Predicting Impact Outcomes and Maximum Spreading of Drop Impact on Heated Nanostructures Using Machine Learning

Accurate prediction of droplet behavior upon impact on a heated nanostructured surface is vital for various industrial applications. In this study, we leverage multiple data-driven machine learning (ML) techniques to model the impact outcome and droplet spreading, employing existing experimental data. Our approach incorporates a comprehensive range of critical control parameters, such as the impact velocity (V), surface temperature (T), nanopillars' packing fraction (ϕ), and surface roughness (r). We obtain optimal results when utilizing the artificial neural network classification (ANNC) to construct a phase diagram that encompasses all of the experimental impact behaviors. Additionally, we utilize the support vector regression (SVR) method to model the maximum spreading factor (β) as a function of the Weber number (We), defined as the ratio of droplet kinetic to surface energy, and T for each surface combination. Consistent with previous experimental observations, our results illustrate that nanostructures not only introduce distinct impact behaviors, such as central jetting, but also influence the boundaries among the deposition, rebound, and splashing regimes within the phase diagram. An increase in ϕ at a constant r promotes deposition and spreading events, while increasing r at a constant ϕ results in enhanced heat transfer to promote the Leidenfrost effect for the rebound regime and a greater disturbance of the liquid lamella to trigger splashing. The SVR prediction reveals the existence of a We-number threshold governed by the nanostructure parameters. Beyond this threshold, the maximum spreading factor (β) of a spreading droplet becomes independent of the surface temperature (T) as We increases, suggesting that fluid properties are likely the dominating factors influencing the spreading dynamics in the extreme We range.