Toward Developing Machine-Learning-Aided Tools for the Thermomechanical Monitoring of Nuclear Reactor Components

TL;DR

This paper presents a CNN-based approach combined with thermomechanical modeling to estimate temperature, stress, and strain in nuclear reactor fuel rods using limited surface temperature data, aiding predictive maintenance.

Contribution

It introduces a novel CNN and simulation-based methodology for real-time thermomechanical monitoring of nuclear fuel rods, enhancing predictive maintenance capabilities.

Findings

CNN achieved high accuracy in temperature prediction

Method enabled real-time stress and strain estimation

Model trained without overfitting over 1,000 epochs

Abstract

Proactive maintenance strategies, such as Predictive Maintenance (PdM), play an important role in the operation of Nuclear Power Plants (NPPs), particularly due to their capacity to reduce offline time by preventing unexpected shutdowns caused by component failures. In this work, we explore the use of a Convolutional Neural Network (CNN) architecture combined with a computational thermomechanical model to calculate the temperature, stress, and strain of a Pressurized Water Reactor (PWR) fuel rod during operation. This estimation relies on a limited number of temperature measurements from the cladding's outer surface. This methodology can potentially aid in developing PdM tools for nuclear reactors by enabling real-time monitoring of such systems. The training, validation, and testing datasets were generated through coupled simulations involving BISON, a finite element-based nuclear fuel performance code, and the MOOSE Thermal-Hydraulics Module (MOOSE-THM). We conducted eleven simulations, varying the peak linear heat generation rates. Of these, eight were used for training, two for validation, and one for testing. The CNN was trained for over 1,000 epochs without signs of overfitting, achieving highly accurate temperature distribution predictions. These were then used in a thermomechanical model to determine the stress and strain distribution within the fuel rod.