Identification of reactor vessel failures using spatiotemporal neural networks

Abstract

Identification of vessel failures provides operators and technical support center personnel with important information to manage severe accidents in a nuclear power plant. It may be very difficult, however, for operators to identify a reactor vessel failure simply by watching temporal trends of some parameters because they have not experienced severe accidents. Therefore, we propose a methodology on the identification of pressurized water reactor (PWR) vessel failure for severe accident management using spatiotemporal neural network (STN). STN can deal directly with the spatial and temporal aspects of input signals and can well identify a time-varying problem. Target patterns of seven parameter signals were generated for training the network from the modular accident analysis program (MAAP) code, which simulates severe accidents in nuclear power plants. We integrated MAAP code with STN in on-line system to mimic real accident situation in nuclear power plants. Using new patterns of signals that had never been used for training, the identification capability of STN was tested in a real-time manner. At the tests, STN developed in this study demonstrated acceptable performance in identifying the occurrence of a vessel failure. It is found that STN techniques can be extended to the identification of other key events such as onset of core uncovery, coremelt initiation, containment failure, etc.