ML13255A376

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Soarca Peach Bottom Uncertainty Analysis (UA) Acrc Briefing - Sept 2013
ML13255A376
Person / Time
Site: Peach Bottom  Constellation icon.png
Issue date: 09/16/2013
From: Tina Ghosh
NRC/RES/DSA
To:
Ghosh T
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SOARCA Peach Bottom Uncertainty Analysis (UA)ACRSBriefing ACRS BriefingTinaGhosh,PhD Tina Ghosh, PhD RES/DSA/AABSeptember 16, 2013 AgendaACRStMACCS2th

  • ACRS commen t s on MACCS2 wea th er uncertainty integration and convergence of resultsandstaffresponses results , and staff responses*MELCORparametersofinterest
  • MELCOR parameters of interestMACCS2parametersofinterest
  • MACCS2 parameters of interest 2 MELCOR -MACCS2 -WeatherUncertainty Weather Uncertainty Integration ACRS Comment: *For the combined MELCOR-MACCS2 results, the report currentlypresentsonlyresultsaveragedovertheweather currently presents only results averaged over the weather trials. *The report should also present results that include and dilthfllthlttit di sp l ay th e f u ll wea ther a l ea t ory uncer t a i n t y 3 Conditional mean, individual latent cancer fatalit y (LCF) risk (p er y()(pevent) for combined results (865) with LNT model 0 10 0 20 0 30 0 40 0 50 0-10 miles 0-20 miles 0-30 miles 0-40 miles 0-50 miles 5 th3110 54910 53410 52210 51910 5 percentile 3.1 x 10-5 4.9 x 10-5 3.4 x 10-5 2.2 x 10-5 1.9 x 10-5 Median 1.3x10-4 1.9x10-4 1.3x10-4 8.7x10-5 7.1x10-5 Mean 1.7x10-4 2.8x10-4 2.0x10-4 1.3x10-4 1.0x10-4 95 th percentile 4.2x10-4 7.7x10-4 5.3x10-4 3.4x10-4 2.7x10-4 SOARCA UA 4 SOARCA UABaseCase 9.0x10-5 8.3x10-5 5.8x10-5 3.7x10-5 3.0x10-5 Conditional Individual LCF Risk (per Event) CCDFs for Combined Aleatory dEitiUtitd an d E p i s t em i c U ncer t a i n t y an d Epistemic Uncertainty with Aleatory

Means 1 0.8 0.9 10-10 miles Aleatory Mean0-20 miles Aleatory Mean0-50 miles Aleatory Mean0-10 miles Epistemic & Aleatory 020ilEiti&Alt 05 0.6 0.7 0-20 m il es E p i s t em i c & Al ea t ory0-50 miles Epistemic & Aleatory D F 0.3 0.4 0.5 CC D 0 0.1 0.2 5 Individual LCF Risk 01.0E-061.0E-051.0E-041.0E-031.0E-02 MACCS2 and Weather UncertaintiesforPrompt Uncertainties for Prompt Fatality RiskACRSComment:

ACRS Comment: *Select the MELCOR realization that produced the largest conditional prompt fatality consequences in the current SOARCA uncertainty results. *For that realization, sample from the 350 MACCS2 input parametersandforeachepistemicsamplegenerate984 parameters , and for each epistemic sample generate 984 weather cases to derive an uncertainty distribution for the conditional prompt fatality consequences at each dit di s tance. *Demonstrate convergence of the combined MACCS2-weatheruncertaintyanalysisresults.

6 weather uncertainty analysis results.

MACCS2 and Weather UncertaintiesforPrompt Uncertainties for Prompt Fatality Risk (cont.)

Approach: Approach: *MELCOR Replicate 2, Realization 291 identified as the source term that produced the largest conditional prompt fatality risk consequence *For that source term, three Monte Carlo runs of sample size1000werecompleted(Runs345)usingthree size 1000 were completed (Runs 3 , 4 , 5) using three different LHS random seeds for the 350 MACCS2 input

parameters*The same 984 weather trials were used 7

Conditional, mean, individual p rom p t-fatalit y risk (p er event) ppy(p)statistics for the MACCS2 Uncertainty Analysis for specified circularareas(Run1) circular areas (Run 1)0-1.3 miles 0-2.5 miles 0-3.5 miles 0-7 miles 0-10 miles miles miles Mean 4.5x10-7 8.9x10-8 3.5x10-8 8.3x10-9 4.8x10-9 Median 00 00 00 00 00 Median 0.0 0.0 0.0 0.0 0.0 75 th p ercent0.00.00.00.00.0 p-ile 95 th percent19x10-635x10-8 00 00 00 8 percent-ile 1.9x10 3.5x10 0.0 0.0 0.0 Run 3-5 conditional, mean, individual p rom p t-fatalit y risk (p er ppy(p event) statistics for specified

circular areas 0-1.3 miles 0-2.5 miles 0-3.5 miles 0-7 miles 0-10 miles Run 33.3E-061.0E-063.4E-074.7E-089.5E-09 M R433E 0694E 0730E 0742E 0889E 09 M ean R un 4 3.3E-06 9.4E-07 3.0E-07 4.2E-08 8.9E-09 Run 53.2E-069.8E-073.0E-074.7E-081.3E-08 Run 34.9E-071.2E-070.00.00.0 MedianRun433E 0694E 07 00 00 00 Median Run 4 3.3E-06 9.4E-07 0.0 0.0 0.0 Run 53.2E-069.8E-070.00.00.0 75 th Run 34.0E-061.0E-062.0E-073.8E-090.0 percentRun437E 0688E 0722E 0711E 08 00 percent Run 4 3.7E-06 8.8E-07 2.2E-07 1.1E-08 0.0-ile Run 53.9E-069.6E-071.9E-078.2E-090.0 95 th Run 31.4E-054.1E-061.5E-062.1E-071.2E-08 percentRun416E 0547E 0618E 0623E 07 00 9 percent Run 4 1.6E-05 4.7E-06 1.8E-06 2.3E-07 0.0-ile Run 51.4E-054.4E-061.6E-062.0E-070.0 Runs 3-5 and Run1 Conditional, Mean Individual Prompt Fatality Risk (per Event) Epistemic Uncertainty

CCDF, at 1.3 Miles 1 0.1 0-1.3 miles Run 1 D F 0.01 0-1.3 miles Run 3 0-1.3 miles Run 4 CC D 0.001 0-1.3 miles Run 5 10 Individual Prompt Fatality Risk per Event1.0E-111.0E-101.0E-091.0E-081.0E-071.0E-061.0E-051.0E-04 Runs 3-5 and Run1 Conditional, Mean Individual Prompt Fatality Risk (per Event) Epistemic Uncertainty

CCDF, at 3.5 Miles 1 0.1 0-3.5 miles Run 1 0-35milesRun3 D F 0.01 0 3.5 miles Run 3 0-3.5 miles Run 4 0-3.5 miles Run 5 CC D0001 11 Individual Prompt Fatality Risk per Event 0.0011.0E-121.0E-111.0E-101.0E-091.0E-081.0E-071.0E-061.0E-05 MACCS2 and Weather Uncertainties for LCF Risk 1ACRSComment:

ACRS Comment: *Select the MELCOR realization that produced the largest conditional LCF fatality consequences in the current SOARCA uncertainty results. *For that realization, sample from the 350 MACCS2 input parametersandforeachepistemicsamplegenerate984 parameters , and for each epistemic sample generate 984 weather cases to derive an uncertainty distribution for the conditional LCF fatality consequences at each distance. *Demonstrate convergence of the combined MACCS2-weather uncertainty analysis results.

12 MACCS2 and Weather UncertaintiesforLCFRisk1 Uncertainties for LCF Risk 1 (cont.)Approach: Approach: *MELCOR Replicate 3, Realization 46 identified as the source term that produced the largest conditional LCF risk consequence *For that source term, three Monte Carlo runs of sample size1000werecompleted(Runs678)usingthree size 1000 were completed (Runs 6 , 7 , 8) using three different LHS random seeds for the 350 MACCS2 input

parameters*The same 984 weather trials were used 13 Run 6-8 Combined Aleatory and Epistemic Uncertainty Conditional Individual LCF Risk (per Event) CCDF 1 07 0.8 0.9 0-10 miles Run 6 0-10 miles Run 7 0-10milesRun8 0.5 0.6 0.7 C DF 0-10 miles Run 8 0-50 miles Run 6 0-50 miles Run 7 02 0.3 0.4 C C 0-50 miles Run 8 0 0.1 0.2 14 Individual Latent Cancer Fatality Risk per Event1.E-051.E-041.E-031.E-02 Runs 6-8 and Run 1 Epistemic Uncertainty with Aleatory Mean, Conditional Individual LCF Risk (per

Event) CCDFs 1 010milesRun1 07 0.8 0.9 0-10 miles Run 1 0-10 miles Run 6 0-10 miles Run 7 0-10milesRun8 0.5 0.6 0.7 0 10 miles Run 8 0-50 miles Run 1 0-50 miles Run 6 0-50 miles Run 7 D F 0.3 0.4 0-50 miles Run 8 CC D 0 0.1 0.2 15 Individual Latent Cancer Fatality Risk per Event 01.0E-051.0E-041.0E-03 MACCS2 and Weather Uncertainties for LCF Risk 2ACRSComment:

ACRS Comment: *Select a MELCOR realization that produced a small, but non-zero, contribution to the conditional LCF fatality consequences in the current SOARCA uncertainty

results.

  • Forthatrealizationsamplefromthe350MACCS2input For that realization , sample from the 350 MACCS2 input parameters, and for each epistemic sample generate 984 weather cases to derive an uncertainty distribution for the ditilLCFftlitthdit con diti ona l LCF f a t a lity consequences a t eac h di s tance. *Demonstrate convergence of the combined MACCS2-weatheruncertaintyanalysisresults.

16 weather uncertainty analysis results.

MACCS2 and Weather UncertaintiesforLCFRisk2 Uncertainties for LCF Risk 2 (cont.)Approach: *Three re presentative source terms were chosen p*First an initial MACCS2 run (Run 2) used all 865 source terms while all MACCS2 parameters were set to their SOARCApointestimatevalues SOARCA point estimate values.-To assess the influence of the source term when MACCS2 parameters are fixed 17 Run 2 Conditional Mean, Individual LCF Risk (per Event) for 865 Source Terms and Fixed CCDF 0.9 1 0-10miles 0.7 0.8 0-10 miles 0-20 miles 0-30 miles D F 04 0.5 0.6 0-40 miles 0-50 miles CC D 0.2 0.3 0.4 0 0.110E 0510E 0410E 0310E 02 18 Individual LCF Risk per Event 1.0E-05 1.0E-04 1.0E-03 1.0E-02 MACCS2 and Weather UncertaintiesforLCFRisk2 Uncertainties for LCF Risk 2 (cont.)*A set of 11 results have then been used as metrics to select three representative source terms:LatentCancerFatality(LCF)riskat5differentlocations(1020

-Latent Cancer Fatality (LCF) risk at 5 different locations (10 , 20 , 30, 40 and 50 miles)-Fraction of inventory released for 5 radionuclides (Cs, I, Ba, Ce, Te)Te)-Release time*Goal is to choose three source terms whose metrics' ranks come closest to 1/6, 1/2, and 5/6 among the population 19 Results: Cobweb Graph for Selected Source Terms 1.0 0.8 F 0.6 CD F 02 0.4 0.0 0.2 20 Metric 50 miles 20 miles 10 miles 30 miles 40 milesCsIodineBaCeTe(hr)

MACCS2 and Weather UncertaintiesforLCFRisk2 Uncertainties for LCF Risk 2 (cont.)Approach(cont):

Approach (cont.): *With respect to conditional LCF risk: -MELCOR Replicate 3, Realization 187 identified as the representative low source term-MELCOR Replicate 1, Realization 75 identified as the representative medium source term-MELCOR Replicate 1, Realization 290 identified as the representative high source term*For each of these source terms

, three Monte Carlo runs

,of sample size 1000 were completed (Runs 9-11,12-14, 15-17 respectively) using three different LHS random seedsforthe350MACCS2inputparameters 21 seeds for the 350 MACCS2 input parameters*The same 984 weather trials were used.

Runs 9-11 (Low Source Term) Conditional,Mean,IndividualLCFRun#0 10 0 20 0 30 0 40 0 50 Conditional, Mean, Individual LCF Risk (per event) Statistics Statistic Run #0-10 miles 0-20 miles 0-30 miles 0-40 miles 0-50 miles Run 91.1E-041.2E-048.3E-055.4E-054.4E-05 MeanRun1011E-0412E-0483E-0554E-0544E-05 Mean Run 10 1.1E-04 1.2E-04 8.3E-05 5.4E-05 4.4E-05Run 111.1E-041.2E-048.3E-055.4E-054.4E-05 Run 98.8E-051.0E-047.2E-054.7E-053.9E-05 MedianRun1086E-0510E-0474E-0548E-0539E-05 Median Run 10 8.6E-05 1.0E-04 7.4E-05 4.8E-05 3.9E-05Run 118.8E-051.0E-047.2E-054.7E-053.9E-05 5 th Run 92.3E-053.8E-052.7E-051.7E-051.4E-05 percentile Run 1022E 0538E 0526E 0517E 0514E 05 percentile Run 10 2.2E-05 3.8E-05 2.6E-05 1.7E-05 1.4E-05Run 112.3E-054.0E-052.7E-051.8E-051.4E-05 95 th Run 92.5E-042.4E-041.7E-041.1E-048.9E-05 percentileRun1026E 0424E 0417E 0412E 0495E 05 22 percentile Run 10 2.6E-04 2.4E-04 1.7E-04 1.2E-04 9.5E-05Run 112.7E-042.4E-041.7E-041.1E-049.4E-05 Runs 9-11 and Run 1 Epistemic Uncertainty Conditional, Mean, Individual LCF Risk (per Event) CCDFs 09 1 0-10 miles Run 1 010milesRun9 07 0.8 0.9 0-10 miles Run 9 0-10 miles Run 100-10 miles Run 11 050ilR1 D F 0.5 0.6 0.7 0-50 m il es R un 1 0-50 miles Run 9 0-50 miles Run 10 CC D 0.3 0.40-50 miles Run 11 0.1 0.2 23 Individual LCF Risk per Event 01.0E-061.0E-051.0E-041.0E-03 Runs 12-14 (medium) and Run 1 Epistemic Uncertainty Conditional, Mean, Individual LCFRik(Et)CCDF LCF Ri s k (per E ven t) CCDF s 09 1 0-10 miles Run 1 0-10milesRun12 0.7 0.8 0.9 0-10 miles Run 12 0-10 miles Run 13 0-10 miles Run 14 D F 0.5 0.6 0-50 miles Run 1 0-50 miles Run 12 0-50 miles Run 13 CC D 02 0.3 0.4 0-50 miles Run 14 0 0.1 0.2 24 Individual LCF Risk per Event 01.0E-061.0E-051.0E-041.0E-03 Runs 15-17 (high) and Run 1 Epistemic Uncertainty Conditional, Mean, Individual LCF Risk (per Event) CCDFs 0.9 1 0-10 miles Run 1 0-10milesRun15 0.7 0.8 0 10 miles Run 15 0-10 miles Run 16 0-10 miles Run 17 0-50 miles Run 1 050milesRun15 D F 0.5 0.6 0-50 miles Run 15 0-50 miles Run 16 0-50 miles Run 17 CC D 02 0.3 0.4 0 0.1 0.2 25 Individual LCF Risk per Event 01.0E-061.0E-051.0E-041.0E-03 Average difference between the three se parate LHS runs over all p Aleatory Weather Distributions (1 st to 99 th percentile)Source Term Conditional LCF Risk Conditional LCF Risk0-10 miles0-50 miles Highest Prompt Fatality Risk-Runs3-50.8%0.8%Risk Runs 3 5 Highest LCF Risk -

Runs 6-80.8%0.9%L R9 1109%08%Low -R uns 9-11 0.9%0.8%Medium -Runs12-140.8%0.9%High-Runs15-1710%06%26 High Runs 15 17 1.0%0.6%Overall Average0.9%0.8%

MACCS2 Stability Analysis Using Bootstrap Approach Approach: Approach:*MACCS2 code modified to allow simple random sampling *The 'hi gh' source term (i.e., Re plicate 1 Realization 290

) g(p)and the SOARCA UA MACCS2 Analysis (Run 1) were selected to compare between Simple Random Sampling (SRSorMC)andLatinHypercubeSampling(LHS)in (SRS or MC) and Latin Hypercube Sampling (LHS) in order to validate the use of LHS*Bootstrapping performed (similar to approach with MELCOR results) to estimate confidence boundsConclusion:Resultsoftheuncertaintyanalysisarewell 27*Conclusion:

Results of the uncertainty analysis are well converged and LHS use is valid Run 1 (CAP17) Conditional, Mean, Individual LCF Risk (per Event) CCDF with LHS and MC Sampling 09 1 0.7 0.8 0.9 D F 0.5 0.6 0-10 miles CAP17-LHS CC D 0.3 0.4 0-10 miles CAP17-MC 0-50 miles CAP17-LHS 0 0.1 0.2 0-50 miles CAP17-MC 28 Individual LCF Risk per Event 01.0E-061.0E-051.0E-041.0E-03 Run 1 (CAP17) Conditional, Mean, Individual Prompt Fatality Risk (per Event) CCDF with LHS and MC Sampling 1 01 D F 0.1 CC D 0.01 0-1.3 miles CAP17-LHS 0-1.3 miles CAP17-MC 02milesCAP17 LHS0001 0-2 miles CAP17-LHS 0-2 miles CAP17-MC 0-3.5 miles CAP17-LHS 0-3.5 miles CAP17-MC 29 Individual Prompt Fatality Risk per Event 0.0011.0E-121.0E-111.0E-101.0E-091.0E-081.0E-071.0E-061.0E-05 10-mile Conditional, Mean, Individual LCF Risk (per Event) CDF for Run 15 (CAP37) and 95% ConfidenceIntervalUpperandLowerBounds Confidence Interval Upper and Lower Bounds for Runs 16 & 17 (CAP38 & 39) with SRS 30 50-mile Conditional, Mean, Individual LCF Risk (per Event) CDF for Run 15 (CAP37) and 95% ConfidenceIntervalUpperandLowerBounds Confidence Interval Upper and Lower Bounds for Runs 16 & 17 (CAP38 & 39) with SRS 31 MELCOR Parameters of Interest SRVLAM -SRV stochastic failure to reclose 33 CHEMFORM -Iodine and cesium fraction ParameterDistributionCHEMFORM:FivealternativecombinationsofRN classes2,4,16,and17(CsOH,I 2,CsI,andCs 2 MoO 4)Discrete distributionCombination #1 = 0.125Combination#2

=0125Notethefractioncesiumbelowrepresentsthe distributionof'residual'cesiumwhichisthemassof cesiumremainingafterfirstreactingwiththeamountof iodine assumed to form CsI Combination

  1. 2 0.125Combination #3 = 0.125 Combination #4 = 0.125Combination#5=0500 iodine assumed to form CsI.Combination
  1. 5 = 0.500Five AlternativesSpecies (MELCOR RN Class)CsOH (2)I 2 (4)CsI (16)Cs 2 MO 4 (17)fraction iodine--0 03 0 97--Combination#1 fraction iodine 0.03 0.97fractioncesium1----0Combination#2fractioniodine--0.0020.998fractioncesium0.5----0.5 fraction iodine--0 00298 0 99702--Combination#3 fraction iodine 0.00298 0.99702fractioncesium0----1Combination#4fractioniodine--0.07570.9243--fractioncesium0.5----0.5 fraction iodine--0 0277 0 9723--34Combination#5 fraction iodine--0.0277 0.9723--fractioncesium0----1SOARCAestimateFractioniodine--0.01.0--Fractioncesium0.0----1.0 FL904A -Drywell liner failure flow area 35 BATTDUR -Battery Duration 36 SRVOAFRAC -SRV open area fraction 37 SLCRFRAC -Main steam line creep rupture area fraction 38 Radial debris relocation time constants -RDSTC (solid) 39 Radial debris relocation time constants -RDMTC (liquid)40 RRIDRFRAC, RODRFRAC -Railroad door open fraction 41 H2IGNC -Hydrogen ignition criteria 42 RHONOM -Particle density 43 FFC -Fuel failure criterion 44 FFC -Fuel failure criterion (continued) 45 SC1141(2) -Molten clad drainage rate 46 Other MELCOR Items of Interest*Surrogateparameters
  • Surrogate parametersLhdttifil*L ower h ea d pene t ra ti on f a il uresDlllifildl
  • D rywe ll li ner f a il ure mo d e l*Operator actions 47 MACCS2 Parameters of Interest DOSNRM, DOSHOT -Normal and Hotspot Relocation Doses 49 TIMNRM, TIMHOT -Normal and Hotspot Relocation Times 50 ESPEED -Evacuation speed 51 GSHFAC -Groundshine Shielding Factor 52 Next Ste p s p*ANSPSAConferencepresentationand papers*ANS PSA Conference presentation and papers and CSARP presentation

-September2013 September 2013 *Send final NUREG/CR-7155 report for publication -Fall 2013 53 Questions and Comments Note that all results in these presentation slides are conditional (per event) on the potentialoccurrenceofalong

-termstation potential occurrence of a long term station blackout (LTSBO) scenario, and modeling the SOARCA unmitigated LTSBO. ThLTSBOifititd Th e LTSBO scenar i o f requency i s es ti ma t e d in SOARCA to be ~3x10

-6per reactor year.

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