South Texas Project Elements of Risk Informed Resolution

1. South Texas ProjectElements of Risk Informed Resolution Bruce Letellier Los Alamos National Laboratory Los Alamos, NM NEI Chemical Effects Summit NRC Public Meeting January 26 and 27, 2012

2. Goals of Risk-Informed Analysis • Assess a full spectrum of accident scenarios considering both likelihood and outcome • Retain deterministic assumptions like DEGB (where physically plausible), but balance by probability of occurrence • Exercise phenomenologic models where possible to understand realistic time-dependent behavior • Retain prudent conservatism where lack of knowledge limits predictive confidence • Express alternative risk management strategies as differential impacts on Core Damage Frequency and Large Early Release Frequency (∆CDF and ∆LERF) • Requires interface with existing plant PRA • Initial STP quantification compares present plant to “perfect” plant with no debris or chemical induced recirculation failures • Evaluate scenarios to identify any required actions to reduce and/or manage risk (RG 1.174 guidelines)

3. Regulatory Guide 1.174 • STP initial quantification compares present plant to perfect plant with no debris or chemical induced recirculation failures: “Can something be done to obtain a significant risk benefit (figure on left)?” • STP will quantify benefit of new strainers vs old to show decreasing marginal risk reduction • Threshold criteria presently include ECCS NPSH and in-core fiber accumulation • Approximate treatment of chemical effects based on strainer tests with WCAP products

4. Interface with PRA • PWR PRAs include a top event for “sump strainer failure” under various plant states (conditions of equipment functionality, or recirculation success - ECCS) • Typically, a new top event is required to accommodate evaluation of downstream blockage in the core • CASA Grande supports PRA by quantifying failure probabilities at the strainer and at the core • Thousands of break scenarios evaluated relative to thresholds of NPSHreq, fiber mass/fuel assembly, etc. • Chemical effects (at STP) are treated as time-dependent phenomena that do not pose immediate challenges to head loss or core blockage (≥24 hr delay)

5. Risk-Informed Resolution Path Chart 3 from Risk Informed Road Map

6. Key Attributes of Risk-Informed Process • Initiating event frequencies • Weld failure, RCP LOCA, PRT release • Spatial description of plant geometry • Time-dependent scenario development • Debris and chemical challenges competing with equipment operation and operator action • Uncertainty quantification for major phenomena • Categorization of primary behavior • Small, Medium, Large LOCA • Spray vs no Spray phases • High temp/pressure phase vs. atmospheric • Active corrosion vs. passivation • Surface deposition of chemical products vs. ppt in solution

7. Statistical Risk Evaluation • Evaluate thousands of break scenarios over full spectrum of sizes, conditions and uncertain parameter values • Robust sampling of all uncertain parameters ensures precision of extreme consequence tail (DEGB, min water levels, etc.) • Weight the outcome of each random scenario by its respective likelihood to form probability distribution of any performance measure (NPSH, void fraction, etc) • Compare Pr distribution to “threshold of concern” to determine “failure” probability

8. Containment Accident Stochastic Analysis (CASA) Grande Demo

9. Risk Assessment Philosophy Develop and understand the entire distribution of possible outcomes for a performance metric Quantify the proportion of scenarios that exceed some acceptable standard (failure) Prioritize and manage the risk of failure posed by this one issue among all other risk contributors Threshold of Concern Probability Density (# per unit performance) Probability of Exceeding Threshold Performance Level

10. Competing Risk Determinants • Blue distribution of all possible physical outcomes • Red threshold of concern • Steep decline indicates rapid improvement with increased tolerance • Risk Reduction obtained by increasing tolerance and/or decreasing conservatism and/or impact Increased Tolerance Decreased Physical Conservatism

11. STP Prototypical Head Loss Histories • Steep increase indicates arrival of coating debris (and chemical products) • No cases found in parameter space that exceed limiting NPSH of 18 ft H2O • Even with conservative head loss treatment shown (x5) there may be margin for chemicals

12. Risk-Informed Chemical Test Strategy • Limited long-term tests establish trends for major sections of risk spectrum (Small, Med or small Large, Large LOCA) • Thermodynamic equilibrium calculationsguide supporting bench-scale tests to quantify likelihood of adverse effects cited by PIRT near each trend • Utilize CASA debris distributions to support selection of test conditions • Containment simulations to establish pool temperature histories Relate each test to a region of the risk spectrum Chemical findings alone are not useful for risk-informed resolution unless there is a corresponding understanding of likelihood

13. Topics for Discussion • Existing experience with uncertainty quantification and propagation to industrial water quality issues • Importance of high-temp corrosion/spray above atmospheric pressure • Existing evidence of surface precipitation (rather than ppt from bulk solution) • Measures taken to reduce or mitigate chemical product formation or head loss • Evidence that cyclic temp variations help/hinder issues • Is Boron pptconsidered a “chemical effect” for GSI-191 and/or is it an important debris source • Assumptions Re fuel damage from pressure transient that would cause sprays to remain on • Assumptions Re release of scale on fuel during thermal/pressure transient that represents additional particulate source

14. Tutorial Slides

15. Uncertainty in Decision Thresholds Question: Does the datum drawn from blue physical dist exceed the threshold drawn from green tolerance dist? Repeated answers to this question form basis for binomial failure probability.

16. Sampling a Probability Distribution Difference between upper and lower exceedanceprob = bin prob (weight) Parameter values chosen from each bin to obey dist Smaller of 2 bins spanning MLOCA range

17. UQ “Rules of Thumb” • Partition (bin) the entire range of each random variable and calculate the probability spanned by each bin. Can have different number of bins for each variable, but assignments are easier if all variables have the same #. • Select a value to represent each bin using the probability distribution • Carry the bin value AND the local probability weight hand-in-hand. • Evaluate as many combinations of variables as practical using each bin of a variable an equal number of times. • “Evaluate” means run the code/model with each random combination of variables • The “answers” are assigned a weight equal to the product of bin weights for every variable involved in the calculation. • The sum of multiplicative weights for all of the results is used as the denominator to calculate the relative proportion of each answer. • Rank order the result values and form a cumlative sum of the relative weights to obtain a properly normalized distribution of results. Plot CumSumvs ordered values.