Prediction of plutonium content of BWR/MOX fuel using a neural network

Orgonasi, C.*; Nishihara, Kenji ; Luzieux, C.*; Abe, Takumi ; Okamura, Tomohiro*; Nakase, Masahiko*

This study addresses the inaccuracy of NMB fuel cycle simulator due to the one-group cross-section as well as the computational burden by developing and integrating an Multi-Layer Perceptron (MLP) model into NMB. A custom PyTorch MLP was trained on a dataset of 5,000 depletion calculations generated with the SERPENT code for an ATRIUM-10 boiling water reactor geometry. The developed MLP, in conjunction with a dichotomy search algorithm, determined the plutonium content with an error below 1% and achieved calculation speed of approximately 60 times for determination of Pu enrichment and 4 times for overall calculation.