空气动力学学报2025，Vol.43Issue(5)：53-77,25.DOI:10.7638/kqdlxxb-2025.0061
大型结构风效应流固耦合机器学习研究进展
Advances in machine learning for wind-induced fluid-structure interaction of large-scale structures
摘要
Abstract
With the rapid development of computational technology and data science,machine learning provides a novel research paradigm for addressing complex fluid-structure interaction problems in large-scale structural wind effects.This paper systematically reviews recent advances in machine learning applications for wind effects on large-scale structures,focusing on four key aspects:structural surface wind pressure prediction,wind-induced response analysis and modeling,intelligent identification of aerodynamic equations,and reinforcement learning-based structural vibration control.For structural surface wind pressure prediction,machine learning effectively captures complex nonlinear wind pressure characteristics on structural surfaces.In the analysis and modeling of structural wind-induced responses,machine learning techniques enables accurate identification and modeling of abnormal large-amplitude vibrations of large-scale structures.Regarding intelligent identification of aerodynamic equations,data-driven machine learning significantly enhances the automation and accuracy of nonlinear equation identification.For structural vibration control,reinforcement learning offers optimized real-time active control strategies.However,challenges persist in data fusion,model generalization,and physical interpretability.Future studies should integrate physical mechanisms with data-driven models to develop machine learning approaches characterized by high generalization,robustness,and physical interpretability,thus further advancing the intelligent development of structural wind engineering.关键词
机器学习/风工程/大型结构/风效应/流固耦合/振动控制Key words
machine learning/wind engineering/large-scale structure/wind effect/fluid-structure interaction/vibration control分类
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张泽宇,李惠,周旭曦,许楠,王浩炜,杨子鉴,庄简,黎善武,赖马树金,陈文礼..大型结构风效应流固耦合机器学习研究进展[J].空气动力学学报,2025,43(5):53-77,25.基金项目
国家重点研发计划项目(2022YFC3005303) （2022YFC3005303）
