论文标题
CFD数值模拟中气体液体接口重建的机器学习模型
Machine Learning model for gas-liquid interface reconstruction in CFD numerical simulations
论文作者
论文摘要
流体（VOF）方法的体积被广泛用于多相流量模拟中，以跟踪和定位两个不混溶的流体之间的界面。 VOF方法的主要瓶颈是界面重建步骤，因为其高计算成本和非结构化网格的精度较低。我们建议基于图神经网络（GNN）的机器学习增强的VOF方法，以加速通用非结构化网格上的接口重建。我们首先开发了一种基于在非结构化网格上离散的抛物面表面生成合成数据集的方法。然后，我们训练基于GNN的模型并执行概括测试。我们的结果表明，在工业背景下，基于GNN的界面重建方法的效率。
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.
