Machine Learning model for gas-liquid interface reconstruction in CFD numerical simulations
This addresses a bottleneck in computational fluid dynamics for industrial applications, but appears incremental as it enhances an existing method.
The authors tackled the high computational cost and low accuracy of interface reconstruction in volume of fluid (VoF) simulations by proposing a machine learning method using Graph Neural Networks (GNN) on unstructured meshes, demonstrating its efficiency in industrial multi-phase flow simulations.
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.