BubbleID: A Deep Learning Framework for Bubble Interface Dynamics Analysis
This work addresses bubble dynamics analysis for boiling heat transfer applications, but it is incremental as it combines existing segmentation and tracking methods.
The paper tackles the problem of analyzing bubble interface dynamics in boiling images by developing BubbleID, a deep learning framework that identifies static and dynamic bubble attributes, achieving comprehensive analysis across diverse conditions and comparing dynamics before and after critical heat flux.
This paper presents BubbleID, a sophisticated deep learning architecture designed to comprehensively identify both static and dynamic attributes of bubbles within sequences of boiling images. By amalgamating segmentation powered by Mask R-CNN with SORT-based tracking techniques, the framework is capable of analyzing each bubble's location, dimensions, interface shape, and velocity over its lifetime, and capturing dynamic events such as bubble departure. BubbleID is trained and tested on boiling images across diverse heater surfaces and operational settings. This paper also offers a comparative analysis of bubble interface dynamics prior to and post-critical heat flux (CHF) conditions.