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Volume 11 Issue 6
Jun.  2024

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  >  11(6): 1331-1341
Z. Zhang, S. Gao, M. C. Zhou, M. Yan, and  S. Cao,  “Mapping network-coordinated stacked gated recurrent units for turbulence prediction,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1331–1341, Jun. 2024. doi: 10.1109/JAS.2024.124335
Citation: Z. Zhang, S. Gao, M. C. Zhou, M. Yan, and  S. Cao,  “Mapping network-coordinated stacked gated recurrent units for turbulence prediction,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1331–1341, Jun. 2024. doi: 10.1109/JAS.2024.124335
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Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction

doi: 10.1109/JAS.2024.124335
Funds:  This work was partially 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), and the State Key Laboratory of Disaster Reduction in Civil Engineering (CE19-A-01)
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    Abstract

  • 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

    .

     

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    Highlights

    • This study proposes a novel deep learning method for solving turbulence prediction
    • The proposed method can extract the spatial-temporal feature in the turbulence well
    • The proposed method outperforms other state-of-the-art methods
    • This study is the first method that uses one point to predict turbulence

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