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Abstract Drop impact causes severe surface erosion, dictating many important natural, environmental and engineering processes and calling for substantial prevention and preservation efforts. Nevertheless, despite extensive studies on the kinematic features of impacting drops over the last two decades, the dynamic process that leads to the drop-impact erosion is still far from clear. Here, we develop a method of high-speed stress microscopy, which measures the key dynamic properties of drop impact responsible for erosion, i.e., the shear stress and pressure distributions of impacting drops, with unprecedented spatiotemporal resolutions. Our experiments reveal the fast propagation of self-similar noncentral stress maxima underneath impacting drops and quantify the shear force on impacted substrates. Moreover, we examine the deformation of elastic substrates under impact and uncover impact-induced surface shock waves. Our study opens the door for quantitative measurements of the impact stress of liquid drops and sheds light on the origin of low-speed drop-impact erosion.
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Predictive modeling of drop impact force on concave targets
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.
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- PAR ID:
- 10376734
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- Physics of Fluids
- Volume:
- 34
- Issue:
- 10
- ISSN:
- 1070-6631
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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