A surrogate machine learning model for advanced gas-cooled reactor graphite core safety analysis

Abstract

A surrogate machine learning model was developed with the aim of predicting seismic graphite core displacements from crack configurations for the advanced gas-cooled reactor. The model was trained on a dataset generated by a software package which simulates the behaviour of the graphite core during a severe earthquake. Several machine learning techniques, such as the use of convolutional neural networks, were identified as highly applicable to this particular problem. Through the development of the model, several observations and insights were garnered which may be of interest from a graphite core analysis and safety perspective. The best performing model was capable of making 95% of test set predictions within a 20 percentage point margin of the ground truth.