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Developing a new mesh quality evaluation method based on convolutional neural network
Engineering Applications of Computational Fluid Mechanics ( IF 6.1 ) Pub Date : 2020-02-05 , DOI: 10.1080/19942060.2020.1720820
Xinhai Chen 1 , Jie Liu 1 , Yufei Pang 2 , Jie Chen 2 , Lihua Chi 1 , Chunye Gong 1
Affiliation  

One of the difficult requirements imposed on high-quality CFD mesh generation has been the ability to evaluate the mesh quality efficiently. Due to the lack of a general and effective evaluating criterion, the current mesh quality evaluation task mainly relies on various quality metrics for the shape of mesh elements, such as angle, radius, edge and contextual information collected by pre-processing software. However, this line of methods greatly increases the pre-processing cost and may not guarantee a precise quality result. In this paper, we provide a solution to solve the mentioned issues, resulting in a CNN model GridNet and the first mesh dataset NACA-Market. GridNet takes the mesh file as input and then automatically evaluates the mesh quality. Experiment results show that GridNet is capable of performing automatic mesh quality evaluation and outperforms the widely used classifiers. We hope that the proposed large benchmark collection and network could fill in the gaps in the fields of CNN-based mesh quality evaluation and provide potential future research directions in this field.



中文翻译:

开发基于卷积神经网络的网格质量评估方法

对高质量CFD网格生成施加的困难要求之一是有效评估网格质量的能力。由于缺乏通用有效的评估标准,当前的网格质量评估任务主要依赖于网格元素形状的各种质量度量,例如角度,半径,边缘和预处理软件收集的上下文信息。但是,这一系列方法大大增加了预处理成本,并且可能无法保证精确的质量结果。在本文中,我们提供了解决上述问题的解决方案,从而产生了CNN模型GridNet和第一个网格数据集NACA-Market。GridNet将网格文件作为输入,然后自动评估网格质量。实验结果表明,GridNet能够执行自动网格质量评估,并且性能优于广泛使用的分类器。我们希望所提议的大型基准收集和网络能够填补基于CNN的网格质量评估领域的空白,并为该领域提供潜在的未来研究方向。

更新日期:2020-04-20
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