Understanding and predicting the transition from nucleate boiling regime to the boiling crisis in a thin liquid film is important for numerous applications, such as the thermal management of power electronics and nuclear power plants. The processes of bubble coalescence, their breakup, and dry patch formation precede the boiling crisis. Here, we present a study on the dynamics of bubbles and dry patches, as well as the contact lines formed around dry patches and at the periphery of a spreading liquid droplet when it impacts a hot, smooth surface at different Weber numbers and substrate temperatures. The investigation is conducted using high-speed imaging, machine learning-assisted computer vision, and statistical analysis. The automated pipeline has been developed for the tracking and analysis of hundreds of bubbles and dry patches with high temporal resolution. This Convolutional Neural Network analysis extracts quantitative descriptors of size and interaction of the bubbles and dry patches, contact line length, and density. Finally, the transition from contact boiling to transition boiling, which subsequently leads to a boiling crisis, can be predicted through analysis of the dry patch and contact line dynamics.
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Sitnikov et al. (2026) studied this question.
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