The design of water-repellent surfaces is of great importance as water repellency of surfaces against impacting water drops is a promising approach for most of applications as anti-icing and self-cleaning. To comprehensively investigate drop interactions with hydrophobic and superhydrophobic surfaces, we conducted a large suite of experimental tests to evaluate the morphology of impacting drops on these surfaces as a function of drop properties (drop diameter, density, viscosity, and surface tension), kinematic parameters (velocity), and surface features (contact angle, contact angle hysteresis, and surface roughness). Following analyzing the experimental results, we utilized a novel approach in this field by applying a predictive approach based on machine learning to predict the behavior of impacting drops on hydrophobic and superhydrophobic surfaces. Our developed model, based on a random-forest approach, predicted drop behavior at up to 98% accuracy. Aiming at finding those conditions favorable for producing a bouncing behavior upon drop impact, we predicted the outcome of an impinging drop for a wide range of Weber numbers, i.e., impact velocities, and numerous hypothetical surfaces. Our results offer some design criteria for creating superhydrophobic surfaces that favor bouncing upon drop impact on these surfaces.

拒水表面的设计非常重要，因为对于大多数应用（例如防冰和自清洁）而言，拒水表面对撞击水滴的拒水性是一种有前途的方法。为了全面研究液滴与疏水和超疏水表面的相互作用，我们进行了一系列实验测试，以评估液滴在这些表面上的冲击形态，这是液滴特性（液滴直径，密度，粘度和表面张力），运动学参数的函数（速度）和表面特征（接触角，接触角滞后和表面粗糙度）。分析实验结果后，我们通过基于机器学习的预测方法在该领域中采用了一种新颖的方法来预测疏水性和超疏水性表面上撞击液滴的行为。我们基于随机森林方法开发的模型可预测掉落行为，准确性最高可达98％。为了找到那些有利于在跌落冲击时产生弹跳行为的条件，我们预测了范围广泛的韦伯数（即冲击速度和大量假想表面）撞击降落的结果。我们的结果为创建超疏水表面提供了一些设计标准，这些表面倾向于在跌落冲击时反弹。为了找到那些有利于在跌落冲击时产生弹跳行为的条件，我们预测了范围广泛的韦伯数（即冲击速度和大量假想表面）撞击降落的结果。我们的结果为创建超疏水表面提供了一些设计标准，这些表面倾向于在跌落冲击时反弹。为了找到那些有利于在跌落冲击时产生弹跳行为的条件，我们预测了范围广泛的韦伯数（即冲击速度和大量假想表面）撞击降落的结果。我们的结果为创建超疏水表面提供了一些设计标准，这些表面倾向于在跌落冲击时反弹。