Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction  

作　　者：Zhiming Zhang Shangce Gao MengChu Zhou Mengtao Yan Shuyang Cao 

机构地区：[1]Faculty of Engineering,University of Toyama,Toyama 930-8555,Japan [2]Helen and John C.Hartmann Department of Electrical and Computer Engineering,New Jersey Institute of Technology,Newark NJ 07102 USA [3]Gaoling School of Artificial Intelligence,Renmin University of China,Beijing 100872,China [4]College of Civil Engineering,Tongji University,Shanghai 200092,China [5]IEEE

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第6期1331-1341,共11页自动化学报（英文版）

基　　金：supported by the Japan Society for the Promotion of Science(JSPS)KAKENHI(JP22H03643);Japan Science and Technology Agency(JST)Support for Pioneering Research Initiated by the Next Generation(SPRING)(JPMJSP2145);JST Through the Establishment of University Fellowships Towards the Creation of Science Technology Innovation(JPMJFS2115);the National Natural Science Foundation of China(52078382);the State Key Laboratory of Disaster Reduction in Civil Engineering(CE19-A-01)。

摘　　要：Accurately predicting fluid forces acting on the sur-face 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 net-work-coordinated stacked gated recurrent units(MSU),for pre-dicting pressure on a circular cylinder from velocity data.Specifi-cally,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 measure-ment 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 estab-lished 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.

关 键 词：Convolutional neural network deep learning recurrent neural network turbulence prediction wind load predic-tion. 

分 类 号：O357.5[理学—流体力学] TP18[理学—力学]