Transcript Attendee Introduction - Nuclear Energy Institute

Risk-Informed GSI-191
South Texas Project
Elements 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
Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA
Slide 1
Risk-Informed GSI-191
Goals of Risk-Informed Analysis

Assess a full spectrum of accident scenarios considering both
likelihood and outcome
•

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)
•
•

Retain deterministic assumptions like DEGB (where physically
plausible), but balance by probability of occurrence
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)
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Slide 2
Risk-Informed GSI-191
Regulatory Guide 1.174
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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 incore fiber accumulation
Approximate treatment of
chemical effects based on
strainer tests with WCAP
products
August 22, 2011, NRC PreLicensing Meeting
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Slide 3
Risk-Informed GSI-191
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)
August 22, 2011, NRC PreLicensing Meeting
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Slide 4
Risk-Informed GSI-191
Risk-Informed Resolution Path
Chart 3 from Risk Informed Road Map
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Slide 5
Risk-Informed GSI-191
Key Attributes of Risk-Informed Process
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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
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Uncertainty quantification for major phenomena

Categorization of primary behavior
•
•
•
•
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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
August 22, 2011, NRC PreLicensing Meeting
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Slide 6
Risk-Informed GSI-191
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
August 22, 2011, NRC PreLicensing Meeting
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Slide 7
Risk-Informed GSI-191
Containment Accident Stochastic
Analysis (CASA) Grande
Demo
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Slide 8
Risk-Informed GSI-191
Develop and understand the entire distribution of
possible outcomes for a performance metric
Threshold of Concern
Probability Density (# per unit performance)
Risk Assessment Philosophy
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
Probability of
Exceeding
Threshold
Performance Level
August 22, 2011, NRC PreLicensing Meeting
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Slide 9
Risk-Informed GSI-191
Competing Risk Determinants
Increased
Tolerance
Decreased
Physical
Conservatism
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Blue distribution of all
possible physical outcomes
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Red threshold of concern
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Steep decline indicates rapid
improvement with increased
tolerance

Risk Reduction obtained by
increasing tolerance and/or
decreasing conservatism
and/or impact
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Slide 10
Risk-Informed GSI-191
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
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Slide 11
Risk-Informed GSI-191
Risk-Informed Chemical Test Strategy
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

Limited long-term tests establish trends for major sections of
risk spectrum (Small, Med or small Large, Large LOCA)

Thermodynamic equilibrium calculations guide 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
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Containment simulations to establish pool temperature histories
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Slide 12
Risk-Informed GSI-191
Topics for Discussion

Existing experience with uncertainty quantification and propagation
to industrial water quality issues
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Importance of high-temp corrosion/spray above atmospheric
pressure
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Existing evidence of surface precipitation (rather than ppt from bulk
solution)
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Measures taken to reduce or mitigate chemical product formation
or head loss
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Evidence that cyclic temp variations help/hinder issues
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Is Boron ppt considered a “chemical effect” for GSI-191 and/or is it
an important debris source
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Assumptions Re fuel damage from pressure transient that would
cause sprays to remain on
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Assumptions Re release of scale on fuel during thermal/pressure
transient that represents additional particulate source
August 22, 2011, NRC PreLicensing Meeting
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Slide 13
Risk-Informed GSI-191
Tutorial Slides
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Slide 14
Risk-Informed GSI-191
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.
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Slide 15
Risk-Informed GSI-191
Sampling a Probability Distribution
Difference between upper
and lower exceedance
prob = bin prob (weight)
Parameter values chosen
from each bin to obey dist
X, Break
Size (in.)
0.50
0.75
1.00
1.50
2.00
2.83
4.00
5.66
6.00
6.80
7.20
10.00
14.14
16.97
F(LOCA ≥X)
5.18E-08
3.11E-08
2.21E-08
1.40E-08
7.49E-09
3.12E-09
1.67E-09
7.09E-10
6.19E-10
4.71E-10
4.14E-10
2.16E-10
1.11E-10
7.56E-11
Smaller of 2 bins
spanning MLOCA range
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Slide 16
Risk-Informed GSI-191
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
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Carry the bin value AND the local probability weight hand-in-hand.
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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 CumSum vs ordered
values.
August 22, 2011, NRC PreLicensing Meeting
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Slide 17