Electrical Resistivity Reconstruction of Graphite Moderator Bricks From Multi-Frequency Measurements and Artificial Neural Networks

Abstract

This paper discusses an artificial neural inversion approach for non-destructive testing of the graphite moderator bricks that make up the cores of Advanced Gas-Cooled Reactors (AGR). The study employed fully connected feedforward neural networks consisting of four hidden layers and trained with backpropagation approach using Levenberg-Marquardt optimisation algorithm. The approach is based on multi-frequency (MF) eddy current (EC) measurements, and combinations of simulated and measured datasets from a laboratory sample and one of the operating reactor core, along with different regularisation parameters were used to train and test the networks. Various types of artificially generated errors were added to the data during training procedures, which in turn allowed for error tolerance and improved the generalisation of the ANNs to unseen test datasets. First, the ANN was tested using unseen simulated data, followed by the measurements collected from laboratory sample and one of the operating reactor core. The first test from the unseen simulated data showed mean profile error ranging between 1.30% and 8.20%, whereas the profiles estimated from reactor core measurements showed mean profile error ranging between 1.84% and 17.80% when compared with the resistivity measurements from trepanned graphite sample taken out of the reactor core. Further comparison of the network outputs against the profiles estimated using traditional iterative inversion algorithm indicates reasonable agreement between the two approaches with the exception of one case, but the solution time for the ANN was found to be over three orders of magnitude faster than the iterative inversion algorithm.