MQENet: A Mesh Quality Evaluation Neural Network Based on Dynamic Graph Attention
This addresses the need for more comprehensive and objective mesh evaluation in industrial fluid simulation applications, representing an incremental improvement over previous methods.
The paper tackled the problem of evaluating mesh quality for computational fluid dynamics simulations, proposing MQENet, a neural network based on dynamic graph attention, which achieved effectiveness on the NACA-Market benchmark dataset.
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.