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

• URL: http://arxiv.org/abs/2207.05684v1
• Date: Tue, 12 Jul 2022 17:07:46 GMT
• Title: Machine Learning model for gas-liquid interface reconstruction in CFD numerical simulations
• Authors: Tamon Nakano, Alessandro Michele Bucci, Jean-Marc Gratien, Thibault Faney, Guillaume Charpiat
• Abstract summary: 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.
• Score: 59.84561168501493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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