Prediction of DNBR Using Fuzzy Support Vector Regression and Uncertainty Analysis

It is very important for operators to be informed of the departure from nucleate boiling ratio (DNBR) to prevent the fuel cladding from melting and causing a boiling crisis. Artificial intelligence methods such as neural networks and support vector regression (SVR) have extensively and successfully been applied to nonlinear function approximation. In this paper, fuzzy support vector regression (FSVR) combined with a fuzzy concept and SVR is presented to precisely predict the minimum DNBR by using the measured signals of a reactor coolant system, such as reactor power, reactor pressure, and control rod positions. Also, the prediction uncertainty for the predicted minimum DNBR is assessed. It is demonstrated that FSVR is accurate enough to be used in protection and monitoring algorithms for departure from nucleate boiling (DNB). Therefore, FSVR can be used to effectively monitor and predict the minimum DNBR in the reactor core.