  Deep learning has been employed to identify flow characteristics from
Computational Fluid Dynamics (CFD) databases to assist the researcher to better
understand the flow field, to optimize the geometry design and to select the
correct CFD configuration for corresponding flow characteristics. Convolutional
Neural Network (CNN) is one of the most popular algorithms used to extract and
identify flow features. However its use, without any additional flow field
interpolation, is limited to the simple domain geometry and regular meshes
which limits its application to real industrial cases where complex geometry
and irregular meshes are usually used. Aiming at the aforementioned problems,
we present a Graph Neural Network (GNN) based model with U-Net architecture to
identify the vortex in CFD results on unstructured meshes. The graph generation
and graph hierarchy construction using algebraic multigrid method from CFD
meshes are introduced. A vortex auto-labeling method is proposed to label
vortex regions in 2D CFD meshes. We precise our approach by firstly optimizing
the input set on CNNs, then benchmarking current GNN kernels against CNN model
and evaluating the performances of GNN kernels in terms of classification
accuracy, training efficiency and identified vortex morphology. Finally, we
demonstrate the adaptability of our approach to unstructured meshes and
generality to unseen cases with different turbulence models at different
Reynolds numbers.
