Hybridization of front tracking and level set for multiphase flow simulations: a machine learning approach

A machine learning (ML) based approach is proposed to hybridize two well-established methods for multiphase flow simulations: the front tracking (FT) and the level set (LS) methods. Based on the geometric information of the Lagrangian marker elements which represents the phase interface in FT simulations, the distance function field, which is the key feature for describing the interface in LS simulations, is predicted using an ML model. The trained ML model is implemented in our conventional numerical framework, and we finally demonstrate that the FT-based interface representation can easily and immediately be switched to an LS-based representation whenever needed during the simulation period.