An improved dynamic model for wind-turbine wake flow

Feng，Dachuan1,2,3; Gupta，Vikrant2,3; Li，Larry K.B.1,4; Wan，Minping2,3,5

2024-03-01

10.1016/j.energy.2023.130167

Energy   影响因子和分区

0360-5442

We present an improved dynamic model to predict the time-varying characteristics of the far-wake flow behind a wind turbine. Our model, based on the FAST.Farm engineering model, is novel in that it estimates the turbulence generated by convective instabilities, which selectively amplifies the inflow velocity fluctuations. Our model also incorporates scale dependence when calculating the wake meandering induced by the passive wake meandering mechanism. For validation, our model is compared with FAST.Farm and large-eddy simulation (LES). For the mean flow, our model agrees well with LES in terms of the wake deficit and wake width, but the FAST.Farm model underestimates the former and overestimates the latter. For the instantaneous flow, our model predicts well the wake-center deflection and turbulent kinetic energy, reducing the discrepancies in the spectral characteristics by more than a factor of two relative to LES, depending on the Strouhal number. By incorporating two key mechanisms governing the far-wake dynamics, our model can predict more accurately the dynamic wake evolution, making it suitable for real-time calculations of wind farm performance.

Wake dynamics Wake modeling Wind energy Wind turbine

SCI ; EI

英语

通讯

ENGINEERING

2-s2.0-85182397590

Scopus

1.Department of Mechanical and Aerospace Engineering,The Hong Kong University of Science and Technology,Clear Water Bay,Hong Kong
2.Guangdong Provincial Key Laboratory of Turbulence Research and Applications,Department of Mechanics and Aerospace Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.Guangdong–Hong Kong–Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications,Southern University of Science and Technology,Shenzhen,518055,China
4.Guangdong–Hong Kong–Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications,The Hong Kong University of Science and Technology,Clear Water Bay,Hong Kong
5.Jiaxing Research Institute,Southern University of Science and Technology,Jiaxing,314031,China

Feng，Dachuan,Gupta，Vikrant,Li，Larry K.B.,et al. An improved dynamic model for wind-turbine wake flow[J]. Energy,2024,290.

Feng，Dachuan,Gupta，Vikrant,Li，Larry K.B.,&Wan，Minping.(2024).An improved dynamic model for wind-turbine wake flow.Energy,290.

Feng，Dachuan,et al."An improved dynamic model for wind-turbine wake flow".Energy 290(2024).