Convolutional neural network-based pattern recognition in natural circulation instability images

Abstract

Heat removal systems employing natural circulation are key in new nuclear power plant designs for mitigating accidents. This study applies Convolutional Neural Networks (CNNs) to classify 'chugging' instability phases, analyzing 1152 two-phase flow images from a Natural Circulation Circuit. Three CNN models, including one incorporating transfer learning from the ImageNet database, were trained via five-fold cross-validation to fine-tune hyperparameters. This involved comparing models with and without transfer learning against a baseline linear model. A model using a pre-trained Resnet50 with transfer learning accurately classified all 230 samples, outperforming the baseline linear model with an F1-Score of 0.859. The results endorse the use of CNNs with transfer learning for thermohydraulic image analysis in identifying natural circulation instability stages.