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, PhDRES/DSA/AABSeptember 16, 2013 AgendaACRStMACCS2th

  • ACRS comments on MACCS2 weather uncertainty integration and convergence of resultsandstaffresponses results, and staff responses
  • MELCORparametersofinterest
  • MELCOR parameters of interestMACCS2parametersofinterest
  • MACCS2 parameters of interest2 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 display the full weather aleatory uncer tainty3 Conditional mean, individual latent cancer fatalit y (LCF) risk (per y()(pevent) for combined results (865) with LNT model 0100200300400500-10 miles0-20 miles0-30 miles0-40 miles0-50 miles5th3110549105341052210519105percentile 3.1x10-54.9x10-53.4x10-52.2x10-51.9x10-5Median1.3x10-41.9x10-41.3x10-48.7x10-57.1x10-5Mean1.7x10-42.8x10-42.0x10-41.3x10-41.0x10-495thpercentile 4.2x10-47.7x10-45.3x10-43.4x10-42.7x10-4SOARCAUA4SOARCAUABaseCase9.0x10-58.3x10-55.8x10-53.7x10-53.0x10-5 Conditional Individual LCF Risk (per Event) CCDFs for Combined Aleatory dEitiUtitd and Epistemic Uncertainty and Epistemic Uncertainty with Aleatory

Means10.80.910-10 miles Aleatory Mean0-20 miles Aleatory Mean0-50 miles Aleatory Mean0-10 miles Epistemic & Aleatory 020ilEiti&Alt 050.6 0.70-20 miles Epistemic & Aleatory0-50 miles Epistemic & Aleatory DF0.3 0.40.5CCD00.1 0.25Individual 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 ditdistance. *Demonstrate convergence of the combined MACCS2-weatheruncertaintyanalysisresults.

6weather 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 prompt-fatalit y risk (per event) ppy(p)statistics for the MACCS2 Uncertainty Analysis for specified circularareas(Run1) circular areas (Run 1)0-1.3 miles0-2.5 miles0-3.5 miles0-7 miles0-10 milesmilesmilesMean4.5x10-78.9x10-83.5x10-88.3x10-94.8x10-9Median0000000000Median0.00.00.00.00.075thpercent0.00.00.00.00.0 p-ile95thpercent19x10-635x10-80000008percent-ile1.9x103.5x100.00.00.0 Run 3-5 conditional, mean, individual prompt-fatalit y risk (per ppy(pevent) statistics for specified

circular areas 0-1.3 miles0-2.5 miles0-3.5 miles0-7 miles 0-10 milesRun 33.3E-061.0E-063.4E-074.7E-089.5E-09 MR433E0694E0730E0742E0889E09MeanRun 43.3E-069.4E-073.0E-074.2E-088.9E-09Run 53.2E-069.8E-073.0E-074.7E-081.3E-08 Run 34.9E-071.2E-070.00.00.0 MedianRun433E0694E07000000MedianRun 43.3E-069.4E-070.00.00.0Run 53.2E-069.8E-070.00.00.0 75thRun 34.0E-061.0E-062.0E-073.8E-090.0 percentRun437E0688E0722E0711E0800percentRun 43.7E-068.8E-072.2E-071.1E-080.0-ileRun 53.9E-069.6E-071.9E-078.2E-090.0 95thRun 31.4E-054.1E-061.5E-062.1E-071.2E-08 percentRun416E0547E0618E0623E07009percentRun 41.6E-054.7E-061.8E-062.3E-070.0-ileRun 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 10.10-1.3 miles Run 1 DF0.010-1.3 miles Run 3 0-1.3 miles Run 4 CCD0.0010-1.3 miles Run 5 10Individual 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 10.10-3.5 miles Run 1 0-35milesRun3 DF0.0103.5 miles Run 30-3.5 miles Run 4 0-3.5 miles Run 5 CCD000111Individual 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 1070.80.90-10 miles Run 6 0-10 miles Run 7 0-10milesRun8 0.50.60.7CDF0-10 miles Run 80-50 miles Run 6 0-50 miles Run 7 020.30.4CC0-50 miles Run 8 00.10.214Individual 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 1010milesRun1 070.80.90-10 miles Run 10-10 miles Run 6 0-10 miles Run 7 0-10milesRun8 0.50.60.7010 miles Run 80-50 miles Run 1 0-50 miles Run 6 0-50 miles Run 7 DF0.30.40-50 miles Run 8 CCD00.1 0.215Individual 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 conditional LCF fatality consequences a t each distance. *Demonstrate convergence of the combined MACCS2-weatheruncertaintyanalysisresults.

16weather 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 0.910-10miles0.70.80-10 miles0-20 miles 0-30 miles DF040.5 0.60-40 miles 0-50 miles CCD0.2 0.30.400.110E0510E0410E0310E0218Individual LCF Risk per Event 1.0E-051.0E-041.0E-031.0E-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 1.00.8F0.6CDF020.40.00.220Metric50 miles20 miles10 miles30 miles40 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 21seeds for the 350 MACCS2 input parameters*The same 984 weather trials were used.

Runs 9-11 (Low Source Term) Conditional,Mean,IndividualLCFRun#010020030040050Conditional, Mean, Individual LCF Risk (per event) Statistics Statistic Run #0-10 miles0-20 miles0-30 miles0-40 miles0-50 milesRun 91.1E-041.2E-048.3E-055.4E-054.4E-05 MeanRun1011E-0412E-0483E-0554E-0544E-05MeanRun 101.1E-041.2E-048.3E-055.4E-054.4E-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-05MedianRun 108.6E-051.0E-047.4E-054.8E-053.9E-05Run 118.8E-051.0E-047.2E-054.7E-053.9E-05 5thRun 92.3E-053.8E-052.7E-051.7E-051.4E-05 percentile Run1022E0538E0526E0517E0514E05percentile Run 102.2E-053.8E-052.6E-051.7E-051.4E-05Run 112.3E-054.0E-052.7E-051.8E-051.4E-05 95thRun 92.5E-042.4E-041.7E-041.1E-048.9E-05 percentileRun1026E0424E0417E0412E0495E0522percentile Run 102.6E-042.4E-041.7E-041.2E-049.5E-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 0910-10 miles Run 1 010milesRun9 070.80.90-10 miles Run 90-10 miles Run 100-10 miles Run 11 050ilR1DF0.50.60.70-50 miles Run 10-50 miles Run 9 0-50 miles Run 10 CCD0.30.40-50 miles Run 11 0.1 0.223Individual 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 Risk (per Event) CCDFs0910-10 miles Run 1 0-10milesRun12 0.70.80.90-10 miles Run 120-10 miles Run 13 0-10 miles Run 14 DF0.5 0.60-50 miles Run 1 0-50 miles Run 12 0-50 miles Run 13 CCD020.30.40-50 miles Run 14 00.10.224Individual 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 0.910-10 miles Run 1 0-10milesRun15 0.70.8010 miles Run 150-10 miles Run 16 0-10 miles Run 17 0-50 miles Run 1 050milesRun15 DF0.5 0.60-50 miles Run 150-50 miles Run 16 0-50 miles Run 17 CCD020.30.400.10.225Individual LCF Risk per Event 01.0E-061.0E-051.0E-041.0E-03 Average difference between the three separate LHS runs over all pAleatory Weather Distributions (1 stto 99thpercentile)Source Term Conditional LCF RiskConditional LCF Risk0-10 miles0-50 miles Highest Prompt Fatality Risk-Runs3-50.8%0.8%Risk Runs 35Highest LCF Risk -

Runs 6-80.8%0.9%LR91109%08%Low -Runs 9-110.9%0.8%Medium -Runs12-140.8%0.9%High-Runs15-1710%06%26High Runs 15171.0%0.6%Overall Average0.9%0.8%

MACCS2 Stability Analysis Using Bootstrap Approach Approach:

Approach:*MACCS2 code modified to allow simple random sampling *The 'high' source term (i.e., Replicate 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 0910.70.80.9DF0.5 0.60-10 miles CAP17-LHS CCD0.30.40-10 miles CAP17-MC 0-50 miles CAP17-LHS 00.1 0.20-50 miles CAP17-MC 28Individual 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 101DF0.1CCD0.010-1.3 miles CAP17-LHS 0-1.3 miles CAP17-MC 02milesCAP17 LHS00010-2 miles CAP17-LHS0-2 miles CAP17-MC 0-3.5 miles CAP17-LHS 0-3.5 miles CAP17-MC 29Individual 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 reclose33 CHEMFORM -Iodine and cesium fraction ParameterDistributionCHEMFORM:FivealternativecombinationsofRN classes2,4,16,and17(CsOH,I 2,CsI,andCs 2MoO4)Discrete distributionCombination #1 = 0.125Combination#2

=0125Notethefractioncesiumbelowrepresentsthe distributionof'residual'cesiumwhichisthemassof cesiumremainingafterfirstreactingwiththeamountof iodineassumedtoformCsICombination

  1. 2 0.125Combination #3 = 0.125 Combination #4 = 0.125Combination#5=0500 iodineassumedtoformCsI.Combination
  1. 5 = 0.500Five AlternativesSpecies (MELCOR RN Class)CsOH (2)I 2 (4)CsI (16)Cs 2MO4 (17)fractioniodine--003097--Combination#1 fractioniodine0.030.97fractioncesium1----0Combination#2fractioniodine--0.0020.998fractioncesium0.5----0.5 fractioniodine--000298099702--Combination#3 fractioniodine0.002980.99702fractioncesium0----1Combination#4fractioniodine--0.07570.9243--fractioncesium0.5----0.5 fractioniodine--0027709723--34Combination#5 fractioniodine--0.02770.9723--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
  • Drywell liner failure model*Operator actions 47 MACCS2 Parameters of Interest DOSNRM, DOSHOT -Normal and Hotspot Relocation Doses 49 TIMNRM, TIMHOT -Normal and Hotspot Relocation Times50 ESPEED -Evacuation speed 51 GSHFAC -Groundshine Shielding Factor52 Next Stepsp*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 longterm station blackout (LTSBO) scenario, and modeling the SOARCA unmitigated LTSBO. ThLTSBOifititd The LTSBO scenario frequency is estimated in SOARCA to be ~3x10

-6per reactor year.

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