Towards a Deep Unified Framework for Nuclear Reactor Perturbation Analysis
share
This paper proposes the first step towards a novel unified framework for the analysis of perturbations occurring in nuclear reactors in both Time and Frequency domain. The identification of type and source of such perturbations is fundamental for monitoring core reactors and guarantee safety even while running at nominal conditions. A 3D Convolutional Neural Network (3D-CNN) was employed to analyse perturbations happening in the frequency domain, such as the alteration of an absorber of variable strength or propagating perturbation. Recurrent neural networks (RNN), specifically Long Short-Term Memory (LSTM) was used to study signal sequences related to perturbations induced in the time domain, including the vibrations of fuel assemblies and the fluctuation of thermalhydraulic parameters at the inlet of the reactor coolant loops. 512-dimensional representations were extracted from the 3D-CNN and LSTM architectures, and used as input to a fused multi-sigmoid classification layer to recognise the perturbation type. If the perturbation is frequency domain related, a separate fully-connected layer utilises said representations to regress the coordinates of its source. The results showed that perturbation type can be recognised with high accuracy in both domains, and frequency domain scenario sources can be localised with high precision.
READ FULL TEXT