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Volume 11 Issue 6
Jun.  2024

IEEE/CAA Journal of Automatica Sinica

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Article Contents
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

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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  • 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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  • [1]
    A. Corbetta, V. Menkovski, R. Benzi, and F. Toschi, “Deep learning velocity signals allow quantifying turbulence intensity,” Sci. Adv., vol. 7, no. 12, p. eaba7281, Mar. 2021. doi: 10.1126/sciadv.aba7281
    [2]
    V. Fabbro and L. Feral, “Comparison of 2D and 3D electromagnetic approaches to predict tropospheric turbulence effects in clear sky conditions,” IEEE Trans. Antennas Propag., vol. 60, no. 9, pp. 4398–4407, Sept. 2012. doi: 10.1109/TAP.2012.2207070
    [3]
    J. N. Kutz, “Deep learning in fluid dynamics,” J. Fluid Mech., vol. 814, pp. 1–4, Mar. 2017. doi: 10.1017/jfm.2016.803
    [4]
    H. Kim, J. Kim, S. Won, and C. Lee, “Unsupervised deep learning for super-resolution reconstruction of turbulence,” J. Fluid Mech., vol. 910, p. A29, Mar. 2021. doi: 10.1017/jfm.2020.1028
    [5]
    D. Jin, Y. Chen, Y. Lu, J. Chen, P. Wang, Z. Liu, S. Guo, and X. Bai, “Neutralizing the impact of atmospheric turbulence on complex scene imaging via deep learning,” Nat. Mach. Intell., vol. 3, no. 10, pp. 876–884, Oct. 2021. doi: 10.1038/s42256-021-00392-1
    [6]
    Z. Lei, S. Gao, Z. Zhang, H. Yang, and H. Li, “A chaotic local searchbased particle swarm optimizer for large-scale complex wind farm layout optimization,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1168–1180, May 2023. doi: 10.1109/JAS.2023.123387
    [7]
    B. J. West and M. Turalska, “The fractional Landau model,” IEEE/CAA J. Autom. Sinica, vol. 3, no. 3, pp. 257–260, Jul. 2016. doi: 10.1109/JAS.2016.7508800
    [8]
    J. N. K. Liu, K. M. Kwong, and P. W. Chan, “Chaotic oscillatory-based neural network for wind shear and turbulence forecast with LiDAR data,” IEEE Trans. Syst. Man. Cybern. C Appl. Rev., vol. 42, no. 6, pp. 1412–1423, Nov. 2012. doi: 10.1109/TSMCC.2012.2188284
    [9]
    R. J. A. M. Stevens and C. Meneveau, “Flow structure and turbulence in wind farms,” Annu. Rev. Fluid Mech., vol. 49, pp. 311–339, Jan. 2017. doi: 10.1146/annurev-fluid-010816-060206
    [10]
    P. Milan, M. Wächter, and J. Peinke, “Turbulent character of wind energy,” Phys. Rev. Lett., vol. 110, no. 13, p. 138701, Mar. 2013. doi: 10.1103/PhysRevLett.110.138701
    [11]
    X. Guo, W. Li, and F. Iorio, “Convolutional neural networks for steady flow approximation,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, San Francisco, USA, 2016, pp. 481–490.
    [12]
    S. B. Pope, Turbulent Flows. Cambridge, UK: Cambridge University Press, 2000.
    [13]
    G. Chen, Q. Xiong, P. J. Morris, E. G. Paterson, A. Sergeev, and Y.-C. Wang, “OpenFOAM for computational fluid dynamics,” Not. AMS, vol. 61, no. 4, pp. 354–363, Apr. 2014.
    [14]
    Z. Zhang, Z. Lei, M. Omura, H. Hasegawa, and S. Gao, “Dendritic learning-incorporated vision transformer for image recognition,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 539–541, Feb. 2024. doi: 10.1109/JAS.2023.123978
    [15]
    Z. Liu, Z. Zhang, Z. Lei, M. Omura, R.-L. Wang, and S. Gao, “Dendritic deep learning for medical segmentation,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 3, pp. 803–805, Mar. 2024. doi: 10.1109/JAS.2023.123813
    [16]
    C. Lee, H. Hasegawa, and S. Gao, “Complex-valued neural networks: A comprehensive survey,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1406–1426, Aug. 2022. doi: 10.1109/JAS.2022.105743
    [17]
    J. Lü, G. Wen, R. Lu, Y. Wang, and S. Zhang, “Networked knowledge and complex networks: An engineering view,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1366–1383, Aug. 2022. doi: 10.1109/JAS.2022.105737
    [18]
    M. Raissi, P. Perdikaris, and G. E. Karniadakis, “Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,” J. Comput. Phys., vol. 378, pp. 686–707, Feb. 2019. doi: 10.1016/j.jcp.2018.10.045
    [19]
    V. Sekar, Q. Jiang, C. Shu, and B. C. Khoo, “Fast flow field prediction over airfoils using deep learning approach,” Phys. Fluids, vol. 31, no. 5, p. 057103, May 2019. doi: 10.1063/1.5094943
    [20]
    Z. C. Cao, C. R. Lin, M. C. Zhou, and R. Huang, “Scheduling semiconductor testing facility by using cuckoo search algorithm with reinforcement learning and surrogate modeling,” IEEE Trans. Autom. Sci. Eng., vol. 16, no. 2, pp. 825–837, Apr. 2019. doi: 10.1109/TASE.2018.2862380
    [21]
    Y. Wan, J. Qin, X. Yu, T. Yang, and Y. Kang, “Price-based residential demand response management in smart grids: A reinforcement learning-based approach,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 123–134, Jan. 2021.
    [22]
    Z. Zhang, H. Liu, M. C. Zhou, and J. Wang, “Solving dynamic traveling salesman problems with deep reinforcement learning,” IEEE Trans. Neural Netw. Learn. Syst., vol. 34, no. 4, pp. 2119–2132, Apr. 2023. doi: 10.1109/TNNLS.2021.3105905
    [23]
    H. J. Bae and P. Koumoutsakos, “Scientific multi-agent reinforcement learning for wall-models of turbulent flows,” Nat. Commun., vol. 13, no. 1, p. 1443, Mar. 2022. doi: 10.1038/s41467-022-28957-7
    [24]
    C. Y. Li, Z. Chen, T. K. T. Tse, A. U. Weerasuriya, X. Zhang, Y. Fu, and X. Lin, “A parametric and feasibility study for data sampling of the dynamic mode decomposition: Spectral insights and further explorations,” Phys. Fluids, vol. 34, no. 3, p. 035102, Mar. 2022. doi: 10.1063/5.0082640
    [25]
    X. Jin, P. Cheng, W.-L. Chen, and H. Li, “Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder,” Phys. Fluids, vol. 30, no. 4, p. 047105, Apr. 2018. doi: 10.1063/1.5024595
    [26]
    K. Bi, L. Xie, H. Zhang, X. Chen, X. Gu, and Q. Tian, “Accurate medium-range global weather forecasting with 3D neural networks,” Nature, vol. 619, no. 7970, pp. 533–538, Jul. 2023. doi: 10.1038/s41586-023-06185-3
    [27]
    Y. Zhang, S. Cao, L. Zhao, and J. Cao, “A case application of WRF-UCM models to the simulation of urban wind speed profiles in a typhoon,” J. Wind Eng. Ind. Aerodyn., vol. 220, p. 104874, Jan. 2022. doi: 10.1016/j.jweia.2021.104874
    [28]
    Y. Zhang, S. Cao, and J. Cao, “Implementation of an embedded LES model with parameter assessment for predicting surface pressure and surrounding flow of an isolated building,” Build. Environ., vol. 243, p. 110633, Sept. 2023. doi: 10.1016/j.buildenv.2023.110633
    [29]
    S. Chen, W. Zhao, and D. Wan, “Turbulent structures and characteristics of flows past a vertical surface-piercing finite circular cylinder,” Phys. Fluids, vol. 34, no. 1, p. 015115, Jan. 2022. doi: 10.1063/5.0078526
    [30]
    H. C. Lim, I. P. Castro, and R. P. Hoxey, “Bluff bodies in deep turbulent boundary layers: Reynolds-number issues,” J. Fluid Mech., vol. 571, pp. 97–118, Jan. 2007. doi: 10.1017/S0022112006003223
    [31]
    K. Butler, S. Cao, A. Kareem, Y. Tamura, and S. Ozono, “Surface pressure and wind load characteristics on prisms immersed in a simulated transient gust front flow field,” J. Wind Eng. Ind. Aerodyn., vol. 98, no. 6–7, pp. 299–316, Jun.–Jul. 2010. doi: 10.1016/j.jweia.2009.11.003
    [32]
    B. Alem, A. Abedian, and K. Nasrollahi-Nasab, “Impact of sensor geometric dimensions and installation accuracy on the results of instantaneous SHM based on wave propagation using wafer active sensors,” J. Aerosp. Eng., vol. 34, no. 1, p. 04020100, Jan. 2021. doi: 10.1061/(ASCE)AS.1943-5525.0001217
    [33]
    N. Wandel, M. Weinmann, M. Neidlin, and R. Klein, “Spline-PINN: Approaching PDEs without data using fast, physics-informed Hermite-spline CNNs,” in Proc.36th AAAI Conf. Artificial Intelligence, 2022, pp. 8529–8538. Vancouver, Canada.
    [34]
    C. H. K. Williamson, “Vortex dynamics in the cylinder wake,” Annu. Rev. Fluid Mech., vol. 28, no. 1, pp. 477–539, Jan. 1996. doi: 10.1146/annurev.fl.28.010196.002401
    [35]
    L. Zhu, W. Zhang, J. Kou, and Y. Liu, “Machine learning methods for turbulence modeling in subsonic flows around airfoils,” Phys. Fluids, vol. 31, no. 1, p. 015105, Jan. 2019. doi: 10.1063/1.5061693
    [36]
    X. Hui, J. Bai, H. Wang, and Y. Zhang, “Fast pressure distribution prediction of airfoils using deep learning,” Aerosp. Sci. Technol., vol. 105, p. 105949, Oct. 2020. doi: 10.1016/j.ast.2020.105949
    [37]
    S. Ye, Z. Zhang, X. Song, Y. Wang, Y. Chen, and C. Huang, “A flow feature detection method for modeling pressure distribution around a cylinder in non-uniform flows by using a convolutional neural network,” Sci. Rep., vol. 10, no. 1, p. 4459, Mar. 2020. doi: 10.1038/s41598-020-61450-z
    [38]
    L. Cheng, H. Zang, Y. Xu, Z. Wei, and G. Sun, “Augmented convolutional network for wind power prediction: A new recurrent architecture design with spatial-temporal image inputs,” IEEE Trans. Industr. Inform., vol. 17, no. 10, pp. 6981–6993, Oct. 2021. doi: 10.1109/TII.2021.3063530
    [39]
    J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” in Proc. NIPS Workshop on Deep Learning, 2014. Montreal, Canada.
    [40]
    M. Xia, H. Shao, X. Ma, and C. W. de Silva, “A stacked GRU-RNN-based approach for predicting renewable energy and electricity load for smart grid operation,” IEEE Trans. Industr. Inform., vol. 17, no. 10, pp. 7050–7059, Oct. 2021. doi: 10.1109/TII.2021.3056867
    [41]
    O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in Proc. 18th Int. Conf. Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 2015, pp. 234–241.
    [42]
    J. Ling, A. Kurzawski, and J. Templeton, “Reynolds averaged turbulence modelling using deep neural networks with embedded invariance,” J. Fluid Mech., vol. 807, pp. 155–166, Nov. 2016. doi: 10.1017/jfm.2016.615
    [43]
    A. Kashefi, D. Rempe, and L. J. Guibas, “A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries,” Phys. Fluids, vol. 33, no. 2, p. 027104, Feb. 2021. doi: 10.1063/5.0033376
    [44]
    S. Jadon, “A survey of loss functions for semantic segmentation,” in Proc. IEEE Conf. Computational Intelligence in Bioinformatics and Computational Biology, Via del Mar, Chile, 2020, pp. 1–7.
    [45]
    P. Parnaudeau, J. Carlier, D. Heitz, and E. Lamballais, “Experimental and numerical studies of the flow over a circular cylinder at Reynolds number 3900,” Phys. Fluids, vol. 20, no. 8, p. 085101, Aug. 2008. doi: 10.1063/1.2957018
    [46]
    P. Y. Zhang, S. Shu, and M. C. Zhou, “An online fault detection model and strategies based on SVM-grid in clouds,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 445–456, Mar. 2018. doi: 10.1109/JAS.2017.7510817
    [47]
    L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing, vol. 415, pp. 295–316, Nov. 2020. doi: 10.1016/j.neucom.2020.07.061
    [48]
    D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proc. 3rd Int. Conf. Learning Representations, San Diego, USA, 2015.
    [49]
    Y. Weng and S. G. Paal, “Machine learning-based wind pressure prediction of low-rise non-isolated buildings,” Eng. Struct., vol. 258, p. 114148, May 2022. doi: 10.1016/j.engstruct.2022.114148
    [50]
    Q. Zhu, J. Chen, D. Shi, L. Zhu, X. Bai, X. Duan, and Y. Liu, “Learning temporal and spatial correlations jointly: A unified framework for wind speed prediction,” IEEE Trans. Sustain. Energy, vol. 11, no. 1, pp. 509–523, Jan. 2020. doi: 10.1109/TSTE.2019.2897136
    [51]
    R. Wang, R. Walters, and R. Yu, “Incorporating symmetry into deep dynamics models for improved generalization,” in Proc. 9th Int. Conf. Learning Representations, Vienna, Austria, 2021.
    [52]
    H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, and W. Zhang, “Informer: Beyond efficient transformer for long sequence time-series forecasting,” in Proc. 35th AAAI Conf. Artificial Intelligence, Vancouver, Canada, 2021, pp. 11106–11115.
    [53]
    H. Wu, J. Xu, J. Wang, and M. Long, “Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting,” in Proc. 34th Int. Conf. Neural Information Processing Systems, Sydney, Australia, 2021, pp. 22419–22430.
    [54]
    P. W. Bearman, “On vortex shedding from a circular cylinder in the critical Reynolds number régime,” J. Fluid Mech., vol. 37, no. 3, pp. 577–585, Jul. 1969. doi: 10.1017/S0022112069000735
    [55]
    D. Fan, L. Yang, Z. Wang, M. S. Triantafyllou, and G. E. Karniadakis, “Reinforcement learning for bluff body active flow control in experiments and simulations,” in Proc. Natl. Acad. Sci. USA, vol. 117, no. 42, pp. 26091–26098, Oct. 2020. doi: 10.1073/pnas.2004939117
    [56]
    H. Jiang and L. Cheng, “Large-eddy simulation of flow past a circular cylinder for Reynolds numbers 400 to 3900,” Phys. Fluids, vol. 33, no. 3, p. 034119, Mar. 2021. doi: 10.1063/5.0041168
    [57]
    C. Norberg, “Fluctuating lift on a circular cylinder: Review and new measurements,” J. Fluids Struct., vol. 17, no. 1, pp. 57–96, Jan. 2003. doi: 10.1016/S0889-9746(02)00099-3
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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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