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If safety injection systems (SISs) do not work in the event of a loss-of-coolant accident (LOCA), the accident can progress to a severe accident in which the reactor core is exposed and the reactor vessel fails. Therefore, it is considered that a technology that provides recoverable maximum time for SIS actuation is necessary to prevent this progression. In this study, the corresponding time was defined as the golden time. To achieve the objective of accurately predicting the golden time, the prediction was performed using the deep fuzzy neural network (DFNN) with rule-dropout. The DFNN with rule-dropout has an architecture in which many of the fuzzy neural networks (FNNs) are connected and is a method in which the fuzzy rule numbers, which are directly related to the number of nodes in the FNN that affect inference performance, are properly adjusted by a genetic algorithm. The golden time prediction performance of the DFNN model with rule-dropout was better than that of the support vector regression model. By using the prediction result through the proposed DFNN with rule-dropout, it is expected to prevent the aggravation of the accidents by providing the maximum remaining time for SIS recovery, which failed in the LOCA situation.

Many studies are undertaken into nuclear power plants (NPPs) in preparation for accidents exceeding design standards. In this paper, we analyze the applicability of various wireless communication technologies as accident countermeasures in different NPP environments. In particular, a commercial wireless communication module (WCM) is investigated by measuring leakage current and packet error rate (PER), which vary depending on the intensity of incident radiation on the module, by testing at a Co-60 gamma-ray irradiation facility. The experimental results show that the WCMs continued to operate after total doses of 940 and 1097 Gy, with PERs of 3.6% and 0.8%, when exposed to irradiation dose rates of 185 and 486 Gy/h, respectively. In short, the lower irradiation dose rate decreased the performance of WCMs more than the higher dose rate. In experiments comparing the two communication protocols of request/response and one-way, the WCMs survived up to 997 and 1177 Gy, with PERs of 2% and 0%, respectively. Since the request/response protocol uses both the transmitter and the receiver, while the one-way protocol uses only the transmitter, then the electronic system on the side of the receiver is more vulnerable to radiation effects. From our experiments, the tested module is expected to be used for design-based accidents (DBAs) of “Category A″ type, and has confirmed the possibility of using wireless communication systems in NPPs.

Acquiring instrumentation signals generated from nuclear power plants (NPPs) is essential to maintain nuclear reactor integrity or to mitigate an abnormal state under normal operating conditions or severe accident circumstances. However, various safety-critical instrumentation signals from NPPs cannot be accurately measured on account of instrument degradation or failure under severe accident circumstances. Reactor vessel (RV) water level, which is an accident monitoring variable directly related to reactor cooling and prevention of core exposure, was predicted by applying a few signals to deep neural networks (DNNs) during severe accidents in NPPs. Signal data were obtained by simulating the postulated loss-of-coolant accidents at hot- and cold-legs, and steam generator tube rupture using modular accident analysis program code as actual NPP accidents rarely happen. To optimize the DNN model for RV water level prediction, a genetic algorithm was used to select the numbers of hidden layers and nodes. The proposed DNN model had a small root mean square error for RV water level prediction, and performed better than the cascaded fuzzy neural network model of the previous study. Consequently, the DNN model is considered to perform well enough to provide supporting information on the RV water level to operators. Keywords: Deep neural networks, Genetic algorithm, Reactor vessel water level, Safety-critical instrumentation signals, Severe accident