Subcooled-boiling detection by data-driven acoustic diagnosis

Ueki, Yoshitaka*; Hirako, Itsuki*; Tezuka, Kosuke*; Aizawa, Kosuke ; Ara, Kuniaki*

With a final goal of early detection and understanding of the transition of coolant boiling events in the core of sodium-cooled fast reactors, our present aim is to obtain and maintain the basic knowledge necessary for developing anomaly detection technology associated with local anomalies in the core, and to demonstrate basic feasibility. We constructed a deep learning method of convolutional neural network and evaluated its performance to detect the occurrence and understand the transition of subcooled boiling using acoustic identification. In addition to being able to identify the occurrence of boiling with high accuracy, the visualization of the identification basis based on Grad-CAM revealed the acoustic frequency bands that the deep learning model determined to be of high importance. We also constructed a regression analysis-type deep learning model and demonstrated that it is possible to predict boiling heat flux values with high accuracy.