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Using Graph Neural Network for gas-liquid interface reconstruction in Volume Of Fluid methods
- Source :
- 8th European Congress on Computational Methods in Applied Sciences and Engineering.
- Publication Year :
- 2022
- Publisher :
- CIMNE, 2022.
-
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.
Details
- Database :
- OpenAIRE
- Journal :
- 8th European Congress on Computational Methods in Applied Sciences and Engineering
- Accession number :
- edsair.doi.dedup.....21564f4c26f8372b72b6c7f1fb8927b0