空气动力学学报2025，Vol.43Issue(5)：124-133,10.DOI:10.7638/kqdlxxb-2025.0031
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
摘要
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 more 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 Re=6000 show prediction errors of merely 0.06 m/s(lateral)and 0.02 m/s(vertical)at the reference point[1.5,0],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
1∶5矩形断面/动力学模态分解/表面压力分布/速度场反演/BP神经网络/桥梁Key words
1∶5 rectangular cylinder/dynamic mode decomposition/surface pressure distribution/velocity field inversion/backpropagation neural network/bridge分类
航空航天引用本文复制引用
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):124-133,10.基金项目
国家重点研发计划(2022YFC3005301) （2022YFC3005301）
国家自然科学基金(52378527,52478552) （52378527,52478552）
