ML13255A376

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Soarca Peach Bottom Uncertainty Analysis (UA) Acrc Briefing - Sept 2013
ML13255A376
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Site: Peach Bottom  
Issue date: 09/16/2013
From: Tina Ghosh
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SOARCA Peach Bottom Uncertainty Analysis (UA)

ACRS Briefing ACRS Briefing Tina Ghosh, PhD Tina Ghosh, PhD RES/DSA/AAB September 16, 2013

Agenda ACRS t

MACCS2 th

  • ACRS comments on MACCS2 weather uncertainty integration and convergence of results and staff responses results, and staff responses
  • MELCOR parameters of interest
  • MELCOR parameters of interest MACCS2 parameters of interest
  • MACCS2 parameters of interest 2

MELCOR - MACCS2 -

Weather Uncertainty Weather Uncertainty Integration ACRS Comment:

  • For the combined MELCOR-MACCS2 results, the report currently presents only results averaged over the weather currently presents only results averaged over the weather trials.
  • The report should also present results that include and di l

th f ll th l

t t i t display the full weather aleatory uncertainty 3

Conditional mean, individual latent cancer fatality (LCF) risk (per y (

)

(p event) 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 5th 3 1 10 5 4 9 10 5 3 4 10 5 2 2 10 5 1 9 10 5 percentile 3.1x10-5 4.9x10-5 3.4x10-5 2.2x10-5 1.9x10-5 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 95th percentile 4.2x10-4 7.7x10-4 5.3x10-4 3.4x10-4 2.7x10-4 SOARCA UA 4

SOARCA UA Base Case 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 d E i t i

U t i t d

and Epistemic Uncertainty and Epistemic Uncertainty with Aleatory Means 1

0.8 0.9 1

0-10 miles Aleatory Mean 0-20 miles Aleatory Mean 0-50 miles Aleatory Mean 0-10 miles Epistemic & Aleatory 0 20 il E i t i & Al t

0 5 0.6 0.7 0-20 miles Epistemic & Aleatory 0-50 miles Epistemic & Aleatory DF 0.3 0.4 0.5 CCD 0

0.1 0.2 5

Individual LCF Risk 0

1.0E-06 1.0E-05 1.0E-04 1.0E-03 1.0E-02

MACCS2 and Weather Uncertainties for Prompt Uncertainties for Prompt Fatality Risk ACRS Comment:

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 parameters and for each epistemic sample generate 984 parameters, and for each epistemic sample generate 984 weather cases to derive an uncertainty distribution for the conditional prompt fatality consequences at each di t distance.
  • Demonstrate convergence of the combined MACCS2-weather uncertainty analysis results.

6 weather uncertainty analysis results.

MACCS2 and Weather Uncertainties for Prompt 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 size 1000 were completed (Runs 3 4 5) using three 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-fatality risk (per event) p p

y (p

)

statistics for the MACCS2 Uncertainty Analysis for specified circular areas (Run 1) 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 0 0 0 0 0 0 0 0 0 0 Median 0.0 0.0 0.0 0.0 0.0 75th percent 0.0 0.0 0.0 0.0 0.0 p

-ile 95th percent 1 9x10-6 3 5x10-8 0 0 0 0 0 0 8

percent

-ile 1.9x10 3.5x10 0.0 0.0 0.0

Run 3-5 conditional, mean, individual prompt-fatality risk (per p

p y

(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 3 3.3E-06 1.0E-06 3.4E-07 4.7E-08 9.5E-09 M

R 4

3 3E 06 9 4E 07 3 0E 07 4 2E 08 8 9E 09 Mean Run 4 3.3E-06 9.4E-07 3.0E-07 4.2E-08 8.9E-09 Run 5 3.2E-06 9.8E-07 3.0E-07 4.7E-08 1.3E-08 Run 3 4.9E-07 1.2E-07 0.0 0.0 0.0 Median Run 4 3 3E 06 9 4E 07 0 0 0 0 0 0 Median Run 4 3.3E-06 9.4E-07 0.0 0.0 0.0 Run 5 3.2E-06 9.8E-07 0.0 0.0 0.0 75th Run 3 4.0E-06 1.0E-06 2.0E-07 3.8E-09 0.0 percent Run 4 3 7E 06 8 8E 07 2 2E 07 1 1E 08 0 0 percent Run 4 3.7E-06 8.8E-07 2.2E-07 1.1E-08 0.0

-ile Run 5 3.9E-06 9.6E-07 1.9E-07 8.2E-09 0.0 95th Run 3 1.4E-05 4.1E-06 1.5E-06 2.1E-07 1.2E-08 percent Run 4 1 6E 05 4 7E 06 1 8E 06 2 3E 07 0 0 9

percent Run 4 1.6E-05 4.7E-06 1.8E-06 2.3E-07 0.0

-ile Run 5 1.4E-05 4.4E-06 1.6E-06 2.0E-07 0.0

Runs 3-5 and Run 1 Conditional, Mean Individual Prompt Fatality Risk (per Event) Epistemic Uncertainty CCDF, at 1.3 Miles 1

0.1 0-1.3 miles Run 1 DF 0.01 0-1.3 miles Run 3 0-1.3 miles Run 4 CCD 0.001 0-1.3 miles Run 5 10 Individual Prompt Fatality Risk per Event 1.0E-11 1.0E-10 1.0E-09 1.0E-08 1.0E-07 1.0E-06 1.0E-05 1.0E-04

Runs 3-5 and Run 1 Conditional, Mean Individual Prompt Fatality Risk (per Event) Epistemic Uncertainty CCDF, at 3.5 Miles 1

0.1 0-3.5 miles Run 1 0-3 5 miles Run 3 DF 0.01 0 3.5 miles Run 3 0-3.5 miles Run 4 0-3.5 miles Run 5 CCD 0 001 11 Individual Prompt Fatality Risk per Event 0.001 1.0E-12 1.0E-11 1.0E-10 1.0E-09 1.0E-08 1.0E-07 1.0E-06 1.0E-05

MACCS2 and Weather Uncertainties for LCF Risk 1 ACRS Comment:

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 parameters and for each epistemic sample generate 984 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 Uncertainties for LCF Risk 1 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 size 1000 were completed (Runs 6 7 8) using three 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

0 7 0.8 0.9 0-10 miles Run 6 0-10 miles Run 7 0-10 miles Run 8 0.5 0.6 0.7 CDF 0-10 miles Run 8 0-50 miles Run 6 0-50 miles Run 7 0 2 0.3 0.4 CC 0-50 miles Run 8 0

0.1 0.2 14 Individual Latent Cancer Fatality Risk per Event 1.E-05 1.E-04 1.E-03 1.E-02

Runs 6-8 and Run 1 Epistemic Uncertainty with Aleatory Mean, Conditional Individual LCF Risk (per Event) CCDFs 1

0 10 miles Run 1 0 7 0.8 0.9 0-10 miles Run 1 0-10 miles Run 6 0-10 miles Run 7 0-10 miles Run 8 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 DF 0.3 0.4 0-50 miles Run 8 CCD 0

0.1 0.2 15 Individual Latent Cancer Fatality Risk per Event 0

1.0E-05 1.0E-04 1.0E-03

MACCS2 and Weather Uncertainties for LCF Risk 2 ACRS Comment:

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.
  • For that realization sample from the 350 MACCS2 input 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 diti l LCF f t lit t

h di t conditional LCF fatality consequences at each distance.

  • Demonstrate convergence of the combined MACCS2-weather uncertainty analysis results.

16 weather uncertainty analysis results.

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

Approach:

  • Three representative 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 SOARCA point estimate values 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

0-10 miles 0.7 0.8 0-10 miles 0-20 miles 0-30 miles DF 0 4 0.5 0.6 0-40 miles 0-50 miles CCD 0.2 0.3 0.4 0

0.1 1 0E 05 1 0E 04 1 0E 03 1 0E 02 18 Individual LCF Risk per Event 1.0E-05 1.0E-04 1.0E-03 1.0E-02

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

  • A set of 11 results have then been used as metrics to select three representative source terms:

Latent Cancer Fatality (LCF) risk at 5 different locations (10 20

- 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 CDF 0 2 0.4 0.0 0.2 20 Metric 50 miles 20 miles 10 miles 30 miles 40 miles Cs Iodine Ba Ce Te (hr)

MACCS2 and Weather Uncertainties for LCF Risk 2 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 seeds for the 350 MACCS2 input parameters 21 seeds for the 350 MACCS2 input parameters
  • The same 984 weather trials were used.

Runs 9-11 (Low Source Term)

Conditional, Mean, Individual LCF Run #

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 9 1.1E-04 1.2E-04 8.3E-05 5.4E-05 4.4E-05 Mean Run 10 1 1E-04 1 2E-04 8 3E-05 5 4E-05 4 4E-05 Mean Run 10 1.1E-04 1.2E-04 8.3E-05 5.4E-05 4.4E-05 Run 11 1.1E-04 1.2E-04 8.3E-05 5.4E-05 4.4E-05 Run 9 8.8E-05 1.0E-04 7.2E-05 4.7E-05 3.9E-05 Median Run 10 8 6E-05 1 0E-04 7 4E-05 4 8E-05 3 9E-05 Median Run 10 8.6E-05 1.0E-04 7.4E-05 4.8E-05 3.9E-05 Run 11 8.8E-05 1.0E-04 7.2E-05 4.7E-05 3.9E-05 5th Run 9 2.3E-05 3.8E-05 2.7E-05 1.7E-05 1.4E-05 percentile Run 10 2 2E 05 3 8E 05 2 6E 05 1 7E 05 1 4E 05 percentile Run 10 2.2E-05 3.8E-05 2.6E-05 1.7E-05 1.4E-05 Run 11 2.3E-05 4.0E-05 2.7E-05 1.8E-05 1.4E-05 95th Run 9 2.5E-04 2.4E-04 1.7E-04 1.1E-04 8.9E-05 percentile Run 10 2 6E 04 2 4E 04 1 7E 04 1 2E 04 9 5E 05 22 percentile Run 10 2.6E-04 2.4E-04 1.7E-04 1.2E-04 9.5E-05 Run 11 2.7E-04 2.4E-04 1.7E-04 1.1E-04 9.4E-05

Runs 9-11 and Run 1 Epistemic Uncertainty Conditional, Mean, Individual LCF Risk (per Event) CCDFs 0 9 1

0-10 miles Run 1 0 10 miles Run 9 0 7 0.8 0.9 0-10 miles Run 9 0-10 miles Run 10 0-10 miles Run 11 0 50 il R

1 DF 0.5 0.6 0.7 0-50 miles Run 1 0-50 miles Run 9 0-50 miles Run 10 CCD 0.3 0.4 0-50 miles Run 11 0.1 0.2 23 Individual LCF Risk per Event 0

1.0E-06 1.0E-05 1.0E-04 1.0E-03

Runs 12-14 (medium) and Run 1 Epistemic Uncertainty Conditional, Mean, Individual LCF Ri k (

E t) CCDF LCF Risk (per Event) CCDFs 0 9 1

0-10 miles Run 1 0-10 miles Run 12 0.7 0.8 0.9 0-10 miles Run 12 0-10 miles Run 13 0-10 miles Run 14 DF 0.5 0.6 0-50 miles Run 1 0-50 miles Run 12 0-50 miles Run 13 CCD 0 2 0.3 0.4 0-50 miles Run 14 0

0.1 0.2 24 Individual LCF Risk per Event 0

1.0E-06 1.0E-05 1.0E-04 1.0E-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 0-10 miles Run 15 0.7 0.8 0 10 miles Run 15 0-10 miles Run 16 0-10 miles Run 17 0-50 miles Run 1 0 50 miles Run 15 DF 0.5 0.6 0-50 miles Run 15 0-50 miles Run 16 0-50 miles Run 17 CCD 0 2 0.3 0.4 0

0.1 0.2 25 Individual LCF Risk per Event 0

1.0E-06 1.0E-05 1.0E-04 1.0E-03

Average difference between the three separate LHS runs over all p

Aleatory Weather Distributions (1st to 99th percentile)

Source Term Conditional LCF Risk Conditional LCF Risk 0-10 miles 0-50 miles Highest Prompt Fatality Risk - Runs 3-5 0.8%

0.8%

Risk Runs 3 5 Highest LCF Risk -

Runs 6-8 0.8%

0.9%

L R

9 11 0 9%

0 8%

Low - Runs 9-11 0.9%

0.8%

Medium - Runs 12-14 0.8%

0.9%

High - Runs 15-17 1 0%

0 6%

26 High Runs 15 17 1.0%

0.6%

Overall Average 0.9%

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 (SRS or MC) and Latin Hypercube Sampling (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 bounds

==

Conclusion:==

Results of the uncertainty analysis are well 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 0 9 1

0.7 0.8 0.9 DF 0.5 0.6 0-10 miles CAP17-LHS CCD 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 0

1.0E-06 1.0E-05 1.0E-04 1.0E-03

Run 1 (CAP17) Conditional, Mean, Individual Prompt Fatality Risk (per Event) CCDF with LHS and MC Sampling 1

0 1 DF 0.1 CCD 0.01 0-1.3 miles CAP17-LHS 0-1.3 miles CAP17-MC 0 2 miles CAP17 LHS 0 001 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 0.001 1.0E-12 1.0E-11 1.0E-10 1.0E-09 1.0E-08 1.0E-07 1.0E-06 1.0E-05

10-mile Conditional, Mean, Individual LCF Risk (per Event) CDF for Run 15 (CAP37) and 95%

Confidence Interval Upper and Lower Bounds 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%

Confidence Interval Upper and Lower Bounds 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 Parameter Distribution CHEMFORM:

Five alternative combinations of RN classes 2, 4, 16, and 17 (CsOH, I2, CsI, and Cs2MoO4)

Discrete distribution Combination #1 = 0.125 Combination #2 = 0 125 Note the fraction cesium below represents the distribution of 'residual' cesium which is the mass of cesium remaining after first reacting with the amount of iodine assumed to form CsI Combination #2 0.125 Combination #3 = 0.125 Combination #4 = 0.125 Combination #5 = 0 500 iodine assumed to form CsI.

Combination #5 = 0.500 Five Alternatives Species (MELCOR RN Class)

CsOH (2)

I2 (4)

CsI (16)

Cs2MO4 (17) fraction iodine 0 03 0 97 Combination #1 fraction iodine 0.03 0.97 fraction cesium 1

0 Combination #2 fraction iodine 0.002 0.998 fraction cesium 0.5 0.5 fraction iodine 0 00298 0 99702 Combination #3 fraction iodine 0.00298 0.99702 fraction cesium 0

1 Combination #4 fraction iodine 0.0757 0.9243 fraction cesium 0.5 0.5 fraction iodine 0 0277 0 9723 34 Combination #5 fraction iodine 0.0277 0.9723 fraction cesium 0

1 SOARCA estimate Fraction iodine 0.0 1.0 Fraction cesium 0.0 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

  • Surrogate parameters
  • Surrogate parameters L

h d

t ti f il

ll li f il d l

  • 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 Times 50

ESPEED - Evacuation speed 51

GSHFAC - Groundshine Shielding Factor 52

Next Steps p

  • ANS PSA Conference presentation and papers
  • ANS PSA Conference presentation and papers and CSARP presentation

- September 2013 September 2013

Questions and Comments

Note that all results in these presentation slides are conditional (per event) on the potential occurrence of a long-term station potential occurrence of a long term station blackout (LTSBO) scenario, and modeling the SOARCA unmitigated LTSBO.

Th LTSBO i

f i

ti t d The LTSBO scenario frequency is estimated in SOARCA to be ~3x10-6 per reactor year.

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