ML112770176

Licensee Handouts from 10/3/11 Meeting STP LOCA Frequency

ML112770176
Person / Time
Site:	South Texas
Issue date:	10/03/2011
From:	Mosleh A South Texas
To:	Balwant Singal Plant Licensing Branch IV
Singal, B K, NRR/DORL, 301-415-301
Shared Package
ML112770162	List: ML112770170 ML112770176 ML112770203 ML112770230 ML112770237 ML112770246 ML112770251
References
TAC ME5358, TAC ME5359
Download: ML112770176 (24)

Text

Technical Review of STP LOCA Frequency E ti ti M Estimation Methodology th d l Ali Mosleh Work Performed for South Texas Project Electric Generation Station Oct 3, 2011 1

Scope of Review y LOCA Frequency Estimation Model y Interpretation and use of NUREG-1829 information y Other keyy assumptions p and computational p steps p used to generate the numerical results y Two rounds of review were done, one on an earlier draft (Early August, 2011), and one on the final report (dated Sept 2011) 2

LOCA Frequency Estimation Model Overview y Current report has focused on estimation of the frequency of LOCAs initiated at or near the location of pipe and nozzle welds y Future F t workk will ill include i l d LOCAs LOCA d due tto pipe i failures f il att other locations and non-pipe related failures in the RCS pressure boundary 3

Basic Estimation Model Overview y The model for estimating the frequency of a LOCA of a given size is given by the following equations:

F(LOCAx ) = mi ix i

jx = ix = ik P(Rx Fik )Iik k

nik nik ik = =

ik fik N iTi y x refers to the various break size ranges such as those used in NUREG-1829 to describe the 6 LOCA categories.

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Definitions 5

Definitions 6

Basic Estimation Model Overview y Different locations are characterized and assigned rupture frequencies based on 45 combinations of system type, weld type, degradation mechanisms, and pipe size.

y The estimation process is a bottom-up approach building the LOCA estimates by combining various contributing components, failure mechanisms, and conditional probabilities.

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Basic Estimation Model Comments and Observations y The general decomposition formula follows known principles of calculus of probability y The formula represents the commonly made assumption of constant failure rate model for the selected period in plant life y The formulation enables the consideration of known susceptibilities to the damage mechanisms in quantification of the failure rates and probabilities y It allows tailoring the probabilities for component-specific vulnerabilities to degradation mechanisms 8

Basic Estimation Model Comments and Observations y Parameter Iik, determined through a previously developed and peer-reviewed Markov model, provides the ability to account for the effects of integrity management programs on reducing likelihood of catastrophic failures y As a physical model the decomposition formula views the pipe rupture (LOCA) process as a two stage process of (1) stochastic occurrence of degraded functional conditions, and (2) stochastic rupture events given a degraded condition y This is a reasonable first order approximation of a physical process model. Similar formulations are seen elsewhere in PRA such as Precursor Event methodology and some of the commonly used methods for common cause failure analysis analysis 9

Basic Estimation Model Comments and Observations y A key advantage of the approach is the ability to make use of existing operational data y Assumptions in implementation y Various degradations (crack, small leak, leak etc) are lumped together as one homogenous class irrespective of their implied severity y This means that the same conditional rupture probability applies to all degraded states. The effects is overestimation for some (e.g., crack) and underestimation for other degraded conditions (e.g.,

leak) 10

Basic Estimation Model Comments and Observations y The contributions of various DMs at a specific piping location are combined linearly to determine the total LOCA frequency distribution (for each weld /location) y This is a first order approximation to a significantly more complex case where synergetic effects of multiple DMs are considered explicitly. However, the state of the art in terms of experimental results and theoretical models for the synergistic effects is quite limited y To some extent the the net effect of synergistic phenomena are accounted for via the use of actual data in the failure frequency estimation estimation stage 11

Failure Rate Development Steps y 1.1 Determination of component and weld types y 1.2 Perform data query for failure counts y 1.3 Estimate component exposure y 1.4* Develop component failure rate prior distributions for each damage mechanism (DM) y 1.5* Perform Bayes update for each exposure case (combination of weld count case and DM susceptibility case) y 1.6* Develop mixture distribution to combine results for different exposure hypotheses to yield conditional failure rate distributions given STP specific applicable DMs y 1.7 Calculate total failure rate over all applicable damage mechanisms

• Subject of specific comments on next viewgraph 12

Observations on Failure Rate Development Steps y STEP 1.4 Develop component failure rate prior distributions for each damage mechanism (DM) y From earlier work in EPRI RI-ISI, peer reviewed y Very wide lognormal distributions, based on existing pipe failure rates, engineering judgment, and use of data y STEP 1 1.5 5P Perform f B Bayes update d t for f each h exposure case (combination of weld count case and DM susceptibility case) y Standard use of Bayesian updating (mostly with 0 failures), well established and accepted in PRAs y Computations were done with aid of a validated and widely used computer code 13

Observations on Failure Rate Development Steps y STEP 1.6 Develop mixture distribution to combine results for different exposure hypotheses to yield conditional failure rate distributions given STP specific applicable DMs y Use of (equally weighted) mixture of distributions to generate a composite distribution for different data assumptions is an accepted (advanced) methodology within the Bayesian framework.

y The report has applied this methodology in a very systematic and rigorous way y The applied Monte Carlo sampling technique and numerical procedures were not subjects of this review y While the process and assumptions are clearly stated (see for example table 3-

1) the scope of this review did not include an evaluation of the technical basis for assumptions made in the process of estimating population size for each class (weld, pipe segment, ect,) and in assessing the level of susceptibility of each class to various failure mechanisms 14

Conditional Rupture Probability Estimation Methodology y The goal is to develop a set of conditional rupture probabilities (CRPs) vs. break size for each component category y CRPs are estimated by anchoring the the generic total frequency of each of the six LOCA classes on two distinct sources of NUREG-1829 data y Base case analyses of specific PWR components (hot leg, surge line, HPI line for a specific 3-loop PWR) developed by B. Lydell using methodology similar to that used by the current study y Estimates of 9 experts of LOCA frequencies vs. break size for many PWR components for the entire fleet of U.S. PWRs 15

Conditional Rupture Mode Probability Model Steps y 2.1 Select components to define conditional rupture probability (CRP) model categories y *2.2 Obtain expert reference LOCA distributions from NUREG-1829 y *2.3 Obtain expert multiplier distributions for 40yr LOCA frequencies from NUREG-1829 y *2.4 Determine 40yr LOCA distributions (product of steps 2.2 and 2.3) for each expert, fit to lognormal y *2.5 Determine geometric mean of expert distributions from Step 2.4 (lognormal)

• Subject of specific comments on next viewgraph 16

Observation on CRP Estimation Steps y STEPS 2.2 and 2.3 y Some NUREG-1829 experts had provided asymmetric inputs for lower, middle, and upper values, inconsistent with lognormal distribution characteristics y NUREG-1829 used split lognormal distributions to treat this asymmetry.t TheTh STP approach h fits fit the th asymmetric t i cases to t standard t d d lognormal distribution in part to simplify the procedure y Of the three possible alternatives, this reviewer recommended use of Upper and Mid values to determine the corresponding distribution y preserves the central tendency of the distribution and y keeps the experts upper bound estimate which carries more risk significance 17

Observation on CRP Estimation Steps y STEP 2.4 Determine 40yr LOCA distributions (product of steps 2.2 and 2.3) for each expert, fit to lognormal y This is done for each experts estimate prior to aggregation of the result of 9 experts (correct approach among alternatives) y The procedure follows basis probability rules for developing distribution of product of lognormally distributed quantities 18

Observation on CRP Estimation Steps y STEP 2.5 Determine geometric mean of expert distributions from Step 2.4 y The report investigated two approaches for forming composite distributions, y Mixture Distribution Method (with equal weight for all experts) y Geometric Mean Method (by taking the geometric means of two parameters of the experts lognormal distributions, medians and range factors) y The Geometric Mean method, which is the approach taken by NUREG-1829 was also adopted by the report y In this reviewers opinion, in general the mixture distribution approach to expert opinion aggregation has a stronger technical basis. However, an equally important factor is the engineering assessment by NUREG-1829 and the STP report of the overall suitability of the results produce by each approach.

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Conditional Rupture Mode Probability Model Steps y 2.6a Benchmark Lydell Base Case Analysis for selected components y 2.6 b Determine failure rate distribution for Lydell Base Case analysis in NUREG-1829; fit to lognormal y 2.6c Apply Lydell CRP model from Base Case Analysis y 2 6d D 2.6d Determine t i LOCA frequency f distribution di t ib ti from f L d ll Base Lydell B C Case Analysis A l i y *2.7 Determine mixture distribution of NUREG-1829 GM (from Step 2.5) and Lydell LOCA frequency (from Step 2.8) to obtain Target LOCA frequency Distribution for each CRP category component y *2.8 Apply formulas to calculate CRP distributions to be used as prior distributions for each component assigned to each CRP category y *2.9 For each component in CRP category, perform Bayes update with evidence of failure and rupture counts from service data.

• Subject of specific comments on next viewgraph 20

Observation on CRP Estimation Steps y STEP 2.7 Determine mixture distribution of NUREG-1829 GM (from Step 2.5) and Lydell LOCA frequency (from Step 2.8) to obtain Target LOCA Frequency distribution for each CRP category component y The report has viewed NUREG-1829 expert opinions and y

Lydells approach pp as two largely g y separate p sources of information on LOCA frequencies.

y This reviewer agrees that the overlap of the two sources are less pronounced than the differences in corresponding modeling and quantification approaches y Of possible options for use of these two sources of information, the report has chosen a mixture distribution of GM and Lydell results, following a recommendation form this reviewer.

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Observation on CRP Estimation Steps y STEP 2.8 Apply formulas to calculate CRP distributions to be used as prior distributions for each component assigned to each CRP category y This step is straightforward conceptually, but requires careful numerical (MC) procedures. This review however did not look into the details of computational implementation y STEP 2.9 For each component in CRP category, perform Bayes update with evidence of failure and rupture counts from service data.

y This step follows standard approach to Bayesian updating (beta prior and binomial likelihood) y Updating is done with marginal distributions (beta) of each of the 6 CRP categories given a failure. The joint distribution of CRPs is a multinomial distribution but separate updating with marginal (beta) distribution for each CRP is a reasonable simplification with no visible impact on results give the low values of CRPs 22

Summary and Conclusions y Comments and suggestion on earlier version have been addressed in the final report. Examples include y Addition of a more detailed summary of the methodology and charts explaining the steps y Better explanation of assumptions y More consistent application of probabilistic methods in some areas 23

Summary and Conclusions y The modeling and parameter estimation approaches are found to be sound, acknowledging a number of common approximations and certain simplifying assumptions, some imposed by the limitations in the state of the art y The reports careful and comprehensive treatment of known sources of uncertainty as well as relatively broad rages assigned to distributions are expected to cover much of the impact of the assumptions and limitations in the state of the art 24