Boiling acoustics (BA) offers a noninvasive approach for two-phase flow regime identification, yet its correlation with specific boiling regimes under flow conditions remains poorly characterized. This study simulates flow boiling and corresponding acoustic emissions in a helical tube to establish the relationship between flow regimes and their acoustic signatures. Machine learning (ML) models, particularly a convolutional neural network (CNN), are trained to automate regime classification. The results reveal distinct acoustic features: plug flow exhibits high-frequency dominance, wavy flow is characterized by low-frequency components, and slug flow combines both. BA-based classification is numerically validated and further enhanced by the CNN model, which achieves 97.5% accuracy even under strong white noise (SD = 0.7). These findings demonstrate BA’s potential as a robust, real-time tool for monitoring and controlling flow boiling processes in industrial systems.

The study focuses on understanding vapor bubble dynamics and heat transfer characteristics in pool boiling influenced by electrowetting (EW), a critical phenomenon for enhancing heat transfer efficiency in applications such as power generation and electronic cooling. EW is a promising method that allows precise control over fluid interfaces through electric fields, altering bubble growth, detachment, and contact angles. This study leverages numerical simulations using the phase-field interface tracking approach to examine the impact of direct current (DC) and alternating current (AC) EW on bubble dynamics. The results demonstrate that increased applied voltages enhance bubble detachment and decrease the contact angle, improving heat transfer rates. In the case of AC electrowetting, as the frequency increases, the fluid interface begins to experience difficulty in keeping up with the rapid voltage oscillations, and then the detachment time is slow. Moreover, it is demonstrated that compared to the impact of AC and DC electrowetting on bubble dynamics, the bubble tends to grow more rapidly when DCEW is applied. This is because the applied voltage is constant, which provides a steady force, resulting in more uniform and potentially faster bubble growth.