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Impacting drops are ubiquitous and the corresponding impact force is their most studied dynamic quantity. However, impact forces arising from collisions with curved surfaces are understudied. In this study, we impact small cups with falling drops across drop Reynolds number 2975–12 800, isolating five dominant parameters influencing impact force: drop height and diameter, surface curvature and wettability, and impact eccentricity. These parameters are effectively continuous in their domain and have stochastic variability. The unpredictable dynamics of the system incentivize the implementation of tools that can unearth relationships between parameters and make predictions about impact force for parameter values for which there is not explicit experimental data. We predict force due to the impacting drop in a concave target using an ensemble learning algorithm comprised of four base algorithms: a random forest regressor, k-nearest neighbor, a gradient boosting regressor, and a multi-layer perceptron. We train and test our algorithm with original experimental data comprising 387 total trials using four cup radii with two wetting conditions each. Our approach permits the determination of relative importance of the input features in producing impact force and force predictions which can be compared to scaling relations modified from those for flat targets. Algorithmic predictions indicate that deformation of the drop and surface wettability, often neglected in scaling for impact force on flat surfaces, are important for concave targets. Finally, our approach provides another opportunity for the application of machine learning to characterize complex systems' fluid mechanics for which experimental variables are numerous and vary independently. 

The high frequency, low amplitude wing motion that mosquitoes employ to dry their wings inspires the study of drop release from millimetric, forced cantilevers. Our mimicking system, a 10-mm polytetrafluoroethylene cantilever driven through ±1 mm base amplitude at 85 Hz, displaces drops via three principal ejection modes: normal-to-cantilever ejection, sliding and pinch-off. The selection of system variables such as cantilever stiffness, drop location, drop size and wetting properties modulates the appearance of a particular ejection mode. However, the large number of system features complicate the prediction of modal occurrence, and the transition between complete and partial liquid removal. In this study, we build two predictive models based on ensemble learning that predict the ejection mode, a classification problem, and minimum inertial force required to eject a drop from the cantilever, a regression problem. For ejection mode prediction, we achieve an accuracy of 85% using a bagging classifier. For inertial force prediction, the lowest root mean squared error achieved is 0.037 using an ensemble learning regression model. Results also show that ejection time and cantilever wetting properties are the dominant features for predicting both ejection mode and the minimum inertial force required to eject a drop.