Mapping Network-coordinated Stacked Gated Recurrent Units for Turbulence Prediction

doi:10.1109/JAS.2024.124335

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	Mapping Network-coordinated Stacked Gated Recurrent Units for Turbulence Prediction
	Zhiming Zhang; Shangce Gao; MengChu Zhou; Mengtao Yan; Shuyang Cao
刊名	IEEE/CAA Journal of Automatica Sinica
	2024
卷号	11 期号:6 页码:1331-1341
关键词	Convolutional neural network deep learning recurrent neural network turbulence prediction wind load prediction
ISSN号	2329-9266
DOI	10.1109/JAS.2024.124335
英文摘要	Accurately predicting fluid forces acting on the surface of a structure is crucial in engineering design. However, this task becomes particularly challenging in turbulent flow, due to the complex and irregular changes in the flow field. In this study, we propose a novel deep learning method, named mapping network-coordinated stacked gated recurrent units (MSU), for predicting pressure on a circular cylinder from velocity data. Specifically, our coordinated learning strategy is designed to extract the most critical velocity point for prediction, a process that has not been explored before. In our experiments, MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the cylinder. This method significantly reduces the workload of data measurement in practical engineering applications. Our experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual aspects. Furthermore, the comparison results show that MSU predicts more precise results, even outperforming models that use all velocity field points. Compared with state-of-the-art methods, MSU has an average improvement of more than 45% in various indicators such as root mean square error (RMSE). Through comprehensive and authoritative physical verification, we established that MSU’s prediction results closely align with pressure field data obtained in real turbulence fields. This confirmation underscores the considerable potential of MSU for practical applications in real engineering scenarios. The code is available at https://github.com/zhangzm0128/MSU.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/56452]
专题	自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Zhiming Zhang,Shangce Gao,MengChu Zhou,et al. Mapping Network-coordinated Stacked Gated Recurrent Units for Turbulence Prediction[J]. IEEE/CAA Journal of Automatica Sinica,2024,11(6):1331-1341.
APA	Zhiming Zhang,Shangce Gao,MengChu Zhou,Mengtao Yan,&Shuyang Cao.(2024).Mapping Network-coordinated Stacked Gated Recurrent Units for Turbulence Prediction.IEEE/CAA Journal of Automatica Sinica,11(6),1331-1341.
MLA	Zhiming Zhang,et al."Mapping Network-coordinated Stacked Gated Recurrent Units for Turbulence Prediction".IEEE/CAA Journal of Automatica Sinica 11.6(2024):1331-1341.