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Journal Articles

CNN-based acoustic identification of gas-liquid jet; Evaluation of noise resistance and visual explanation using Grad-CAM

Mikami, Nao*; Ueki, Yoshitaka*; Shibahara, Masahiko*; Aizawa, Kosuke; Ara, Kuniaki*

International Journal of Multiphase Flow, 171, p.104688_1 - 104688_13, 2024/01

https://doi.org/10.1016/j.ijmultiphaseflow.2023.104688

Times Cited Count：2 Percentile：34.95(Mechanics)

Journal Articles

State sensing of bubble jet flow based on acoustic recognition and deep learning

Mikami, Nao*; Ueki, Yoshitaka*; Shibahara, Masahiko*; Aizawa, Kosuke; Ara, Kuniaki

International Journal of Multiphase Flow, 159, p.104340_1 - 104340_8, 2023/02

https://doi.org/10.1016/j.ijmultiphaseflow.2022.104340

Times Cited Count：7 Percentile：57.48(Mechanics)

This study covers the accidental generation of bubble jet flow caused by steam generator (SG) tubes damaging in sodium cooled fast reactors (SFRs). The main objective of this study is to develop a novel state sensing method of bubble jet flow based on acoustic recognition and deep learning. Prior to the application of this method to actual SFRs, we utilize air and water as simulant fluids in order to perform the proof of concept. This study is divided into three phases. The first phase is the acquisition and analysis of pipe flow sound and bubble jet flow sound, each of which simulates the normal and anomaly sound from SG tubes in SFRs. The second phase is the preprocessing of acoustic signals and feature extraction. The third phase is the building of deep learning models and performance evaluation. As a result, every of our proposed models could distinguish between pipe flow sound and bubble jet sound with an accuracy of almost 100.00%, and the best model could classify pipe flow sound and three types of bubble jet flow sound with an accuracy of 99.76%. This result suggests that the acoustic recognition with deep learning has great potential to sense the state of bubble jet flow in actual SFRs.

Journal Articles