1∶5矩形断面速度场降阶动力学模态智能预测模型

赵林; 刘鹏; 崔巍

doi:10.7638/kqdlxxb-2025.0031

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1∶5矩形断面速度场降阶动力学模态智能预测模型

研究专栏——大型结构风效应流固耦合与人工智能 | 更新时间：2026-05-28

- 1∶5矩形断面速度场降阶动力学模态智能预测模型
- Intelligent prediction model for reduced-order dynamic modes of velocity field around a 1∶5 rectangular section
- 空气动力学学报 2025年43卷第5期页码：124-133
- 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：
  
  赵林（1974—），男，黑龙江牡丹江人，教授，博导，研究方向：结构风工程研究. E-mail：zhaolin@tongji.edu.cn
  崔巍*（1988—），男，山西长治人，助理教授，硕导，研究方向：结构风工程研究. E-mail：cuiwei@tongji.edu.cn
- 基金信息：
  
  国家重点研发计划（2022YFC3005301）;国家自然科学基金（52378527，52478552）
- DOI：10.7638/kqdlxxb-2025.0031
  中图分类号： V214.3;TU311.3;TP183
- 纸质出版：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

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