Machine Learning-Based Prediction and Interpretation of Collision Outcomes for Binary Seawater Droplets

The collision dynamics of binary seawater droplets are pivotal in marine engineering applications, like spray desalination and engine cooling. While high-fidelity simulations can resolve these dynamics, they are computationally prohibitive for rapid design and analysis. This study introduces the first interpretable machine learning (ML) framework to predict and elucidate the collision outcomes of head-on binary seawater droplets. A high-fidelity numerical dataset, generated via Modified Coupled Level Set-VOF (M-CLSVOF) simulations across a broad Weber number (We) range, serves as the foundation for training multiple classifiers. Among the tested algorithms, the Random Forest model achieved superior performance with 96.2% accuracy. The model’s predictions precisely identified the critical Weber number for the transition from coalescence to reflexive separation at We ≈ 22.3 for seawater. Moving beyond black-box prediction, we employed SHapley Additive exPlanations (SHAP) to quantitatively deconstruct the model’s decision-making process. SHAP analysis confirmed the dominance of the Weber number (75% contribution) and revealed the context-dependent role of the Reynolds number (25% contribution) in modulating the collision outcome. Furthermore, a comparative analysis with freshwater droplets quantified a 6% elevation in the critical Weber number for seawater, attributed to salinity-induced modifications in fluid properties. Finally, a machine-learned regime map in the We-Ohnesorge space was constructed, delineating the coalescence and separation boundaries. This work establishes ML as a powerful, interpretable surrogate model that not only delivers rapid, accurate predictions but also extracts fundamental physical insights, offering a valuable paradigm for optimizing marine spray systems.