Prediction of the power peaking factor in a boron-free small modular reactor based on a Support Vector Regression model and control rod bank positions

Abstract

In order to ensure safety in a nuclear power plant, operation and protection systems must take into account safety parameters, whether to guide operators or to trip the reactor in emergency cases. Especially in a boron-free small modular reactor (SMR) where reactivity and power are controlled exclusively by rod banks, the power distribution is mostly influenced by its movements affecting the power peaking factor (PPF), which is an important parameter to be considered. The PPF relates the maximum local linear power density to the average power density in a fuel rod indicating a high neutron flux that can cause fuel rod damage. In this technical note, 2117 samples from simulations of an idealized boron-free SMR controlled exclusively by rod banks were used to generate a Support Vector Machine (SVM) model capable of estimating the PPF as a function of control rod bank positions. Such model could be used to predict the maximum PPF in the reactor core by carrying out simple calculation. Residing in a SVM parameter grid search and a 10-cross-validation process in the training set to reach an optimized and robust model, the results have shown a root-mean- squared error of about 0.1% consistent for both training and testing sets.