An Airfoil Mesh Quality Criterion using Deep Neural Networks
Xinhai Chen, Jie Liu, Chunye Gong, Yufei Pang, Bo Chen · 2020
The quality of the mesh is one of the most critical aspects for solving partial differential equations (PDEs) in applications of Computational Fluid Dynamics. Many geometry criteria have been proposed and are widely used in business preprocessing software like ICEM CFD, PointWise, Gambit. However, these traditional geometry criteria fail to recognize some quality features that seriously affect the accuracy of numerical calculations, such as density and distribution of mesh elements. These quality features are usually evaluated based on engineering experience, which heavily increases the pre-processing cost and requires extensive engineering experience. In this paper, we introduce a deep learning model to solve the mentioned issues by offline learning. The proposed model is small and fast and can be embedded in pre-processing software. Experiment results show that the derived model is capable of performing the quality evaluating task and achieve an accuracy of 93.8%.