Abstract:Mesh generation and adaptation are bottleneck problems restricting future development of computational fluid dynamics (CFD). Automatic and intelligent mesh generation is still worth continuous investigation. With the rapid progress in high-performance computing power and big data technology, artificial intelligence, represented by machine learning, has been successfully applied to multiple fields including fluid dynamics, which has revolutionarily boosted the development of these fields. This paper reviews briefly the application of machine learning in the unstructured mesh generation in CFD and analyzes the key issues in the mesh generation based on machine learning. Meanwhile, the sample data format is designed and the automatic extraction of unstructured mesh sample data sets is realized. By integrating the advancing front (AFT) method with the artificial neural network, a novel two-dimensional triangular grid generation method is developed based on machine learning. Finally, several isotropic unstructured grids and hybrid grids (2D cylinder, 2D NACA0012 airfoil, and 30p30n three-element airfoil) are generated and mesh quality and elapsed time are counted, it indicates that mesh quality is generally equivalent to commercial software and the efficiency is 30% higher than the traditional AFT method.