Will it flood? Classifying entrainment outcomes via machine learning

Multiphase flow simulations are advancing to the extent at which high-fidelity results can guide engineering decision-making. Accurate simulations, however, carry a high computational cost, often due to resolution constraints and the inclusion of complex, physics-driven models: question-to-answer iteration times are routinely on the order of weeks. In this study, we accelerate an engineering analysis of a benchmark flow: that of a falling film reactor. We investigate how an injected gas stream drives droplet entrainment, with the intent of predicting the certainty at which this harmful process occurs. We first train a machine learning (ML) classifier via a low-fidelity, though sufficiently representative, volume-of-fluid solver, thus mapping the class boundary demarcating flooding. Additional high-fidelity simulations along the class boundary are then used to improve the ML classifier. We quantify the savings in computational time versus prediction accuracy using this two-step ML-augmented approach, as opposed to a conventional full parameter sweep with the high-fidelity model.