东南大学学报（英文版）2025，Vol.41Issue(4)：437-445,9.DOI:10.3969/j.issn.1003-7985.2025.04.005
基于多功能递归模糊神经网络的非定常风力机尾流降阶模型
Reduced-order model of unsteady wind turbine wake based on a multifunctional recurrent fuzzy neural network
摘要
Abstract
To enhance the prediction accuracy of unsteady wakes behind wind turbines,a novel reduced-order model is proposed by integrating a multifunctional recurrent fuzzy neural network(MFRFNN)and proper orthogonal decom-position(POD).First,POD is employed to reduce the di-mensionality of the wind field data,extracting spatiotempo-rally correlated modal coefficients and modes.These reduced-order variables can effectively capture the essential features of unsteady wake behaviors.Next,MFRFNN is utilized to predict the time series of modal coefficients.Fi-nally,by combining the predicted modal coefficients with their corresponding modes,a flow field is reconstructed,al-lowing accurate prediction of unsteady wake dynamics.The predicted wake data exhibit high consistency with large eddy simulation results in both the near-and far-wake re-gions and outperform existing data-driven methods.This ap-proach offers significant potential for optimizing wind farm design and provides a new solution for the precise prediction of wind turbine wake behavior.关键词
计算流体动力学(CFD)/降阶模型/深度学习/风力机/尾流模型Key words
computational fluid dynamics(CFD)/reduced-order model/deep learning/wind turbine/wake model分类
能源科技引用本文复制引用
张洪福,温嘉豪,周蕾..基于多功能递归模糊神经网络的非定常风力机尾流降阶模型[J].东南大学学报（英文版）,2025,41(4):437-445,9.基金项目
The National Natural Science Foundation of China(No.51908107). （No.51908107）
