Estimation of Minimum DNBR Using Cascaded Fuzzy Neural Networks

It is very important for plant operators to be informed of the departure from nucleate boiling ratio (DNBR) to prevent the fuel cladding from melting and a boiling crisis in a nuclear reactor. The reactor core monitoring and protection systems require a minimum DNBR value to monitor reactor coolant conditions. In this study, in order to estimate the minimum DNBR value, a cascaded fuzzy neural network (CFNN) method was used. The CFNN model can be used to estimate the minimum DNBR value through the process of adding fuzzy neural networks (FNNs) repeatedly. The proposed DNBR estimation algorithm was verified by applying the nuclear and thermal data acquired from many numerical simulations of the optimized power reactor 1000 (OPR1000). The CFNN model was compared to previously developed models and was found to be superior to them. Therefore, this model can be used to effectively monitor and predict the minimum DNBR in the reactor core.