In this study, we consider a simple system of water drops impacting cantilevered, circular fibers with velocity $1.0\text{–}2.4\mathrm{m∕s}$. The dynamics of the system are complicated because the motion of the drop and deflecting fiber at impact are highly coupled and the outcome is influenced by several continuous variables. The unpredictable dynamics of the system call for the use of computational tools that can reveal relationships between variables and make predictions about the physical outcomes for parameter values for which there is no experimental data. This study considers three fibers of contrasting properties, with hydrophilic and hydrophobic wetting conditions, exposed to a range of falling drop diameters and velocities. We predict maximal fiber deflection due to the impacting drop using an ensemble machine learning algorithm combining three base algorithms: a random forest regressor, a gradient boosting regressor, and a multi-layer perceptron. We train and test our algorithm with experimental datasets comprising 405 total trials using three different fibers and five input variables per fiber. The approach allows the determination of relative dominance of the input features in the prediction, reveals shortcomings of traditional scaling approaches for momentum transfer, and shows that drop momentum plays only a minor role in fiber deflection. Finally, our approach provides another example for the application of machine learning to characterize complex and coupled systems in fluid mechanics.

在这项研究中，我们考虑了一个简单的水滴系统，该系统以速度影响悬臂式圆形纤维 $\mathrm{1个}。0\text{–}\mathrm{2个}。4\mathrm{∕}$。该系统的动力学很复杂，因为在撞击时液滴和偏转光纤的运动高度耦合，并且结果受几个连续变量的影响。系统的不可预测的动态性要求使用计算工具，该工具可以揭示变量之间的关系，并可以预测没有实验数据的参数值的物理结果。这项研究考虑了三种具有对比特性的纤维，具有亲水性和疏水性润湿条件，并暴露于一系列下降的液滴直径和速度。我们使用结合了三种基本算法的集成机器学习算法预测由于撞击降而导致的最大光纤偏转：随机森林回归器，梯度提升回归器和多层感知器。我们使用实验数据集训练和测试我们的算法，该实验数据集包含405个试验，使用三种不同的光纤，每根光纤五个输入变量。该方法可以确定预测中输入特征的相对优势，揭示了动量传递的传统缩放方法的缺点，并显示出下降动量在光纤偏转中仅起很小的作用。最后，我们的方法为机器学习在流体力学中表征复杂和耦合系统的应用提供了另一个示例。并表明，下降动量在光纤挠度中仅起较小作用。最后，我们的方法为机器学习在流体力学中表征复杂和耦合系统的应用提供了另一个示例。并表明，下降动量在光纤挠度中仅起很小的作用。最后，我们的方法为机器学习在流体力学中表征复杂和耦合系统的应用提供了另一个示例。