Intelligent prediction model for reduced-order dynamic modes of velocity field around a 1∶5 rectangular section

ZHAO Lin; LIU Peng; CUI Wei

doi:10.7638/kqdlxxb-2025.0031

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Intelligent prediction model for reduced-order dynamic modes of velocity field around a 1∶5 rectangular section

更新时间：2026-05-28

- Intelligent prediction model for reduced-order dynamic modes of velocity field around a 1∶5 rectangular section
- Vol. 43, Issue 5, Pages: 124-133(2025)
- 作者机构：
  
  1. 广西大学土木建筑工程学院,南宁,530004
  2. 同济大学土木工程防灾减灾全国重点实验室,上海,200092
- 作者简介：
- 基金信息：
- DOI：10.7638/kqdlxxb-2025.0031
  CLC： V214.3;TU311.3;TP183
- Published：2025
- 稿件说明：
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赵林, 刘鹏, 崔巍. 1∶5矩形断面速度场降阶动力学模态智能预测模型[J]. 空气动力学学报, 2025, 43(5): 124−133.

ZHAO Lin, LIU Peng, CUI Wei. Intelligent prediction model for reduced-order dynamic modes of velocity field around a 1∶5 rectangular section[J]. 2025, 43(5): 124-133.
赵林, 刘鹏, 崔巍. 1∶5矩形断面速度场降阶动力学模态智能预测模型[J]. 空气动力学学报, 2025, 43(5): 124−133. DOI： 10.7638/kqdlxxb-2025.0031

ZHAO Lin, LIU Peng, CUI Wei. Intelligent prediction model for reduced-order dynamic modes of velocity field around a 1∶5 rectangular section[J]. 2025, 43(5): 124-133. DOI： 10.7638/kqdlxxb-2025.0031.

摘要

钝体断面绕流研究虽可通过粒子图像测速和CFD方法获取速度场特征，但受限于雷诺数效应、实桥测试条件和数值模拟精度，而足尺桥梁表面压力测量技术更具工程实用性。鉴于桥梁断面表面压力场与速度场存在固有耦合关系，本文基于表面压力分布建立了综合动力学模态分解方法（dynamic mode decomposition

DMD）和BP神经网络模型的1∶5矩形断面速度场降阶关联与预测模型。通过DMD技术提取不同雷诺数（

1000

～

20000

）下表面压力分布和速度场的模态特征，并利用隐式神经网络建立其模态间的映射关系，实现了从矩形断面表面压力分布到速度场的预测。结果表明：在

6000

工况下，尾部参考点[1.5

]

处的横向与竖向速度预测误差分别为0.06 m/s和0.02 m/s，验证了该模型从表面压力分布预测速度场的有效性。相关研究可为桥梁断面尾流区流场反演、气动措施比选提供重要参考。

Abstract

Although the research on flow around bluff body sections can obtain the characteristics of the velocity field through particle image velocimetry (PIV) and computational fluid dynamics (CFD) methods

it is limited by the Reynolds number effect

onsite test conditions

and the accuracy of numerical simulation. However

the full-scale bridge surface pressure measurement technology is mo

re engineering practical. Building upon the inherent coupling between the surface pressure field and velocity field of bridge sections

this paper proposes a reduced-order correlation and prediction model for the velocity field of a 1:5 rectangular section using surface pressure distribution. The developed model‌ integrates dynamic mode decomposition (DMD) and a BP neural network to: (1) extract pressure and velocity field modes across Reynolds numbers (

1000

–

20000

); (2) establish their mapping relationship through an implicit neural network; and (3) achieve velocity field prediction from pressure data. ‌Validation results‌ at

6000

show prediction errors of merely 0.06 m/s (lateral) and 0.02 m/s (vertical) at the reference point [1.5

]

demonstrating the model's effectiveness. This research provides valuable insights for wake flow field reconstruction and aerodynamic measure evaluation in bridge sections.

关键词

Keywords

references

KUTA J N, BRUNTON S L, BRUNTON B W, et al. Dynamic mode decomposition: Data-driven modeling of complex systems [M]. PhiladelphiaPAUnited States: SIAM-Society for Industrial and Applied Mathematics, 2016.

ZHANG W F, ZHANG Z T, ZHANG X X. Numerical investigation on the wind-field-dependence property of bridge section aerodynamic admittances[J]. Acta Aerodynamica Sinica, 2018, 36(4): 677−686,694(in Chinese). doi: 10.7638/kqdlxxb-2016.0067

LIU Z J, YANG Y X. Flow field mechanism and air energy characteristic of H-shape section in flutter[J]. China Civil Engineering Journal, 2013, 46(4): 110−116,141(in Chinese).

FRANDSEN J B. Simultaneous pressures and accelerations measured full-scale on the Great Belt East suspension bridge[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2001, 89(1): 95−129. doi: 10.1016/S0167-6105(00)00059-3

LI H, LAIMA S J, OU J P, et al. Investigation of vortex-induced vibration of a suspension bridge with two separated steel box girders based on field measurements[J]. Engineering Structures, 2011, 33(6): 1894−1907. doi: 10.1016/j.engstruct.2011.02.017

JIN X W, CHENG P, CHEN W L, et al. Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder[J]. Physics of Fluids, 2018, 30(4): 047105. doi: 10.1063/1.5024595

HU C X, ZHAO L, GE Y J. A simplified vortex model for the mechanism of vortex-induced vibrations in a streamlined closed-box girder[J]. Wind and Structures, 2021, 32(4): 309−19. doi: 10.12989/was.2021.32.4.309

HU C X, ZHAO L, GE Y J. Multiple-order vertical vortex-induced vibration mechanism of a typical streamlined closed-box girder[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2022, 227: 105066. doi: 10.1016/j.jweia.2022.105066

KOU J Q, ZHANG W W. Dynamic mode decomposition and its applications in fluid dynamics[J]. Acta Aerodynamica Sinica, 2018, 36(2): 163−179 (in Chinese). doi: 10.7638/kqdlxxb-2017.0134

DU M H, TIAN L L, ZHU C L. IDDES simulations of flow separation around the iced airfoil and its modal analysis[J]. Acta Aerodynamica Sinica, 2023, 41(11): 46−55(in Chinese). doi: 10.7638/kqdlxxb-2022.0140

SCHMID P J. Dynamic mode decomposition of numerical and experimental data[J]. Journal of Fluid Mechanics, 2010, 656: 5−28. doi: 10.1017/s0022112010001217

KOU J Q, ZHANG W W, GAO C Q. Modal analysis of transonic buffet based on POD and DMD method[J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(9): 2679−2689(in Chinese). doi: 10.7527/S1000-6893.2016.0003

HU C X, ZHAO L, GUAN X L, et al. Mitigation mechanism of torsional vortex-induced vibrations using aerodynamic countermeasures: Case study on a typical closed-box girder[J]. Engineering Structures, 2024, 318: 118611. doi: 10.1016/j.engstruct.2024.118611

SCHEWE G. Reynolds-number-effects in flow around a rectangular cylinder with aspect ratio 1: 5[J]. Journal of Fluids and Structures, 2013, 39: 15−26. doi: 10.1016/j.jfluidstructs.2013.02.013

NAKAMURA Y, OHYA Y, OZONO S, et al. Experimental and numerical analysis of vortex shedding from elongated rectangular cylinders at low Reynolds numbers 200−103[J]. Journal of Wind Engineering and Industrial Aerodynamics, 1996, 65(1-3): 301−308. doi: 10.1016/S0167-6105(97)00048-2

BERRONE S, GARBERO V, MARRO M. Numerical simulation of low-Reynolds number flows past rectangular cylinders based on adaptive finite element and finite volume methods[J]. Computers Fluids, 2011, 40(1): 92−112. doi: 10.1016/j.compfluid.2010.08.014

HOURIGAN K, THOMPSON M C, TAN B T. Self-sustained oscillations in flows around long blunt plates[J]. Journal of Fluids and Structures, 2001, 15(3-4): 387−398. doi: 10.1006/jfls.2000.0352

WU B, LI S P, LI K, et al. Large-eddy simulation of the near wake of a 5∶1 rectangular cylinder in oscillating flows at Re = 670[J ] . Journal of Wind Engineering and Industrial Aerodynamics, 2020, 196: 104050. doi: 10.1016/j.jweia.2019.104050

BRUNO L, SALVETTI M V, RICCIARDELLI F. Benchmark on the aerodynamics of a rectangular 5∶1 cylinder: An overview after the first four years of activity[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2014, 126: 87−106. doi: 10.1016/j.jweia.2014.01.005

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