Prediction and uncertainty quantification of SAFARI-1 axial neutron flux profiles with neural networks

Abstract

In this study, Deep Neural Networks (DNNs) are used to predict the assembly axial neutron flux profiles in the SAFARI-1 research reactor, with quantified uncertainties in the DNN predictions and testing using data from cycles that are unseen in the training process. The training dataset consists of copper-wire activation measurements from the reactor’s historical cycles. Uncertainty Quantification of the regular DNN models’ predictions is performed using Monte Carlo Dropout (MCD) and Bayesian Neural Networks solved by Variational Inference (BNN VI). The regular DNNs, DNNs solved with MCD and BNN VI results agree very well with each other and with the new measured dataset not used in the training process, thus indicating good prediction and generalization capability. The uncertainty bands produced by MCD and BNN VI agree very well and fully envelop the noisy measurement data points.