Fuel failure analysis in Boiling Water Reactors (BWR) using Machine Learning. : A comparison of different machine learning algorithms and their performance at predicting fuel failures.

Sammanfattning

In collaboration with Westinghouse Electric AB this project aims to study the possibilities with using machine learning methods to predict fuel failure in a Boiling Water Reactors (BWRs). The main objective has been to create a dataset consisting of both empirical measurements and simulated samples from a physics model and evaluate different machine learning algorithms, that use these datasets to predict fuel defects. The simulated data is created using a physics model derived from the ANS-5.4 standard which allows for good control over specific parameter values. Three machine learning algorithms were deemed fit for this type of problem and used throughout the project: Random Forest (RF), K-Nearest Neighbor (KNN) and Neural Network (NN). Both classification and regression type problems have been assessed. All three methods showed good results for the classification problems, where the goal was to predict if there was a fuel failure or not. All models reached an accuracy above 97% and performed well, the RF model had the highest overall, with an accuracy of 98.2 %. However, the NN method made the fewest false negative predictions and can therefore be seen as the best model for this purpose. For the regression, problems with the aim of predicting escape rates, both the RF and KNN had similar promising results with very small errors overall. Yet, there is a slight increase in errors when predicting higher escape rates for both models. This is most likely due to the available data being of mostly low escape rates. The NN did not perform well with this problem, the predictions having large error for both low and high escape rates, a possible explanation is the lack of data. To improve the results, and create even better models, the empirical measurements need to contain more information such as defect location and fuel failure size, also an increase in the number of samples taken at fuel failure operation would be valuable.