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

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

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.

• Jezik: Angleški jezik
• Ključne besede: droplet impact, superhydrophobic surface, machine learning, maximum spreading coefficient, droplet rebound, rebound efficiency, laser-textured surface
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2025
• Št. strani: 18 str.
• Številčenje: Vol. 10, iss. 16, art. 357
• PID: 20.500.12556/RUL-169664
• UDK: 532
• ISSN pri članku: 2313-7673
• DOI: 10.3390/biomimetics10060357
• COBISS.SI-ID: 238595587
• Datum objave v RUL: 06.06.2025

Gradivo je del revije

• Naslov: Biomimetics
• Skrajšan naslov: Biomimetics
• Založnik: MDPI
• ISSN: 2313-7673
• COBISS.SI-ID: 526328601

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.