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2024年08月29日

【Awards and Commendations】Hanyu WANG, Okamoto-Miwa lab, Department of Nuclear Engineering and Management, (D1)

【Awards and Commendations】Hanyu WANG, D1, Okamoto-Miwa Lab., Department of Nuclear Engineering and Management, received “Best Presentation Award” at the 20th conference of Japan Society of Maintenology (JSM)

〈Name of award and short explanation about the award〉

Best Presentation Award

The 20th conference of Japan Society of Maintenology held in University of Tokyo Komaba campus, Tokyo, Japan on August 6-7, 2024, presented the “Best Presentation Award” to the research for their outstanding and high-quality presentation in the student session of JSM.

 

〈About awarded research〉

Two-phase Flow-Induced Vibration (FIV) poses a potential risk to pipelines and structures in various industrial systems and is closely related to two-phase flow regimes. This study employs feature-based and 1D Convolutional Neural Network (CNN)-based approaches using two-phase FIV acceleration signals for flow regime identification. An accelerometer was utilized to measure acceleration signals in three directions of a vertical 90-degree elbow. A general feature extraction and selection methodology is proposed for feature-based approaches. Additionally, in response to the limitations of the feature-based approach, an end-to-end solution for flow regime identification is proposed based on the modified Multi-Channel Deep Convolutional Neural Network (MC DCNN) model, which enables automatic feature extraction and exhibits good resistance to overfitting. The results indicate an average accuracy of 89.9% and an improved ability to differentiate between bubbly and slug flow regimes. This study provides practical methods for two-phase flow regime classification. The accessibility and low cost of accelerometers suggest broad application potential, particularly in industrial settings.

 

〈Your impression & future plan〉

It is really pleasure and an honor to receive this award in such a conference with many researchers from multidisciplinary. I extend my heartfelt thanks to my supervisors, Professors Miwa, Okamoto, and Suzuki, for their invaluable guidance, support, and encouragement throughout my research journey. Additionally, I appreciate Prof. Pellegrini and the members of the Visualization Laboratory for their valuable insights during our daily interactions and meetings. The excellent organization of JSM-20 has left me with many memorable experiences. From now on, I am excited to continue my research in advancing thermal hydraulics and nuclear engineering through artificial intelligence.

 

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