ML21302A247
| ML21302A247 | |
| Person / Time | |
|---|---|
| Issue date: | 11/04/2021 |
| From: | David Esh, Timothy Mccartin Office of Nuclear Material Safety and Safeguards |
| To: | |
| Tomeka Terry NMSS/DFM/IOB 301-415-1488 | |
| References | |
| Download: ML21302A247 (22) | |
Text
NRCs Development and Use of Performance Assessment David Esh and Timothy McCartin Office of Nuclear Material, Safety and Safeguards U.S. Nuclear Regulatory Commission Nuclear Waste Technical Review Board November 4, 2021
Outline
- Key Aspects in Development of a PA Model
- Scope/Level of Detail
- PA Development Process
- Challenges and Lessons Learned 2
PA Example Mathematical model (abstraction)
Real system Estimated future performance 1.E+0 Tc99 1.E-1 Tc99 I129 I129 1.E-2 Np237 Np237 Sn126 1.E-3 Se79 Sn126 Se79 Pu239 Model 1.E-4 C14 1.E-5 Pb210 Pu240 Dose (mSv/yr)
Support 1.E-6 Ra226 Ac227 0.6 1.E-7 Pa231 0.5 0.4 1.E-8 0.3 0 2000 4000 6000 8000 10000 0.2 Time(yr) 0.1 0
1 3 10 30 35 0 .0 3 0 .1 0 .3 100 300 1000 3000 10000 30000 Kd (ml/g) 3
Key Aspects
- Purpose Why do you need a PA model and what question(s) are you trying to answer?
- Scope of the assessment (Conceptual)
What to include - why and how (disruptive vs. nominal)
- Modeling (Numerical)
Complexity, system vs. process, data, model abstraction
- Uncertainty Epistemic/aleatory, propagation, risk dilution
- Model Support 4
Purpose (Decisions) -
HLW Example
- Site Recommendation
~20 years of site characterization
- Construction Authorization
~10 years for license application preparation and regulatory review
- License Approval (emplacement of waste)
~3-5 years of data collection during construction application preparation and regulatory review
- Permanent Closure
~95 years of performance confirmation (continual learning) data 5
Scope/Level of Detail
- Features, events, and processes (FEPs) widely used Bottom up (FEP screening), top down (safety function), mix
- May be iterative Expert judgment and external review important
- Real world can be dynamic and complex
- One of the harder steps 6
Scope/Level of Detail -
non-HLW Examples Beatty WIPP 7
Model Development
- Model development is iterative
- Modeling generally progresses from simple to complex
- Initially data may be sparse
- Designs may be evolving 8
Model Development - Initial
- Scoping/screening - more simple models, limited data, large uncertainties
- Example - HLW site characterization and design general concepts and parameters large uncertainties (limited data) evolving design limited availability of site- and design-specific models and parameters limited detailed process level models 9
Model Development - HLW (Purpose/Approach)
- Demonstrate capability for conducting PA (NUREG-1327) - 1992 integrated release standard
- Include all steps of PA limited scenarios (e.g., gaseous release, drilling, pluvial conditions) considered disruptive events (igneous activity, faulting) sensitivity and uncertainty analyses identify model improvements and data needs 10
Model Development (Mid-stage)
- Based on initial assessments make improvements enhance/modify models collect new data add or remove scenarios modify the design 11
Model Development - HLW
- Model enhancements enhance flow model (e.g., more dimensions, temporal variability) improve transport model (e.g., fracture matrix interaction) include additional processes (e.g., thermal impact, include mechanistic models for waste package failure and water contacting the waste)
- Data needs non-vertical flow, plutonium geochemistry, waste package corrosion, waste form behavior igneous activity 12
Uncertainty
- Including, evaluating, and understanding the impacts of uncertainties is essential
- NRC has learned many lessons (e.g., risk dilution, representativeness)
- In general, a small number of uncertainty parameters or alternate conceptual models drive the uncertainty in the overall results
- These are not known a priori!
13
Model Support
- At a minimum, should have elements of verification and validation:
- Verification - Solving the equations right
- Validation - Solving the right equations
- A variety of elements can be part of the model support process:
- internal review (QA)
- independent external review
- documentation of verification efforts
- multi-faceted confidence building effort: comparison to lab experiments, field experiments, analogs, etc.
14
Lessons Learned
- Performance Assessment useful even when data and design are in initial stages of development
- Iterative development is expected - assists integration of data collection and model/parameter enhancements
- Detailed analyses outside of performance assessment useful to informing and assisting model development 15
Lessons Learned (cont.)
- Sensitivity and uncertainty analyses were conducted with every version of the PA model
- Given decades of development computer tools will evolve (Fortran - Goldsim)
- Flexibility to incorporate alternative approaches and scenarios - recognition that performance assessment will be used throughout facility development (e.g., performance confirmation) 16
Independent Modeling
- Continued evaluation and improvement
- Promotes technical discussions with experts
- Informed by data
- Enhances staff capabilities to review licensee models 17
Thank you for your Attention!
Questions?
18
Backup Slides 19
Performance Assessment Improvements and Analyses
- NUREG-1464 (1995)
More mechanistic source term model, inclusion of seismicity and magmatic scenarios, dose capability Executive module
- NUREG-1746 (2001)
Secondary mineral formation on spent fuel dissolution, correlation between sampled sorption parameters for similar species Mechanical failure of waste package due to seismically induced rockfall 20
Performance Assessment Improvements and Analyses (cont.)
- CNWRA 2002-05 (2004)
Finer spatial discretization of repository, inclusion of drip shield, alternative waste package failure mode for igneous scenario, variable flow paths in alluvium, stylized human intrusion scenario Evaluation of barrier capabilities, consideration of 100,000-year time period
- NUREG-1762 (2005)
Integrated Issue Resolution Status Report Key technical issues 21
Model Support - Example Tim Cohn USGS - The Value of Paleoflood Information When Estimating Flood Risk (8/23/11) 22