【Awards and Commendations】

Wen ZHOU, D2, Okamoto-Miwa Lab., Department of Nuclear Engineering and Management, received “Best Poster Award” at the 31th International Conference on Nuclear Engineering (ICONE31)

〈Name of award and short explanation about the award〉

Best Poster Award

The 31th International Conference on Nuclear Engineering (ICONE31) held in Prague, Czech Republic on May 4-8, 2024, presented the “Best Poster Award” to the researches for their outstanding and high-quality poster submitted to the track of student competition in ICONE31.

〈About awarded research〉

Subcooled flow boiling plays a pivotal role in various industrial applications, including nuclear reactors and thermal management systems. However, the rapid and complex changes of condensing bubbles experience from their inception to collapse in subcooled flow boiling present substantial obstacles for conventional bubble detection methods, especially when it comes to condensation bubbles. In light of this, a state-of-the-art AI method is developed and validated for the efficient detection and tracking of condensation bubbles in subcooled flow boiling, thereby enabling the effective execution of thermal hydraulic analyses. This study initially employs computer vision technology to efficiently construct a bubble dataset. Subsequently, a bubble detection model is trained based on this dataset and the YOLOv8 with attention mechanism. Utilizing the results of bubble detection, a multi-object tracking algorithm is then applied to track all bubbles in each frame. The developed AI-based method exhibits a strong ability to detect 95% of condensation bubbles in subcooled flow boiling, additionally, it streamlines the extraction of critical thermal hydraulic parameters, including void fraction, bubble lifetime, and nucleation site density. The model’s accuracy and consistency is demonstrated compared to empirical correlations, affirming its reliability in analyzing the intricate dynamics of subcooled flow boiling.

〈Your impression & future plan〉

It is both a pleasure and an honor to receive this award amidst such strong competition from outstanding students worldwide. I extend my heartfelt thanks to my supervisors, Professors Miwa, Okamoto, and Suzuki, for their invaluable guidance, support, and encouragement throughout my research journey. I am also grateful to Professor Okawa for their assistance and efforts in my study. 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 ICONE31 has left me with many memorable experiences. Looking ahead, I am excited to continue my research in advancing thermal hydraulics and nuclear engineering through artificial intelligence.