Self-supervised learning of shedding droplet dynamics during steam condensation

Knowledge of condensate shedding droplet dynamics provides important information for the characterization of two-phase heat and mass transfer phenomena. Detecting and segmenting the droplets during shedding requires considerable time and effort if performed manually. Here, we developed a self-supervised deep learning model for segmenting shedding droplets from a variety of dropwise and filmwise condensing surfaces. The model eliminates the need for image annotation by humans in the training step and, therefore, reduces labor significantly. The trained model achieved an average accuracy greater than 0.9 on a new unseen test dataset. After extracting the shedding droplet size and speed, we developed a data-driven model for shedding droplet dynamics based on condensation heat flux and surface properties such as wettability and tube diameter. Our results demonstrate that condensate droplet departure size is both heat flux and tube size dependent and follows different trends based on the condensation mode. The results of this work provide an annotation-free methodology for falling droplet segmentation as well as a statistical understanding of droplet dynamics during condensation.