Alam Md Erfanul, Wu Dazhong, Dickerson Andrew K
Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, USA.
Proc Math Phys Eng Sci. 2020 Sep;476(2241):20200467. doi: 10.1098/rspa.2020.0467. Epub 2020 Sep 16.
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.
蚊子用于晾干翅膀的高频、低振幅翅膀运动激发了对毫米级受迫悬臂梁液滴释放的研究。我们的模拟系统是一个10毫米的聚四氟乙烯悬臂梁，在85赫兹下通过±1毫米的基座振幅驱动，通过三种主要喷射模式排出液滴：垂直于悬臂梁喷射、滑动和夹断。诸如悬臂梁刚度、液滴位置、液滴大小和润湿性等系统变量的选择会调节特定喷射模式的出现。然而，大量的系统特征使得预测模式的出现以及完全和部分液体去除之间的转变变得复杂。在本研究中，我们基于集成学习构建了两个预测模型，一个预测喷射模式（一个分类问题），另一个预测从悬臂梁喷射液滴所需的最小惯性力（一个回归问题）。对于喷射模式预测，使用袋装分类器我们实现了85%的准确率。对于惯性力预测，使用集成学习回归模型实现的最低均方根误差为0.037。结果还表明，喷射时间和悬臂梁润湿性是预测喷射模式和喷射液滴所需最小惯性力的主要特征。
