Nuclear power plant simulation using multilayer perceptron

Ono, Tomio*; Subekti, M.*; Maruyama, Yuta*; Nabeshima, Kunihiko  ; Kudo, Kazuhiko*

In this research, we present nuclear power plant simulation method using Multilayer Perceptron, which is one of the models of Artificial Neural Networks(ANNs). The major characteristics of ANNs are to obtain the model through learning, analogy and very high speed processing. Furthermore, 'time synchronizing signal' and 'progress synchronizing signal' are added as the inputs to adapt the abnormal events with various scales or progress rates. This ANN, learned some sample data, can be flexibly adapted to simulate the abnormal events with different scales including explicit progress rates. In the verification using PWR simulator, we confirmed that this method could model NPP abnormal events by learning data and simulate the data which have different progress rates from learning data.