Universal Model for Predicting Maximum Spreading of Drop Impact on a Smooth Surface Developed Using Boosting Machine Learning Models

Abstract

Drop impact on a solid surface is a fundamental phenomenon in nature and engineering. Prediction of the maximum spreading ratio during drop impact is critical for modeling and optimizing the relevant processes. However, accurately modeling the maximum spreading using empirical and numerical methods remains challenging. Machine learning (ML) has recently provided a promising way to understand and model complex fluid phenomena. Thus, in this study, a universal model is developed by using machine learning methods to predict the maximum spreading. TPE (Tree-Structured Parzen Estimator) algorithm, a variant of Bayesian optimization, is applied to optimize the hyperparameters to improve the predictive performance of ML models. An extensive database containing 1015 experimental data points has been constructed from 24 research sources and the present experimental results. Four boosting ML models were compared with conventional models, and the results show that the mean absolute percentage error (MAPE) of ML models is 2.62− 3.40%, which is less than a third that of the best conventional model. Among these ML models, the TPE-based CatBoost model is superior to others, with an MAPE of 1.67% and an R 2 of 0.952. Then, SHAP (SHapley Additive exPlanations) was used to address the black-box nature of the ML-based models. Parameter analysis indicates that the developed ML model robustly captures the physical variation trend of the maximum spreading for various working fluids. The results presented here demonstrate that the TPEbased CatBoost model can model the drop maximum spreading ratio with high accuracy and broad applicability.

Effect of Surfactants on the Splashing Dynamics of Drops Impacting Smooth Substrates

“…Regarding rough and superhydrophobic surfaces, Wang et al found that surface roughness minimally affects the spreading diameter of dilute SDS droplets on micropillared arrays . However, higher surfactant concentrations decrease the droplet-receding velocity and can prevent bouncing on superhydrophobic surfaces. ,, Additionally, surfactant molecules entering micro or nanostructures on superhydrophobic surfaces change the surface wettability, with faster impact speeds enhancing this interaction. − Efforts at developing a prediction of maximum spreading ratios are numerous and take into account a variety of variables (fluid properties or wetting characteristics) but generally do not account for the unique properties of surfactant-laden liquids. − …”

“… 43 − 46 Efforts at developing a prediction of maximum spreading ratios are numerous and take into account a variety of variables (fluid properties or wetting characteristics) but generally do not account for the unique properties of surfactant-laden liquids. 47 − 49 …”