  The volume of fluid (VoF) method is widely used in multi-phase flow
simulations to track and locate the interface between two immiscible fluids. A
major bottleneck of the VoF method is the interface reconstruction step due to
its high computational cost and low accuracy on unstructured grids. We propose
a machine learning enhanced VoF method based on Graph Neural Networks (GNN) to
accelerate the interface reconstruction on general unstructured meshes. We
first develop a methodology to generate a synthetic dataset based on paraboloid
surfaces discretized on unstructured meshes. We then train a GNN based model
and perform generalization tests. Our results demonstrate the efficiency of a
GNN based approach for interface reconstruction in multi-phase flow simulations
in the industrial context.
