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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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NRC approves V.C. Summer’s second license renewal
Dominion Energy’s V.C. Summer nuclear power plant, in Jenkinsville, S.C., has been authorized to operate for 80 years, until August 2062, following the renewal of its operating license by the Nuclear Regulatory Commission for a second time.
Yuichi Morimoto, Masanori Akaike, Satoshi Takeo, Hiromi Maruyama
Nuclear Technology | Volume 205 | Number 12 | December 2019 | Pages 1652-1660
Technical Paper | doi.org/10.1080/00295450.2019.1580529
Articles are hosted by Taylor and Francis Online.
The Fukushima Daiichi Nuclear Power Plants (1FNPPs) are thought to be subcritical, but the condition will be changed during the fuel debris retrieval. Subcriticality control is one of the most important processes to eliminate the possibility of criticality through the decommissioning. For the subcriticality control, it is important to properly evaluate the status of criticality. We propose a statistical evaluation method for the criticality of the 1FNPPs with various uncertainties. Although physical parameters related to the criticality are still uncertain, conservative assumptions may lead to excessive requirements for the criticality control system. The goal of the proposed method is to construct a methodology to evaluate the realistic status of the plants based on useful information about the fuel debris observed by current and future in-core investigations and obtained by accident analysis codes. The method is composed of sampling methods for physical parameters, a criticality evaluation method based on a continuous-energy Monte Carlo code, and processing methods to evaluate the results. Physical parameters related to criticality such as debris size, porosity fraction, structure material contamination, corrosion depth, and so on are sampled from predetermined probability distributions based on knowledge for the in-core status. Calculated results are processed statistically to give probability distributions of neutron multiplication factors. From these results, physical parameters that have strong correlations with the neutron multiplication factor can be identified. In the case that the neutron multiplication factor is estimated from some other observation results, posterior distribution of physical parameters can be determined by the Bayesian estimation method. To demonstrate the method, statistical criticality evaluations are made for 1FNPP Unit 1. The fuel debris of the 1FNPP is assumed to be located at the lower plenum, the pedestal, and the drywell. The distribution of the fuel debris is located by the results of the severe accident code MAAP. Physical parameters are determined according to the characteristics list given by the fuel debris characterization project. The Bayesian estimate of stainless steel fraction based on the neutron multiplication factor evaluated by the ratio of 88Kr to 135Xe was reported. The results suggest that the criticality risk is extremely small for 1FNPP Unit 1.
