English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper introduces a novel neural network - flow completion network (FCN)- to infer the fluid dynamics, includ-ing the flow field and the force actingon the body, from the incomplete data based on Graph ConvolutionAttentionNetwork. The FCN is composed of several graph convolution layers andspatial attention layers. It is designed to inferthe velocity field and thevortex force contribution of the flow field when combined with the vortex forcemap (VFM)method. Compared with other neural networks adopted in fluid dynamics,the FCN is capable of dealing with bothstructured data and unstructured data.The performance of the proposed FCN is assessed by the computationalfluiddynamics (CFD) data on the flow field around a circular cylinder. Theforce coefficients predicted by our model arevalidated against those obtaineddirectly from CFD. Moreover, it is shown that our model effectively utilizesthe exist-ing flow field information and the gradient informationsimultaneously, giving a better performance than the traditionalconvolutionneural network (CNN)-based and deep neural network (DNN)-based models.Specifically, among all thecases of different Reynolds numbers and differentproportions of the training dataset, the results show that the proposedFCNachieves a maximum norm mean square error of 5.86% in the test dataset, whichis much lower than those of thetraditional CNN-based and DNN-based models(42.32% and 15.63% respectively).

Flow Completion Network: Inferring the Fluid Dynamics from Incomplete Flow Information using Graph Neural Networks