2024 journal article
Clustering and uncertainty analysis to improve the machine learning-based predictions of SAFARI-1 control follower assembly axial neutron flux profiles
ANNALS OF NUCLEAR ENERGY, 206.
author keywords: Deep Neural Networks; Uncertainty Quantification; Monte Carlo Dropout; Clustering analysis; Gaussian Process
open access via arxiv.org (repository)
Source: Web Of Science
Added: June 24, 2024
The goal of this work is to develop accurate Machine Learning (ML) models for predicting the assembly axial neutron flux profiles in the SAFARI-1 research reactor, trained by measurement data from historical cycles. Affinity Propagation and k-means algorithms are used to identify clusters in the set of measured axial neutron flux profiles. Pair-counting-based and information-theoretic measures are applied to compare the clusterings. Deep Neural Network (DNN) and Gaussian Process (GP) ML models are then trained for different clusters, with prediction uncertainties quantified with the Monte Carlo Dropout. The proposed procedure improves the prediction accuracy for the control assemblies and reduces the prediction uncertainty, with axial flux shapes predicted by DNN and GP being very close and the overall accuracy becoming comparable to the fuel assemblies.