Power Reconstruction of Fuel Rods by Support Vector Regression for CANDU Reactors

Abstract

A support vector regression (SVR) model has been presented for reconstructing fuel rod powers from Canada deuterium uranium core calculations performed with a coarse-mesh finite difference diffusion approximation and single-assembly lattice calculations. The SVR is to nonlinearly map the original data into a higher dimensional feature space. Parameters related to the SVR are optimized by a genetic algorithm using the partial core calculation results of two 6 times 6 fuel bundle models (for training data). Verification has been conducted for two other partial core benchmark problems composed of 6 times 6 and 3 times 3 fuel bundles (for test data). The reconstructed fuel rod powers are compared with the reference solutions obtained with the detailed collision probability calculations using the HELIOS lattice analysis code. It is known from simulation results that the proposed rod power reconstruction algorithm is accurate, yielding the error due to the reconstruction scheme of less than 0.35%.