Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2025 , Vol. 59 >Issue 5: 684 - 690

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2023.213

Mechanical Engineering

Data-Driven Method of Modeling Sparse Flow Field Data

  • WANG Hongxin ,
  • XU Degang ,
  • ZHOU Kaiwen ,
  • LI Linwen ,
  • WEN Xin
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  • 1. School of Automation, Central South University, Changsha 410083, China
    2. Shanghai Aircraft Design and Research Institute, Shanghai 201210, China
    3. School of Mechanical and Power Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received date: 2023-05-29

  Revised date: 2023-07-04

  Accepted date: 2023-08-22

  Online published: 2023-09-11

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Abstract

Real-time perception and prediction of flow field have very important application value in aviation and navigation, and pose challenges such as high flow field dimension and less real-time measurement information. To solve such problem, a data-driven flow field modeling method framework is proposed, which realizes real-time reconstruction of online flow field by establishing sparse data and high-dimensional flow field mapping offline. In offline modeling, aimed at the high-dimensional challenge of the flow field, the eigenortho decomposition and other methods are used to reduce the dimensionality of the data and extract the spatial mode of the main flow field. The QR decomposition method is used to mine the modal sensitivity characteristics of the flow field and optimize the measurement point position. Dynamic modal decomposition with time delay significantly reduces the number of measurement points. In the online reconstruction, based on real-time sparse measurement data and data-driven models, the prediction of the current and future full-field flow field is realized. In the test of cylinder wake flow, using this method and using 20 sparse measurement points, the full-field reconstruction error obtained can reach less than 10%.

Key words： data-driven; reduced order model; dynamic mode decomposition; location optimization; sparse data; flow field reconstruction

Cite this article

WANG Hongxin , XU Degang , ZHOU Kaiwen , LI Linwen , WEN Xin . Data-Driven Method of Modeling Sparse Flow Field Data[J]. Journal of Shanghai Jiaotong University, 2025 , 59(5) : 684 -690 . DOI: 10.16183/j.cnki.jsjtu.2023.213

References

[1] VOGEL J M, KELKAR A G. Aircraft control augmentation and health monitoring using flush air data system feedback[C]// AIAA Applied Aerodynamics Conference. Hawaii, USA: AIAA, 2008: 1-16.
[2] TRIANTAFYLLOU M S, WEYMOUTH G D, MIAO J. Biomimetic survival hydrodynamics and flow sensing[J]. Annual Review of Fluid Mechanics, 2016, 48: 1-24.
[3] LIEW J, G??MEN T, LIO W H, et al. Streaming dynamic mode decomposition for short-term forecasting in wind farms[J]. Wind Energy, 2022, 25(4): 719-734.
[4] WILLCOX K. Unsteady flow sensing and estimation via the gappy proper orthogonal decomposition[J]. Computers and Fluids, 2006, 35(2): 208-226.
[5] MANOHAR K, BRUNTON B W, KUTZ J N, et al. Data-driven sparse sensor placement for reconstruction: Demonstrating the benefits of exploiting known patterns[J]. IEEE Control Systems, 2018, 38(3): 63-86.
[6] ZHOU K, ZHOU L, ZHAO S, et al. Data-driven method for flow sensing of aerodynamic parameters using distributed pressure measurements[J]. AIAA Journal, 2021, 59(9): 3504-3516.
[7] DENG Z, CHEN Y, LIU Y, et al. Time-resolved turbulent velocity field reconstruction using a long short-term memory (LSTM)[J]. Physics of Fluids, 2019, 31(075108): 1-13.
[8] SCHMID P J. Dynamic mode decomposition of numerical and experimental data[J]. Journal of Fluid Mechanics, 2010, 656: 5-28.
[9] 刘余丹, 周楷文, 刘应征, 等. 基于卡尔曼滤波的翼型表面压力实时重构方法[J]. 空气动力学学报, 2022, 41(4): 64-72.
  LIU Yudan, ZHOU Kaiwen, LIU Yingzheng, et al. Reconstruction of airfoil surface pressure by Kalman filter[J]. Acta Aerodynamica Sinica, 2022, 41(4): 64-72.
[10] WILLIAMS M O, KEVREKIDIS I G, ROWLEY C W. A data-driven approximation of the Koopman operator: Extending dynamic mode decomposition[J]. Journal of Nonlinear Science, 2015, 25(6): 1307-1346.
[11] WILLIAMS M O, ROWLEY C W, KEVREKIDIS I G. A kernel-based method for data-driven koopman spectral analysis[J]. Journal of Computational Dynamics, 2015, 2(2): 247-265.
[12] YUAN Y, ZHOU K, ZHOU W, et al. Flow prediction using dynamic mode decomposition with time-delay embedding based on local measurement[J]. Physics of Fluids, 2021, 33(9): 1-18.
[13] SHI Z Y, ZHOU K W, QIN C, et al. Experimental study of dynamical airfoil and aerodynamic prediction[J]. Actuators, 2022, 11(2): 1-14.
[14] BERKOOZ G, HOLMES P, LUMLEY J L. The proper orthogonal decomposition in the analysis of turbulent flows[J]. Annual Review of Fluid Mechanics, 1993(25): 539-575.
[15] SARLóS T. Improved approximation algorithms for large matrices via random projections[C]// 2006 47th Annual IEEE Symposium on Foundations of Computer Science. Berkeley, USA: IEEE, 2006:143-152.
[16] MARTINSSON P G, ROKHLIN V, TYGERT M. A randomized algorithm for the decomposition of matrices[J]. Applied and Computational Harmonic Analysis, 2011, 30(1): 47-68.
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