排灌机械工程学报2025，Vol.43Issue(3)：260-267,8.DOI:10.3969/j.issn.1674-8530.23.0202
耦合风速测量的风力机时空尾流重构
Spatiotemporal wake field reconstruction of wind turbine coupled with wind speed measurements
摘要
Abstract
To measure the detailed flow field information of the dynamic wake of a wind turbine,a deep learning method for physical information was proposed to solve this dilemma,which combined a small number of sparse measurements and fluid dynamics equations to achieve spatiotemporal recon-struction of dynamic wake.Specifically,Navier-Stokes equations were embedded in the neural network to constrain the output physical quantities,including downwind speed,crosswind speed and pressure,to ensure the explainability and rationality of the output.Taking the dynamic wake during yaw as an example,only a small amount of internal wake measurement data is used as a training set,and the spatiotemporal reconstruction of the local dynamic wake is completed.This method successfully captures the dynamic trend of wake flow during yaw,and accurately predicts the wake trajectory and deflection.In the spatiotemporal reconstruction of global wake,the proposed method completely restores the flow evolution process of the real flow field,which has great potential in the intelligent con-trol of wind farms.关键词
水平轴风力机/动态尾流/离散测量/深度学习/时空重构Key words
horizontal-axis wind turbine/dynamic wake/sparse measurement/deep leaning/spatiotemporal reconstruction分类
农业工程引用本文复制引用
王龙滟,陈梦,袁建平..耦合风速测量的风力机时空尾流重构[J].排灌机械工程学报,2025,43(3):260-267,8.基金项目
国家自然科学基金资助项目(12002137) （12002137）
江苏省博士后基金资助项目(2021K110B) （2021K110B）
江苏大学高级人才启动基金资助项目(20JDG065) （20JDG065）
