State-of-the-Art Reactor Consequence Analyses Project: Uncertainty Analysis of the Unmitigated Short-Term Station Blackout of the Surry Power Station (NUREG/CR-7262)

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Publication Information

Manuscript Completed: June 2019
Date Published: December 2022

Prepared by:
Severe Accident Analysis Department
Sandia National Laboratories
P.O. Box 5800 MS-0848
Albuquerque, NM 87185-0748

S. Tina Ghosh, NRC Project Manager

Office of Nuclear Regulatory Research
U.S. Nuclear Regulatory Commission
Washington DC 20555-0001

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Abstract

The State of the Art Reactor Consequence Analyses (SOARCA) project published best estimate analyses for select accident scenarios at the Peach Bottom Atomic Power Station and Surry Power Station in 2012. This work was followed by an integrated uncertainty analysis (UA) performed on the unmitigated long-term station blackout (LTSBO) scenario for Peach Bottom, a draft UA on the unmitigated short-term station blackout (STSBO) scenario for Surry, and a UA on the unmitigated STSBO scenario for Sequoyah. The approach developed for the Peach Bottom UA was further enhanced for application to the subsequent UAs. Consequently, the current Surry UA not only benefits from additional knowledge gained since the original Surry SOARCA best-estimate calculation but also the other UAs. The UA projects include an integrated Monte Carlo analysis using the MELCOR and MACCS codes. Regression analyses and separate sensitivity analyses were conducted to understand the contributions of uncertain input parameters to the uncertainty in key figures of merit, such as radionuclide release to the environment and individual latent cancer fatality risk to the offsite public. Single realizations were analyzed to investigate individual parameter effects, typically associated with the extreme bounds, and to confirm phenomenological explanations of variations in system behavior and results. Consistent with the previous UAs, rank regression, quadratic regression, recursive partitioning, and multivariate adoptive regression splines (MARS) techniques were used to identify the importance of the input parameters regarding the uncertainty of the results. Using the insights gained from the previous UAs including the draft Surry UA and their respective ACRS reviews, the Surry model, the uncertain parameter definitions, and the uncertainty characterizations were revised for the UA documented in this report. Like the previous UAs, the analyses herein corroborate the conclusions of the original SOARCA study and further extend the body of knowledge on severe reactor accidents.