State sensing of bubble jet flow based on acoustic identification of deep learning

三上 奈生*; 植木 祥高*; 芝原 正彦*; 相澤 康介; 荒 邦章

Proceedings of 17th International Heat Transfer Conference (IHTC-17) (Internet), 9 Pages, 2023/08

To increase the safety of sodium-cooled fast reactors, it is necessary to develop a method to identify the states of bubble jet flow caused when a heat transfer tube is damaged in steam generators (SGs). For this issue, we propose a novel state sensing method with time-frequency representations (TFRs) and convolutional neural networks (CNNs). This study consists of three phases. First, using water and air as simulant fluids to perform the proof of concept, pipe flow sound and bubble jet flow sound are acquired, each of which simulates normal and anomaly sound. Second, three TFRs are extracted from raw signals based on short-time Fourier transform (STFT), continuous wavelet transform (CWT), and synchrosqueezed wavelet transform (SWT). Third, typical CNNs including AlexNet, VGG16, and ResNet18 are introduced for the identification of pipe flow sound and three types of bubble jet flow sound. As a result, the model combining ResNet18 and STFT reaches the highest accuracy and correctly identifies 1984 out of 200 test data. These results demonstrate that our proposed method based on the acoustic identification of deep learning has great potential to sense the states of bubble jet flow in actual SFRs.