ML111890380

South Texas Project, Units 1 and 2 - Licensee Slides, LOCA Initiating Event Frequencies and Uncertainties(Draft)(Tac Nos. ME5358 and ME5359)

ML111890380
Person / Time
Site:	South Texas
Issue date:	07/07/2011
From:	Fleming K N, Lydell B O KNF Consulting Services, Scandpower, Risk Management
To:	Singal B K Plant Licensing Branch IV
Singal, B K, NRR/DORL, 301-415-301
Shared Package
ML111890371	List: ML111890376 ML111890380 ML111890391 ML111890408 ML111890413 ML111890420
References
TAC ME5358, TAC ME5359, GSI-191
Download: ML111890380 (68)

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LOCA Initiating Event Frequencies and Uncertainties (Draft)Risk Informed GSI-191 Resolution ThdJl720117/5/11 Pre-Licensing Meeting 1 Th urs d ay, J u l y 7 , 20111:00 pm -2:00 p.m EDT Public Meeting with STP Nuclear Operating CompanyKarl N. Fleming KNF Consulting Services LLCBengt O. Y. Lydell Discussion Topics*LOCA frequencies scope and objectives*Technical approach

• StepbystepprocedurewithexamplesRisk Informed GSI-1917/5/11 Pre-Licensing Meeting 2 Step by step procedure with examples*Technical issues to be addressed

• Resolution of NRC questions from June 2011 meeting LOCA Frequencies Objectives*Incorporate insights from previous work on LOCA frequencies*Characterize LOCA initiating events and there frequencies with respect to:-Specific components, materials, dimensions-Specific locations

-Range of break sizes

-Degradation mechanisms and mitigation effectivenessOtherbreakcharacteristicsegspeedRisk Informed GSI-191

-Other break characteristics , e.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 37/5/11 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*Utilize information from NUREG-1829 and NUREG/CR-5750 in optimum manner*Consider probabilistic fracture mechanics evaluation on selected locations as may be

required 47/5/11 Pre-Licensing Meeting LOCA IE Frequency Model 1 of 2=i ix i x m LOCA F)( (1) ik ik x k ik ix jx I F R P)(== (2)Where: =)(x LOCA F Frequency of LOCA of size x, per reactor calendar-year; subject to epistemic uncertainty calculated via Monte Carlo

=i m Number of pipe welds of type i; each type determined by pipe size, weld type, applicable damage mechanisms, and inspection status (leaktestandNDE);nouncertaintyforSTP 57/5/11 Pre-Licensing Meeting (leak test and NDE); no uncertaintyfor 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)

=)(ik x F R P 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)

=ik I Integrity management factor for weld type iand 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.

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

i i ik ik ik ik ik T N f n n== (3)

=ik n 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 67/5/11 Pre-Licensing Meeting expert opinion

=ik f 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.

=i N 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

=i T Total number of reactor years exposure for the data collection for component type i; little or no uncertainty

Step by Step Procedure1.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 failureratedistributions;computeunconditionalfailureratesforlocationswithuncertain failure rate distributions; compute unconditional failure rates for locations with uncertain DM status7.Develop conditional probability of rupture size given failure probabilities for each weld type and associated epistemic uncertainties8.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 component9.Apply Markov Model to specialize rupture frequencies for differences in integrity management10.For intermediate LOCA categories and break sizes, interpolate the results of Step 10 via log-log linear interpolation11.Calculate total LOCA frequencies from all components and reconcile differences with earlier LOCA frequency estimates7/5/11 Pre-Licensing Meeting 7 Steps 1 and 2 Failure Data Query*Data query limited to Westinghouse and Framatome PWRs similar to STP*Failure defined as any event that involved repair or replacement of damaged component

• Dataquerycoversoperatingexperiencefrom1970

• 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 QueryExamplechosen SYSTEMNominal Pipe Size (NPS)Pipe Failures by Mode(1), (2), (3)TotalCrack-FullCrack-PartSmall Leak LeakLarge Leak CVC 1"716 2" ø 4"716Safety Injection 1" 22 4" ø 10"63111Pressurizer-Sample 2"541Pressurizer-PORV4" ø 10" 22Pressurizer-SPRAY 1"4121 4" ø 10"3217/5/11 Pre-Licensing Meeting 9 Example chosento illustrate FR ApproachPressurizer-SRV4" ø 10"761Pressurizer-Surge14" 33 RCS 2"764105345RCS Cold Leg32" 44RCS Hot Leg32"651 RHR 1" 66RHR4" ø 10" 11RC Hot Leg -S/G-Inlet 32"1919S/G-System 2"8224TOTALS16612598366Notes(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.

Step by Step Procedure1.Determination of weld types (i)2.Perform data query for failure counts (n)3.Estimate component exposure (T) and uncertainty4.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 status7.Develop conditional probability of rupture size given failure probabilities for each weld type and associated epistemic uncertainties8.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 component9.Apply Markov Model to specialize rupture frequencies for differences in integrity management10.For intermediate LOCA categories and break sizes, interpolate the results of Step 10 via log-log linear interpolation11.Calculate total LOCA frequencies from all components and reconcile differences with earlier LOCA frequency estimates7/5/11 Pre-Licensing Meeting 10 Step 3 Component Exposure*Need Reactor-years of service experience in failure data query*Need estimates of component populations perplant per plant*Need fractions of the component population susceptible to each damage mechanism (DM) for conditional failure

rates given knowledge of applicable

damage mechanisms7/5/11 Pre-Licensing Meeting 11 Reactor Years in Data QueryWE Type RxReactor-Calendar Years Initial Grid Connection Initial Criticality7/5/11 Pre-Licensing Meeting 12 Connection2-Loop570.1581.43-Loop2052.62096.1 4-Loop1193.91236.5Total3816.63914 Data for Estimating Surge Line Weld Population Plant PWR TypeWeld Population[Note (1)]B-F WeldsInline B-J WeldsBranch Connection to Hot LegBraidwood-14-Loop182Braidwood-24-Loop172Byron-14-Loop162Byron-24-Loop162 Kewaunee2-Loop162 Koeberg-13-Loop152 Koeberg-23-Loop152STP-14-Loop182STP-24-Loop182 V.C. Summer3-Loop1102Note(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 damageandfailuremechanisms 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 rates7/5/11 Pre-Licensing Meeting 14 Example Surge Line Damage Mechanism CharacterizationLocationConfidence LevelWeld Susceptibility FractionsC-FD&CECSCCFrettingIGSCCPWSCCTFTGSCCTAEV-F B-FLowN/A1N/AN/AN/A1N/AN/AN/AN/AMediumN/A1N/AN/AN/A1N/AN/AN/AN/A HighN/A1N/AN/AN/A1N/AN/AN/AN/A B-JLowN/A1N/AN/AN/AN/A0.01N/AN/AN/AMediumN/A1N/AN/AN/AN/A0.05N/AN/AN/AHighN/A1N/AN/AN/AN/A0.25N/AN/AN/A RC-HL Branch CtiLowN/A1N/AN/AN/AN/A1N/AN/AN/A

////////Connec ti onMediumN/A1N/AN/AN/AN/A1N/AN/AN/AHighN/A1N/AN/AN/AN/A1N/AN/AN/A7/5/11 Pre-Licensing Meeting 15IDDescriptionC-FCorrosion-FatigueD&CDesign & Construction FlawsECSCCExternal Chloride-induced SCCIGSCCIntergranular SCCLC-FATLow-Cycle FatiguePWSCCPrimary Water SCCOVLDOverloadTFThermal FatigueTGSCCTransgranular SCCTAEThermal Aging EmbrittlementV-FVibration Fatigue Example DM evaluation for Surge Line Welds*All B-F welds at the pressurizer nozzle are susceptible to PWSCC -no uncertainty for this

DM*A ll 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 location7/5/11 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*BC Welds-No DM uncertaintySlttltibilitibfbhti

-S ome p l an t t o p l an t var i a bilit y i n num ber o f b ranc h connec ti ons-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 FatigueWelds/Rx6.9Rx-yrs3914Base Exposure27006.6Weld Count UncertaintyFraction of B-J Welds Susceptible to Thermal FatigueExposure Case ProbabilityExposure Multiplierp=.250.06250.513,503 weld-yrsHigh (.25 x Base)p=.25p=.500.1250.12,701 weld-yrsHigh (2 X Base)Medium (.05 x Base)Exposure7/5/11 Pre-Licensing Meeting 18p=.250.06250.02540 weld-yrsLow (.01 x Base)p=.250.1250.256,752 weld-yrsHigh (.25 x Base)p=.50p=.500.250.051,350 weld-yrsMedium (1.0 X Base)Medium (.05 x Base)p=.250.1250.01270 weld-yrsLow (.01 x Base)p=.250.06250.1253,376 weld-yrsHigh (.25 x Base)p=.25p=.500.1250.025675 weld-yrsLow (0.5 X Base)Medium (.05 x Base)p=.250.06250.005135 weld-yrsLow (.01 x Base)

Step by Step Procedure1.Determination of weld types (i)2.Perform data query for failure counts (n) 3.Estimate component exposure (T) and uncertainty4.Develop component failure rate prior distributions for each DM5.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 failureratedistributions;computeunconditionalfailureratesforlocationswithuncertain failure rate distributions; compute unconditional failure rates for locations with uncertain DM status7.Develop conditional probability of rupture size given failure probabilities for each weld type and associated epistemic uncertainties8.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 component9.Apply Markov Model to specialize rupture frequencies for differences in integrity management10.For intermediate LOCA categories and break sizes, interpolate the results of Step 10 via log-log linear interpolation11.Calculate total LOCA frequencies from all components and reconcile differences with earlier LOCA frequency estimates7/5/11 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 10-6per year and allocation down to welds based on weld count estimates in EPRI TR-111880

• Lognormaldistributionswithlargerangefactors(100)

• 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 rates7/5/11 Pre-Licensing Meeting 20 Example Prior DistributionsDamage MechanismPrior Distribution (Failures perWeld-Year)Dist. TypeMeanMedian Range FactorStress Corrosion CrackingLognormal4.27E-058.48E-07100Design and ConstructionLognormal2.75E-065.46E-08100Thermal FatigueLognormal1.34E-052.66E-071007/5/11 Pre-Licensing Meeting 21 Step by Step Procedure1.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 DM5.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 filtditibtitditilfiltfltiithti f a il ure ra t e di s t r ib u ti ons; compu t e uncon diti ona l f a il ure ra t es f or l oca tions w ith uncer t a i n DM status7.Develop conditional probability of rupture size given failure probabilities for each weld type and associated epistemic uncertainties8.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 component9.Apply Markov Model to specialize rupture frequencies for differences in integrity management10.For intermediate LOCA categories and break sizes, interpolate the results of Step 10 via log-log linear interpolation11.Calculate total LOCA frequencies from all components and reconcile differences with earlier LOCA frequency estimates7/5/11 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

• Foreachfailurerateweperform1Bayes

'updatefor*For each failure rate we perform 1 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-PlusŽ software7/5/11 Pre-Licensing Meeting 23 Step 5 Example Bayes' Updates for Surge Line WeldsWeld Type and DM[Note (3)]Weld Count Case DM Susceptibility CasePrior Distribution[Note (1)]Evidence[Note (2)]Bayes' Posterior Distribution[Note (1)]TypeMedianRFFailuresExposureMean5 th 50 th 95 th RFSurge BF SCBaseBaseLognormal8.48E-07100339145.62E-041.23E-044.83E-041.27E-033.2Surge BF DCBaseBaseLognormal5.46E-08100039141.41E-065.41E-105.33E-084.77E-0693.9Surge BC TFBaseBaseLognormal2.66E-07100078283.25E-062.53E-092.34E-071.47E-0576.1Surge BC DCBaseBaseLognormal5.46E-08100078281.17E-065.37E-105.24E-084.37E-0690.1 Surge BJ TFLowLowLognormal2.66E-0710001359.75E-062.66E-092.65E-072.58E-0598.5 Sur ge BJ TFLowMediumLo gnormal2.66E-0710006757.17E-062.64E-092.61E-072.36E-0594.6 g gSurge BJ TFLowHighLognormal2.66E-07100033764.48E-062.59E-092.48E-071.85E-0584.5 Surge BJ TFMediumLowLognormal2.66E-0710002708.70E-062.65E-092.64E-072.51E-0597.4Surge BJ TFMediumMediumLognormal2.66E-07100013505.98E-062.62E-092.57E-072.18E-0591.2Surge BJ TFMediumHighLognormal2.66E-07100067523.46E-062.54E-092.37E-071.54E-0577.7Surge BJ TFHighLowLognormal2.66E-0710005407.55E-062.64E-092.62E-072.41E-0595.4 Surge BJ TFHighMediumLognormal2.66E-07100027014.83E-062.60E-092.51E-071.94E-0586.4Surge BJ TFHighHighLognormal2.66E-071000135032.58E-062.47E-092.22E-071.21E-0569.8Surge BJ DCLowBaseLognormal5.46E-081000135039.83E-075.33E-105.14E-083.96E-0686.2Surge BJ DCMediumBaseLognormal5.46E-081000270077.66E-075.25E-104.94E-083.34E-0679.8Surge BJ DCHighBaseLognormal5.46E-081000540135.77E-075.12E-104.65E-082.67E-0672.2Notes(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 weld7/5/11 Pre-Licensing Meeting 24 Step by Step Procedure1.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 status7.Develop conditional probability of rupture size given failure probabilities for each weld type and associated epistemic uncertainties8.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 component9.Apply Markov Model to specialize rupture frequencies for differences in integrity management10.For intermediate LOCA categories and break sizes, interpolate the results of Step 10 via log-log linear interpolation11.Calculate total LOCA frequencies from all components and reconcile differences with earlier LOCA frequency estimates7/5/11 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&CTFfiltiditil(fiE(3)1)

-TF f a il ure ra t e i s uncon diti ona l ( f i n E q. (3)= 1)*Case 3 FR for B-J weld not susceptible to TF-Includes only contributions from D&C7/5/11 Pre-Licensing Meeting 27Case No.Evaluation CaseMean5%tile50%tile95%tile1B-J Total Conditional on TF6.75E-069.60E-093.23E-071.55E-05 2B-J Total Unconditional2.62E-068.21E-092.23E-077.69E-06 3B-J Total Conditional on no TF7.66E-075.25E-104.94E-083.34E-06 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 Procedure1.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 DMstatus DM status7.Develop conditional probability of rupture size given failure probabilities for each weld type and associated epistemic uncertainties8.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 component9.Apply Markov Model to specialize rupture frequencies for differences in integrity management10.For intermediate LOCA categories and break sizes, interpolate the results of Step 10 via log-log linear interpolation11.Calculate total LOCA frequencies from all components and reconcile differences with earlier LOCA frequency estimates7/5/11 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 fa ilures are precursors to LOCAssuchthatLOCAfrequenciesaretheproductof 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 ruptures7/5/11 Pre-Licensing Meeting 30 Step 7 Conditional Probability of Pipe Rupture*Step 7.1 Benchmark of Lydell's 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

• Step73SetTargetLOCAfrequenciesthatencompass

• Step 7.3 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 LOCAs7/5/11 Pre-Licensing Meeting 31

7.1 Benchmark

of Lognormal Model to Lydell HPI Base Case -HPI7/5/11 Pre-Licensing Meeting 32

7.1 Benchmark

of Lognormal Model to Lydell HPI Base Case -Surge Line7/5/11 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 foreachLOCACategoryfrequency for each LOCA Category frequency*Lognormal distributions combined using posterior weighting procedure to pr oduce 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 results7/5/11 Pre-Licensing Meeting 35 Steps 7.2 and 7.3 Selection of HPI Target LOCA Frequencies7/5/11 Pre-Licensing Meeting 36 Steps 7.2 and 7.3 Selection of Surge Line Target Frequencies7/5/11 Pre-Licensing Meeting 37 Step 7.4 Benchmarking Target Frequencies -HPI7/5/11 Pre-Licensing Meeting 38 Step 7.4 Benchmarking Target Frequencies -Surge Line7/5/11 Pre-Licensing Meeting 39 Step 7.4 CRPs that Match HPI Targets7/5/11 Pre-Licensing Meeting 40 Step 7.4 CRPs that Match Surge Line Targets7/5/11 Pre-Licensing Meeting 41 Step 7.4 CRPs that Match Hot Leg Targets7/5/11 Pre-Licensing Meeting 42 Step 7.4 Example Conditional Probability DistributionsComponent LOCA CategoryBreak Size (in.)Distribution Input ParametersTruncated Distribution ParametersTypeMedian[Note (1)Range FactorMedianMean5%tile95%tile RCS-Hot Leg1.51.39E-042.09E+011.39E-047.69E-046.69E-062.89E-0321.52.49E-052.94E+012.49E-052.02E-048.37E-077.38E-04 338.65E-063.64E+018.62E-069.59E-052.36E-073.24E-04 46.762.43E-064.76E+012.43E-063.75E-055.20E-081.16E-04 5148.10E-075.90E+017.96E-072.38E-051.34E-084.82E-05Lognormal truncated at 1.0631.52.20E-077.53E+012.19E-076.79E-062.96E-091.66E-05RCS-Surge Line1.54.73E-021.40E+024.73E-031.19E-012.66E-046.11E-0121.56.06E-031.92E+026.06E-035.83E-022.89E-053.49E-01 332.06E-032.45E+022.06E-033.85E-027.96E-062.23E-01 46.766.43E-043.68E+026.43E-042.54E-021.69E-061.31E-01 5142.24E-044.85E+022.24E-041.70E-024.51E-077.26E-02HPI Line1.55.85E-032.20E+015.78E-032.15E-023.77E-048.88E-0221.51.20E-038.61E+001.18E-035.87E-036.34E-052.30E-02334.56E-048.61E+004.59E-042.61E-032.07E-051.01E-02Note (1) These are medians to specify the input distribution to Crystal BallŽprior 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 significance7/5/11 Pre-Licensing Meeting 44 Step 7.5 Bayes' Update of Surge CRP Priors: 0 LOCAs in 3 FailuresLOCA Category Break Size (in.)DistributionType(1)Distribution Parameters(2)Mean5%tile50%tile95%tile Range Factor(3)10.5PriorTruncated Lognormal1.05E-015.38E-042.59E-025.36E-0153.6PosteriorLognormal-Binomial4.43E-024.11E-041.45E-021.95E-0121.821.5PriorTruncated Lognormal3.46E-027.41E-053.97E-031.76E-0148.8PosteriorLognormal-Binomial175E 02675E 05317E 03845E 023547/5/11 Pre-Licensing Meeting 45PosteriorLognormalBinomial 1.75E-02 6.75E-05 3.17E-03 8.45E-02 35.433PriorTruncated Lognormal1.85E-021.99E-051.34E-038.24E-0264.3PosteriorLognormal-Binomial1.00E-021.88E-051.17E-034.79E-0250.546.76PriorTruncated Lognormal9.09E-034.34E-063.88E-043.32E-0287.5PosteriorLognormal-Binomial5.27E-034.22E-063.60E-042.33E-0274.3514PriorTruncated Lognormal5.16E-031.12E-061.34E-041.55E-02117PosteriorLognormal-Binomial3.10E-031.11E-061.28E-041.21E-02105Notes(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)

Step by Step Procedure1.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 DMstatus DM status7.Develop conditional probability of rupture size given failure probabilities for each weld type and associated epistemic uncertainties8.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

component9.Apply Markov Model to specialize rupture frequencies for differences in integrity management10.For intermediate LOCA categories and break sizes, interpolate the results of Step 10 via log-log linear interpolation11.Calculate total LOCA frequencies from all components and reconcile differences with earlier LOCA frequency estimates7/5/11 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 probabilit y of LOCA vs.

y 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 component7/5/11 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 Case7/5/11 Pre-Licensing Meeting 48 LOCA Frequencies for Each Weld TypeWeld Type Parameter Distribution Parameters RF(1) Mean 5%tile 50%tile 95%tile B-F Failure Rate 5.61E-041.37E-044.38E-041.40E-033.2Category 1 2.13E-051.44E-073.81E-069.35E-0525.5Category 2 1.03E-052.44E-081.01E-064.26E-0541.7Category 3 6.71E-066.94E-094.10E-072.38E-0558.6Category 4 3.67E-061.55E-091.36E-071.16E-0586.6Category 5 2.22E-064.27E-105.05E-085.95E-06118.0FailureRate4.16E-069.75E-092.91E-071.17E-0534.77/5/11 Pre-Licensing Meeting 49Branch Connection Failure Rate4.16E 069.75E 092.91E 071.17E 0534.7Category 1 1.43E-072.65E-112.58E-092.87E-07104.1Category 2 8.14E-085.02E-126.87E-101.12E-07149.3Category 3 4.35E-081.51E-122.81E-105.73E-08194.9Category 4 2.87E-083.68E-139.32E-112.55E-08263.1Category 5 1.45E-081.06E-133.45E-111.23E-08339.4 B-J Failure Rate 2.03E-072.66E-112.86E-093.74E-07118.6Category 1 4.38E-081.12E-122.68E-106.33E-08238.3Category 2 2.13E-082.34E-137.40E-112.38E-08319.0Category 3 1.49E-087.21E-142.98E-111.15E-08398.5Category 4 8.68E-091.85E-149.77E-124.92E-09515.9Category 5 2.07E-091.26E-132.23E-113.84E-09175.0Note (1) RF = SQRT(95%tile/5%tile)

LOCA Frequencies for Surge LineWeld Type Parameter Distribution Parameters RF(2) Mean 5%tile 50%tile 95%tile Base Case Total Surge Line(1) Failure Rate5.71E-041.37E-04 4.39E-041.42E-033.2Category 12.19E-051.44E-07 3.81E-069.45E-0525.6Category 2 1.06E-05 2.45E-08 1.02E-06 4.29E-05 41.9 Category 36.90E-066.94E-09 4.11E-072.40E-0558.8Category 43.79E-061.55E-09 1.36E-071.17E-0586.97/5/11 Pre-Licensing Meeting 50Category 52.26E-064.28E-10 5.07E-086.00E-06118.4Total Surge Line Case with B-F weld overlay and no TF Susceptibility for B-J welds Failure Rate1.50E-052.37E-08 9.77E-075.13E-0546.5Category 14.39E-075.35E-11 5.45E-097.25E-07116.4Category 2 3.04E-07 1.01E-11 1.46E-09 3.02E-07 172.9 Category 31.90E-073.04E-12 6.01E-101.68E-07235.3Category 41.46E-077.41E-13 2.02E-108.45E-08337.8Category 58.73E-082.17E-13 8.12E-115.29E-08493.6Note (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)

Comparison of Calculated Surge Line LOCA Frequencies7/5/11 Pre-Licensing Meeting 51 Step by Step Procedure1.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 failureratedistributions;computeunconditionalfailureratesforlocationswithuncertain failure rate distributions; compute unconditional failure rates for locations with uncertain DM status7.Develop conditional probability of rupture size given failure probabilities for each weld type and associated epistemic uncertainties8.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 component9.Apply Markov Model to specialize rupture frequencies for differences in integrity management10.For intermediate LOCA categories and break sizes, interpolate the results of Step 10 via log-log linear interpolation11.Calculate total LOCA frequencies from all components and reconcile differences with earlier LOCA frequency estimates7/5/11 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 to PBMR to su pport new ASME Code develo pment for pppppin-service inspections*Applied in NUREG-1829 LOCA frequency update

• 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 537/5/11 Pre-Licensing Meeting Markov Model Of Pipe Element S FµS F S S F FµPipe Element StatesS -success, no detectable damageF -detectable flawL -detectable leakR -rupture L RFL L R L L R RFLState Transition Rates-flaw occurrence rate-leak failure rateF-rupture failure rate given flawL-rupture failure rate given leak-repair rate via ISI exams

µ-repair rate via leak detection 547/5/11 Pre-Licensing Meeting 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 557/5/11 Pre-Licensing Meeting Modeling Impact Of NDE Inspections*Capture by : the repair rate for flaws

()=+PP T TFIFD I R where:-P FI = probability that segment element with flaw will be inspected

-P FD= probability that flaw is detected given inspection

-T I= mean time between inspections

-T R = mean time to repair after detectionis set to 0.0 for weld that is not in ISI program

()+T T I R 567/5/11 Pre-Licensing Meeting Modeling Impact of Leak Tests and Inspections*Capture by

µ: the repair rate for leaks

µ=+P T T LD LI R ()where:-P LD= probability that leak is detected given inspection

-T I= mean time between inspections

-T R = mean time to repair after detection

µis set to 0.0 if there is no leak inspections

+T T LI R ()577/5/11 Pre-Licensing Meeting Example Application of Markov Model to Evaluate Strategies for Fire Protection Piping 1.0E-061.0E-05 1.0E-04 or Equal to X (events per ROY-ft.)Current Study w/ WHCurrent Study no WHEPRI 1013141 FP NPS > 10"Current Study No WH + Yearly Leak TestCurrent Study No WH + Quaterly Leak Test1.0E-09 1.0E-08 1.0E-070.010.101.0010.00100.00 X, Equivalent Break Size (in.) Frequency of Rupture Size Greater than o 587/5/11 Pre-Licensing Meeting BWR Recirculation Pipe LOCA Frequency Example from NUREG-1860 1.0E-061.0E-05 1.0E-04 OCA Frequency/yea rNo ISI/No Leak InspectionNo ISI/ Leak Inspection 1/Refueling OutageNo ISI/ Leak Inspection 1/WeekISI/Leak Inspection 1/Refueling OutageISI/Leak Inspection 1/Week1.0E-09 1.0E-08 1.0E-0751525354555Plant Age (Years)BWR Recirculation Piping L O 597/5/11 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 Procedure1.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 DMstatus DM status7.Develop conditional probability of rupture size given failure probabilities for each weld type and associated epistemic uncertainties8.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 component9.Apply Markov Model to specialize rupture frequencies for differences in integrity management10.For intermediate LOCA categories and break sizes, interpolate the results of Step 10 via log-log linear interpolation11.Calculate total LOCA frequencies from all components and reconcile differences with earlier LOCA frequency estimates7/5/11 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 onceeveryrefuelingcycle 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 impacts7/5/11 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 wellbehaved 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 def ine 6 LOCA categories*STP model will extrapolate curves to account for double ended break of each pipe at the location of the weld7/5/11 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, andNDEprogramstatusandassignedto 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 Carlo7/5/11 Pre-Licensing Meeting 64 Step 12*Results of Step 11 will be compared against other available LOCA frequency sources, e.g. NUREG-1150, NUREG/CR-5750 5750*Differences will be identified and reconciled; may lead to refinements in

technical approach7/5/11 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 iNUREG1829iddltiditit i n NUREG-1829 prov id e d cumu l a tive vs. di scre t e i npu t*Need to confirm that CRP method can be easily extended to other components*Need to incorporate insight s about DMs since EPRI RI-ISI program developed7/5/11 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 uncertaint y method yyields 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 selection7/5/11 Pre-Licensing Meeting 67 For more information, please contact: Karl Fleming fleming@ti-sd.com Bengt Lydell bly@scandpower.comKarl Fleming Consulting Service LLC 687/5/11 Pre-Licensing Meeting