An automatic isotropic/anisotropic hybrid grid generation technique for viscous flow simulations based on an artificial neural network
Peng Lü, Nianhua Wang, Xinghua Chang, Laiping Zhang, Yadong Wu
Abstract
Based on the author's previous research, a novel hybrid grid generation technique is developed by introducing an Artificial Neural Network (ANN) approach for realistic viscous flow simulations. An initial hybrid grid over a typical geometry with anisotropic quadrilaterals in the boundary layer and isotropic triangles in the off-body region is generated by the classical mesh generation method to train two ANNs on how to predict the advancing direction of the new point and to control the grid size. After inputting the initial discretized fronts, the ANN-based Advancing Layer Method (ALM) is adopted to generate the anisotropic quadrilaterals in boundary layers. When the high aspect ratio of the anisotropic grid reaches a specified value, the ANN-based Advancing Front Method (AFM) is adopted to generate isotropic triangles in the off-body computational domain. The initial isotropic triangles are smoothed to further improve the grid quality. Three typical cases are tested and compared with experimental data to validate the effectiveness of grids generated by the ANN-based hybrid grid generation method. The experimental results show that the two ANNs can predict the advancing direction and the grid size very well, and improve the adaptability of the isotropic/anisotropic hybrid grid generation for viscous flow simulations.