ML111890380

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Licensee Slides, LOCA Initiating Event Frequencies and Uncertainties(Draft)
ML111890380
Person / Time
Site: South Texas  STP Nuclear Operating Company icon.png
Issue date: 07/07/2011
From: Kreslyon Fleming, Lydell B
KNF Consulting Services, Scandpower, Risk Management
To: Balwant Singal
Plant Licensing Branch IV
Singal, B K, NRR/DORL, 301-415-301
Shared Package
ML111890371 List:
References
TAC ME5358, TAC ME5359, GSI-191
Download: ML111890380 (68)
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Text

LOCA Initiating Event Frequencies and Uncertainties (Draft)

Risk Informed GSI-191 Resolution Th d Thursday, July J l 7, 7 2011 1:00 pm - 2:00 p.m EDT Public Meeting with STP Nuclear Operating Company Karl N. Fleming KNF Consulting Services LLC Bengt O. Y. Lydell 7/5/11 Pre-Licensing Meeting 1

Risk Informed GSI-191 Discussion Topics

  • LOCA frequencies scope and objectives
  • Technical approach
  • Step by step procedure with examples
  • Technical issues to be addressed
  • Resolution of NRC questions from June 2011 meeting 7/5/11 Pre-Licensing Meeting 2

Risk Informed GSI-191 LOCA Frequencies Objectives

  • Incorporate insights from previous work on LOCA frequencies

- Specific components, materials, dimensions

- Specific locations

- Range of break sizes

- Degradation mechanisms and mitigation effectiveness

- Other break characteristics, characteristics ee.g.

g speed

  • Quantify both aleatory and epistemic uncertainties; augment with sensitivity studies
  • Support interfaces with other parts of the GSI-191 evaluation

- LOCA initiating event frequencies for PRA modeling

- Break characterization for evaluation of debris formation

  • Participate in NRC workshops 3 7/5/11 Pre-Licensing Meeting

LOCA Frequency Technical Approach

  • Utilize passive component reliability methods and data from RI-ISI technology
  • Utilize PIPExp database to help resolve uncertainties in failure rates
  • Consider probabilistic fracture mechanics evaluation on selected locations as may be required 4 7/5/11 Pre-Licensing Meeting

LOCA IE Frequency Model 1 of 2 F ( LOCAx ) = mi ix (1) i jx = ix = ik P( Rx Fik ) I ik (2) k Where:

F ( LOCAx ) = Frequency of LOCA of size x, per reactor calendar-year; subject to epistemic uncertainty calculated via Monte Carlo mi = Number of pipe welds of type i; each type determined by pipe size, weld type, applicable damage mechanisms, and inspection status (leak test and NDE); no uncertainty for STP ix = Frequency of rupture of pipe location j belonging to component type i with break size x, subject to epistemic uncertainty calculated via Monte Carlo and uncertainties on the RHS of Equation (2) ik = Failure rate per weld-year for pipe component type i due to failure mechanism k; subject to epistemic uncertainty determined by RI-ISI Bayes method and Eq. (3)

P( Rx Fik ) = Conditional probability of rupture of size x given failure of pipe component type i due to damage mechanism k; subject to epistemic uncertainty determined via expert elicitation (NUREG-1829)

I ik = Integrity management factor for weld type i and failure mechanism k; calculated via Markov Model; subject to epistemic uncertainty determined by Monte Carlo propagation of input parameters using Markov model equations and input parameter uncertainties.

5 7/5/11 Pre-Licensing Meeting

LOCA IE Frequency Model 2 of 2 For a Point Estimate of the Failure Rate for type i and failure mechanism k:

nik nik ik = = (3) ik f ik N iTi nik = Number of failures in pipe component (i.e. weld) type i due to failure mechanism k, very little epistemic uncertainty ik = Component exposure population for welds of type i susceptible to failure mechanism k, subject to epistemic uncertainty determined by expert opinion f ik = Estimate of the fraction of the component exposure population for weld type i that is susceptible to failure mechanism k, subject to epistemic uncertainty; estimated from results of RI-ISI for population of plants and expert opinion.

Ni = Estimate of the average number of pipe welds of type i per reactor in the applicable reactor years exposure for the data collection; subject to plant to plant variability and epistemic uncertainty; estimated from results of RI-ISI for sample population of plants and expert opinion Ti = Total number of reactor years exposure for the data collection for component type i; little or no uncertainty 6 7/5/11 Pre-Licensing Meeting

Step by Step Procedure

1. Determination of weld types (i)
2. Perform data query for failure counts (n)
3. Estimate component exposure (T) and uncertainty
4. Develop component failure rate prior distributions for each DM
5. Perform Bayes update for each exposure case (combination of weld count and DM susceptibility)
6. Apply posterior weighting to combine results for different hypothesis yield conditional failure rate distributions; compute unconditional failure rates for locations with uncertain DM status
7. Develop conditional probability of rupture size given failure probabilities for each weld type and associated epistemic uncertainties
8. Combine the results of Step 6 and Step 7 by Monte Carlo in Eq. (1) for component LOCA frequencies and total LOCA frequencies for each component
9. Apply Markov Model to specialize rupture frequencies for differences in integrity management
10. For intermediate LOCA categories and break sizes, interpolate the results of Step 10 via log-log linear interpolation
11. Calculate total LOCA frequencies from all components and reconcile differences with earlier LOCA frequency estimates 7/5/11 Pre-Licensing Meeting 7

Steps 1 and 2 Failure Data Query

  • Failure defined as any event that involved repair or replacement of damaged component
  • Data query covers operating experience from 1970 through 2010
  • Supports Steps 1 (define weld types) and 2 (failure counts) 7/5/11 Pre-Licensing Meeting 8

Preliminary Results of Data Query Pipe Failures by Mode (1), (2), (3)

Nominal SYSTEM Pipe Size (NPS) Crack- Crack- Small Large Total Leak Full Part Leak Leak 1" 7 1 6 CVC 2" ø 4" 7 1 6 1" 2 2 Safety Injection 4" ø 10" 6 3 1 1 1 Pressurizer-Sample 2" 5 4 1 Pressurizer-PORV 4" ø 10" 2 2 1" 4 1 2 1 Pressurizer-SPRAY 4" ø 10" 3 2 1 Pressurizer-SRV 4" ø 10" 7 6 1 Example chosen Pressurizer-Surge 14" 3 3 to illustrate FR RCS 2" 76 4 10 53 4 5 RCS Cold Leg 32" 4 4 Approach RCS Hot Leg 32" 6 5 1 RHR 1" 6 6 RHR 4" ø 10" 1 1 RC Hot Leg - S/G- 19 19 32" Inlet S/G-System 2" 8 2 2 4 TOTALS 166 12 59 83 6 6 Notes (1) Query accounts for 3914 reactor years based on date of initial criticality from 1970-2010.

(2) Failure is defined as any event that required repair or replacement of damaged component.

(3) Small leaks have leak flows << 1gpm; Leaks < 1gpm; Large Leaks < 10gpm.

7/5/11 Pre-Licensing Meeting 9

Step by Step Procedure

1. Determination of weld types (i)
2. Perform data query for failure counts (n)
3. Estimate component exposure (T) and uncertainty
4. Develop component failure rate prior distributions for each DM
5. Perform Bayes update for each exposure case (combination of weld count and DM susceptibility)
6. Apply posterior weighting to combine results for different hypothesis yield conditional failure rate distributions; compute unconditional failure rates for locations with uncertain DM status
7. Develop conditional probability of rupture size given failure probabilities for each weld type and associated epistemic uncertainties
8. Combine the results of Step 6 and Step 7 by Monte Carlo in Eq. (1) for component LOCA frequencies and total LOCA frequencies for each component
9. Apply Markov Model to specialize rupture frequencies for differences in integrity management
10. For intermediate LOCA categories and break sizes, interpolate the results of Step 10 via log-log linear interpolation
11. Calculate total LOCA frequencies from all components and reconcile differences with earlier LOCA frequency estimates 7/5/11 Pre-Licensing Meeting 10

Step 3 Component Exposure

  • Need Reactor-years of service experience in failure data query
  • Need estimates of component populations per plant
  • Need fractions of the component population susceptible to each damage mechanism (DM) for conditional failure rates given knowledge of applicable damage mechanisms 7/5/11 Pre-Licensing Meeting 11

Reactor Years in Data Query Reactor-Calendar Years WE Type Rx Initial Grid Initial Criticality Connection 2-Loop 570.1 581.4 3-Loop 2052.6 2096.1 4-Loop 1193.9 1236.5 3816.6 3914 Total 7/5/11 Pre-Licensing Meeting 12

Data for Estimating Surge Line Weld Population Weld Population[Note (1)]

PWR Branch Connection to Plant B-F Welds Inline B-J Welds Type Hot Leg Braidwood-1 4-Loop 1 8 2 Braidwood-2 4-Loop 1 7 2 Byron-1 4-Loop 1 6 2 Byron-2 4-Loop 1 6 2 Kewaunee 2-Loop 1 6 2 Koeberg-1 3-Loop 1 5 2 Koeberg-2 3-Loop 1 5 2 STP-1 4-Loop 1 8 2 STP-2 4-Loop 1 8 2 V.C. Summer 3-Loop 1 10 2 Note (1) Kewaunee surge line is NPS10, Remaining plants are NPS 14 to 16 7/5/11 Pre-Licensing Meeting 13

Damage Mechanism Characterization

  • EPRI developed screening criteria to evaluated susceptibility of piping to known damage mechanisms to support RI-ISI
  • Criteria applied in STP RI-ISI application to all welds on Class 1 and 2 pressure boundary
  • PIPExp data base also identifies generic susceptibilities to some damage and failure mechanisms

- Bi-metallic welds with Ni-based allows susceptible to PWSCC

- All welds especially field welds subject to design and construction defects

  • If we know that a specific weld location is susceptible to a given damage mechanism (s) we can specialize the failure rates to that knowledge, i.e. conditional failure rates
  • If we do not know the specific susceptibility to DMs we apply unconditional failure rates 7/5/11 Pre-Licensing Meeting 14

Example Surge Line Damage Mechanism Characterization Confidence Weld Susceptibility Fractions Location Level C-F D&C ECSCC Fretting IGSCC PWSCC TF TGSCC TAE V-F Low N/A 1 N/A N/A N/A 1 N/A N/A N/A N/A B-F Medium N/A 1 N/A N/A N/A 1 N/A N/A N/A N/A High N/A 1 N/A N/A N/A 1 N/A N/A N/A N/A Low N/A 1 N/A N/A N/A N/A 0.01 N/A N/A N/A B-J Medium N/A 1 N/A N/A N/A N/A 0.05 N/A N/A N/A High N/A 1 N/A N/A N/A N/A 0.25 N/A N/A N/A RC-HL Branch Low N/A 1 N/A N/A N/A N/A 1 N/A N/A N/A C

Connection ti Medium N/A

/ 1 N/A

/ N/A

/ N/A

/ N/A

/ 1 N/A

/ N/A

/ N/A

/

High N/A 1 N/A N/A N/A N/A 1 N/A N/A N/A ID Description C-F Corrosion-Fatigue D&C Design & Construction Flaws ECSCC External Chloride-induced SCC IGSCC Intergranular SCC LC-FAT Low-Cycle Fatigue PWSCC Primary Water SCC OVLD Overload TF Thermal Fatigue TGSCC Transgranular SCC TAE Thermal Aging Embrittlement V-F Vibration Fatigue 7/5/11 Pre-Licensing Meeting 15

Example DM evaluation for Surge Line Welds

  • All B-F welds at the pressurizer nozzle are susceptible to PWSCC - no uncertainty for this DM
  • All branch connection welds are susceptible to TF - no uncertainty for this DM
  • Some unknown fraction of B-J welds susceptible to TF - uncertainty in the fraction susceptible for this DM
  • For STP we have a deterministic evaluation of DM Susceptibility for each location 7/5/11 Pre-Licensing Meeting 16

Example Exposure Uncertainty Model for Surge Line Welds

  • BF Welds (at pressurizer nozzle)

- No DM Uncertainty or Weld count Uncertainty

- Only need one Bayes update for one case of exposure

- No DM uncertainty

- Some S plant l t tto plant l t variability i bilit iin numberb off bbranch h connections ti

- Need three updates for weld count uncertainty one for each of high, medium, and low estimates for weld counts

  • BJ Welds

- DM Uncertainty

- Weld Count Uncertainty

- Need Bayes update of priors for each combination of weld count and DM susceptibility Cases ( 3 X 3 = 9) 7/5/11 Pre-Licensing Meeting 17

Treatment of Exposure Uncertainty for B-J Welds and Thermal Fatigue Welds/Rx 6.9 Rx-yrs 3914 Base Exposure 27006.6 Fraction of B-J Welds Exposure Weld Count Exposure Susceptible to Thermal Case Exposure Uncertainty Multiplier Fatigue Probability p=.25 0.0625 0.5 13,503 weld-yrs High (.25 x Base) p=.25 p=.50 0.125 0.1 2,701 weld-yrs High (2 X Base) Medium (.05 x Base) p=.25 0.0625 0.02 540 weld-yrs Low (.01 x Base) p=.25 0.125 0.25 6,752 weld-yrs High (.25 x Base) p=.50 p=.50 0.25 0.05 1,350 weld-yrs Medium (1.0 X Base) Medium (.05 x Base) p=.25 0.125 0.01 270 weld-yrs Low (.01 x Base) p=.25 0.0625 0.125 3,376 weld-yrs High (.25 x Base) p=.25 p=.50 0.125 0.025 675 weld-yrs Low (0.5 X Base) Medium (.05 x Base) p=.25 0.0625 0.005 135 weld-yrs Low (.01 x Base) 7/5/11 Pre-Licensing Meeting 18

Step by Step Procedure

1. Determination of weld types (i)
2. Perform data query for failure counts (n)
3. Estimate component exposure (T) and uncertainty
4. Develop component failure rate prior distributions for each DM
5. Perform Bayes update for each exposure case (combination of weld count and DM susceptibility)
6. Apply posterior weighting to combine results for different hypothesis yield conditional failure rate distributions; compute unconditional failure rates for locations with uncertain DM status
7. Develop conditional probability of rupture size given failure probabilities for each weld type and associated epistemic uncertainties
8. Combine the results of Step 6 and Step 7 by Monte Carlo in Eq. (1) for component LOCA frequencies and total LOCA frequencies for each component
9. Apply Markov Model to specialize rupture frequencies for differences in integrity management
10. For intermediate LOCA categories and break sizes, interpolate the results of Step 10 via log-log linear interpolation
11. Calculate total LOCA frequencies from all components and reconcile differences with earlier LOCA frequency estimates 7/5/11 Pre-Licensing Meeting 19

Step 4 Prior Distributions

  • Initially developed in EPRI RI-ISI Program (EPRI TR-111880)
  • Based on Wash-1400 era state of knowledge on LOCA frequencies with estimates for SLOCA ranging from 10-2 to 10-6 per year and allocation down to welds based on weld count estimates in EPRI TR-111880
  • Lognormal distributions with large range factors (100)
  • Means adjusted based on gross estimates from service data
  • Justification for STP priors will be provided if different from EPRI TR-111880
  • Priors updated with results of data queries and exposure estimates to determine failure rates 7/5/11 Pre-Licensing Meeting 20

Example Prior Distributions Prior Distribution (Failures per Weld-Year)

Damage Mechanism Range Dist. Type Mean Median Factor Stress Corrosion Cracking Lognormal 4.27E-05 8.48E-07 100 Design and Construction Lognormal 2.75E-06 5.46E-08 100 Thermal Fatigue Lognormal 1.34E-05 2.66E-07 100 7/5/11 Pre-Licensing Meeting 21

Step by Step Procedure

1. Determination of weld types (i)
2. Perform data query for failure counts (n)
3. Estimate component exposure (T) and uncertainty
4. Develop component failure rate prior distributions for each DM
5. Perform Bayes update for each exposure case (combination of weld count and DM susceptibility)
6. Apply posterior weighting to combine results for different hypothesis yield conditional f il failure rate t di distributions; t ib ti compute t unconditional diti l ffailure il rates t ffor llocations ti with ith uncertain t i DM status
7. Develop conditional probability of rupture size given failure probabilities for each weld type and associated epistemic uncertainties
8. Combine the results of Step 6 and Step 7 by Monte Carlo in Eq. (1) for component LOCA frequencies and total LOCA frequencies for each component
9. Apply Markov Model to specialize rupture frequencies for differences in integrity management
10. For intermediate LOCA categories and break sizes, interpolate the results of Step 10 via log-log linear interpolation
11. Calculate total LOCA frequencies from all components and reconcile differences with earlier LOCA frequency estimates 7/5/11 Pre-Licensing Meeting 22

Step 5 Bayes Updates

  • Need library of failure rates for each weld type and damage mechanism
  • This library was first developed in EPRI TR 111880 for the EPRI RI-ISI program; will be updated for STP to reflect operating experience through 2010
  • For each failure rate we perform 1 Bayes Bayes update for each exposure hypothesis and then combine the results of those into one probabilistically weighted distribution
  • For each weld type and damage mechanism need

- Unconditional failure rates for cases where DM status at a location is unknown

- Conditional failure rates for cases where DM status is known

  • Perform using RDAT-Plus' software 7/5/11 Pre-Licensing Meeting 23

Step 5 Example Bayes Updates for Surge Line Welds Weld Type DM Prior Distribution[Note (1)] Evidence[Note (2)] Bayes Posterior Distribution[Note (1)]

Weld and DM[Note Susceptibility (3)]

Count Case Type Median RF Failures Exposure Mean 5th 50th 95th RF Case Surge BF SC Base Base Lognormal 8.48E-07 100 3 3914 5.62E-04 1.23E-04 4.83E-04 1.27E-03 3.2 Surge BF DC Base Base Lognormal 5.46E-08 100 0 3914 1.41E-06 5.41E-10 5.33E-08 4.77E-06 93.9 Surge BC TF Base Base Lognormal 2.66E-07 100 0 7828 3.25E-06 2.53E-09 2.34E-07 1.47E-05 76.1 Surge BC DC Base Base Lognormal 5.46E-08 100 0 7828 1.17E-06 5.37E-10 5.24E-08 4.37E-06 90.1 Surge BJ TF Low Low Lognormal 2.66E-07 100 0 135 9.75E-06 2.66E-09 2.65E-07 2.58E-05 98.5 Surge g BJ TF Low Medium Lognormal g 2.66E-07 100 0 675 7.17E-06 2.64E-09 2.61E-07 2.36E-05 94.6 Surge BJ TF Low High Lognormal 2.66E-07 100 0 3376 4.48E-06 2.59E-09 2.48E-07 1.85E-05 84.5 Surge BJ TF Medium Low Lognormal 2.66E-07 100 0 270 8.70E-06 2.65E-09 2.64E-07 2.51E-05 97.4 Surge BJ TF Medium Medium Lognormal 2.66E-07 100 0 1350 5.98E-06 2.62E-09 2.57E-07 2.18E-05 91.2 Surge BJ TF Medium High Lognormal 2.66E-07 100 0 6752 3.46E-06 2.54E-09 2.37E-07 1.54E-05 77.7 Surge BJ TF High Low Lognormal 2.66E-07 100 0 540 7.55E-06 2.64E-09 2.62E-07 2.41E-05 95.4 Surge BJ TF High Medium Lognormal 2.66E-07 100 0 2701 4.83E-06 2.60E-09 2.51E-07 1.94E-05 86.4 Surge BJ TF High High Lognormal 2.66E-07 100 0 13503 2.58E-06 2.47E-09 2.22E-07 1.21E-05 69.8 Surge BJ DC Low Base Lognormal 5.46E-08 100 0 13503 9.83E-07 5.33E-10 5.14E-08 3.96E-06 86.2 Surge BJ DC Medium Base Lognormal 5.46E-08 100 0 27007 7.66E-07 5.25E-10 4.94E-08 3.34E-06 79.8 Surge BJ DC High Base Lognormal 5.46E-08 100 0 54013 5.77E-07 5.12E-10 4.65E-08 2.67E-06 72.2 Notes (1) Failure rates in units of failures per weld-year (2) Exposure in units of weld-years (3) SC = stress corrosion cracking; TF = thermal fatigue; DC = design and construction defects; BF = B-F weld; BC = Branch connection weld; BJ = B-J weld 7/5/11 Pre-Licensing Meeting 24

Step by Step Procedure

1. Determination of weld types (i)
2. Perform data query for failure counts (n)
3. Estimate component exposure (T) and uncertainty
4. Develop component failure rate prior distributions for each DM
5. Perform Bayes update for each exposure case (combination of weld count and DM susceptibility)
6. Apply posterior weighting to combine results for different hypothesis yield conditional failure rate distributions; compute unconditional failure rates for locations with uncertain DM status
7. Develop conditional probability of rupture size given failure probabilities for each weld type and associated epistemic uncertainties
8. Combine the results of Step 6 and Step 7 by Monte Carlo in Eq. (1) for component LOCA frequencies and total LOCA frequencies for each component
9. Apply Markov Model to specialize rupture frequencies for differences in integrity management
10. For intermediate LOCA categories and break sizes, interpolate the results of Step 10 via log-log linear interpolation
11. Calculate total LOCA frequencies from all components and reconcile differences with earlier LOCA frequency estimates 7/5/11 Pre-Licensing Meeting 25

Step 6 Posterior Weighting

  • Purpose is to develop a single uncertainty distribution for the failure rate that probabilistically weights each exposure hypothesis
  • Use a discrete probability distribution across the cases as developed in the previous event tree
  • Method established and applied in EPRI RI-ISI program
  • Method referred to as Bayes posterior weighting 7/5/11 Pre-Licensing Meeting 26

Failure Rate Options for Thermal Fatigue in B-J Welds

  • Case 1 FR for B-J Weld susceptible to TF

- Sum of applicable contributions: TF+D&C

- TF failure rate conditional on TF susceptibility ( f in Eq. (3) < 1)

  • Case 2 FR for B-J weld whose susceptibility to TF is unknown

- Sum of applicable contributions: TF+D&C

- TF failure f il rate t isi unconditional diti l ( f iin E Eq. (3)

(3)= 1)

  • Case 3 FR for B-J weld not susceptible to TF

- Includes only contributions from D&C Case No. Evaluation Case Mean 5%tile 50%tile 95%tile 1 B-J Total Conditional on TF 6.75E-06 9.60E-09 3.23E-07 1.55E-05 2 B-J Total Unconditional 2.62E-06 8.21E-09 2.23E-07 7.69E-06 3 B-J Total Conditional on no TF 7.66E-07 5.25E-10 4.94E-08 3.34E-06 7/5/11 Pre-Licensing Meeting 27

Impact of RI-ISI Damage Mechanism Evaluation on RCS Weld Failure Rates (2005 RI-ISI for Koeberg) 7/5/11 Pre-Licensing Meeting 28

Step by Step Procedure

1. Determination of weld types (i)
2. Perform data query for failure counts (n)
3. Estimate component exposure (T) and uncertainty
4. Develop component failure rate prior distributions for each DM
5. Perform Bayes update for each exposure case (combination of weld count and DM susceptibility)
6. Apply posterior weighting to combine results for different hypothesis yield conditional failure rate distributions; compute unconditional failure rates for locations with uncertain DM status
7. Develop conditional probability of rupture size given failure probabilities for each weld type and associated epistemic uncertainties
8. Combine the results of Step 6 and Step 7 by Monte Carlo in Eq. (1) for component LOCA frequencies and total LOCA frequencies for each component
9. Apply Markov Model to specialize rupture frequencies for differences in integrity management
10. For intermediate LOCA categories and break sizes, interpolate the results of Step 10 via log-log linear interpolation
11. Calculate total LOCA frequencies from all components and reconcile differences with earlier LOCA frequency estimates 7/5/11 Pre-Licensing Meeting 29

Step 7 Conditional Probability of Pipe Rupture

  • Service experience includes 178 pipe failures and millions of weld-years of exposure, which is sufficient to support failure rate estimates
  • Model assumption that pipe failures are precursors to LOCAs such that LOCA frequencies are the product of failure rates and conditional LOCA probabilities
  • NUREG-1829 viewed as most relevant and up to date source of information on LOCA frequencies and uncertainty
  • Our approach for this step is to convert information in terms of LOCA frequencies into conditional probabilities of pipe ruptures 7/5/11 Pre-Licensing Meeting 30

Step 7 Conditional Probability of Pipe Rupture

  • Step 7.1 Benchmark of Lydells Base Case LOCA frequencies for PWR hot leg, surge line, and HPI line
  • Step 7.2 Compare results of individual expert elicitation LOCA Frequencies from NUREG-1829 to base case
  • Step 7 7.3 3 Set Target LOCA frequencies that encompass elicitation results
  • Step 7.4 Derive conditional rupture probability distributions that when combines with Lydell failure rate estimates match the target LOCA frequencies
  • Step 7.5 Perform Bayes updates that incorporate evidence on pipe failures without LOCAs 7/5/11 Pre-Licensing Meeting 31

7.1 Benchmark of Lognormal Model to Lydell HPI Base Case -HPI 7/5/11 Pre-Licensing Meeting 32

7.1 Benchmark of Lognormal Model to Lydell HPI Base Case -Surge Line 7/5/11 Pre-Licensing Meeting 33

Individual Estimates by Component in Appendix L NUREG-1829 7/5/11 Pre-Licensing Meeting 34

Step 7.2 Review of NUREG-1829 Data

  • Used supporting information for NUREG-1829 recently released by NRC some of which is in Appendix L
  • 9 experts provided estimates for LOCA frequencies for specific components
  • Each expert estimate treated as lognormal distribution for each LOCA Category frequency
  • Lognormal distributions combined using posterior weighting procedure to produce a single composite experts distribution
  • Each of the nine experts given equal weight
  • Sanity check performed by comparing results to the component failure rate distribution in the Lydell Base case results 7/5/11 Pre-Licensing Meeting 35

Steps 7.2 and 7.3 Selection of HPI Target LOCA Frequencies 7/5/11 Pre-Licensing Meeting 36

Steps 7.2 and 7.3 Selection of Surge Line Target Frequencies 7/5/11 Pre-Licensing Meeting 37

Step 7.4 Benchmarking Target Frequencies - HPI 7/5/11 Pre-Licensing Meeting 38

Step 7.4 Benchmarking Target Frequencies - Surge Line 7/5/11 Pre-Licensing Meeting 39

Step 7.4 CRPs that Match HPI Targets 7/5/11 Pre-Licensing Meeting 40

Step 7.4 CRPs that Match Surge Line Targets 7/5/11 Pre-Licensing Meeting 41

Step 7.4 CRPs that Match Hot Leg Targets 7/5/11 Pre-Licensing Meeting 42

Step 7.4 Example Conditional Probability Distributions Distribution Input Parameters Truncated Distribution Parameters LOCA Break Component Median Range Category Size (in.) Type [Note (1)

Median Mean 5%tile 95%tile Factor 1 .5 1.39E-04 2.09E+01 1.39E-04 7.69E-04 6.69E-06 2.89E-03 2 1.5 2.49E-05 2.94E+01 2.49E-05 2.02E-04 8.37E-07 7.38E-04 RCS-Hot Leg 3 3 8.65E-06 3.64E+01 8.62E-06 9.59E-05 2.36E-07 3.24E-04 4 6.76 2.43E-06 4.76E+01 2.43E-06 3.75E-05 5.20E-08 1.16E-04 5 14 8.10E-07 5.90E+01 7.96E-07 2.38E-05 1.34E-08 4.82E-05 6 31.5 2.20E-07 7.53E+01 2.19E-07 6.79E-06 2.96E-09 1.66E-05 Lognormal 1 .5 4.73E-02 1.40E+02 4.73E-03 1.19E-01 2.66E-04 6.11E-01 truncated at RCS-Surge 2 1.5 6.06E-03 1.92E+02 6.06E-03 5.83E-02 2.89E-05 3.49E-01 1.0 Line 3 3 2.06E-03 2.45E+02 2.06E-03 3.85E-02 7.96E-06 2.23E-01 4 6.76 6.43E-04 3.68E+02 6.43E-04 2.54E-02 1.69E-06 1.31E-01 5 14 2.24E-04 4.85E+02 2.24E-04 1.70E-02 4.51E-07 7.26E-02 1 .5 5.85E-03 2.20E+01 5.78E-03 2.15E-02 3.77E-04 8.88E-02 HPI Line 2 1.5 1.20E-03 8.61E+00 1.18E-03 5.87E-03 6.34E-05 2.30E-02 3 3 4.56E-04 8.61E+00 4.59E-04 2.61E-03 2.07E-05 1.01E-02 Note (1) These are medians to specify the input distribution to Crystal Ball' prior to truncation; the median of the truncated distribution is generally different following truncation.

7/5/11 Pre-Licensing Meeting 43

Step 7.5 Bayes Update to Incorporate Service Data

  • Results of Steps 7.1 - 7.4 produce Bayes prior distributions for CRP for each LOCA category
  • Prior distributions are Lognormal truncated at CRP = 1.0
  • We update these priors with the service data for surge line welds: 0 LOCAs in each Category out of 3 failures.
  • Even though this is weak evidence, it impacts the upper tails and changes the mean CRPs -

could impact risk significance 7/5/11 Pre-Licensing Meeting 44

Step 7.5 Bayes Update of Surge CRP Priors: 0 LOCAs in 3 Failures Distribution Parameters(2)

LOCA Break Distribution Type (1) Range Category Size (in.) Mean 5%tile 50%tile 95%tile Factor(3) 1 0.5 Prior Truncated Lognormal 1.05E-01 5.38E-04 2.59E-02 5.36E-01 53.6 Posterior Lognormal-Binomial 4.43E-02 4.11E-04 1.45E-02 1.95E-01 21.8 2 1.5 Prior Truncated Lognormal 3.46E-02 7.41E-05 3.97E-03 1.76E-01 48.8 Posterior Lognormal-Binomial Lognormal Binomial 1 75E 02 1.75E-02 6 75E 05 6.75E-05 3 17E 03 3.17E-03 8 45E 02 8.45E-02 35 4 35.4 3 3 Prior Truncated Lognormal 1.85E-02 1.99E-05 1.34E-03 8.24E-02 64.3 Posterior Lognormal-Binomial 1.00E-02 1.88E-05 1.17E-03 4.79E-02 50.5 4 6.76 Prior Truncated Lognormal 9.09E-03 4.34E-06 3.88E-04 3.32E-02 87.5 Posterior Lognormal-Binomial 5.27E-03 4.22E-06 3.60E-04 2.33E-02 74.3 5 14 Prior Truncated Lognormal 5.16E-03 1.12E-06 1.34E-04 1.55E-02 117 Posterior Lognormal-Binomial 3.10E-03 1.11E-06 1.28E-04 1.21E-02 105 Notes (1) Prior lognormal distributions truncated at 1.0.

(2) Values for means and percentiles represent conditional probability of LOCA category given pipe failure.

(3) Range Factor = SQRT(95%tile/5%tile) 7/5/11 Pre-Licensing Meeting 45

Step by Step Procedure

1. Determination of weld types (i)
2. Perform data query for failure counts (n)
3. Estimate component exposure (T) and uncertainty
4. Develop component failure rate prior distributions for each DM
5. Perform Bayes update for each exposure case (combination of weld count and DM susceptibility)
6. Apply posterior weighting to combine results for different hypothesis yield conditional failure rate distributions; compute unconditional failure rates for locations with uncertain DM status
7. Develop conditional probability of rupture size given failure probabilities for each weld type and associated epistemic uncertainties
8. Combine the results of Step 6 and Step 7 by Monte Carlo in Eq. (1) for component LOCA frequencies and total LOCA frequencies for each component
9. Apply Markov Model to specialize rupture frequencies for differences in integrity management
10. For intermediate LOCA categories and break sizes, interpolate the results of Step 10 via log-log linear interpolation
11. Calculate total LOCA frequencies from all components and reconcile differences with earlier LOCA frequency estimates 7/5/11 Pre-Licensing Meeting 46

Step 8 Calculate Component LOCA Frequencies

  • Need to calculate LOCA frequencies for each component
  • LOCA frequency is the product of the failure rate and the conditional probability y of LOCA vs.

Break Size

  • Calculated via Monte Carlo simulation by sampling from the applicable failure rate and CRP distributions
  • Will be performed on a STP specific basis for each component 7/5/11 Pre-Licensing Meeting 47

Step 8 Calculate Component LOCA Frequencies

  • Base Case Results

- Assume all BJ welds are susceptible to thermal fatigue (TF) and D&C

- All BC welds are susceptible to TF and D&C

- All BF welds are susceptible to SC and D&C

  • Sensitivity Case

- Assume no BJ welds are susceptible to TF

- Assume BF welds mitigate SC via weld overlay

- Other assumptions same as Base Case 7/5/11 Pre-Licensing Meeting 48

LOCA Frequencies for Each Weld Type Distribution Parameters (1)

Weld Type Parameter RF Mean 5%tile 50%tile 95%tile Failure Rate 5.61E-04 1.37E-04 4.38E-04 1.40E-03 3.2 Category 1 2.13E-05 1.44E-07 3.81E-06 9.35E-05 25.5 Category 2 1.03E-05 2.44E-08 1.01E-06 4.26E-05 41.7 B-F Category 3 6.71E-06 6.94E-09 4.10E-07 2.38E-05 58.6 Category 4 3.67E-06 1.55E-09 1.36E-07 1.16E-05 86.6 Category 5 2.22E-06 4.27E-10 5.05E-08 5.95E-06 118.0 Failure Rate 4.16E-06 4.16E 06 9.75E-09 9.75E 09 2.91E-07 2.91E 07 1.17E-05 1.17E 05 34.7 Category 1 1.43E-07 2.65E-11 2.58E-09 2.87E-07 104.1 Branch Category 2 8.14E-08 5.02E-12 6.87E-10 1.12E-07 149.3 Connection Category 3 4.35E-08 1.51E-12 2.81E-10 5.73E-08 194.9 Category 4 2.87E-08 3.68E-13 9.32E-11 2.55E-08 263.1 Category 5 1.45E-08 1.06E-13 3.45E-11 1.23E-08 339.4 Failure Rate 2.03E-07 2.66E-11 2.86E-09 3.74E-07 118.6 Category 1 4.38E-08 1.12E-12 2.68E-10 6.33E-08 238.3 Category 2 2.13E-08 2.34E-13 7.40E-11 2.38E-08 319.0 B-J Category 3 1.49E-08 7.21E-14 2.98E-11 1.15E-08 398.5 Category 4 8.68E-09 1.85E-14 9.77E-12 4.92E-09 515.9 Category 5 2.07E-09 1.26E-13 2.23E-11 3.84E-09 175.0 Note (1) RF = SQRT(95%tile/5%tile) 7/5/11 Pre-Licensing Meeting 49

LOCA Frequencies for Surge Line Distribution Parameters (2)

Weld Type Parameter RF Mean 5%tile 50%tile 95%tile Failure Rate 5.71E-04 1.37E-04 4.39E-04 1.42E-03 3.2 Category 1 2.19E-05 1.44E-07 3.81E-06 9.45E-05 25.6 Base Case Category 2 1.06E-05 2.45E-08 1.02E-06 4.29E-05 41.9 Total Surge (1) Category 3 6.90E-06 6.94E-09 4.11E-07 2.40E-05 58.8 Line Category 4 3.79E-06 1.55E-09 1.36E-07 1.17E-05 86.9 Category 5 2.26E-06 4.28E-10 5.07E-08 6.00E-06 118.4 Total Surge Failure Rate 1.50E-05 2.37E-08 9.77E-07 5.13E-05 46.5 Line Case with Category 1 4.39E-07 5.35E-11 5.45E-09 7.25E-07 116.4 B-F weld Category 2 3.04E-07 1.01E-11 1.46E-09 3.02E-07 172.9 overlay and no TF Category 3 1.90E-07 3.04E-12 6.01E-10 1.68E-07 235.3 Susceptibility Category 4 1.46E-07 7.41E-13 2.02E-10 8.45E-08 337.8 for B-J welds Category 5 8.73E-08 2.17E-13 8.12E-11 5.29E-08 493.6 Note (1) Total surge line results are based on 1 B-F weld, 2 BC welds, and 6.9 B-J welds.

(2) RF = SQRT( 95%tile/5%tile) 7/5/11 Pre-Licensing Meeting 50

Comparison of Calculated Surge Line LOCA Frequencies 7/5/11 Pre-Licensing Meeting 51

Step by Step Procedure

1. Determination of weld types (i)
2. Perform data query for failure counts (n)
3. Estimate component exposure (T) and uncertainty
4. Develop component failure rate prior distributions for each DM
5. Perform Bayes update for each exposure case (combination of weld count and DM susceptibility)
6. Apply posterior weighting to combine results for different hypothesis yield conditional failure rate distributions; compute unconditional failure rates for locations with uncertain DM status
7. Develop conditional probability of rupture size given failure probabilities for each weld type and associated epistemic uncertainties
8. Combine the results of Step 6 and Step 7 by Monte Carlo in Eq. (1) for component LOCA frequencies and total LOCA frequencies for each component
9. Apply Markov Model to specialize rupture frequencies for differences in integrity management
10. For intermediate LOCA categories and break sizes, interpolate the results of Step 10 via log-log linear interpolation
11. Calculate total LOCA frequencies from all components and reconcile differences with earlier LOCA frequency estimates 7/5/11 Pre-Licensing Meeting 52

Step 9 Markov Model

Background

  • Purpose of model is to evaluate the impact of changes to inspection on pipe failure rates
  • Markov Model originally developed for EPRI RI-ISI Program
  • Applied to 26 plant specific RI-ISI programs in U.S. and South Africa
  • Applied pp to PBMR to support pp new ASME Code development p for in-service inspections
  • Currently being applied to address CANDU feeder pipe cracking issue
  • Recently applied to LWRs to guide efforts to reduce internal flood and HELB contributions to CDF
  • Enhanced version of model developed in DOE/INL RISMC to address aging issues; transition rates based on physics of failure 53 7/5/11 Pre-Licensing Meeting

Markov Model Of Pipe Element S

Pipe Element States S - success, no detectable damage F - detectable flaw L - detectable leak F R - rupture State Transition Rates

- flaw occurrence rate F L - leak failure rate F - rupture failure rate given flaw L - rupture failure rate given leak L - repair rate via ISI exams

- repair rate via leak detection R

7/5/11 Pre-Licensing Meeting 54

Estimating Input Parameters

  • Degradation related parameters

- Uses failure rates for flaws and leaks and rupture frequencies as developed in previous slides

- Leaks estimated using leak data and conditional leak given failure model similar to that used for ruptures

- Flaws estimated as a multiple of leaks based on insights from service data

- Modeled solved separately for each rupture mode (LOCA category)

  • Test and inspection parameters estimated using simple and easy to quantify models

- One model for leak tests and inspections

- One model for NDE 55 7/5/11 Pre-Licensing Meeting

Modeling Impact Of NDE Inspections

  • Capture by : the repair rate for flaws PF I PF D

=

(T I + TR )

where:

- PFI = probability that segment element with flaw will be inspected

- PFD= probability that flaw is detected given inspection

- TI = mean time between inspections

- TR = mean time to repair after detection is set to 0.0 for weld that is not in ISI program 56 7/5/11 Pre-Licensing Meeting

Modeling Impact of Leak Tests and Inspections

  • Capture by : the repair rate for leaks PLD

=

( TLI + TR )

where:

- PLD= probability that leak is detected given inspection

- TI = mean time between inspections

- TR = mean time to repair after detection is set to 0.0 if there is no leak inspections 57 7/5/11 Pre-Licensing Meeting

Example Application of Markov Model to Evaluate Strategies for Fire Protection Piping 1.0E-04 Frequency of Rupture Size Greater than o or Equal to X (events per ROY-ft.)

Current Study w/ WH 1.0E-05 Current Study no WH EPRI 1013141 FP NPS > 10" Current Study No WH + Yearly Leak Test Current Study No WH + Quaterly Leak Test 1.0E-06 1.0E-07 1.0E-08 1.0E-09 0.01 0.10 1.00 10.00 100.00 X, Equivalent Break Size (in.)

58 7/5/11 Pre-Licensing Meeting

BWR Recirculation Pipe LOCA Frequency Example from NUREG-1860 1.0E-04 No ISI/No Leak Inspection No ISI/ Leak Inspection 1/Refueling Outage No ISI/ Leak Inspection 1/Week ISI/Leak Inspection 1/Refueling Outage BWR Recirculation Piping LO OCA Frequency/year 1.0E-05 ISI/Leak Inspection 1/Week 1.0E-06 1.0E-07 1.0E-08 1.0E-09 5 15 25 35 45 55 Plant Age (Years) 59 7/5/11 Pre-Licensing Meeting

Impact of RIM Strategies on SC Susceptible RCS Weld Failure Rate (2005 RI-ISI for Koeberg) 7/5/11 Pre-Licensing Meeting 60

Step by Step Procedure

1. Determination of weld types (i)
2. Perform data query for failure counts (n)
3. Estimate component exposure (T) and uncertainty
4. Develop component failure rate prior distributions for each DM
5. Perform Bayes update for each exposure case (combination of weld count and DM susceptibility)
6. Apply posterior weighting to combine results for different hypothesis yield conditional failure rate distributions; compute unconditional failure rates for locations with uncertain DM status
7. Develop conditional probability of rupture size given failure probabilities for each weld type and associated epistemic uncertainties
8. Combine the results of Step 6 and Step 7 by Monte Carlo in Eq. (1) for component LOCA frequencies and total LOCA frequencies for each component
9. Apply Markov Model to specialize rupture frequencies for differences in integrity management
10. For intermediate LOCA categories and break sizes, interpolate the results of Step 10 via log-log linear interpolation
11. Calculate total LOCA frequencies from all components and reconcile differences with earlier LOCA frequency estimates 7/5/11 Pre-Licensing Meeting 61

Step 9 Application of Markov Model

  • Failure rates and rupture frequencies calculated in Step 8 are for an average integrity management program
  • For Class 1 welds in the service data an average integrity management has

- 25% that are included in NDE program and subjected to leak testing once every refueling cycle

- 75% that are not included in NDE program and subjected to leak testing once every refueling cycle

  • For STP welds the integrity management factors will be:

- Greater than 1.0 for welds not in ISI program

- Less than 1.0 for welds in ISI program

  • If some specific weld locations have an unusually high potential for debris induced core damage, inspection locations can be added or changed to offset risk impacts 7/5/11 Pre-Licensing Meeting 62

Steps 10 Interpolation for Intermediate LOCA Sizes

  • LOCA categories used in PRA and NUREG-1829 are defined as discrete ranges over continuum of possible break sizes
  • Results of expert elicitation for 6 LOCA categories are well behaved
  • For a given pipe size there is no reason to expect sharp discontinuties over the range of possible break sizes
  • STP model will assume linear interpolation between break sizes used to define 6 LOCA categories
  • STP model will extrapolate curves to account for double ended break of each pipe at the location of the weld 7/5/11 Pre-Licensing Meeting 63

Step 11 Aggregation of Results for PRA Model

  • LOCA frequency distributions to be developed for all unique weld-types typified by pipe size, weld type, DM status, and NDE program status and assigned to each location for Case Grande debris formation analysis
  • Results will be probabilistically summed up over each PRA LOCA category (small, medium, large LOCA) via Monte Carlo 7/5/11 Pre-Licensing Meeting 64

Step 12

  • Differences will be identified and reconciled; may lead to refinements in technical approach 7/5/11 Pre-Licensing Meeting 65

Technical Issues

  • Need to better understand aggregation methods used in NUREG-1829 and reason why uncertainties in aggregated results appear much smaller than those of the component level expert estimates
  • Need to resolve questions about whether all the experts i NUREG in NUREG-1829 1829 provided id d cumulative l ti vs. di discrete t input i t
  • Need to confirm that CRP method can be easily extended to other components
  • Need to incorporate insights about DMs since EPRI RI-ISI program developed 7/5/11 Pre-Licensing Meeting 66

Summary of LOCA Frequency Approach

  • Method of deriving CRP distributions from NUREG-1829 has been demonstrated for hot leg, surge line, and HP injection line; appears to be applicable to other components
  • Adjustments needed to prevent CRP from exceeding 1.0
  • CRP method combined with failure rate uncertainty y method yields very large uncertainties in component level LOCA frequencies
  • Capability to specialize frequencies to address key variables impacting pipe reliability (e.g. pipe size, materials, damage mechanisms, inspection status)
  • Capability to augment RI-ISI program to optimize NDE element selection 7/5/11 Pre-Licensing Meeting 67

For more information, please contact:

Karl Fleming fleming@ti-sd.com Bengt Lydell bly@scandpower.com Karl Fleming Consulting Service LLC 68 7/5/11 Pre-Licensing Meeting

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