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Measuring the Interaction Force Between a Droplet and a Super-hydrophobic Substrate by the Optical Lever Method
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Investigation of Droplet Spreading and Rebound Dynamics on Superhydrophobic Surfaces Using Machine Learning
Samo Jereb1, Jure Berce1, Robert Lovšin1
1Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva cesta 6, SI-1000 Ljubljana, Slovenia.
Biomimetics (Basel, Switzerland)
|June 25, 2025
Summary
Machine learning models predict droplet behavior on superhydrophobic surfaces. Impact velocity is key, while surface features influence rebound efficiency, improving prediction accuracy.
Area of Science:
- Fluid dynamics
- Surface science
- Materials science
Background:
- Droplet impact on superhydrophobic surfaces is complex, influenced by surface, fluid, and environmental factors.
- Understanding these interactions is crucial for applications in various fields.
Purpose of the Study:
- To develop a machine learning model predicting droplet spreading and rebound on superhydrophobic surfaces.
- To elucidate the influence of individual parameters on droplet behavior.
Main Methods:
- Conducted 1498 water-glycerin droplet impact experiments.
- Utilized laser-structured aluminum samples with varying microtopography.
- Trained, validated, and optimized a machine learning regression model.
Main Results:
- Droplet impact velocity is the dominant factor in spreading.
- Spreading is independent of surface microtopography (depth, width).
- Unwetted area fraction significantly impacts rebound efficiency, especially with smaller microchannel distances.
Conclusions:
- Developed accurate empirical correlations for maximum spreading coefficient and rebound efficiency, outperforming existing models.
- The study bridges macroscale droplet-surface interactions with microscale properties and fluid thermophysics.
