Development Of Pipework System Failure Rates

Where do the numbers come from and why

should we believe them?

By:

Karl N. Fleming, President

KNF Consulting Services LLC

Presented to:

CRA’s UK’s 5th Probabilistic Safety Analysis & Human Factors Assessment Forum

What lies behind the numbers? 17-18 September 2014

Scope of PRA Issues

n  Pipe failure rate issues in PRA n  Loss of coolant accident initiating events n  Internal flooding and high energy line

breaks n  Risk-informed inservice inspection n  Generic safety issue for emergency cooling

system blockage

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Approach to Solving Issues

n  Technical approach to piping reliability n  Sources of pipe failure data n  Treatment of uncertainty n  Key results and insights

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Two types of pipe failure data:

n  Type 1 Databases of events involving failure or degradation of piping system components with information on causes, failure modes, corrective actions etc.

n  Type 2 Estimates of piping system failure rates and rupture frequencies for PRA development and risk-informed applications

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Sources of Type 1 Failure Data n  EPRI-SKI Collaboration

n  SKI 96:20 Bush and Chockie records of events involving pipe failures

n  EPRI RI-ISI Project n  EPRI TR-111880 estimates of pipe failure rates and rupture

frequencies for RI-ISI

n  PIPExp Comprehensive database of worldwide NPP service experience developed by Bengt Lydell

n  OECD/NEA Pipe Data Exchange – international effort to collect and analyze pipe failure data

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PIPExp Database n  Evolved from EPRI-SKI collaboration n  Continuously updated since 1994 n  Summary available in “PIPExp-2014 High-Level

Summary of Database Content as of June 30, 2014, Sigma Phase Inc.”

n  Currently has nearly 10,000 pipe failure records n  Non-through wall defects (e.g., cracks and wall thinning) n  Small leaks resulting in piping repair or replacements; n  Leaks; n  Severance (i.e., pressure boundary failure due to external impact); n  Rupture (significant structural failure).

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PIPExp Plant Level Data Summary Showing Aging Effects

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PIPExp Distribution of System and Failure Mode

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Technical Approach To Pipe Failure Rate Estimation

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March 2011 PSA 2011 10

Pipe Rupture Frequency Model

ikt

M

kxikik

M

kikxixt IFRP

ii

∑∑==

==11

}{λρρ

ρixt = total rupture frequency for component i for rupture mode x at plant age t

ρikx = rupture frequency of component i due to damage mechanism k for rupture mode x

λik = failure rate of pipe component i due to damage mechanism k Pik{Rx|F} = conditional probability of rupture mode x given failure for pipe

component i and damage mechanism k Mi = Number of different damage mechanisms for component i Iikt = Age and Integrity management factor for component i and

damage mechanism k ;this factor adjusts the rupture frequency to account for plant age t and integrity management strategy which may be different than the components in the service data.

March 2011 PSA 2011 11

Markov Model Background n  Markov Model originally developed for EPRI RI-ISI

Program n  Applied to 26 plant specific RI-ISI programs in U.S.

and South Africa n  Applied to PBMR to support new ASME Code

development for in-service inspections n  Applied in NUREG-1829 LOCA frequency update n  Currently being applied to address CANDU feeder

pipe cracking issue n  Recently applied to LWRs to guide efforts to reduce

internal flood and HELB contributions to CDF

March 2011 PSA 2011 12

MARKOV MODEL OF PIPE ELEMENT

S

F

L

R

φ ω

µ

λ

ρF

ρL

S

F

L

R

SS

FF

LL

RR

φ ω

µ

λ

ρF

ρL

Pipe Element States

S – success, no detectable damageF – detectable flawL – detectable leakR - rupture

State Transition Rates

φ – flaw occurrence rateλ – leak failure rateρF – rupture failure rate given flawρL – rupture failure rate given leakω – repair rate via ISI examsµ – repair rate via leak detection

March 2011 PSA 2011 13

Modeling Impact Of NDE Inspections

n  Capture by ω: the repair rate for flaws

where: n  PFI = probability that segment element with flaw will be

inspected n  PFD= probability that flaw is detected given inspection n  TI = mean time between inspections n  TR = mean time to repair after detection

( )ω =+

P PT TFI FD

I R

March 2011 PSA 2011 14

Modeling Impact Of Leak Tests

n  Capture by µ: the repair rate for leaks

where: n  PLD= probability that leak is detected given inspection n  TI = mean time between inspections n  TR = mean time to repair after detection

µ =+

PT T

LD

LI R( )

March 2011 PSA 2011 15

BWR Recirculation Pipe LOCA Frequency from NUREG-1829

1.0E-09

1.0E-08

1.0E-07

1.0E-06

1.0E-05

1.0E-04

5 15 25 35 45 55

Plant Age (Years)

BW

R R

ecirc

ulat

ion

Pipi

ng L

OC

A F

requ

ency

/yea

r

No ISI/No Leak InspectionNo ISI/ Leak Inspection 1/Refueling OutageNo ISI/ Leak Inspection 1/WeekISI/Leak Inspection 1/Refueling OutageISI/Leak Inspection 1/Week

March 2011 PSA 2011 16

Application to Justify On-line Leakage Monitoring for PBMR

0

0

1

10

100

RIM Case 1: T= 27yrs -LWR RIM

RIM Case 2: T= 27yrs - No

RIM

RIM Case 3: T= 40yrs -LWR RIM

RIM Case 4: T= 40yrs - No

RIM

RIM Case 5: T= 40yrs -

Primary NDE

RIM Case 6: T= 40yrs -

OLLD - NoNDE

RIM Case 7: T= 40yrs -OLLD -

SecondaryNDE

RIM Case 8: T= 40yrs -OLLD -

Primary NDE

Plant Age (T) and RIM Strategy

Inte

grity

Man

agem

ent F

acto

r

LWR RIM:RIM = reliability and integrity management100% Leak Tested @90% POD every 1.5yrs50% NDE @50% POD every 10 yrsPOD = Probability of detectionNDE = Non-destructive examination

PBMR RIMOLLD - On-line leak detection with 90% POD in 24hrsSecondary NDE - 50%POD every 12yrsPrimary NDE - 90% POD every 12 years

PBMR RIM CasesLWR RIM Cases

Pipe Failure Rates For Internal Flooding PRA

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Guidance Available for IFPRA n  Guidelines for Performance of Internal Flooding

Probabilistic Risk Assessment* 1019194 Final Report, December 2009

n  Pipe Rupture Frequencies for Internal Flooding Probabilistic Risk Assessments* Revision 3 3002000079 Final Report, May 2013

n  ASME/ANS PRA Standard RA-Sb-2013, Part 3** * Available from EPRI ** Available from ASME or ANS

Scope of Pipe Failure Rate Cases in EPRI Report 3002000079

SystemReactor

Type Type ASME ClassNominal

Pipe SizesClass 3

Non-SafetyClass 3

Non-SafetyClass 3

Non-SafetyClass 3

Non-SafetyClass 3

Non-SafetyClass 3

Non-SafetyNormal System

Protected Against Water HammerSIR Outside Cont All N/A Class 3 4,10,24 (>2)

CCW and CST All N/A Non-Safety 24 (>2)BWR N/APWR N/ABWR N/APWR N/ABWR N/APWR N/ABWR N/APWR N/A

Piping Non-SafetyExpansion Joints Non-Safety

10,24 (>2)Non-Safety

BWR

PWR

2,4,10,24

Fire Protection All Non-Safety 4,6,24

HP Steam

LP Steam

Ext Steam

Service Water

Circulating Water All >24

River

Lake

Sea

River

Lake

Sea

FWC

Failure Rates And Rupture Frequencies For Different Piping Systems

1.0E-10

1.0E-09

1.0E-08

1.0E-07

1.0E-06

1.0E-05

1.0E-04

PWRFeedwater

FireProtection

>6"

Circ Water >24"

PWRCondensate

BWR SeaSW Class 3 >

10"

ECCS Class2 >10"

BWR LakeSW Class 3 >

10"

CCW Class 3>10"

BWR Class 1Pipe toNozzle

Failu

re R

ate,

per

line

ar fo

ot-y

ear

All Failure ModesCatastrophic Failure Modes

Non Safety Class Safety Class

Pressure Boundary Failure Modes Considered in IFPRA n  Spray Events

n  Can screen out leaks < 1gpm n  Initial flood rates from 1gpm to 100gpm n  Typically within drain and sump capacities n  Damage is typically localized (within∼10ft)

n  Flood Events n  Would also involve sprays n  May involve pipe whip effects n  Initial flood rates from 100gpm to 2,000gpm (or lower limit based on system capacity) n  Typically in excess of drain and sump capacities n  Flood volume and damage dependent on time to isolate, flood propagation paths, etc.

n  Major Flood Events n  Initial flood rates > 2,000 and up to system capacity n  May involve pipe whip effects n  Well in excess of any drain or sump capacity outside containment n  Flood volume and damage also dependent on time to isolate, flood propagation paths,

etc. n  High Energy Line Breaks

n  Pressure and temperature effects n  Steam jet and pipe whip effects n  Inadvertent fire protection (sprinkler) operation n  Other effects of flooding

Pipe Failure Rates for Screening Analysis

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BWR River Site Class 3 Service Water Results – Baseline Results for 24” Pipe

1.0E-12

1.0E-11

1.0E-10

1.0E-09

1.0E-08

1.0E-07

1.0E-06

1.0E-05

1.0E-04

0.01 0.10 1.00 10.00 100.00

X, Equivalent Break Size (in.)

Freq

uenc

y of

Rup

ture

Siz

e G

reat

er th

an o

r Equ

al to

X (e

vent

s pe

r RO

Y-ft.

)

95%tileMean50%tile (Median)5%tile

March 2011 PSA 2011 24

Impact of Design Features to Reduce FP Susceptibility to Water Hammer

1.0E-08

1.0E-07

1.0E-06

1.0E-05

1.0E-04

0.01 0.10 1.00 10.00 100.00

X, Equivalent Break Size (in.)

Freq

uenc

y of

Rup

ture

Siz

e G

reat

er th

an o

r Equ

al to

X (e

vent

s pe

r RO

Y-ft.

)

Current Study w/ WHCurrent Study no WHEPRI 1013141 FP NPS > 10"

Spray1-100gpm

Flood100-2,000gpm

Major Flood> 2,000gpm

Unfavorable Trend in FP Performance Since 2004

March 2011 PSA 2011 25

Impact of Integrity Management Strategies for Fire Protection Piping

1.0E-09

1.0E-08

1.0E-07

1.0E-06

1.0E-05

1.0E-04

0.01 0.10 1.00 10.00 100.00

X, Equivalent Break Size (in.)

Freq

uenc

y of

Rup

ture

Siz

e G

reat

er th

an o

r Equ

al to

X (e

vent

s pe

r RO

Y-ft.

)

Current Study w/ WHCurrent Study no WHEPRI 1013141 FP NPS > 10"Current Study No WH + Yearly Leak TestCurrent Study No WH + Quaterly Leak Test

Plant Level Flood Frequencies Added in Revision 3 of Failure Rate Data Report

n  Plant level flood events identified in U.S. service data from 1980-2011 encompass about 3,500 reactor years

n  Flood events analyzed by system, building, event type, cause, flood rate, flood volume, and event description

n  160 flood events identified in four major event types n  Pressure boundary failures (PBF) including HELB, piping and

component failures n  Maintenance induced floods not involving PBF n  Spurious fire protection system actuation n  Design deficiency

Distribution of Flood Events by Flood Type

Risk Evaluation Of LOCA Debris Blockage Of ECCS

(GSI-191)

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GSI-191 Modifies LOCA Success Criteria n  Swedish BWR event in 1980’s identified new failure mechanism

involving LOCA induced debris formation and flow blockage n  Normal PRA LOCA success criteria

n  based on capability to control inventory and remove heat via different systems

n  Independent of break location n  3 to 5 LOCA size categories (e.g. small, medium, large) from 0.5 in. n  break sizes greater than about 6in. have same criteria

n  GSI-191 PRA LOCA success criteria n  Debris formation highly dependent on break size, location, and

orientation within containment relative to insulation distribution n  Debris formation not significant for break sizes less than ≅10in. n  need to consider a continuous break size distribution up to and

including a DEGB at each location within RCS pressure boundary PSA 2013 29

Evolution of LOCA Frequency Estimates n  WASH-1400

n  Derived from gas-pipeline data and expert judgments to credit ASME nuclear piping codes

n  Used for all PRAs from 1975 to mid-1990s

n  NUREG/CR-5750 n  Incorporated some nuclear power plant service data n  Used for PRAs until NUREG-1829

n  NUREG-1829 (NUREG/CR-6928) n  Expert elicitation informed by service data and fracture mechanics n  Basis for most current PRAs

n  GSI-191 n  New requirements for more refined location and break size

dependent LOCA frequencies PSA 2013 30

LOCA Frequencies Objectives n  Incorporate insights from previous work on LOCA frequencies n  Characterize LOCA initiating events and their frequencies with

respect to: n  Specific components, materials, dimensions n  Specific locations n  Range of break sizes n  Damage / Degradation mechanisms and mitigation effectiveness

n  Quantify both aleatory and epistemic uncertainties; augment with sensitivity studies

n  Support interfaces with other parts of the GSI-191 evaluation n  Support submittal and RAI responses

31 PSA 2013

Risk Informed GSI-191

Pipe Rupture Model

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n  Total LOCA frequency of a given break size x is the sum of frequencies over a set of homogenous weld categories i, x treated as a continuous function, mi is the number of welds in the category, ρix is the rupture frequency per weld.

F(LOCAx ) = miρixi∑

Pipe Rupture Model

n  Rupture frequency for category i and size x is product of three terms: failure rate, conditional rupture size probability, and integrity management factor

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ρix = λikP Rx Fik( )k∑ Iik

Impact of Inspections on Weld Failure Rates

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Model Characteristics n  Failure defined as events involving repair or replacement n  All failures are assumed to be pre-cursors of pipe ruptures n  Failure rates based on Bayes’ analysis of pooled vendor

specific service data using accepted RI-ISI methodology n  Failure rates are conditional on the susceptibility to damage

mechanisms using RI-ISI damage mechanism criteria n  CRPs derived from expert elicitation inputs to NUREG-1829 n  Integrity management factors from Markov model from RI-

ISI program n  Epistemic uncertainties via Monte Carlo or via log normal

formulas for product terms

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Weld Categories

n  Service data indicates vast majority of pipe failures occur at or near welds

n  Weld locations provide convenient roadmap where pipe failures can occur

n  Weld categories define by: n  System (e.g. hot leg, cold leg, surge line..) n  Pipe Size n  Applicable susceptible damage mechanisms

determined via (EPRI) RI-ISI program

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Example Weld Categories

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System  Case   System   Component  

Case  Weld  Type   Applicable  DM  

STP  Total  No.  of  Welds  

Pipe  Size  (in.)  

DEGB  Size  (in.)  

1   RC  Hot  Leg  1A   B-­‐F   SC,  D&C   4   29   41.0  1B   B-­‐J   D&C   11   29   41.0  1C   B-­‐J   TF,  D&C   1   29   41.0  

2   RC  SG  Inlet   2   B-­‐F   SC,  D&C   4   29   41.0  

3   RC  Cold  Leg  

3A   B-­‐F  SC,  D&C  

4   27.5   38.9  3B   B-­‐J   4   31   43.8  3C   B-­‐J  

D&C   12   27.5   38.9  3D   B-­‐J   24   31   43.8  

4   RC  Surge  

4A   B-­‐F   SC,  TF,  D&C   1   16   22.6  4B   B-­‐J  

TF,  D&C  7   16   22.6  

4C   BC   2   16   22.6  4D   B-­‐J   6   2.5   3.5  

 

Failure Rate Development n  Pooled data for reactor vendor population

n  Westinghouse PWR data for STPEGs n  CE data for Calvert Cliffs

n  Separate failure rates for each of 40 to 50 weld categories n  Start with broad priors with means anchored to industry data

and RF =100 n  Bayes updates for different hypotheses about weld counts and

DM susceptibilities based on RI-ISI evaluations n  Mixture distributions to combine Bayes’ Updates n  Same methodology as developed for EPRI RI-ISI program

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Conditional Rupture Size Probabilities n  Reverse engineered from NUREG-1829 expert

elicitation inputs for reference systems (hot leg, cold leg, surge line, HPI line)

n  Inputs aggregated at system level using geometric mean method

n  Results for target LOCA frequency distributions converted to CRP distributions for reference systems

n  CRP distributions used as priors in Bayes’ update using industry data with zero ruptures and many failures.

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Example Failure Data Query

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Summary of Component Exposure Estimates W-PWRs

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System  Case   System   Component  

Case  Weld  Type  

Best  Estimate  

Upper  Bound  

Lower  Bound  

1   RCS  Hot  Leg  1A   B-­‐F   21,732   24,147   12,074  

1B,  1C   B-­‐J   32,297   36,221   24,147  2   RCS  SG  Inlet   2   B-­‐F   12,074   12,074   12,074  

3   RCS  Cold  Leg  3A   B-­‐F   22,315   24,794   12,397  3B   B-­‐J   123,764   177,279   99,177  

4   RCS  Surge  4A   B-­‐F   3,914   3,914   3,914  4B   B-­‐J   27,007   54,013   13,503  4C   BC   7,828   7,828   7,828  

5   PZR  5A–5D   B-­‐J   351,127   496,158   286,245  5E–5G   B-­‐F   19,083   19,083   19,083  

6   SB   6A–6B   B-­‐J   744,237   1,144,980   366,394  

7  SIR  Lines  Excl.  Accumulator   7A–7L   B-­‐J   590,797   637,190   507,518  

SIR  Accumulator  Lines   7M–7O   B-­‐J   175,067   277,693   132,810  

8   CVCS  8A–8D   B-­‐J   562,348   627,324   403,018  8E,  8F   BC   81,393   90,797   58,332  

Total  Estimated  Weld-­‐Yrs   2,774,983   3,633,494   1,958,513    

Mean Failure Rate Results for STP Class 1 Components

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Example Results – Hot Leg B-F Weld at RPV Nozzle

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Epistemic Uncertainties for Individual Welds –Case 1B

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New Paradigm for LWR PRA LOCA Frequencies

n  Historically LOCA frequencies only dependent on reactor type gross size range (e.g. small, medium, large) with minor differences on size ranges

n  Historically PRAs have relied on generic estimates from a single source of data

n  GSI-191 identifies the need to develop plant specific LOCA frequencies

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Plant to Plant Variability

n  Among Westinghouse PWRs there are 2 loop, 3 loop, and 4 loop designs

n  CE PWRs use different piping materials and different weld types; 1/3 larger hot legs

n  All plants have different numbers of welds and different weld categories incl. pipe sizes

n  Strong evidence to suggest plant to plant variability in LOCA frequencies

n  Lack of variability in PRAs due only to single source of generic data PSA 2013 46

Aggregated LOCA Frequencies are Plant Specific!

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PRA  LOCA  Category  

WCGS   STPEGS  Unit  1  

Break  Size  Range  (in.)  

Mean  Frequency  (/rx-­‐yr)  

Break  Size  Range  (in.)  

Mean  Frequency  (/rx-­‐yr)  

Small  LOCA  0.375  -­‐  .50  

1.71E-­‐04  Not  Modeled  

.50  -­‐  2.0   .5  -­‐  2.0   3.54E-­‐04  

Medium  LOCA   2.0  -­‐  6.0   9.38E-­‐06   2.0  -­‐  6.0   2.01E-­‐05  

Large  LOCA   >  6.0   5.77E-­‐07   >  6   2.29E-­‐06  

 

Comparison STP Pipe Induced Mean LOCA Frequencies with NUREG-1829

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Comparison of Calvert Cliffs, STPEGS, and NUREG-1829

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Contributions to LOCA Category 6 (> 31”) Frequency

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GSI-191 Results Summary n  Bottom-up LOCA frequencies for STP comparable to NUREG-1829 for pipe

induced LOCAs; n  Bottom-up LOCA frequencies for Category 6 for Calvert Cliffs appear to be

significantly smaller than for STP and NUREG-1829; no hot leg or SG inlet B-F welds in CE PWRs

n  Large variability in LOCA frequencies for different weld types; more than 3 orders of magnitude variation in mean failure rates

n  Uncertainties in local LOCA frequencies much larger than those for total LOCA frequencies; this result is magnified when debris-induced failures only occur in limited number of locations

n  After applying this method to STPEGS, Calvert Cliffs, and Vogtle there is strong evidence for plant to plant variability in total LOCA frequencies

PSA 2013 51

Summary

n  Pipe failure rate estimates involve large uncertainties; uncertainty modeling and quantification is a must

n  PIPExp database and OECD pipe data exchange provide excellent Type 1 sources

n  Uncertainties in estimating component exposure (“success data”) addressed

n  Experience and expertise required to interpret data, make sensible database queries, and apply failure rate models

PSA 2013 52