Investigation of droplet spreading and rebound dynamics on superhydrophobic surfaces using machine learning

Jereb, Samo (author), Berce, Jure (author), Lovšin, Robert (author), Zupančič, Matevž (author), Može, Matic (author), Golobič, Iztok (author)

The spreading and rebound of impacting droplets on superhydrophobic interfaces is a complex phenomenon governed by the interconnected contributions of surface, fluid and environmental factors. In this work, we employed a collection of 1498 water–glycerin droplet impact experiments on monolayer-functionalized laser-structured aluminum samples to train, validate and optimize a machine learning regression model. To elucidate the role of each influential parameter, we analyzed the model-predicted individual parameter contributions on key descriptors of the phenomenon, such as contact time, maximum spreading coefficient and rebound efficiency. Our results confirm the dominant contribution of droplet impact velocity while highlighting that the droplet spreading phase appears to be independent of surface microtopography, i.e., the depth and width of laser-made features. Interestingly, once the rebound transitions to the retraction stage, the importance of the unwetted area fraction is heightened, manifesting in higher rebound efficiency on samples with smaller distances between laser-fabricated microchannels. Finally, we exploited the trained models to develop empirical correlations for predicting the maximum spreading coefficient and rebound efficiency, both of which strongly outperform the currently published models. This work can aid future studies that aim to bridge the gap between the observed macroscale surface-droplet interactions and the microscale properties of the interface or the thermophysical properties of the fluid.

• Language: English
• Keywords: droplet impact, superhydrophobic surface, machine learning, maximum spreading coefficient, droplet rebound, rebound efficiency, laser-textured surface
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: 18 str.
• Numbering: Vol. 10, iss. 16, art. 357
• PID: 20.500.12556/RUL-169664
• UDC: 532
• ISSN on article: 2313-7673
• DOI: 10.3390/biomimetics10060357
• COBISS.SI-ID: 238595587
• Publication date in RUL: 06.06.2025

Record is a part of a journal

• Title: Biomimetics
• Shortened title: Biomimetics
• Publisher: MDPI
• ISSN: 2313-7673
• COBISS.SI-ID: 526328601

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.