Saturated pool boiling over vertical tube has significant importance in various practical applications specifically in the safety of nuclear power plant. The process of boiling is characterized by the formation of bubbles within the liquid. These bubbles are composed of vapour of the substance. The formation and growth of bubbles are influenced by various factors which directly regulate the nucleation sites in the liquid. The formation of nucleation sites is directly linked to the heat flux and heat transfer efficiency of the device. The accurate prediction of these parameter is quite important for the equipment design, safety, reliability. Here, we used machine learning approach that correlates high-quality high-speed imaging on dynamic bubbles at different heat flux. The framework leverages cutting-edge deep learning models including convolutional neural networks and object detection algorithms to automatically extract both hierarchical and physics-based features. By training on these features, our model learns physical boiling laws that statistically describe the manner in which bubbles nucleate, coalesce, and depart under boiling conditions, enabling in situ boiling heat flux prediction. The proposed method achieves an average heat flux prediction error of approximately 6%, while maintaining an overall classification accuracy of 88% across all heat flux levels. Our framework offers an automated, learning-based, alternative to conventional boiling heat transfer metrology.
Sha et al. (Sat,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: