Prediction of Fuel Debris Location in Fukushima Nuclear Power Plant using Machine Learning

Alrawash, Saed and Hale, Matthew and Lennox, Barry and Joyce, Malcolm and West, Andrew and Watanabe, Minoru and Zhang, Zhongming and Aspinall, Michael (2024) Prediction of Fuel Debris Location in Fukushima Nuclear Power Plant using Machine Learning. EPJ Web of Conferences, 302. ISSN 2100-014X

Abstract

Accurate fuel debris location is crucial part of the decommissioning of the Fukushima Nuclear Power plants. Conventional methods face challenges due to extreme radiation and complex structure of the materials involved. In this study, we propose a novel approach utilising neutron detection and machine learning to estimate fuel material location. Geant4 simulations and pythonTM scripts have been used to generate a comprehensive dataset to train a machine learning model using MATLAB’s regression learner. A Gaussian Process Regression model was chosen for training and prediction. The results show excellent prediction performance to estimate the corium thickness effectively and to locate the nuclear fuel material with a mean square error (MSE) of 0.01. By combining the machine learning with nuclear simulation codes, this promises to enhance the nuclear decommissioning efforts to retrieve nuclear fuel debris.