Prediction of Axial DNBR Distribution in a Hot Fuel Rod Using Support Vector Regression Models

Abstract

The departure from nucleate boiling ratio (DNBR) is one of the most critical parameters in the safety issues of a nuclear reactor. Most reactor core protection systems of current nuclear power plants calculate the minimum DNBR at a pseudo hot fuel rod position to prevent the departure from nucleate boiling (DNB). On the other hand, it gives rise to a more conservative result, which reduces the operating margin of nuclear power plants. In this paper, the axial DNBR distribution at the actual hot fuel rod position was predicted based on the support vector regression (SVR) model, which is a data-based method using a number of measured signals from the reactor coolant system. SVR models were developed using a learning data set and validated by an independent test data set. These models were applied to the first fuel cycle of the Yonggwang unit 3 nuclear power plant. The root mean square (RMS) error averaged for 13 axial locations of the hot rod was 0.87%. The SVR models estimate DNBR values more accurately at central parts that have relatively lower DNBR values, which are more important in terms of safety. This algorithm can predict the DNBR accurately at each time step and provide reliable protection and monitoring information for nuclear power plant (NPP) operation.