An Automatic Isotropic Triangular Grid Generation Technique Based on an Artificial Neural Network and an Advancing Front Method

Peng Lu, Nianhua Wang, Xinghua Chang, Laiping Zhang, Yadong Wu, Hongying Zhang and Paolo Crippa

Mathematical Problems in Engineering, 2022, vol. 2022, 1-20

Abstract: To improve the efficiency and automation of the traditional advancing front method (AFM) of unstructured grid generation, a novel isotropic triangular generation technique is developed based on an artificial neural network (ANN). First, some existing high-quality triangular grids are used as data sources, and then an automatic extraction method of training dataset is proposed. Second, the dataset is input into the ANN to train the network by the back-propagation (BP) algorithm, and then some typical patterns are identified through iterative learning. Third, after inputting the initial discretized fronts, the grid generator starts from the shortest front, and the adjacent front information is collected as the input of the neural network to choose the most proper pattern and predict the coordinates of the new point until the grid covers the whole computational domain. Finally, the initial grid is smoothed to further improve the grid quality. Some typical two-dimensional (2D) geometries are tested to validate the capability of the ANN-based advancing front triangle generator. The experimental results demonstrate that the efficiency of the proposed ANN-based triangular grid generator is about 30 percent higher than that of the traditional AFM, and grid quality has also been improved significantly.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:8103813

DOI: 10.1155/2022/8103813

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