A GNN-BASED FRAMEWORK TO IDENTIFY FLOW PHENOMENA ON UNSTRUCTURED MESHES -PARCFD2023
Résumé
Driven by the abundant data generated from Computational Fluid Dynamics
(CFD) simulations, machine learning (ML) methods surpass the deterministic criteria on
flow phenomena identification in the way that is independent of case-by-case thresholds by
combining the flow field properties and the topological distribution of the phenomena. The
current most popular and successful ML models based on convolutional neural networks
are limited to structured meshes and unable to directly digest the data generated from
unstructured meshes which are more widely used in the real industrial CFD simulations.
We propose a framework based on graph neural networks with the proposed Fast Gaussian
Mixture Model as the convolution kernel and U-Net architecture to detect flow phenomena
resided on a graph hierarchy generated by the algebraic multigrid method embedded in the
open-source CFD solver, code saturne. We demonstrate the superiority of the proposed
kernel and U-Net architecture, along with the generality of the framework to unstructured
mesh and unseen case on detecting the vortexes behind the backward-facing step. Our
proposed framework can be trivially extended to detect other flow phenomena in 3D cases
which is ongoing work.
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