Machine learning model for gas-liquid interface reconstruction in CFD numerical simulations - Laboratoire Interdisciplinaire des Sciences du Numérique Access content directly
Preprints, Working Papers, ... Year : 2022

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

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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Dates and versions

hal-03721729 , version 1 (12-07-2022)

Identifiers

  • HAL Id : hal-03721729 , version 1

Cite

Tamon Nakano, Michele Alessandro Bucci, Jean-Marc Gratien, Thibault Faney, Guillaume Charpiat. Machine learning model for gas-liquid interface reconstruction in CFD numerical simulations. 2022. ⟨hal-03721729⟩
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