United States Nuclear Regulatory Commission - Protecting People and the Environment

State-of-the-Art Reactor Consequence Analyses Project: Uncertainty Analysis of the Unmitigated Long-Term Station Blackout of the Peach Bottom Atomic Power Station (NUREG/CR-7155, SAND2012-10702P)

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Publication Information

Manuscript Completed: September 2015
Date Published: May 2016

Prepared by:
P. Mattie, R. Gauntt, K. Ross, N. Bixler, D. Osborn,
C. Sallaberry, and J. Jones

Sandia National Laboratories
Severe Accident Analysis Dept. 6232
Albuquerque, NM 87185-0748

T. Ghosh, NRC Technical Lead

NRC Job Code N6306

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

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This document describes the U.S. Nuclear Regulatory Commission's (NRC's) uncertainty analysis of the accident progression, radiological releases, and offsite consequences for the State-of-the-Art Reactor Consequence Analyses (SOARCA) unmitigated long-term station blackout (LTSBO) severe accident scenario at the Peach Bottom Atomic Power Station. The objective of the SOARCA Uncertainty Analysis is to evaluate the robustness of the SOARCA deterministic results and conclusions documented in NUREG-1935, and to develop insight into the overall sensitivity of the SOARCA results to uncertainty in key modeling inputs. As this is a first-of-a-kind analysis in its integrated look at uncertainties in the MELCOR accident progression and the MELCOR Accident Consequence Code System, Version 2 (MACCS) offsite consequence analyses, an additional objective is to demonstrate uncertainty analysis methodology that could be used in future source term, consequence, and Level 3 probabilistic risk assessment studies.

This work assessed key MELCOR and MACCS modeling uncertainties in an integrated fashion to quantify the relative importance of each uncertain input (included in the analysis) on potential accident consequences. A detailed uncertainty analysis was performed for a single-accident scenario at the Peach Bottom pilot plant. Not all possible uncertain input parameters were included in the analysis. Rather, a set of key parameters was carefully chosen to capture important influences on release and consequence results. 21 MELCOR parameters and 350 MACCS parameters (representing 20 parameter groups) were included in the integrated analysis. The uncertainty in these parameters was propagated to consequence results in a two-step Monte Carlo simulation with a total of 865 realizations. This quantitative uncertainty analysis provides measures of the effects for each of the selected uncertain parameters both individually and through interaction with other parameters, through the use of four regression methods. Phenomenological insights are also qualitatively described and corroborated through the analysis of individual Monte Carlo realizations that show different accident progression, release, and consequence behavior.

Sampling the chosen input parameters in this uncertainty analysis revealed three groupings of similar accident progression sequences within the Peach Bottom unmitigated LTSBO scenario: (1) early stochastic failure of the cycling safety-relief valve (SRV), which was the SOARCAestimate scenario in NUREG-1935; (2) thermal failure of the SRV without main steam line (MSL) creep rupture; and (3) thermal failure of the SRV with MSL creep rupture. Even with the sequences that could lead to higher source terms, the results corroborated the SOARCA results and conclusions in NUREG-1935; the projected consequences are still much smaller than previous studies (the 1982 Siting Study in particular) calculated, and the projected early fatality risk is essentially zero.

For the release magnitude (source term) and timing, the regression methods rank the SRV stochastic failure probability, chemical forms of cesium and iodine, station battery duration, SRV open area fraction (after thermal failure), and drywell liner melt-through area as the most important parameters. For the conditional, mean (average over weather variability), individual latent cancer fatality risk, the regression methods rank the MACCS dry deposition velocity, the MELCOR SRV stochastic failure probability, and the MACCS residual cancer risk factor as the most important input parameters.

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