

operations are provided in Table 7.1 1. These values are the baseline values determined without using any

overconfidence adjustment. The ratios of the AM to GM aggregated LOCA frequencies for the baseline

(unadjusted) mean and 9 5th percentile estimates are provided in Table 7.12 by dividing the LOCA

frequencies in Table 7.11 by those in Table 7.1. The largest ratios are greater than a factor of 10 and

occur for the BWR Category 3 and 4 and the PWR Category 5 and 6 LOCA estimates. In each of these

instances, there are one or two individual estimates that are significantly higher than the others. The AM

aggregated group estimates and therefore the ratios are dominated by these high values.

Specifically, a single panelist, C, expects the Category 2 to 4 LOCA frequencies for BWR plants to be

essentially constant due to IGSCC concerns in large recirculation system lines and the expectation that

failure of these lines will propagate directly to a larger LOCA (Section 7.5.2). The expected frequency of

these failures is approximately two orders of magnitude higher than other estimates for Category 4

LOCAs. The AM estimates largely reflect this single panelist's expectation for the Category 3 and 4

LOCAs. The same panelist also has large uncertainty about the Category 5 and 6 PWR LOCA estimates.

While this panelist's median values are consistent with the remainder of the group, the large uncertainty

leads to means that are significantly higher than most other panelists' means. Also, Panelist A's Category

5 and 6 LOCA median estimates are significantly higher than the remaining panelists' medians (Section

7.5.2), which leads to comparatively high mean estimates. These two panelists' estimates cause the

increases in the PWR Category 5 and 6 AM estimates relative to the GM estimates.

7-39
Table 7.11 Total BWR and PWR LOCA Frequencies

(Using Arithmetic-Mean Aggregation)

Current-Day Estimate End-of-Plant-License Estimate

Eff. (per cal. year) (per cal. year)

LOCA Break (25 years fleet average operation) (40 years fleet average operation)

Plant Size Size

Type (gpm) (inch) 51h Per. Median Mean 95'h Per. 5'h Per. Median Mean 95'h Per.

>100 1⁄42 5.8E-05 5.1E-04 1.4E-03 5.OE-03 4.7E-05 4.3E-04 1.3E-03 4.9E-03

>1,500 1 7/8 7.3E-06 1.3E-04 4.1E-04 1.6E-03 5.4E-06 8.6E-05 2.9E-04 1.1E-03

BWR >5,000 3 1/ 3.1E-06 7.OE-05 3.3E-04 1.3E-03 1.8E-06 3.6E-05 1.6E-04 6.4E-04

>25K 7 1.6E-06 5.2E-05 2.6E-04 1.OE-03 8.9E-07 2.7E-05 l.4E-04 5.4E-04

>100K 18 3.5E-08 7.6E-07 5.2E-06 1.9E-05 4.4E-08 9.4E-07 7.1E-06 2.5E-05

>500K 41 4.5E-10 5.OE-09 2.8E-08 4.8E-08 3.7E-10 4.7E-09 5.3E-08 7.2E-08

>100 V2 1.IE-03 4.8E-03 I.OE-02 3.3E-02 7.5E-04 4.2E-03 9.3E-03 3.2E-02

>1,500 1 5/8 5.7E-05 7.0E-04 2.8E-03 I.OE-02 4.3E-05 7.6E-04 3.2E-03 *1.2E-02

PWR >5,000 3 1.8E-06 1.9E-05 6.7E-05 2.5E-04. 1.6E-06 2.2E-05 1.3E-04 4.5E-04

>25K 7 1.3E-07 1.3E-06 7.9E-06 2.1E-05 1.9E-07 2.OE-06 2.2E-05 6.1 E-05

>100K 14 1.6E-08 2.6E-07 1.2E-06 1.9E-06 3.11E-08 3.3E-07 2.3E-06 3.11E-06

>500K 31 6.2E-09 1.51E-07 4.OE-07 7.8E-07 6.3E-09 1.5E-07 6.9E-07 9.9E-07

Table 7.12 Ratio of AM to GM (Baseline) Group Estimates

BWR: Current-Day PWR: Current-Day 95t1 95th

LOCA Mean Percentile LOCA Mean Percentile

Category Ratio Ratio Category Ratio Ratio

1 2.5 2.9 1 1.5 1.8

2 3.8 4.2 2 5.9 6.9

3 14 16 3 5.2 5.7

4 41 47 4 6.3 5.1

5 4.5 4.9 5 11 6.5

6 8.6 5.6 6 26 20

Table 7.13 provides the 25 and 40-year LOCA frequency estimates determined using AM aggregation for

individual estimates that have been adjusted using the error-factor scheme (Section 7.6.2.2). These results

can be directly compared with those in Table 7.7 to evaluate the differences between AM and GM

aggregation for the adjusted individual estimates. The ratios between the AM and GM aggregated results

exhibit trends similar to those in Table 7.12, although they are slightly less because the error-factor

adjustment decreases the diversity of the individual estimates. Greater diversity generally increases the

difference between AM and GM aggregation.

7-40
Table 7.13 Total BWR and PWR LOCA Frequencies

(Using Arithmetic-Mean Aggregation with Error-Factor Overconfidence Adjustment)

Current-Day Estimate End-of-Plant-License Estimate

Eff. (per cal. year) (per cal. year)

LOCA Break (25 years fleet average operation) (40 years fleet average operation)

Plant Size Size

Type (gpm) (inch) 5 h Per. Median Mean 9 5 1h Per. 5 1h Per. Median Mean 9 5"h Per.

>100 V2 5.OE-05 5.1E-04 1.4E-03 5.4E-03 3.8E-05 4.3E-04 1.3E-03 5.2E-03

>1,500 1 7/8 6.4E-06 1.3E-04 4.1 E-04 1.6E-03 4.6E-06 8.6E-05 2.9E-04 1.2E-03

r>5,000 3 1A 3.OE-06 7.OE-05 3.4E-04 1.3E-03 1.6E-06 3.6E-05 1.6E-04 6.5E-04

>25K 7 1.6E-06 5.2E-05 2.6E-04 L.OE-03 8.3E-07 2.7E-05 1.4E-04 5.5E-04

>100K 18 2.5E-08 7.6E-07 5.5E-06 2.1E-05 3.0E-08 9.4E-07 7.5E-06 2.7E-05

>500K 41 1.7E-10 5.OE-09 5.6E-08 1.6E-07 1.513-10 4.7E-09 8.4E-08 1.9E-07

>100 '/2 8.1E-04 4.81E-03 1.013-02 3.6E-02 5.2E-04 4.2E-03 9.2E-03 3.3E-02

>1,500 1 5/8 4.2E-05 7.OE-04 3.OE-03 1.2E-02 3.9E-05 7.6E-04 3.5E-03 1.3E-02

PWR >5,000 3 1.3E-06 t.9E-05 7.3E-05 2.9E-04 1.4E-06 2.2E-05 t.4E-04 5.I E-04

>25K 7 6.9E-08 1.3E-06 9.4E-06 3.OE-05 1.0E-07 2.OE-06 2.11E-05 6.8E-05

>100K 14 9.913-09 2.6E-07 2.4E-06 7.2E-06 1.4E-08 3.3E-07 4.0E-06 1.0E-05

>500K 31 5.9E-09 1.5E-07 1.513-06 5.2E-06 6.1E-09 1.5E-07 1.9E-06 5.5E-06

7.6.4.2 Aggregation Point - There are three possible aggregation points in the elicitation structure

(Section 5.6.4.2). One is after the total LOCA frequencies have been calculated for each panelist (Block

1.18 in Figure 5.1). Another is after each panelist's piping and non-piping LOCA frequencies have been

calculated (Blocks 1.7 and 1.17, respectively, in Figure 5.1). The last is after each panelist's piping

system and non-piping sub-component frequencies have been calculated (Blocks 1.4 and 1.11,

respectively, in Figure 5.1) and is the earliest possible aggregation point in the elicitation structure.

However, this last alternative is problematic because the panelists' elicitation responses did not all have

the same basic structure and the same level of completeness. For instance, each panelist may have used a

slightly different definition of the piping system or non-piping sub-component boundaries. These basic

differences make it difficult to ensure consistent LOCA frequency estimates among the panelists'

responses at this elicitation step. Also, as discussed previously (Section 3), the elicitation questions only

required each panelist to assess the dominant LOCA contributors. Additional assumptions would be

required to impute missing or incomplete responses for each panelist's less-important LOCA contributing

piping systems or non-piping sub-components in order to implement the aggregation. Because of these

issues, aggregation at this earliest point was not performed in this study.

The current elicitation responses are suitable for aggregation at the other two points. The baseline

aggregation point (Section 5.5) is after the total LOCA frequencies have been calculated for each panelist.

As previously discussed (Section 5.5), the group estimate for the baseline analysis is calculated using the

GM of the individual estimates. This aggregation scheme is referred to as individual aggregation within

the rest of this section. The aggregation of the piping and non-piping contributions is accomplished by

first calculating the group estimates separately for the piping and non-piping LOCA frequency parameters

using the GM. As with the baseline approach, the group estimates of the parameters are assumed to be

lognormally distributed and the piping and non-piping group estimates are then summed using the

procedure described in Section 5.3.5. This aggregation scheme is referred to as component aggregation.

The differences resulting from the chosen aggregation point are plotted in Figure 7.30 for the PWR

current-day median, mean and 9 5 h percentile LOCA frequency estimates for both individual and

component aggregation. The differences are small, usually less than a factor of two for all the bottomline parameters, although the differences do increase as the LOCA size increases. The differences in the

BWR results are similar, but they are not a function of the LOCA size. For both plant types, the baseline

(individual) aggregation results lead to slightly higher frequencies. Therefore, while the results are not

7-41
particularly sensitive to the aggregation point, the baseline methodology results in slightly more

conservative estimates.

Individual aggregation is also the better choice based on the elicitation structure. This aggregation

scheme is not affected by panelist differences in the piping and non-piping boundary definitions which

could affect whether contributions (e.g., CRDM failures) were counted as piping or non-piping by a

panelist. Individual aggregation also ensures that the relative piping and non-piping contributions and

their associated uncertainties reflect the correlations expressed by the panelists. For example, a panelist

might expect that piping is the dominant contributor to the total LOCA frequency estimates, but could be

much less confident (large uncertainty) about the non-piping contribution. For this panelist, the total

LOCA frequency estimates would be dominated by the piping contribution and its associated uncertainty.

Thus, the individual aggregation results are dominated by those contributions judged to be most important

by this panelist.

In contrast, with component aggregation, the more uncertain, but less important non-piping contribution

for this panelist is explicitly considered in the non-piping aggregation. Because these contributions have

a direct effect on the combined results, their actual values become important with component aggregation,

while individual aggregation more accurately reflects the opinion of this panelist that the non-piping

contribution should be negligible. In short, individual aggregation after the total LOCA frequencies have

been calculated (baseline methodology) ensures that the results are more consistent with the panelists'

opinions and that only the most important contributors identified by the panelist dominate the total LOCA

frequency estimates.

7.6.4.3. Aggregation Parameters - This sensitivity analysis compares the bottom-line mean estimates

determined from direct aggregation with estimates calculated from the other aggregated percentiles. In

the baseline methodology, as discussed in Section 5.3, the mean estimates are calculated directly from the

elicitation responses for each panelist by summing the means of the piping system (Block 1.4 in Figure

5.1) and non-piping sub-component (Block 1.11 in Figure 5. 1) frequencies to determine the individual

total LOCA frequencies (Block 1.18 in Figure 5.1). The group estimate for the mean is then determined

by aggregation. The baseline methodology also aggregates the other percentiles (i.e., median, 5h , and

9 5 th) directly from the panelists' responses (Section 5.3). An alternative approach is to calculate the

bottom-line mean estimates only after the aggregated percentile estimates have been determined by

assuming a distributional relationship for the percentile estimates.

This alternative scheme is called the median and error factor (MEF) approach. In this approach, the 5"

and 9 5 th percentile estimates are calculated using the GM to aggregate the median and error factors

instead of directly aggregating the percentiles as in the baseline methodology. The bottom-line mean

estimates are then calculated from the group estimates of the median and the 5" and 9 5 'h percentiles by

assuming that the underlying distribution is a split lognormal which is truncated at the 99.91h percentile.

This is the same distributional form assumed for calculating the mean estimates from the individual

elicitation responses in the baseline methodology (Section 5.3).

7-42
PWR: Individual vs. Group Aggregation

10-2 __ Group:_Median

;:k Group: Median 102 I,•..•0"Group: Mean

.--4-- Group: 95h Percentile

10-3. :U,• Individual: Median

- Individual: Mean

104-- Individual: 95th Percentile

N,

10-

a- 105

o 10-8

10-9

10`8 '

10-11

10-to I

1 2 3 4 5 6

LOCA Category

Figure 7.30 Effect of Aggregation Point on PWR LOCA Frequency Estimates

at 25 Years of Operation

The 5' and 9 5th percentile estimates determined by the MEF approach are identical to the estimates

determined by direct aggregation since all the individual estimates can be calculated either directly from

the median and the 5 h and 9 5th percentiles (baseline methodology) or from the median and the UEFs and

LEFs (MEF approach). In fact, the MEF approach was used to calculate the percentiles in order to

validate the baseline calculations. However, the mean can be determined at several points in the analysis

and differences do result from this choice. Another possible calculation point is after the individual

bottom-line percentile estimates are determined. However, it is postulated that, compared with other

calculation points, the MEF approach will maximize differences in the mean estimates from the baseline

approach because the characteristics of the aggregated distributions are most likely to differ from the

individual elicitation response distributions due to the intermediate processing steps (Section 5). Hence,

it is postulated that the means determined after group aggregation (MEF approach) will result in the

largest differences from the means determined by summing the means from the individual responses

(baseline method).

Representative results from this sensitivity analysis are plotted in Figure 7.31 for the current-day PWR

MEF and baseline means and 9 5th percentiles. As expected, the 95t' percentile estimates (as well as the

medians and 5th percentiles) from both approaches are identical. In general, the mean estimates from both

approaches are also consistent. Differences in the means do increase with increasing LOCA size, but the

maximum differences are less than 35% for the PWR results (Figure 7.31) and less than 40% for the

BWR results (not shown). Therefore, the approach used to determine the aggregated means has little

influence on the mean estimates. However, the baseline approach of directly aggregating the mean

estimates from the individual responses always results in slightly higher frequencies.

7-43
10-1

0 Mean: MEF t

10-2 -. 0 - 95th Percentile: MEF --M-- Mean: baseline

10-3.U 95th Percentile: baseline

10-4

C)

10) "_ 10-s

U- S10e

0

-i 10-7

108

1 2 3 4 5 6

LOCA Category

Figure 7.31 Comparison of Baseline and MEF Aggregation Parameters for

PWR LOCA Frequencies at 25 Years

7.6.4.4 Mixture-Distribution Aggregation - An alternative method of aggregating the individual

bottom-line estimates is to use a mixture distribution (Section 5.6.4.4). The mixture distribution used is

the arithmetic average of the panelists' LOCA frequency distributions for each LOCA category and planttype (i.e., BWR or PWR). This aggregation methodology assumes that the expert panel is a random

sample from the population of all experts (Section 5.6.4.4). The mixture-distribution approach does not

develop group estimates of the four bottom-line parameters as does the baseline methodology, but rather

estimates the full LOCA frequency distribution. More information on the mixture-distribution approach

is provided in Section 5.6.4.4.

LOCA frequency distributions were developed using mixture-distribution aggregation for both the

current-day, unadjusted estimates (baseline) and the current-day estimates adjusted for overconfidence

using the error-factor scheme (Sections 5.6.2.2 and 7.6.2.2). The following procedure was used. For each

panelist, a split lognormal distribution was fit to the three estimated bottom-line percentile estimates

(median, 5th, and 9 5th percentiles) calculated as described in Section 5.3. The distribution below the

median was determined using the LEF and the distribution above the median was determined using the

UEF.

The cumulative distribution function (CDF) of the mixture distribution for each plant type and LOCA

category is simply the average of the corresponding split lognormal CDFs of all panelists who provided

bottom-line estimates. Each mixture CDF was calculated as follows. First the frequency range for each

mixture CDF was determined by setting its lower end equal to the 0. Ith percentile of the panelist CDF

with the smallest 5 th percentile and setting its upper end equal to the 9 9 9 th percentile of the panelist CDF

with the largest 9 5th percentile. Next, this frequency range was divided into 1,000 equal logarithmic

intervals and the individual panelist CDFs were calculated and averaged at each of the 1,000 points in the

7-44
frequency range. This procedure yielded a CDF evaluated at 1,000 points for each plant type and LOCA

category.

The 5", 5 0 th, and 9 5 th percentiles were then determined from each mixture CDF. Because the mean of a

mixture distribution is equal to the average of the means of the contributing panelist distributions (Section

5.6.4.4), the mean of each mixture distribution was set equal to the AM of the panelists' mean estimates

from Section 7.6.4. 1. The means determined in this way are not precisely the same as the means

determined directly from the 1,000-point CDF, but the two means are generally within 10 percent of each

other. Table 7.14 provides the mixture-distribution percentiles and means for the current-day, unadjusted

estimates. The means, medians and 95[ percentiles for the mixture distributions are plotted in Figures

7.32 and 7.33 for BWR and PWR LOCA frequencies, respectively. Although confidence bounds have

not been determined for these parameters, they could be calculated from the mixture CDF using a

bootstrap technique [7.3].

Table 7.14 Current-day Total BWR and PWR LOCA Frequencies

(Using Mixture Distribution for Aggregation)

LOCA Size Eff. Break 5t 95th

Plant Type (gpm) Size (inch) Percentile Median Mean Percentile

>100 1⁄2/2 2.6E-05 2.4E-04 1.4E-03 4.7E-03

>1,500 1 7/8 2.2E-06 3.8E-05 4.1E-04 1.6E-03

>5,000 3 1/ 3.8E-07 6.6E-06 3.3E-04 8.3E-04

>25K 7 4.8E-08 1.3E-06 2.6E-04 6.5E-04

>100K 18 4.3E-09 2.5E-07 5.2E-06 1.3E-05

>500K 41 5.3E-14 7.4E-10 2.8E-08 5.5E-08

>100 1/2 6. IE-04 3.9E-03 1.0E-02 3.3E-02

>1,500 1 5/8 6.8E-07 1.3E-04 2.8E-03 9.1E-03

>5,000 3 5.2E-08 3.9E-06 6.7E-05 2.4E-04

>25K 7 4.OE-09 3.4E-07 7.9E-06 1.4E-05

> 100K 14 l.7E-12 1.7E-08 1.2E-06 2.6E-06

__ >500K 31 3.0E- 14 1.6E-09 4.OE-07 1.7E-06

7-45
BWR: Mixture Distribution Results

102

i0,

>-10-4

C-)

>, 10-5

L)i0

010-6

0

10-9

U_

10-10

0.1 1 10 100

Threshold Break Diameter (in)

Figure 7.32 BWR LOCA Frequencies Determined by Mixture-Distribution Aggregation: Currentday Estimates

PWR: Mixture Distribution Results

10-1

10-2

1 03

C-)

;, 10-4

C )

o: 10-5

(1)

I

v

v

1 0 -9

10-1c

0.1 1 10 100

Threshold Break Diameter (in)

Figure 7.33 PWR LOCA Frequencies Determined by Mixture-Distribution Aggregation: Currentday Estimates

7-46
Table 7.15 compares the mixture-distribution and GM (baseline methodology) aggregation techniques by

providing the ratios of the median, mean, and 9 5

th percentile estimates. The median estimates determined

from the two aggregation techniques are relatively consistent, usually within a factor of 2. Because the

AM and mixture mean are identical, the ratios of the means in'Table 7.15 are identical to those contained

in Table 7.12. The 95th percentile ratios in Table 7.15 are also similar to the 95t" percentile ratios in Table

7.12 and usually there is less than a factor of 2 difference between the two ratios.

Table 7.15 Ratios of Mixture-Distribution to Geometric-Mean Aggregated Results

(not adjusted for overconfidence)

LOCA BWR: Current-Day PWR: Current-Day

Cat. Median Mean 9 5 th Median Mean 9 5 th

1 0.8 2.5 2.7 1.0 1.5 1.8

2 0.8 3.8 4.4 1.0 5.9 6.0

3 0.7 14 10 1.2 5.2 5.4

4 0.6 42 30 1.1 6.3 3.5

5 0.9 4.5 3.3 1.5 11 8.8

6 2.5 8.6 6.4 1.4 26 42

The reasons for the discrepancies between the two aggregation schemes for the mean and 9 5"h percentile

estimates as shown in Table 7.15 is the same as for the discrepancies between the AM and GM

aggregation schemes as shown in Table 7.12. When differences between the mixture-distribution and

GM aggregated parameters are large, it is an indication that one or even two panelists' estimates are

significantly greater than the other estimates for that category. This is particularly true for BWR LOCA

Categories 3 and 4 and PWR LOCA Categories 5 and 6 (Section 7.6.4.3). Smaller differences are

indicative of more uniformity in the panelists' estimates. This also explains why the AM aggregated 95"h

percentile estimates are similar to the mixture-distribution 9 5 th percentiles. In this study, because of the

diversity of the estimates, the largest panelist estimate often dominates the AM and mixture-distribution

aggregation schemes.

The similarity of the parameter estimates aggregated by the AM scheme to the mixture-distribution

parameter estimates decreases as the percentiles decrease. This occurs because the AM aggregation is

most greatly influenced by the highest panelist estimates for each parameter while the mixture

distributions become more influenced by the lowest panelist estimates as the percentiles decrease. In the

extreme, the 5th percentile of the mixture distribution (not shown) can be significantly lower than either

the GM or AM 5th percentile estimate if one or two panelist estimates are significantly lower than the

other panelist estimates.

The mixture-distribution aggregation scheme will always result in higher mean and 9 5"h percentile

estimates and lower 5th percentile estimates than the GM aggregation scheme. Consequently, the mixturedistribution scheme exhibits larger differences between the 5 th and 9 5 th percentiles. However, the mean

and 9 5th percentile estimates are often dominated by the maximum panelist estimate while the 5 th

percentile is often dominated by the minimum panelist estimate. This characteristic implies that the

extreme panelist estimates will often dominate the mean, 5 "h, and 9 5 1h percentile group estimates.

The mixture-distribution percentiles and means for the current-day estimates adjusted using the errorfactor scheme are provided in Table 7.16. The trends observed by comparing mixture-distribution

aggregation to both the GM and AM aggregation schemes (Section 7.6.4.3) for the adjusted estimates are

similar to those discussed above for the unadjusted estimates. Ratios of the mixture-distribution median,

7-47
mean and 9 5"h percentiles to the GM parameters are typically within 30 percent of the values in Table

7.15 for the unadjusted results.

Table 7.16 Current-day Total BWR and PWR LOCA Frequencies

(Using Mixture-Distribution Aggregation with Error-Factor Overconfidence Adjustment)

LOCA Size Eff. Break 5h 95h

Plant Type (gpm) Size (inch) Percentile Median Mean Percentile

>100 v2 2.OE-05 2.7E-04 1.4E-03 5.3E-03

>1,500 1 7/8 1.5E-06 4.1E-05 4.1E-04 1.6E-03

>5,000 3 1⁄44 2.7E-07 7.3E-06 3.4E-04 8.4E-04

>25 K 7 3.2E-08 1.4E-06 2.6E-04 6.5E-04

>100K 18 2.9E-09 2.7E-07 5.5E-06 1.5E-05

>500K 41 5.2E- 14 6.4E- 10 5.6E-08 1.4E-07

>100 1⁄2/2 4.8E-04' 3.9E-03 1.0E-02 3.5E-02

>1,500 1 5/8 6.7E-07 1.5E-04 3.OE-03 1.1E-02

PWR >5,000 3 3.8E-08 4.OE-06 7.3E-05 2.8E-04

>25K 7 3.4E-09 3.1E-07 9.4E-06 2.5E-05

>100K 14 1.7E-12 1.7E-08 2.4E-06 5.7E-06

>500K 31 3.OE- 14 1.7E-09 1.5E-06 2.5E-06

7.6.5 Panel Diversity

An important objective of this elicitation is to measure panel diversity (Section 2). In this study, panel

diversity refers to the range of individual LOCA frequency estimates provided by the panelists. Panel

diversity has been evaluated in this study by two methods. The baseline method calculates two-sided

95% statistical confidence intervals assuming that the individual LOCA frequency estimates of the mean

and 9 5rh percentile are lognormally distributed (Section 5.4.2). Under this assumption, the group estimate

is the GM and a measure of panel diversity is a confidence interval centered on the group estimate

(Section 7.5.3).

This sensitivity analysis uses a second method (quartile method) to measure panelist diversity. The

quartile method does not rely on the assumption of an underlying distributional structure for the

individual estimates. Instead, it uses a quartile interal corresponding to the interquartile range of the

individual estimates. The quartile interval is defined by the lower (2 51h percentile) and upper (7 5th

percentile) quartiles of the individual estimates. With the qualtile method, the median (or 5 0 1h percentile

estimate) is a natural measure of central group opinion. As discussed in Section 7.6.4. 1, the GM and

median estimates of group opinion produce similar results in this study. Thus, a quartile interval is a

measure of group diversity about the median while, analogously, a confidence interval is a measure of

group diversity about the GM.

The current-day, GM aggregated (i.e., baseline method) and median group estimates of the mean and 9 5 th

percentile, along with their corresponding diversity measures, are plotted in Figures 7.34 and 7.35 for

BWRs and PWRs, respectively. The BWR confidence and quartile intervals are quite similar for LOCA

Categories 1, 2, and 5 for both the mean and 9 5th percentile estimates. The confidence intervals tend to be

slightly wider than the quartile intervals, but this reflects the fact that the confidence intervals have 90

percent coverage while the quartile intervals cover only the middle 50 percent of the estimates. However,

the upper confidence bounds are approximately a factor of 5 higher than the upper quartiles for both the

means and 9 5th percentiles for BWR LOCA Categories 3 and 4. As before, these differences reflect the

high estimates by a single panelist for these LOCA categories, which affect the confidence intervals but

do not affect the quartile intervals. For LOCA Category 6, the upper confidence bounds for the mean and

9 5 th percentiles are similar to the upper quartile, but the lower confidence bounds are much lower than the

lower quartiles. These differences reflect the very low estimates by a single panelist.

7-48
Baseline LOCA Frequency Estimates:

Mean

Baseline LOCA Frequency Estimates:

95th Percentile

7

0

u0

U-

-J

10.1

10-2

103

10'

10.5

106

107

108

10-9

101C

confidence

intervals

* Median

* Geometric mean

'w--quartile

.intervals

* Median 1

* Geometric mean

-t--quartile

-intervals

10-

102

10-3

104

10-5

106

107

108

109

10-10

r

Values offset for clarity Values offset for clarity

1 2 3 4 5 6

LOCA Category

1 2 3 4 5 6

LOCA Category

Figure 7.34 Measures of Panel Diversity for BWR LOCA Frequencies at 25 Years

Baseline LOCA Frequency Estimates:

Mean

Baseline LOCA Frequency Estimates:

95th Percentile

7

C

C,

0'

U10

10"

10

10"

10

10

10o

10o

10-

10-

• •• Median Median

* Geometric mean "_. * Geometric mean

quartile intervals te

.6 conidencen

V7 confidence Vu intervals

-8 ~ intervals L"

-9

10

1

10,

102

10,3

105

10-6

10/

108

109

1010

1 2 3 4 5 6

LOCA Category

1 2 3 4 5 6

LOCA Category

Figure 7.35 Measures of Panel Diversity for PWR LOCA Frequencies at 25 Years

The PWR mean and 951h percentile diversity measures (Figure 7.35) exhibit many trends similar to the

BWR trends. Once again, the confidence intervals are typically wider than the quartile intervals. The

differences between the confidence and quartile intervals also tend to increase slightly with LOCA size.

The only exception to these trends is for LOCA Category 2, where the quartile intervals are wider than

the confidence intervals. This occurs because two panelists expect relatively high LOCA frequencies, due

to PWSCC concerns, compared with the rest of the group (Section 6.3.2). These two high estimates skew

the upper quartiles with respect to the medians. The upper quartiles for LOCA Categories 5 and 6 are

similarly skewed with respect to the median by two high estimates, and they are similar to the upper

confidences bounds. However, the quartile intervals are not wider than the confidence intervals in these

cases.

In summary, with only a few exceptions, the confidence intervals are wider than the quartile intervals.

Therefore, the baseline method and use of confidence bounds provides a more conservative measure of

7-49

I
panel diversity than the quartile method. Of course, the quartile intervals could be widened to cover a

larger percentage of the panelists' estimates. However, if their coverage was increased to the 90 percent

coverage of the confidence intervals, their spreads would be strongly influenced by the extreme panelists'

estimates.

7.7 Summary Estimates

The baseline LOCA frequency estimates in Table 7.1 were calculated using the analysis framework

described in Section 5.5. The individual and group summary estimates of the means, medians and 5"h and

95th percentiles of the LOCA frequency distributions were determined using the following summary

analysis framework:

(i) The MV, UB, and LB supplied by the panelists for each elicitation question were assumed

to correspond to the median, 9 5 th percentile and 5 "h percentile, respectively, of a split

lognormal distribution, with the mean calculated assuming that the upper tail is truncated at

the 9 9 .9 th percentile.

(ii) Only those panelists whose uncertainty ranges were relatively small were adjusted using an

error-factor adjustment scheme to account for possible overconfidence (Section 7.6.2.2).

(iii) Split lognormal distributions were summed by assuming perfect rank correlation among the

individual terms.

(iv) The individual estimates are the total LOCA frequency parameters (i.e., mean, median, 5 "h

percentile, and 9 5 th percentile) determined for each panelist.

(v) The group estimates of the total LOCA frequency parameters were determined using the

GMs of the individual estimates.

(vi) Panel diversity was characterized with a two-sided 95% confidence interval based on an

assumed lognormal model for the individual estimates.

Based on the sensitivity analyses, the resultant individual and group estimates are consistent with the

elicitation objectives and structure and are a reasonable reflection of the panelists' quantitative judgments.

In particular, the group estimates are not dominated by extreme results, either on the high or low end.

The only difference between the baseline estimates discussed up to this point and the summary estimates

is that the summary estimates are adjusted for overconfidence using the error-factor adjustment scheme in

(ii) above, while the baseline estimates are determined without any such adjustment. For the reasons

given in Sections 5.6.2 and 7.6.2, the adjusted individual LOCA frequency estimates using (ii) above

result in improved group LOCA frequency estimates.

The summary LOCA frequency estimates for the current-day and end-of-plant-license period are provided

in Table 7.17 for both BWR and PWR plant types. Table 7.17 is identical to Table 7.7 and is reproduced

here for convenience. The current-day median, mean, and 9 5th percentile summary estimates are plotted

in Figures 7.36 and 7.37 for BWRs and PWRs, respectively. Figures 7.36 and 7.37 are identical to

Figures 7.21 and 7.22 and are also reproduced here for convenience. As for the baseline estimates, a

measure of the panelists' uncertainty in the summary estimates is.given by the difference between the

median and 5h or 9 5 th percentile in Table 7.17 and Figures 7.36 and 7.37. Also, the 95% confidence

intervals plotted in these figures provide a measure of the diversity among the panelists.

7-50
Table 7.17 Total BWR and PWR LOCA Frequencies

(After Overconfidence Adjustment using Error-Factor Scheme)

(reproduced from Table 7.7)

Current-Day Estimate End-of-Plant-License Estimate

Eff, (per cal. year) (per cal. year)

LOCA Break (25 years fleet average operation) (40 ears fleet average operation)

Plant Size Size

Type (gpm) (inch) 5 h Per. Median Mean 95 h Per. 5 'h Per. Median Mean 9 5th Per.

>100 '/2 3.3E-05 3.OE-04 6.5E-04 2.3E-03 2.8E-05 2.6E-04 6,2E-04 212E-03

>1500 1 7/8 3.OE-06 5.OE-05 1.3E-04 4.8E-04 2.5E-06 4.5E-05 1.2E-04 4.8E-04

BWR >5,000 3 '/A 6.OE-07 9.7E-06 2.9E-05 1.1 E-04 5.4E-07 9.8E-06 3.2E-05 1,3E-04

>25K 7 8.6E-08 2.2E-06 7.3E-06 2.9E-05 7.8E-08 2.3E-06 9.4E-06 3.7E-05

>100K 18 7.7E-09 2.9E-07 1.5E-06 5.9E-06 6.8E-09 3.tE-07 2.]E-06 7.9E-06

>500K 41 6.3E-12 2.9E-10 6.3E-09 1.8E-08 7.5E-12 4.OE-10 tOE-08 2.8E-08

>100 1⁄22 6.9E-04 3.9E-03 7.3E-03 2.3E-02 4.OE-04 2.6E-03 5.2E-03 1.8E-02

> 1,500 1 5/8 7,6E-06 l.4E-04 6.4E-04 2.4E-03 8.3E-06 1.6E-04 7.8E-04 2.9E-03

PWR >5.000 3 2.1E-07 3.4E-06 1.6E-05 6.1 E-05 4.8E-07 7.6E-06 3.6E-05 1.4E-04

>25K 7 1.4E-08 3.IE-07 1.6E-06 6.1E-06 2,8E-08 6.6E-07 3.6E-06 1.4E-05

>100K 14 4.1E-10 1.2E-08 2IOE-07 5.8E-07 t.OE-09 2.8E-08 4.8E-07 I1AE-06

>500K 31 3.5E-1 I 1.2E-09 2.9E-08 8.1E-08 8.7E- II 2.9E-09 7.5E-08 2.1E-07

BWR: Error Factor Adjustment

10-1

10-2

0

C-,

10.3

104

10-5

10-6

107

10-8

10-9

10

0.1 1 10 100

Threshold Break Diameter (in)

Figure 7.36 BWR LOCA Frequencies with Error-Factor Adjustment

(reproduced from Figure 7.21)

7-51
PWR: Error Factor Adjustment

10-1

0 Median

10-2 v Mean

0 95th Percentile

10-3

,- 10-'

0

>, 10-5 -

=,3 07

u 10.6

U._ 10.8

10-8

10.9

median and 95th percentile results offset for clarity 1 0 -1o . . . . . .. . . . . I . I . . . .

0.1 1 10 100

Threshold Break Diameter (in)

Figure 7.37 PWR LOCA Frequencies with Error-Factor Adjustment

(reproduced from Figure 7.22)

7.8 Steam Generator Tube Rupture and Small Break (Category 1) LOCA

Frequencies

Steam generator tube ruptures (SGTRs) are usually analyzed separately from other passive system

failures in PRAs because they have occurred relatively frequently and the event trees associated with

SGTRs are modeled separately from all other passive system LOCA failures. Therefore, it is useful to

identify the SGTR frequency contributions to the total PWR LOCA frequency estimates provided in

Table 7.17. The mean frequencies associated with the Category I (i.e., flow rate > 100 gpm [380 lpm])

PWR LOCA frequencies of other passive system failures can then be simply determined by subtraction.

However, the other frequency parameters cannot be so determined.

As explained in Section 4, a generic Category I SGTR frequency of 3.5E-03 per calendar year was

determined based on operating experience. This estimate was provided to the panelists for use as a base

case. The panelists then assessed the applicability of this base case for estimating current and future

SGTR frequencies. Category 1 LOCA failures are possible with a single tube rupture. Other higher

category LOCAs require multiple tube ruptures due to either a primary or secondary side pressure

transient or another common cause failure mechanism. The assessment of multiple tube ruptures was

specifically defined as a failure scenario and panelists were asked to assess the relative likelihood of

multiple tube rupture if they believed it to be a significantly contributing factor.

The portions of the total LOCA frequencies (Table 7.17) attributed by the panelists to SGTR are provided

in Table 7.18 for the current-day and end-of-plant-license periods. These SGTR frequencies have been

7-52
aggregated from the individual estimates using the GM and the error-factor overconfidence adjustment

used for the summary estimates discussed in Section 7.7. The number of panelists (# of Est.) who made

SGTR frequency estimates for each LOCA category is also provided in Table 7.18. Only group estimates

for LOCA Categories 1 and 2 are provided because the majority of panelists judged that SGTR

contributions are insignificant for LOCA Categories 3 and higher.

Table 7.18 PWR Steam Generator Tube Rupture Frequencies (Geometric Mean Values After

Overconfidence Adjustment)

LOCA # Eff. Current-Day Estimate (per cal. year) Next 15 Year Estimate (per cal. year)

Size of Break (25 year fleet average operation) (End-of-plant license)

(gpm) Est. Size

(inch) 5 h Median Mean 95th 5 th Median Mean 95th

>100 9 V2 5.0E-04 2.6E-03 3.7E-03 1.0E-2 2.6E-04 1.5E-03 2.IE-03 6.0E-03

>1,500 7 , 1 5/8 3.4E-07 4.8E-06 1.5E-05 5.4E-05 1.5E-07 2.3E-06 6.5E-06 2.3E-05

All the panelists who provided PWR elicitation responses identified SGTR as a major contributor to

Category 1 PWR LOCAs. In fact, the current-day mean and median SGTR frequencies from Table 7.18

are 51 and 67 percent, respectively, of the total Category 1 LOCA frequencies from Table 7.17. Similar

results hold for the end-of-plant-license period. Note that the mean current-day Category I SGTR

frequency of 3.7E-03 per calendar year is nearly identical to the base case and operating-experience

estimated frequency of 3.5E-03 per calendar year (Section 4.4.1). Clearly, the panelists as a group did not

believe that these frequencies needed any significant adjustment. As noted in Section 4.4.1, this

frequency estimate is also consistent with the prior estimate from NUREG/CR-5750. The SGTR 9 5 th

percentile of L.0E-02 per calendar year is also less than the mean steam generator leak frequency (Section

4.4.1) of 1.6E-02 per calendar year. This result is not surprising, because the leak frequency can

reasonably be expected to bound the rupture frequency.

From Table 7.18, seven panelists provided SGTR estimates for LOCA Category 2, and the group

estimates reported are based only on these seven estimates. The fact that two of the panelists - C and H -

did not provide estimates means that they believe that SGTR contributions to LOCA Category 2 are

relatively insignificant. However, ignoring the responses of these two panelists might bias the results in

Table 7.18. One way to evaluate this possible bias is to use bounding estimates. After the formal

elicitation was completed, Panelist C provided an UB estimate on the conditional rupture probability of a

second steam generator tube given that one has already failed. This UB estimate is somewhat

conservative because it is likely that more than two tubes would need to severely rupture for the flow rate

to reach 1,500 gpm (5,700 1pm).

A lower estimate was also obtained from Panelist C's responses. Panelist C only provided estimates for

80 percent of the steam generator failure modes that contribute to Category 2 PWR non-piping LOCAs.

Therefore, it was assumed that the remaining 20 percent of these contributions consist of multiple SGTRs.

This is a reasonable assumption because Panelist C provided direct estimates for the frequency

contributions of all the other steam generator tube components that were evaluated in the elicitation

(Appendix B).

There is approximately one order of magnitude difference between these UBs and LBs. Aggregating

Panelist C's UB estimate with the other seven estimates increases the Category 2 value in Table 7.18 by

less than 40 percent. Aggregating the LB estimate decreases the value in Table 7.18 by only 4 percent.

Hence, Panelist C's influence on the Category 2 SGTR frequencies is minimal.

7-53
Similarly, bounding values were determined from Panelist H's responses. A LB value was determined by

assuming that the SGTR Category 2 frequency is equivalent to the RPV shell failure estimation. This was

the smallest Category 2 PWR non-piping estimate that panelist H provided. The UB estimate was

determined by assuming that the 20 percent of the Category 2 PWR non-piping frequency contributions

that were not directly evaluated by this panelist is all attributed to SGTRs. Panelist H's dominant

contribution is from CRDM failures so the implication of this assumption is that multiple SGTRs are

about 4 times less likely than CRDM failures. After the elicitation, Panelist H indicated that these are

suitable bounding assumptions. Unfortunately, there is a large disparity between these UBs and LBs

(approximately 5 orders of magnitude) and there is no additional information in this panelist's response

that can be used to reduce this discrepancy. However, aggregating panelist H's bounding estimates with

the other seven estimates modifies the values in Table 7.18 by a factor of approximately ±2.

From this sensitivity analysis, it can be seen that excluding these two panelists' results has relatively little

effect on the aggregated estimates. Therefore, the Category 2 estimates in Table 7.18 are considered to be

appropriate group estimates for multiple steam generator tube failures.

The SGTR contributions to the Category 2 LOCA frequencies are less than 3 percent of the mean total

LOCA frequencies from Table 7.17, Thus, these contributions are much less significant than the

contributions of SGTRs to the Category I frequencies. Also, the results in Table 7.18 predict slight

decreases in both the SGTR Category I and 2 frequencies over the next 15 years. These expected

decreases are due to continued steam generator replacement and improved programs to mitigate steam

generator tube degradation.

The PWR LOCA frequencies of passive system failures other than SGTRs can also be estimated using the

following procedure. First the LOCA frequencies without SGTR contributions are calculated for each

panelist. Using the previous assumption that the non-piping subcomponent frequencies have perfect rank

correlation (Section 5.3.4.1), the percentiles (i.e., 5rh, 5 0 1h, and 9 5th) are calculated by subtracting the

SGTR percentiles from the corresponding total LOCA frequency percentiles. Also, the means are

calculated by subtracting the SGTR means from the total LOCA frequency means. Because the mean is

linear, it is not necessary to assume a perfect rank correlation structure for this calculation. The

individual estimates are then adjusted for overconfidence using the error-factor scheme as described in

Sections 5.6.2.2 and 7.6.2.2. Finally, group estimates are calculated using the GM to aggregate the

individual estimates as discussed in Section 5.4.

Note that the non-SGTR contributions cannot be estimated by directly subtracting the group SGTR

estimates (Table 7.18) from the total LOCA frequency estimates (Table 7.17) because the overconfidence

adjustment and aggregation are both non-linear. The procedure described above ensures that the

individual estimates are consistent with the original elicitation responses before overconfidence

adjustment and aggregation are performed. This procedure is also consistent with the procedure used to

calculate the total LOCA and SGTR estimates. This estimate is treated as a LB for this analysis.

This procedure was used to calculate both Category I and Category 2 LOCA frequency estimates without

SGTR contributions. No additional assumptions are required for calculating the Category I estimates

without SGTR contributions since all the panelists provided Category 1 SGTR estimates. As discussed

above, all panelists except C and H also provided Category 2 SGTR estimates. Using a bounding

sensitivity analysis, the Category 2 SGTR contributions for Panelists C and H are relatively insignificant.

Specifically, if the UB Category 2 SGTR estimates discussed above for Panelists C and H are used, then

the differences between the means of their individual Category 2 estimates with and without SGTR

contributions are less than 5 and 20 percent, respectively. Therefore, it can be conservatively assumed

that the total LOCA estimates for Panelists C and H are equivalent to their non-SGTR LOCA estimates.

7-54
The panelists also judged that the SGTR failure contributions to Categories 3 - 6 are insignificant.

Therefore, it was not necessary to adjust the estimates for these larger LOCAs to remove SGTR

contributions. The PWR total LOCA frequencies after excluding SGTR contributions are provided in

Table 7.19. The Category 3 - 6 estimates in Table 7.19 are identical to the PWR results in Table 7.17.

The results in Table 7.19 are appropriate to use for PRA applications that separately consider SGTRs.

- Table 7.19 Total PWR LOCA Frequencies without SGTR Contributions

(LOCA Categories 3 - 6 Reproduced from Table 7.17)

Current-Day Estimate End-of-Plant-License Estimate

Eff. (per cal. year) (per cal. year)

LOCA Break (25 years fleet average operation) (40 years fleet average operation)

Plant Size Size

Type (gpm) (inch) 5 h Per. Median Mean 9 5 th Per. 5 'h Per. Median Mean 95th Per.

>100 '/2 6.8E-05 633E-04 1.9E-03 7.1E-03 7.0E-05 7.2E-04 2.IE-03 7.9E-03

>1,500 1 5/8 5.0E-06 8.9E-05 4.2E-04 1.6E-03 6.1 E-06 1.2E-04 5.813-04 2.2E-03

>5,000 3 2.1 E-07 3.4E-06 1.6E-05 6.1E-05 4.8E-07 7.6E-06 3.6E-05 1.4E-04

PWR >25K 7 1.4E-08 3.11E-07 1.6E-06 6.1 E-06 2.8E-08 6.6E-07 3.61E-06 1.4E-05'

>100K 14 4.1E--10 1.2E-08 2.0E-07 5.8E-07 1.0E-09 2.8E-08 4.8E-07 I.4E-06

>500K 31 3.5E-I I 1.2E-09 2.9E-08 8.1E-08 8.7E-I I 2.9E-09 7.5E-08 2.1E-07

7.9 Comparison with Prior Estimates

In this section, selected results from this study are compared with LOCA frequency estimates developed

in prior studies. As noted in Section 1.2, LOCA frequency estimates were previously developed in

WASH-1400 [7.4], NUREG-1 150 [7.5], and NUREG/CR-5750 [7.6], Furthermore, estimates were

developed for each plant type for the individual plant examinations (IPEs) [7.7] using an early 1990's

piping precursor database as documented in EPRI TR-100380 [7.8]. Prior estimates were also obtained

during the pilot elicitation in this exercise as discussed in Section 3.1 [7.9]. As indicated earlier (Section

I), the LOCA frequency estimates from these earlier studies were typically based, at least in part, on the

analysis of operating experience. This elicitation also provided the relevant operating experience to the

panelists and used it as the basis for many of the base case conditions (Section 3.5).

Prior studies differed in their analysis and interpretation of operating experience. It is important to

understand the differences between these prior studies and this elicitation before making comparisons.

First, these prior estimates defined the range of small break LOCAs as 100 to 1,500 gpm (380 to 5,700

lpm) and the range of medium break LOCAs as 1,500 to 5,000 gpm (5,700 to 19,000 1pm). The current

study defines each LOCA category using flow rate thresholds (Section 3.7). To facilitate comparison

with prior estimates, the summary estimates of the mean LOCA frequencies (Table 7.17) are recalculated

as interval-based estimates. Interval-based estimates for the mean LOCA frequencies were determined by

simply subtracting the estimates for adjacent cumulative LOCA categories. This technique cannot strictly

be used for determining other percentiles of the LOCA frequency distribution. However, because the

differences between the results for adjacent LOCA categories in Table 7.17 are much smaller than the

uncertainties, the differences between the cumulative threshold and interval-based estimates arenot

statistically significant. Therefore, the percentiles from Table 7.17 could be directly compared with prior

estimates if desired.

Another important distinction between the current elicitation and prior studies is that the prior studies

generally used the WASH-1400 and NUREG/CR-5750 pipe break to leak rate correlation which

distinguishes between medium and large break LOCAs at an approximately 6-inch (152 mm) effective

pipe break diameter (Section 3.7). In this study, Category 3 (i.e., flow rate > 5000 gpm [19,000 1pm]) and

Category 4 (i.e., flow rate > 25,000 gpm [95,000 lpm]) LOCAs are correlated with generic minimum

effective break diameters of approximately 3 and 7-inches (76 to 178 mm), respectively. Because the

7-55
break size definitions are not consistent, LB LOCA frequencies from prior studies are compared with both

Category 3 and 4 LOCA estimates from this study.

Table 7.20 compares the summary mean frequency estimates of this study with NUREG/CR-5750

estimates, which is the most recent generic study. In this table, as previously discussed, the elicitation

means at 25 years are calculated from Table 7.17 after converting the cumulative frequency estimates to

interval ranges consistent with the NUREG/CR-5750 LOCA definitions. The NUREG/CR-5750 ratio

entries in this table are the ratios of the NUREG/CR-5750 estimates to the summary elicitation estimates.

Note that the NUREG/CR-5750 SB LOCA PWR estimates include SGTR frequency contributions to

make these estimates comparable with the elicitation estimates, which include these events. Furthermore,

as indicated in Sections 4.4.1 and 7.8, the mean SGTR frequency estimates from this study, NUREG/CR5750, and operating experience are all consistent.

Table 7.20 Comparison of Current Elicitation and Selected Prior Mean Estimates

Plant Summary Elicitation Estimates NUREG/CR-5750 Estimates Pilot Elicitation

Type (60 years)

LOCA Mean Mean LOCA Mean Ratio Mean Ratio

Intervals Freq. Freq. Size Freq. Freq.

(25 yrs) (60 yrs)

1-2 5.2E-04 1. 1 E-03 SB 4.OE-04 0.76 1.5E-03 1.36

2-4 1.2E-04 2.OE-04 MB 3.OE-05 0.25 9.1E-05 0.47

>3 2.9E-05 6.1E-05 LB 2.OE-05 0.70 5.2E-05 0.85

>4 7.3E-06 1.7E-05 LB .2.OE-05 2.74 5.2E-05 2.98

1-2 6.6E-03 1.IE-02 SB 7.4E-03 1.12 1.5E-03 0.13

2-4 6.4E-04 1.9E-03 MB 3.OE-05 0.05 6.1E-05 0.03

>3 1.6E-05 1.5E-04 LB 4.OE-06 0.25 7.2E-06 0.05

>4 1.6E-06 1.9E-05 LB 4.OE-06 2.49 7.2E-06 0.38

From Table 7.20, the current mean elicitation estimates are generally consistent with the NUREG/CR5750 estimates [7.6], as the differences between the two studies are generally less than a factor of 4. The

one exception is for the current-day mean PWR MB LOCA estimate which is about twenty times higher

than the corresponding NUREG/CR-5750 estimate (Table 7.20 and Figure 7.38). The increase is largely

due to current PWR PWSCC concerns (Section 6.3.2) in both piping and non-piping (CRDM)

components. However, the expectation that aging will affect intermediate piping to a greater extent than

small or large diameter piping (Section 6.3.3) also contributes to increases in both the current BWR and

PWR MB LOCA estimates (Table 7.17).

The NUREG/CR-5750 BWR and PWR LB LOCA estimates in Table 7.20 are less than a factor of 4

lower than the Category 3 elicitation estimates, but are less than a factor of 3 higher than the Category 4

estimates. For LB LOCAs, the current elicitation Category 4 estimates are more comparable with the

NUREG/CR-5750 estimates because the effective break sizes are closer (i.e., 7 inches (180 mm) for the

elicitation and 6 inches (150 mm) for NUREG/CR-5750). The Category 3 LOCA frequency estimates

correspond to break sizes greater than 3 inches, which is one-half of the threshold break size for LB

LOCAs as defined in NUREG/CR-5750. The generally good agreement between the NUREG/CR-5750

and current elicitation estimates provided in Table 7.20 is a bit surprising given the markedly different

methodologies used to arrive at these results.

Figures 7.38 and 7.39 compare the elicitation estimates of the median, mean, 5 th and 9 5th percentiles with

estimates from prior studies for PWR MB LOCAs and BWR LB LOCAs, respectively. These LOCA

categories were chosen because their differences between the elicitation and NUREG/CR-5750 estimates

are the largest. For PWR MB LOCAs (Figure 7.38), the current elicitation yields mean and 9 5t"

7-56
percentile estimates, while higher than in NUREG/CR-5750. are very consistent with estimates from

WASH-1400 [7.4], NUREG-1 150 [7.51. the IPE submittals [7.7], and EPRI TR-100380 [7.8]. From

Figure 7.39, the BWR LB LOCA mean and 9 5

th percentile estimates are approximately one to two orders

of magnitude less than all the prior estimates. Once again, these comparisons are consistent with the

elicitation rationale provided in Section 6 that intermediate piping is most affected by aging degradation

while the failure of larger diameter piping is less likely than estimated in prior studies.

While these total LOCA frequency estimates are relatively consistent, it is also of interest to compare the

PWR SB LOCA estimates after excluding the large SGTR contribution. As previously discussed in

Sections 4.4.1 and 7.8, the SGTR estimates from this study, NUREG/CR-5750, and operating experience

are all consistent. Therefore, the differences in the total LOCA frequency estimates are largely due to the

failure frequencies of other passive systems. From Table 7.19, the mean PWR LOCA Category I and 2

estimates without SGTR contributions are 1.9E-03 and 4.2E-04 per calendar year, respectively.

Therefore, the interval-based estimate that corresponds to the historical SB LOCA definition (i.e., breaks

between 100 and 1,500 gpm [380 and 5,700 1pm]) is 1.5E-03 per calendar year. This estimate is half an

order of magnitude higher than the mean SB LOCA NUREG/CR-5750 estimate of 4.0E-04 per calendar

year.

PWR: MB LOCA

1.OE-02

1.OE-03 0

1.OE-04

2 1.OE-05

U.

o 1.OE-06 _.,

e 5th

* median

a mean

A 95th

1.0E-07

0@ 4P

N~

Figure 7.38 Comparison of Elicitation Estimates for PWR Medium Break LOCAs

at 25 Years with Estimates from Prior Studies

7-57

I
BWR: LB LOCA

1.0E-02

" 1.OE-03

1.OE-04 [5th

median

C- 1.OE-05 a a" ma mean

LL 1.OE-06 A 95th

4

0 1.OE-07

1.OE-08

NN0

Figure 7.39 Comparison of Elicitation Estimates for BWR Large Break LOCAs

at 25 Years with Estimates from Prior Studies

The NUREG!CR-5750 estimate translates to an expected PWR SB LOCA every 36 years for the current

fleet of 69 operating PWRs and the elicitation frequency translates to an expected PWR SB LOCA every

11 years. This increase reflects the panelists' opinion that the current-day PWR SB LOCA frequency for

components other than steam generator tubes is higher than historical averages, primarily due to current

PWSCC concerns (Section 6.3.2).

Many PRA applications currently utilize NUREG/CR-5750 initiating event frequencies. Based on the

discussion in this section, the current-day, summary estimates may be used to update the NUREG/CR5750 estimates using the results in Table 7.17. Estimates for flow rates greater than 100 gpm (380 1pm),

i.e., effective break sizes greater than 1/2 inch (12.7 mm), can be in lieu of total passive system PWR SB

LOCA. The current-day SGTR estimates in Table 7.18 can be used in applications that analyze these

ruptures separately from all other PWR passive system failures. For these applications, the mean currentday PWR estimates for breaks greater than 100 gpm in Table 7.19 should be also used for the frequency

associated with all other passive system failures. The elicitation estimates for flow rates greater than

1,500 gpm (5,700 1pm) (i.e., breaks greater than 1 7/8 inches (47.6 mm) for BWRs and 1 5/8 inches (41.3

mm) for PWRs) can be used instead of NUREG/CR-5750 MB LOCA frequencies. As indicated above,

the Category 4 LOCA estimates (flow rates greater than 25,000 gpm (95,000 1pm)) should be used instead

of NUREG/CR-5750 LB LOCA estimates because this break size is most similar to the NUREG/CR5750 LB LOCA break size definition.

Table 7.20 also includes the internal NRC pilot elicitation results, which were developed in July 2002

[7.9]. As discussed in Section 3.1, the pilot elicitation used the NUREG/CR-5750 frequencies as the

basis for determining the impact of aging on passive system LOCA frequencies over the next 35 years.

The pilot elicitation panelists expect a moderate increase in these frequencies over the next 35 years.

7-58
Consequently, the pilot elicitation estimates are between factors of 2 to 4 times higher than the

NUREG/CR-5750 estimates for all BWR and PWR LOCA sizes, with the exception of the PWR SB

LOCA estimates (Table 7.20). A comparable increase is apparent in the PWR.SB LOCA estimates

(Table 7.20) if the SGTR frequency of 7E-03 per calendar year is excluded from the NUREG/CR-5750

estimates.

Table 7.20 also compares the summary mean elicitation estimates with the pilot elicitation estimates 35

years into the future (i.e., at 60 years of average plant life) using the ratios of the pilot elicitation estimates

to the summary estimates. The comparison with the 60-year estimates was made because this was the

only time period evaluated in the pilot elicitation (Section 3. 1). More details on the 60-year elicitation

results can be found in Appendix L.

From Table 7.20, the current elicitation BWR SB and LB (Category 4) estimates are approximately one

and one-half and three times lower, respectively, than the pilot elicitation estimates. Conversely, the

current elicitation BWR MB estimates are twice as high as the pilot elicitation estimates. These

differences are not striking when considering that the estimates reflect the next 35 year time period, but

they are supported by the panelists' rationale. The study elicitation panelists appeared to provide more

credit for IGSCC mitigation measures than did the pilot elicitation panelists, which may have led to the

lower SB and LB LOCA estimates. The effectiveness of IGSCC mitigation is offset by the elicitation

panelists' concern for PWSCC cracking in BWR CRDM nozzles. This concern elevated the current

elicitation BWR MB LOCA estimates slightly higher than the pilot elicitation estimates

For PWRs, the differences between the current and pilot elicitation estimates are larger, and the current

elicitation estimates are consistently higher than the pilot elicitation estimates. The largest difference is

for the PWR MB LOCAs, where the current elicitation estimates are 30 times higher than the pilot

estimates. The PWR SB and LB LOCA current elicitation estimates are approximately 5 and 2 times

larger after SGTR contributions are eliminated from the current elicitation estimates. One important

reason for the increased MB and SB estimates is that the current elicitation panel .made a more

comprehensive assessment of the severity of CRDM and PWSCC cracking in PWR plants. Therefore,

this difference is consistent with the panelists' expectation that this mechanism will more severely impact

intermediate and small piping failure frequencies.

7.10 Comparison with Operating Experience Data

There have been very few failures of primary pressure boundary systems that have led to LOCAs. Steam

generator tube rupture is the most common failure, and a few other smaller diameter pipes have also failed.

These events can be classified as either very small (VS), or SB LOCAs depending on the break size. No

MB or LB LOCAs have occurred. Because of the paucity of actual failures, estimating LOCA frequencies

by considering only the number of actual failures becomes less accurate as the break size increases.

Operating experience-based estimates can also be obtained using failure precursor information (e.g., leaks

and part-through-wall cracks). This information is much more prevalent than actual failures, and failure

precursors have been documented on a broad range of primary pressure boundary systems. Loss-ofcoolant accident frequencies can then be estimated if the CFP given a precursor is known. The challenge

is that the CFP depends on many factors whose effects are difficult to quantify, such as the piping system,

degradation mechanism, precursor type, and precursor size. Therefore, using the CFP and failure

precursor information to estimate VS and SB LOCA frequencies is likely to be less accurate than directly

estimating these frequencies from the failure events. Also, as the piping size increases, the uncertainty of

the CFP increases. This effect should be accounted for when estimating MB and LB LOCA frequencies.

7-59
Another consideration is that operating experience reflects only historical performance. New degradation

mechanisms (e.g., PWSCC) can result in systematic increases in generic frequencies while wholesale

mitigation programs (e.g., IGSCC mitigation) can result in systematic decreases in LOCA frequencies over

time. Because LOCAs are so rare, the effects that these changes have on the LOCA frequencies are

typically not reflected in operating, experience until some time (often years) has passed. Therefore, using

operating experience to predict current or future performance is not necessarily conservative. These

considerations, along with other rationales discussed in Section 1, explain why elicitation is preferable to

exclusively relying on operating experience for estimating current and future LOCA frequencies, even for

small break LOCAs.

However, operating experience is still valuable as a basis for identifying trends, assessing historical

performance, and estimating failure precursor frequencies (Section 1). The elicitation procedure in this

study made extensive use of operating experience as the basis for many of the piping and non-piping base

case estimates and sensitivity analyses that the panelists used to anchor their elicitation responses (Section

3.5). Piping and non-piping failure and failure precursor databases were also provided to the panelists so

that they could analyze plant systems and failure mechanisms that were not specifically evaluated in a base

case. Most panelists used operating experience as the basis for their current and future LOCA frequency

estimates and made modifications to account for the current state of knowledge.

Therefore, it is of considerable value to compare the elicitation results with operating experience.

Differences between operating experience and the elicitation results reflect the modifications that the

panelists have deemed necessary to accurately account for current and future changes that may alter the

average failure frequencies associated with past commercial nuclear plant performance. This section only

compares the Category 1, or SB LOCA, frequencies with operating-experience estimates developed

directly from failure events because, as discussed above, these estimates require the least extrapolation or

interpretation of passive-system failure data and should therefore be the most accurate.

As of December 2006, US BWRs had accumulated 1,023 reactor-years of operating experience and no

Category 1 LOCAs have occurred. When no events have occurred, a 50 percent confidence bound is

analogous to a mean frequency estimate. It is assumed that the number of Category I LOCAs in N

reactor years has a Poisson distribution with mean k. With no events, a 50 percent confidence bound on k

is 0.70 [7.10]. The estimated frequency is then 0.70/N per reactor year. Accordingly, an operating

experience-based estimate of the frequency of a BWR Category I LOCA is (0.70/1023) = 6.8E-04 per

reactor year. Multiplying this value by the conversion factor of 0.8 reactor-yr /calendar-yr (Section 4)

results in an estimated BWR SB LOCA frequency of 5.5E-04 per calendar year based on historical plant

performance. From Table 7.t7, the corresponding elicitation estimate of the current-day mean frequency

is 6.5E-04 per calendar year.

Similarly, for US PWRs, there have been 9 total events (8 steam generator tube failures and I piping

failure5

) as of December 2006 resulting in leak rates that were greater than 100 gpm (380 lpm). This is

the threshold leak rate defined for SB LOCAs in NUREG- 1150 and NUREG/CR-5750 and Category 1

LOCAs in this report. Up to that point in time, US PWRs had operated for 1,986 reactor-years.

Therefore, an operating experience-based estimate for the frequency of a PWR Category 1 LOCA

(including the SGTR data) is (9/1986) = 4.5E-03 per reactor year, or 3.6E-03 per calendar year. From

Table 7.17, the corresponding estimate of the current-day mean frequency is 7.3E-03 per calendar year.

The one Category I event involving a US PWR plant was an instrument line fitting failure in November 1991 at Oconee Unit 3

which resulted in an observed maximum flow rate of about 130 gpm (490 1pm) (LER 50-287/1991-008 and IAEA IRS Report

1238). During this event approximately 87,000 gallons (330,000 liters) of reactor coolant was discharged into containment.

7-60
From the analysis above, the BWR elicitation estimate is less than 20 percent higher than the operatingexperience estimate which, given the uncertainty of these estimates, is not statistically significant. There

is a larger difference between the PWR estimates. The PWR elicitation estimate is about 100 percent (or

a factor of two) higher than the operating-experience-based estimate. Additional insight into this

difference can be gained by separately considering SGTR and other passive systems failure frequencies.

Based on the 8 SGTR events listed above, the estimated SGTR Category I LOCA frequency is (8/1986) =

4.OE-03 per reactor year, or 3.2E-03 per calendar year. From Table 7.18, the comparable current-day

elicitation estimate is 3.7E-03 per calendar year. In light of the uncertainties, these two estimates are

statistically equivalent. That is, the panelists' believe that the average, historical SGTR frequency is

applicable to present conditions. This outcome is also supported by the panelists' qualitative rationale

(Section 6.3. 1). Note that the base case SGTR frequency in Section 4 is 3.5E-03 per calendar year and

was determined similarly to the above operating-experience-based estimate The two values are slightly

different due to the additional years of operating experience that have accumulated since the elicitation

was conducted.

Similarly, the operating-experience-based estimate of the non-SGTR Category I LOCA frequency for the

one documented event5 is (1/1986) = 5.OE-04 per reactor year, or 4.OE-04 per calendar year. From Table

7.19, the current-day Category 1 elicitation estimate for non-SGTR events is 1.9E-03 per calendar year.

This value is 5 times the value obtained from operating experience, and encompasses nearly all of the

difference between the total PWR Category I estimates. This discrepancy also raises the possibility that

the elicitation estimate may not be consistent with operating experience.

The importance of this discrepancy between the two frequency estimates can be assessed by first

determining if it is statistically significant. To assess statistical significance, it is necessary to consider

the uncertainty of the estimates. Based on the one observed non-SGTR piping failure, an upper 95

percent confidence bound for the failure frequency is (4.7/1986) = 2.4E-03 per reactor year or 1.9E-03 per

calendar year [7.10]. Because this bound is equivalent to the elicitation estimate of 1.9E-03 per calendar

year, it follows that the difference between the UB operating experience and elicitation frequency

estimates is not statistically significant.

The factor of 5 discrepancy between the operating-experience and elicitation frequency estimates is based

on one piping failure in US PWRs up to December 2006 that apparently exceeded the Category 1 leak

rate threshold. However, one other piping system break was identified in NUREG/CR-5750 (Catawba

Unit I in 1986, LER 413/86-031), with a leak rate of approximately 80 gpm (300 lpm). This leak rate is

just below the Category I threshold of 100 gpm (380 lpm). Considering this event, a conservative

assumption is that there have been two rather than one Category 1 LOCAs in the 1,986 reactor-years of

experience. The resulting operating-experience-based non-SGTR Category I LOCA frequency is 8.OE04 per calendar year with an upper 95 percent confidence bound of (0.8)(6.3)/(1986) = 2.5E-03 per

calendar year. This bound is greater than the elicitation estimate of 1.9E-03 per calendar year. The

conclusion is that the difference between the elicitation and operating-experience-based estimates of the

non-SGTR, PWR Category 1 LOCA frequency, whether based on one or two observed events, is not

statistically significant.

Even though the difference between the elicitation and operating-experience estimates of the non-SGTR

PWR Category I LOCA frequency is not statistically significant, it must be emphasized that this does not

imply that the two estimation procedures are in fact estimating the same frequency. Because the

operating-experience-based estimate is an historical average based on many years of operation, a

difference will exist if the panelists believe that the current failure frequency differs from the historical

average. In fact, the increased elicitation estimate is supported by the panelists' qualitative and

quantitative responses. The panelists indicated that medium and, to a lesser extent, small LOCAs in

7-61
PWRs are most dramatically impacted by PWSCC in relatively small diameter passive system component

(e.g., CRDMs, instrument nozzles, etc.) (Section 6.3.2). This impact was previously reflected in the 20-

fold increase in the MB PWR LOCA elicitation estimates compared to the NUREG/CR-5750 estimates

(Section 7.9), and explains the smaller, five-fold increase in non-SGTR Category 1 failure frequencies.

7.11 References

7.1 Chokshi, N.C., Shaukat, S.K., Hiser A.L., DeGrassi, G., Wilkowski, G., Olson, R., and Johnson, J.J.,

"Seismic Considerations For the Transition Break Size," NUREG-1903, U.S. Nuclear Regulatory

Commission, February 2008.

7.2 Meyer, M.A. and Booker, J.M., "Eliciting and. Analyzing Expert Judgment: A Practical Guide,"

NUREG/CR-5424, U.S. Nuclear Regulatory Commission, January 1990.

7.3 Efron, B. and Tibshirani, R.J., "An Introduction to the Bootstrap," Chapman & Hall, 1993.

7.4 "Reactor Safety Study: An Assessment of Accident Risks in U.S. Commercial Nuclear Power

Plants," WASH-1400, U.S. Nuclear Regulatory Commission, October 1975.

7.5 "Severe Accident Risks: An Assessment for Five U.S. Nuclear Power Plants," NUREG- 1150, U.S.

Nuclear Regulatory Commission, December 1990.

7.6 Poloski, J.P., Marksberry, D.G., Atwood, C.L., and Galyean, W.J., "Rates of Initiating Events at U.S.

Nuclear Power Plants: 1987-1995," NUREG/CR-5750, February 1999.

7.7 "Individual Plant Examination Program: Perspective on Reactor Safety and Plant Performance,"

NUREG-1560, Vol. 2, Parts 2 - 5, December 1997.

7.8 Bush S., "Pipe Failures in U.S. Commercial Nuclear Power Plants," EPRI TR-100380, 1992.

7.9 Memorandum from A.C. Thadani to S.J. Collins, Transmittal of Technical Work to Support Possible

Rulemaking on a Risk-Informed Alternative to 10 CFR 50.46/GDC 35, dated July 31, 2002.

7.10 Lurie, D. and Moore, R.H., "Applying Statistics," NUREG-1475, U.S. Nuclear Regulatory

Commission, February 1994.

7-62
8. BASIS FOR FUTURE LOCA FREQUENCY UPDATES

The LOCA frequencies developed in this report will be periodically reevaluated to determine if they need

to be updated based on information and knowledge gained subsequent to the expert elicitation. There are

several areas of work ongoing or planned for the near future that will support/augment this effort. Those

activities include:

(1) updating the pipe failure database as part of the OPDE project,

(2) developing a new PFM computer code for predicting the frequencies of various size LOCA

events, and

(3) expanding on the efforts of this program through a new international cooperative research

program called MERIT (Maximizing Enhancements in Risk Informed Technology).

The OECD Pipe Failure Data Exchange (OPDE) project is a collaboration between twelve OECD

member countries to capture international nuclear power plant piping event information in a central

database. Because of the rarity of piping failures and precursor events, it is crucial to have both an

accurate count of these events and a large pool of relevant reactor experience to draw from in order to

develop meaningful piping failure frequency estimates. Data captured in the OPDE database allows for

examination of LOCA precursor event occurrences versus time directly from operating experience. The

database is striving for complete international data starting from 1998. Information from events dating

back to the early 1970s will be included for some countries, including the United States. The OPDE

database will form part of the technical database used to periodically update LOCA frequency estimates.

As a means of verifying/augmenting the results from this elicitation effort, additional probabilistic

analyses will be conducted using a probabilistic LOCA code (PRO-LOCA) that is currently under

development. This new code includes many deterministic aspects that have been developed in various

NRC piping programs but not included in any of the currently available PFM codes, such as PRAISE,

SRRA, or PRODIGAL. The PRO-LOCA code includes improved crack initiation/crack growth models,

additional weld residual stress solutions, improved fracture models, improved leak-rate models with

newly developed crack morphology parameters, and new methods for addressing multiple crack initiation

sites and crack coalescence. While the code currently estimates frequencies through direct Monte-Carlo

simulation, new more efficient processing routines (such as Importance Sampling) are currently under

development. Once the development work on this code has been completed and the necessary

benchmarking and quality assurance checks completed, the code will be exercised for a series of test

cases as a means of validating the results from this elicitation effort. The final objective will be to

combine this code with the OPDE database, to either directly calculate LOCA frequencies or use as a

basis for conducting a subsequent elicitation to update LOCA frequency estimates contained in this

report.

Additionally, a new international cooperative research program (MERIT) is being developed as a means

of further developing the tools for making these types of probabilistic-based risk-informed decisions. The

two main outcomes of the MERIT program as related to the determination of LOCA frequencies are the

continued development of the previously mentioned probabilistic LOCA code (PRO-LOCA) and the

further assessment of weld residual stresses and their impact on SCC. As part the continued development

of the PRO-LOCA code, work within the MERIT program will focus on LOCA frequency contributions

from non-piping component degradation. Also, an attempt will be made to reach an international

consensus as to a set of standardized procedures for making these types of assessments. With regards to

8-1
the further assessment of weld residual stresses and their impact on SCC, some of the key activities to be

undertaken as part of the MERIT program are: (1) developing new solutions for additional bimetal weld

locations and weld procedures, (2) examining the effect of weld repairs on the residual stress state, (3)

identifying mitigation techniques/strategies for the re-qualification of leak-before-break (LBB) for

bimetal welds susceptible to PWSCC, and (4) optimizing weld repair techniques to minimize weld

residual stresses.

8-2
9. SUMMARY OF RESULTS AND CONCLUSIONS

An expert elicitation process has been used to consolidate operating experience and insights from PFM

studies with knowledge of plant design, operation, and material performance to estimate LOCA

frequencies. The approach allowed the panelists to decompose complex issues which impact LOCA

frequencies into simpler, more fundamental issues which are easier to assess. The panelists were

provided with frequency estimates for precursor LOCA events associated with degradation in piping and

non-piping components. Additionally, selected panelists developed frequencies for several simplified

base case conditions. Based on their knowledge of passive system component failure, the panelists

extrapolated from this information to respond to a lengthy questionnaire structured to develop LOCA

frequency contributions for potential piping and non-piping passive systems failures. The panelists'

responses were processed to calculate LOCA frequency estimates that reflect individual panelist

uncertainty and panel diversity.

The panelists also provided qualitative insights into a number of topics including evaluation of base case

predictive methodologies; the effect of safety culture on LOCA frequencies; important degradation

mechanisms; piping and non-piping LOCA frequency contributors; the effects of component size and

operating time on LOCA frequencies; the influence of mitigation and maintenance; and uncertainty in

making estimations, to name a few. The following insights were generally shared by most, if not all, of

the panelists. Most panelists believe that operating-experience-based analyses provide the most accurate

basis for estimating current-day (i.e., 25 year) frequencies of the simplified base case conditions.

However, PFM studies also offer valuable insights into the possible future effect of degradation

mechanisms. The use of sensitivity analysis, using either approach, is also valuable for identifying

important contributing factors. Many panelists believe that combining information from both approaches

is the optimal way to estimate LOCA frequencies, even for the simplified base case conditions.

There are several competing factors that affect the general safety culture of the industry and regulatory

bodies. However, the panel members generally expressed the belief that the future safety culture will not

differ dramatically from the current culture. In fact, most panelists expect a small improvement due

mainly to continued nuclear experience and technology advancements. Because of this general

expectation, the facilitation team elected not to adjust the LOCA frequency estimates provided by the

panelists to account for safety culture effects. This decision was endorsed by the elicitation panelists.

The panelists also generally expressed the view that utility and regulatory safety cultures are highly

correlated. Many panelists also believe that safety culture can significantly affect LOCA frequencies at

specific plants, either positively or negatively. However, there is also an expectation that timely

regulatory actions using existing enforcement measures would diminish both the possibility and impact of

safety culture deficiencies, and LOCA frequency increases, at particular plants. Many panelists believe

that the possible effect of a deleterious safety culture motivates the NRC to remain vigilant in addressing

individual plant deficiencies.

Many participants also believe that the number of precursor events (e.g., cracks and leaks) is a good

barometer of the LOCA susceptibility to the associated degradation mechanism. Welds were almost

universally recognized as important because of the high residual stress, the preferential attack of many

degradations mechanisms, and the high likelihood of defects in these locations. Nozzle, elbows, and tees

were also thought to be important locations. The panelists generally identified thermal fatigue, SCC, and

mechanical fatigue as the degradation mechanisms which most significantly contribute to LOCA

frequencies in PWR plants. These mechanisms and FAC are important LOCA frequency contributors for

BWR plants. The panel consensus is that the susceptibility of BWR piping systems to IGSCC is greatly

reduced compared to the past. However, this mechanism remains an important LOCA contributor in

many of the panelists' estimates. The panelists were concerned that PWSCC is an important contributor

9-1
to current-day PWR LOCA frequency estimates. However, most panelists expect that this mechanism

will be mitigated within the next 15 years.

The panelists generally believe that complete rupture of a smaller pipe or non-piping component is more

likely than an equivalent size opening in a larger pipe because of the increased susceptibility to

fabrication or service cracking. Additionally, smaller bore piping, compared to larger bore piping, is

more likely to have fabrication flaws, has lower inspection quality and quantity, and is more susceptible

to external failure mechanisms arising from human error. This is a primary reason why the largest

contributors in each LOCA category tend to be the smallest pipes which can lead to that size LOCA.

There is substantially more disagreement about the most likely contributing systems for the intermediatesize (i.e., Category 3, 4) LOCAs because of the sheer number of possible contributing systems. However,

many panelists thought that aging may have the greatest effect on intermediate diameter (6 to 14-inch)

piping systems.

Generally, non-piping LOCA frequency assessment is believed to be more challenging than the piping

assessments. There are multiple non-piping components to consider, each associated with different

operating requirements, designs, materials, and inspection considerations. Furthermore, there is a dearth

of precursor data available for the non-piping components compared with piping. Nevertheless, the

panelists believe that non-piping components contribute significantly to Category 1 and 2 LOCA

frequencies. For PWRs, non-piping components are the dominant contributor to Category I and 2 LOCA

frequencies due to SGTRs and CRDM cracking. Postulated non-piping failures contribute much less

significantly to Category 3, 4, and 5 LOCA frequency estimates. Non-piping contributions are more

important for LOCA Category 6 because there is little PWR and no BWR piping which can result-in this

size flow rate at failure. Non-piping contributions are more significant in PWR plants because of the

increased number of LOCA-sensitive components and past and current experience with degradation in

these components.

Frequency estimates are not expected to change dramatically over the next 15 years for any size LOCA,

or even over the next 35 years for smaller BWR (< Category 6) and PWR (< Category 3) LOCA

categories. However, larger, (i.e., order of magnitude) frequency increases for the larger LOCA

categories beyond the next 15 years are expected. There are several competing factors which will affect

these future trends. Decreases in LOCA frequencies are expected to be driven by improvements in

mitigation strategies and inspection techniques, material replacement, and continued reduction in

fabrication-related problems. Increases in LOCA frequencies are primarily expected to be driven by

continued aging, which will also be exacerbated by improper maintenance and other human errors.

However, while aging will continue, the panelists' consensus is that mitigation procedures are in place, or

will be implemented in a timely manner, to alleviate significant increases in future LOCA frequencies due

to known degradation mechanisms. Therefore, the significant increases expected beyond the next 15

years are largely due to uncertainty about the future and the concern that new degradation mechanisms

could arise in the operating fleet after this time.

While the panelists generally agreed on the important technical issues and factors contributing to LOCAs,

the quantitative estimates were much more difficult to assess because of the underlying scientific

uncertainty and the lack of truly relevant LOCA data. The panelists generally expressed greater

uncertainty in their predictions as the LOCA size increased. As noted, most panelists also believe that

uncertainty increases with future operating time. Both trends a consequence of the greater extrapolation

required of passive-system failures as the LOCA size and forecasting period increase. Panelist

uncertainty was generally similar for BWR and PWR plants. Although, there are also significant

differences among the panelists' estimates, panel diversity as measured by the quartiles or confidence

9-2
bounds is generally much less than each panelist's uncertainties. Furthermore, panel diversity does not

vary significantly with either LOCA size or plant type.

Baseline LOCA frequency estimates for the 5th percentile, median, mean and 9 5rh percentile (i.e., bottomline parameters) were calculated from each panelist's elicitation responses. These individual responses

were then aggregated into a group estimate using the GM of each of these bottom-line parameter

estimates. Group variability was measured by calculating 95 percent confidence bounds for each bottomline estimate under the assumption that the individual estimates are lognormally distributed. The group

estimates of the bottom-line parameters were calculated for each of the six LOCA size categories and for

each of the three distinct time-periods: current-day (25 years fleet average), end-of-plant-license (40

years fleet average), and end-of-plant-license-renewal (60 years fleet average). Because of the predicted

stability in these estimates over the near-term, it is recommended that the current-day (25 year) results be

used to estimate the LOCA frequencies over the next 15 years of fleet operation.

While it is acknowledged that operating-experience-based estimates do not necessarily reflect current

conditions, it is informative to compare such estimates with the elicitation frequencies for the smallest

LOCAs (Category 1). Because no larger LOCAs have occurred, this comparison requires the least

extrapolation of passive-system failure data. The BWR and PWR Category 1 LOCA frequencies

(including the SGTR frequency for PWRs) were estimated up through December 2006. For BWR plants,

the average SB LOCA frequency based solely on the number of reported events is 5.5E-04 per calendar

year. The mean elicitation BWR SB LOCA estimate is 6.5E-04 per calendar year. Thus, the BWR

elicitation estimate is less than 20 percent higher than the operating-experience-based estimate which,

given the uncertainty of these estimates, is not statistically significant.

For PWR plants, the average SB LOCA frequency is 3.6E-03 per calendar year. The mean elicitation

PWR SB LOCA estimate is 7.3E-03 per calendar year. The PWR elicitation estimate is about 80 percent

higher than the operating-experience-based estimate. Additional insight into this difference can be gained

by partitioning the PWR passive-system failure data into frequencies for SGTRs and for all other passivesystem SB LOCAs. Based on reported failures, the average SGTR LOCA frequency is 3.2E-03 per

calendar year. This result is almost identical to the current-day elicitation estimate of 3.7E-03 per

calendar year. Based on operating experience, the average frequency of all other Category 1 PWR

passive-system failures is 4.OE-04 per calendar year. The corresponding elicitation estimate is 1.9E-03

per calendar year. While this value is 5 times greater than the operating-experience-based estimate, this

difference is explained by the elicitation panelists' assessment of the effect of PWSCC on small diameter

component failures.

There are several prior studies that also estimated LOCA frequencies, including the most recent study

documented in NUREG/CR-5750. Some care is needed when comparing the elicitation LOCA frequency

estimates with these earlier studies because the LOCA categories are defined differently. Specifically, the

current-day, LOCA Category t and 2 estimates in Table 7.17 are comparable to total system SB, MB, and

LB LOCA frequencies, respectively, reported in NUREG/CR-5750. Additionally, current-day SGTR

frequencies (Table 7.18) and PWR LOCA frequencies for all other passive system failures (i.e., .

frequencies for breaks greater than 100 gpm [380 1pm] in Table 7.19) are comparable to NUREG/CR5750 SGTR and PWR SB LOCA frequencies when these failure modes are analyzed separately. The

NUREG/CR-5750 LB LOCA frequency estimates are best compared to the elicitation LOCA Category 4

frequency estimates because the pipe break sizes are most similar.

After accounting for these differences, the elicitation LOCA frequency estimates are generally much

lower than the WASH-1400 estimates and more consistent with the NUR-EG/CR-5750 estimates. The SB

LOCA PWR elicitation estimates after subtracting the SGTR frequencies are approximately 3 times

greater than the NUREG/CR-5750 estimates, due to the aforementioned PWSCC concerns. However, the

9-3
total BWR and PWR SB LOCA frequency estimates are similar once the SGTR frequencies are added to

the NUREG/CR-5750 PWR results. The elicitation MB LOCA estimates are higher than the

NUREG/CR-5750 estimates by factors of approximately 4 and 20 for BWR and PWR plant types,

respectively. These differences are partly due to concerns about PWSCC in piping and non-piping (e.g.,

CRDM) components as well as general aging concerns with piping in this size range. The NUREG/CR5750 LB LOCA frequency estimates are slightly higher (less than a factor of 3) than the current elicitation

results for both PWR and BWR plants. The generally good agreement between the NUREG/CR-5750

and current elicitation estimates is somewhat surprising given the markedly different methodologies used

to arrive at these results.

A number of sensitivity studies were also conducted to examine the robustness of the quantitative results

to the underlying analysis procedure. Sensitivity analyses investigated the effect of distribution shape on

the means, and the effects of correlation structure, panel diversity measure, panelist overconfidence and

aggregation measure on the estimated parameters. The mean calculation in this study used a split

lognormal distribution truncated at the 99.9h percentile to obtain reasonably conservative values

compared with other possible choices. The choice of distribution shape is not significant for distributions

with error factor less than 100, which represents the bulk of the elicitation responses. Split log-triangular

and the baseline truncated split lognormal distribution are the most credible bounds for characterizing the

elicitation responses. The difference in the means calculated for these distributions is less than a factor of

6 for distributions with error factors up to 1,000.

The correlation structure used in this study assumed maximal correlation, which is a reasonable

characterization of the elicitation structure and the panelists' responses. An independent correlation

structure provides an alternative bounding assumption. Based on selected Monte Carlo simulations, the

maximal correlation structure provides 9 5th percentile estimates that are either conservative or not

significantly different from those obtained using a bounding independent correlation structure. However,

the independent correlation structure leads to median and 5" percentile estimates that are as much as one

or two orders of magnitude higher than estimates calculated assuming maximal correlation. These

differences increase with the error factor of the underlying distribution and the number of distributions

which contribute to the total LOCA frequency estimates for each panelist. Because the relatively large

sensitivity to the correlation structure is manifested for only some of the panelists, the sensitivity of the

median and 5 th percentile estimates to the correlation structure is less for the aggregated results. The

means, of course, are unaffected by the choice of the correlation structure.

The assumption of a maximal correlation structure in this study is justified by the observation that the

elicitation responses tend to be strongly correlated. Most panelists noted that degradation mechanisms

that significantly contribute to the LOCA frequencies affect many systems similarly. That is, if

conditions lead to higher failure probabilities in one piping system due to a specific degradation

mechanism, the failure probabilities of other susceptible systems will also increase. This assumption is

also justified since the correlation structure is not important for determining the means and 95"

percentiles of the total LOCA frequency estimates. These are the most important parameters for most

applications.

The analysis procedure in this study used confidence intervals for the group estimates as a measure of

panel diversity. The 95% confidence bounds associated with each LOCA frequency parameter (i.e., the

median, mean, 9 5 'h percentile) were calculated based on an assumed lognormal structure for the panelist's

responses. The confidence bounds are based on the variance of all the panelists' estimates. An

alternative approach used the interquartile ranges of the individual estimates to measure panel diversity.

The interquartile ranges, by definition, encompass approximately one-half of the individual estimates.

With their nominal 95 percent coverage, the confidence intervals are a more conservative measure of

diversity than the interquartile ranges of the individual results. That is, the 95% confidence intervals

9-4
based on the 2.5% and 97.5% confidence bounds cover at least half of all the individual estimates for each

parameter.

The initial estimates discussed in Sections 7.2 - 7.5, and used as the basis for comparison of the

sensitivity analyses in Section 7.6, did not adjust the individual estimates for panelist overconfidence.

These results are referred to throughout the report as the baseline estimates. Sensitivity analyses were

conducted to investigate the effects on the baseline estimates of adjusting individual estimates to account

for panelists' overconfidence in their elicitation responses. The blanket overconfidence adjustments

considered are difficult to justify and can result in large, unsupportable increases in the frequency

estimates The blanket and more severe targeted adjustments generally lead to greatly skewed estimates

(e.g., the mean is much greater than the 9 5 1h percentile) that are not consistent with either the elicitation

structure or the panelists' responses. Additionally, the blanket adjustments can lead to high mean

frequency estimates that are not supported by the operating experience. The targeted adjustment of only

those panelists whose estimates express a relatively large certainty can be supported by the post-adjusted

results. However, all these adjustment schemes require arbitrary assumptions about which panelists to

adjust and the appropriate level of adjustment.

The error-factor adjustment scheme used in this study is less subject to these drawbacks. This adjustment

scheme provides a variable overconfidence adjustment as a function of LOCA size, time period, and plant

type. The adjustment is greatest for those panelists who expressed the least amount of uncertainty

compared to the group average. This adjustment leads to a relatively small increase in the baseline LOCA

frequency estimates and the results are comparable to the least severe targeted adjustment. The estimates

calculated using the error-factor adjustment scheme are referred to throughout the report as the summary

estimates. Because there is a known tendency for people, including experts, to be overconfident when

making subjective judgments, these summar, estimates are deemed to result in improved LOCA

frequencies compared to the baseline estimates.

The largest sensitivity is associated with the method used to aggregate the individual panelist estimates to

obtain group estimates. The baseline and summary estimates were developed using GM aggregation. In

this study, the GM aggregation produces frequency estimates that approximate the median of the

panelists' estimates and can therefore be interpreted as consensus-type results. Hence, the summary

estimates in Table 7.17 are believed to be a reasonable representation of the expert panel's current state of

knowledge regarding LOCA frequencies.

A sensitivity study evaluated the effect of using alternative aggregation methods to calculate the group

estimates. Specifically, mixture-distribution and AM aggregation were evaluated. For the panelists'

responses in this study, these alternative aggregation methods can lead to significantly higher mean and

95t' percentile estimates than those obtained using GM aggregation. For example, while the mixturedistribution increases compared to the baseline estimates are less than a factor of 6 for approximately half

of the six LOCA categories, the increases for the other LOCA categories vary from factors of 9 to 43.

The LOCA categories exhibiting the greatest discrepancies all have a common attribute - there are one or

two individual frequency estimates which are much greater than the other estimates. Alternative LOCA

frequency estimates that are higher than the summary estimates (Table 7.17) can be derived by using

either the summary estimates with 95% confidence bounds (Tables 7.8), the AM aggregated results

(Table 7. 3), or the mixture-distribution results (Table 7.16). These estimates also incorporate the same

overconfidence adjustment as the summary estimates.

Because alternative aggregation methods can lead to significantly different results, a particular set of

LOCA frequency estimates is not generically recommended for all risk-informed applications. The

purposes and context of the application must be considered when determining the appropriateness of any

set of elicitation results. In particular, during the selection of the BWR and PWR transition break sizes

9-5
for the proposed 10 CFR50.46a rulemaking, the NRC staff considered the totality of the results from the

sensitivity studies, rather than only the summary frequency estimates from this study. The NRC

anticipates that a similar approach will be used in selecting appropriate replacement frequencies for the

estimates provided in NUREG/CR-5750 and for other applications that require frequencies for break sizes

other than those in NUREG/CR-5750. While the lack of clear application guidance places an additional

burden on the users of the study results, those users are in the best position to judge which study results

are most appropriate to consider for their particular applications.

9-6
NRC FORM 335 U.S. NUCLEAR REGULATORY COMMISSION 1 REPORT NUMBER

(9-2004) (Assigned by NRC, Add Vol., Supp., Rev.,

NRCMD 3.7 and Addendum Numbers, if any.)

BIBLIOGRAPHIC DATA SHEET

(See instructions on the reverse) NUREG-1829, Vol. 1

2. TITLE AND SUBTITLE 3. DATE REPORT PUBLISHED

Estimating Loss-of-Coolant Accident (LOCA) Frequencies Through the Elicitation Process MONTH YEAR

Main Report April 2008

4. FIN OR GRANT NUMBER

N6360

5. AUTHOR(S) 6. TYPE OF REPORT

Robert Tregoning and Lee Abramson, US NRC

Paul Scott, Battelle Technical

7. PERIOD COVERED (Inclusive Dates)

N/A

8. PERFORMING ORGANIZATION - NAME AND ADDRESS (If NRC. provide Division, Office or Region, U.S. Nuclear Regulatory Commission, and mailing address; if contractor.

provide name and mailing address.)

Division of Engineering Battelle-Columbus

Office of Regulatory Research 505 King Avenue

U.S. Nuclear Regulatory Commission Columbus, OH 43201

Washington, DC 20555-0001

9. SPONSORING ORGANIZATION - NAME AND ADDRESS (If NRC, type "Same as above": if contractor, provide NRC Division, Office or Region, U.S. Nuclear Regulatory Commission,

and mailing address)

Same as Above

10, SUPPLEMENTARY NOTES

A. Csontos, NRC Promect Manager

11. ABSTRACT (200 words or less)

The NRC is developing a risk-informed revision of the design-basis pipe break size requirements in 10 CFR 50.46, Appendix K

to Part 50, and GDC 35 which requires estimates of loss-of-coolant-accident (LOCA) frequencies as a function of break size.

Separate BWR and PWR piping and non-piping passive system LOCA frequency estimates were developed as a function of

effective break size and operating time through the end of license extension. The estimates were based on an expert elicitation

process which consolidated service history data and insights from probabilistic fracture mechanics studies with knowledge of

plant design, operation, and material performance.

The elicitation required each member of an expert panel to qualitatively and quantitatively assess important LOCA contributing

factors and quantify their uncertainty. The quantitative responses were combined to develop BWR and PWR total LOCA

frequency estimates for each contributing panelist. The individual estimates were then aggregated to obtain group estimates,

along with measures of panel diversity. Sensitivity studies were conducted to examine the effects of distribution shape,

correlation structure, panelist overconfidence, measures of panel diversity, and aggregation method. The group estimates are

most sensitive to the method used to aggregate the individual estimates.

12. KEY WORDS/DESCRIPTORS (List vords or phrases that vill assist researchers in locating the report) 13. AVAILABILITY STATEMENT

piping unlimited

risk-informed 14. SECURITY CLASSIFICATION

emergency core cooling system (ECCS) (This Page)

loss-of-coolant accident (LOCA) unclassified

break frequencies (This Report)

design-basis break size unclassified

LOCA frequency estimates

expert elicitation 15. NUMBER OF PAGES

aging

16. PRICE

NRC FORM 335 (9-2004) PRINTED ON RECYCLED PAPER
F.deral Recycling Program