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The volume of fluid (VoF) method is widely used in multi-phase flowsimulations to track and locate the interface between two immiscible fluids. Amajor bottleneck of the VoF method is the interface reconstruction step due toits high computational cost and low accuracy on unstructured grids. We proposea machine learning enhanced VoF method based on Graph Neural Networks (GNN) toaccelerate the interface reconstruction on general unstructured meshes. Wefirst develop a methodology to generate a synthetic dataset based on paraboloidsurfaces discretized on unstructured meshes. We then train a GNN based modeland perform generalization tests. Our results demonstrate the efficiency of aGNN based approach for interface reconstruction in multi-phase flow simulationsin the industrial context.

Machine Learning model for gas-liquid interface reconstruction in CFD numerical simulations