Towards a Deep Unified Framework for Nuclear Reactor Perturbation Analysis

TL;DR

This paper proposes a unified deep learning framework combining 3D-CNNs and LSTMs to analyze and classify nuclear reactor perturbations in both time and frequency domains, enhancing safety monitoring.

Contribution

It introduces a novel integrated approach using deep neural networks for simultaneous perturbation classification and source localization in nuclear reactors.

Findings

High accuracy in perturbation type recognition

Precise localization of frequency domain sources

Effective analysis of time and frequency domain perturbations

Abstract

In this paper, we take the first steps towards a novel unified framework for the analysis of perturbations in both the Time and Frequency domains. The identification of type and source of such perturbations is fundamental for monitoring reactor cores and guarantee safety while running at nominal conditions. A 3D Convolutional Neural Network (3D-CNN) was employed to analyse perturbations happening in the frequency domain, such as an absorber of variable strength or propagating perturbation. Recurrent neural networks (RNN), specifically Long Short-Term Memory (LSTM) networks were used to study signal sequences related to perturbations induced in the time domain, including the vibrations of fuel assemblies and the fluctuations of thermal-hydraulic 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 in the frequency domain, a separate fully-connected layer utilises said representations to regress the coordinates of its source. The results showed that the perturbation type can be recognised with high accuracy in all cases, and frequency domain scenario sources can be localised with high precision.