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

Study of coupled waves of cylinder walls and internal liquid based on cylindrical shell theory and wave equation

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

Journal of Sound and Vibration, 561, p.117797_1 - 117797_14, 2023/05

https://doi.org/10.1016/j.jsv.2023.117797

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

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.

Parallelization of quantum molecular dynamics simulation code

; Kunugi, Tomoaki; Shibahara, Masahiko*;

JAERI-Research 98-008, 25 Pages, 1998/02

JAERI-Research-98-008.pdf:1.32MB

no abstracts in English

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

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

no journal, , 

no abstracts in English

Investigation on anomaly detection technique for cooling system device of sodium fast reactor by acoustic method, 4; Proof of concept of sensing of boiling phenomena using acoustic recognition and deep learning

Ueki, Yoshitaka*; Hashimoto, Shunsaku*; Shibahara, Masahiko*; Aizawa, Kosuke

no journal, , 

no abstracts in English