An Approach for Validating Actinide and Fission Product Burnup Credit Criticality Safety Analyses--Criticality (Keff) Predictions
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
One of the most significant remaining challenges associated with expanded implementation of burnup credit in the United States is the validation of depletion and criticality calculations used in the safety evaluation - in particular, the availability and use of applicable measured data to support validation, especially for fission products. Applicants and regulatory reviewers have been constrained by both a scarcity of data and a lack of clear technical basis or approach for use of the data. U.S. Nuclear Regulatory Commission (NRC) staff have noted that the rationale for restricting their Interim Staff Guidance on burnup credit (ISG-8) to actinide-only is based largely on the lack of clear, definitive experiments that can be used to estimate the bias and uncertainty for computational analyses associated with using burnup credit. To address the issue of validation, the NRC initiated a project with the Oak Ridge National Laboratory to (1) develop and establish a technically sound validation approach (both depletion and criticality) for commercial spent nuclear fuel (SNF) criticality safety evaluations based on best-available data and methods and (2) apply the approach for representative SNF storage and transport configurations/conditions to demonstrate its usage and applicability, as well as to provide reference bias results. The purpose of this paper is to describe the criticality (keff) validation approach and resulting observations and recommendations. Validation of the isotopic composition (depletion) calculations is addressed in a companion paper at this conference. For criticality validation, the approach is to utilize (1) available laboratory critical experiment (LCE) data from the International Handbook of Evaluated Criticality Safety Benchmark Experiments and the French Haut Taux de Combustion (HTC) program to support validation of the principal actinides and (2) calculated sensitivities, nuclear data uncertainties, and the limited available fission product LCE data to predict and verify individual biases for relevant minor actinides and fission products. This paper (1) provides a detailed description of the approach and its technical bases, (2) describes the application of the approach for representative pressurized water reactor and boiling water reactor safety analysis models to demonstrate its usage and applicability, (3) provides reference bias results based on the prerelease SCALE 6.1 code package and ENDF/B-VII nuclear cross-section data, and (4) provides recommendations for application of the results and methods to other code and data packages.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA), Nuclear Criticality Safety Program (NCSP)
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1025400
- Resource Relation:
- Journal Volume: 188; Journal Issue: 3; Conference: 9. International Conference on Nuclear Criticality Safety (ICNC 2011), Edinburgh (United Kingdom), 19-22 Sep 2011
- Country of Publication:
- United States
- Language:
- English
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An Approach for Validating Actinide and Fission Product Burnup Credit Criticality Safety Analyses-Isotopic Composition Predictions
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Related Subjects
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE WASTES FROM NUCLEAR FACILITIES
96 KNOWLEDGE MANAGEMENT AND PRESERVATION
ACTINIDES
BENCHMARKS
BOILING
BURNUP
COMBUSTION
CRITICALITY
FISSION PRODUCTS
IMPLEMENTATION
MANUALS
NUCLEAR FUELS
ORNL
PWR TYPE REACTORS
REACTOR SAFETY
SAFETY
STORAGE
TRANSPORT
VALIDATION
Nuclear Criticality Safety Program (NCSP)
Burnup Credit
Depletion and Criticality Calculations Validation
Safety Evaluation
Validation Support
Fission Products
References
Approach Explanation
SCALE 6.1 Code Package Prerelease
ENDF/B-VII Nuclear Cross-Section Data
Recommendations