Prediction of nuclear reactor vessel water level using deep neural networks

Abstract

Fukushima accident was worse by instrument inability. Eventually, the accident was not mitigated and keeping the integrity of reactor was failed since the operators was not able to quickly understand the situation and take necessary actions. Therefore, in this study, reactor vessel (RV) water level considered as one of the parameters to keep the integrity of reactor is predicted in loss of coolant accident (LOCA) situation using the deep neural network (DNN) method. This is in an effort to provide supporting information under the severe circumstance. The simulation data obtained from modular accident analysis program (MAAP) are applied to the DNN method to check the prediction performance of the RV water level. The prediction performance of RV water level using the proposed DNN model is presented as root mean square error (RMSE). Although the data of several circumstances among a variety of LOCAs are applied, good prediction performance is shown using the proposed DNN method.