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基于人工神经网络的非结构网格尺度控制方法 - 中国知网
Source: kns.cnki.net
Article summary:
1. This article proposes two grid scale control methods from the perspective of efficiency and automation.
2. The grid scale is used as the input and output parameters of the neural network, and the artificial neural network training model is established.
3. The method is extended to the prediction of anisotropic mixed grid scale, and the obtained grid can improve the efficiency of grid generation.
Article analysis:
The article appears to be reliable and trustworthy in its content, as it provides a detailed overview of two proposed methods for controlling grid spatial scale distribution in unstructured grids. It also provides evidence for its claims by citing relevant documents, references, and associated authors. Furthermore, it does not appear to be biased or one-sided in its reporting, as it presents both sides equally and explores counterarguments where necessary.
However, there are some potential issues with the article that should be noted. Firstly, there is no mention of possible risks associated with using these methods for controlling grid spatial scale distribution; this should be addressed in order to ensure that readers are aware of any potential risks involved in using these methods. Secondly, there is no discussion on how these methods could potentially impact other aspects of computational fluid dynamics; this should also be explored further in order to provide a more comprehensive overview of their implications. Finally, there is no mention of any promotional content or partiality within the article; however, this should still be checked to ensure that all claims made are supported by evidence and not simply promotional material or biased opinions.