MQENet: A Mesh Quality Evaluation Neural Network Based on Dynamic Graph Attention

TL;DR

MQENet is a neural network that uses dynamic graph attention to evaluate structured mesh quality, improving accuracy and efficiency in computational fluid dynamics applications.

Contribution

Introduces MQENet, a novel graph neural network with specialized preprocessing algorithms for comprehensive and objective mesh quality evaluation.

Findings

Effective in classifying mesh quality on NACA-Market dataset

Outperforms traditional metrics in evaluation accuracy

Preprocessing algorithms enhance data conversion efficiency

Abstract

With the development of computational fluid dynamics, the requirements for the fluid simulation accuracy in industrial applications have also increased. The quality of the generated mesh directly affects the simulation accuracy. However, previous mesh quality metrics and models cannot evaluate meshes comprehensively and objectively. To this end, we propose MQENet, a structured mesh quality evaluation neural network based on dynamic graph attention. MQENet treats the mesh evaluation task as a graph classification task for classifying the quality of the input structured mesh. To make graphs generated from structured meshes more informative, MQENet introduces two novel structured mesh preprocessing algorithms. These two algorithms can also improve the conversion efficiency of structured mesh data. Experimental results on the benchmark structured mesh dataset NACA-Market show the effectiveness of MQENet in the mesh quality evaluation task.