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.

翻译：随着计算流体动力学的发展，工业应用中对流体仿真精度的要求也不断提高。生成网格的质量直接影响着仿真精度。然而，以往的网格质量评估指标与模型无法全面且客观地评估网格质量。为此，我们提出MQENet——一种基于动态图注意力的结构化网格质量评估神经网络。MQENet将网格评估任务视为图分类任务，用于对输入结构化网格的质量进行分类。为使结构化网格生成的图蕴含更丰富的信息，MQENet引入了两种新颖的结构化网格预处理算法。这两种算法还能提高结构化网格数据的转换效率。在基准结构化网格数据集NACA-Market上的实验结果表明，MQENet在网格质量评估任务中具有有效性。