Data-Driven Real-Time Prediction for Interfacial Fluid Mechanics: Droplet Evaporation

Droplet evaporation plays crucial roles in biodiagnostics, microfabrication, and inkjet printing. Experimentally studying the evolution of a sessile droplet consisting of two or more components needs sophisticated equipment to control the vast parameter space affecting the physical process. On the other hand, non-axisymmetric nature of the problem, attributed to compositional perturbations, introduces challenges to numerical methods. In this work, droplet evaporation problem is studied from a new perspective. We analyze evolution of a sessile methanol droplet through data-driven classification and regression techniques. The models are trained using experimental data of methanol droplet evolution under various environmental humidity levels and substrate temperatures. At higher humidity levels, the interfacial tension and subsequently contact angle increase due to higher water uptake into droplet. Therefore, different regimes of evolution are observed due to adsorption-absorption and possibly condensation of water which turns the droplet into a binary system. We use classification algorithms to predict the regime of droplet with point-by-point analysis of droplet profile. Decision tree demonstrates a better performance compared to Na\text{\"i}ve Bayes (NB) classifier. Furthermore, through utilizing regression techniques, we predict the humidity level surrounding droplet as well as time evolution of macroscopic parameter (diameter or contact angle) of droplet. The prediction results show promising performance for four cases of methanol droplet evolution under conditions that are unseen by the model which demonstrates the capability of the model to capture the complex physics underlying binary droplet evolution.