A Machine Learning Model for the Prediction of Sub-Grid Interfacial Area in Two-Phase Turbulent Flows

Two-phase turbulent flows are ubiquitous with applications in aerospace, energy, and process industries. The interfacial area is an important physical parameter in such flows, since it is used to calculate mass, momentum, and energy transfer between the phases. High-fidelity simulation approaches, like direct numerical simulations (DNS), provide an accurate estimate of the interfacial area, albeit at very high and often unfeasible computational costs. Lower-and mid-fidelity approaches estimate the closure term, namely the subgrid interfacial area, using highly simplified empirical models. In this work, we present a physics-aided machine learning model that predicts the subgrid interfacial area, which can be used as a closure model for the simulation of two-phase turbulent flows.