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Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction
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作者 Zhiming Zhang Shangce Gao +2 位作者 MengChu Zhou Mengtao Yan shuyang cao 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第6期1331-1341,共11页
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 i... 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. 展开更多
关键词 Convolutional neural network deep learning recurrent neural network turbulence prediction wind load predic-tion.
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Comparison and harmonization of building wind loading codes among the Asia-Pacific Economies 被引量:3
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作者 Yaojun GE shuyang cao Xinyang JIN 《Frontiers of Structural and Civil Engineering》 SCIE EI CSCD 2013年第4期402-410,共9页
This paper reviews wind loading codes and standards in the Asia-Pacific Region,in particular in the 15 countries and areas.A general description of wind loading model is given as a famous wind loading chain described ... This paper reviews wind loading codes and standards in the Asia-Pacific Region,in particular in the 15 countries and areas.A general description of wind loading model is given as a famous wind loading chain described by four variables including velocity pressure,exposure factor,pressure coefficient,and gust response factor.Through the APEC-WW Workshops and the extensive calculations for three examples of low,medium and high rise buildings,these four important variables of wind loads are evaluated and compared with statistical parameters,mean values and coefficients of variation.The main results of the comparison show some differences among the 15 economies,and the reasons and further incorporation are discussed and suggested. 展开更多
关键词 wind loading CODIFICATION velocity pressure exposure factor pressure coefficient gust response factor
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