Investigation of Droplet Spreading and Rebound Dynamics on Superhydrophobic Surfaces Using Machine Learning.

IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY
Samo Jereb, Jure Berce, Robert Lovšin, Matevž Zupančič, Matic Može, Iztok Golobič
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Abstract

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.

利用机器学习研究液滴在超疏水表面的扩散和回弹动力学。
液滴在超疏水界面上的扩散和回弹是一个复杂的现象,受表面、流体和环境因素的共同作用。在这项工作中,我们利用1498个水-甘油液滴撞击单层功能化激光结构铝样品的实验来训练、验证和优化机器学习回归模型。为了阐明每个影响参数的作用,我们分析了模型预测的单个参数对该现象的关键描述符(如接触时间、最大扩散系数和回弹效率)的贡献。我们的研究结果证实了液滴撞击速度的主要贡献,同时强调液滴扩散阶段似乎与表面微形貌(即激光制造特征的深度和宽度)无关。有趣的是,一旦回弹过渡到缩回阶段,未湿面积分数的重要性就会提高,在激光制造的微通道之间距离较小的样品上表现为更高的回弹效率。最后,我们利用训练好的模型建立了预测最大扩散系数和反弹效率的经验相关性,这两个模型都明显优于目前发表的模型。这项工作有助于未来的研究,旨在弥合观察到的宏观表面-液滴相互作用与界面的微观性质或流体的热物理性质之间的差距。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
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