Bubble feature extraction in subcooled flow boiling using AI-based object detection and tracking techniques

Abstract

Subcooled flow boiling is a pivotal process prevalent in a myriad of scientific investigations and engineering applications, particularly in the realm of heat transfer system design and the foundational study of phase transition dynamics. The life cycle of bubbles, from nucleation and growth to departure and coalescence, along with their interaction with heat and mass transfer processes, critically influence the overall heat transfer efficiency. Nonetheless, the drastic transformations that bubbles undergo from inception to disappearance in subcooled flow boiling pose significant challenges for conventional bubble detection methods, particularly concerning condensing bubbles. In light of this, a cutting-edge AI-based method for condensing bubble detection and tracking in subcooled flow boiling is developed and validated in the present study. The present approach first identifies bubbles using object detection technique and subsequently tracks them across sequential frames. The proposed method demonstrates a robust capability of detecting approximately 90% of condensing bubbles within subcooled flow boiling. Furthermore, key thermal-hydraulic parameters in subcooled flow boiling such as aspect ratio, Sauter mean diameter, departure diameter, growth time, and bubble lifetime, were successfully extracted using the proposed AI-based model. Its results are compared with empirical correlations, and show a commendable consistency, demonstrating the viability and accuracy of the advanced AI-based model in analyzing the complex dynamics of subcooled flow boiling. The advantage of the newly developed method is preliminarily verified in the present study, and further validation is underway to corroborate its boarded application.