Deep Learning Enabled Comprehensive Evaluation of Jumping-Droplet Condensation and Frosting.

Abstract

Superhydrophobicity-enabled jumping-droplet condensation and frosting have great potential in various engineering applications, ranging from heat transfer processes to antifog/frost techniques. However, monitoring such droplets is challenging due to the high frequency of droplet behaviors, cross-scale distribution of droplet sizes, and diversity of surface morphologies. Leveraging deep learning, we develop a semisupervised framework that monitors the optical observable process of condensation and frosting. This system is adept at identifying transient droplet distributions and dynamic activities, such as droplet coalescence, jumping, and frosting, on a variety of superhydrophobic surfaces. Utilizing this transient and dynamic information, various physical properties, such as heat flux, jumping characteristics, and frosting rate, can be further quantified, conveying the heat transfer and antifrost performances of each surface perceptually and comprehensively. Furthermore, this framework relies on only a small amount of annotated data and can efficiently adapt to new condensation conditions with varying surface morphologies and illumination techniques. This adaptability is beneficial for optimizing surface designs to enhance condensation heat transfer and antifrosting performance.