Publications Details
Soarca Peach Bottom Atomic Power Station long-term station blackout uncertainty analysis: MACCS2 parameters and probabilistic results
Osborn, Douglas; Bixler, Nathan E.; Jones, Joseph A.; Sallaberry, Cedric J.; Mattie, Patrick
This paper describes the MELCOR Accident Consequence Code System, Version 2(MACCS2), parameters and probabilistic results of offsite consequences for the uncertainty analysis of the State-of-the-Art Reactor Consequence Analyses unmitigated long-term station blackout accident scenario at the Peach Bottom Atomic Power Station. Consequence results are presented as conditional risk (i.e., assuming the accident occurs) to individuals of the public as a result of the accident - latent-cancer-fatality (LCF) risk per event or prompt-fatality risk per event. For the mean, individual, LCF risk, all regression methods at each of the circular areas around the plant that are analyzed (10-mile to 50-mile radii are considered) consistently rank the MACCS2 dry deposition velocity, the MELCOR safety relief valve (SRV) stochastic failure probability, and the MACCS2 residual cancer risk factor, respectively, as the most important input parameters. For the mean, individual, prompt-fatality risk (which is zero in over 85% of the Monte Carlo realizations) within circular areas with less than a 2-mile radius, the non-rank regression methods consistently rank the MACCS2 wet deposition parameter, the MELCOR SRV stochastic failure probability, the MELCOR SRV open area fraction, the MACCS2 early health effects threshold for red bone marrow, and the MACCS2 crosswind dispersion coefficient, respectively, as the most important input parameters. For the mean, individual prompt-fatality risk within the circular areas with radii between 2.5-miles and 3.5-miles, the regression methods consistently rank the MACCS2 crosswind dispersion coefficient, the MACCS2 early health effects threshold for red bone marrow, the MELCOR SRV stochastic failure probability, and the MELCOR SRV open area fraction, respectively, as the most important input parameters.