期刊
RENEWABLE ENERGY
卷 183, 期 -, 页码 601-616出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2021.11.037
关键词
NREL 5MW wind turbine; DMD modes; Uniform or turbulent inflow; Turbulent precursor simulation
This study aims to investigate the effect of inflow turbulence on the wake of the NREL-5MW wind turbine, using Sparsity-Promoting Dynamic Mode Decomposition to detect dynamically-relevant flow structures with and without inflow turbulence. The results demonstrate that inflow turbulence significantly changes the dynamically-relevant flow structures, indicating that low-dimensional models of turbine wakes should consider the influence of atmospheric turbulence.
Knowledge of the dynamics of wind turbine wakes and its dependence on the incoming boundary layer is fundamental to optimize and control the power production of wind farms. This work aims at investigating the effect of inflow turbulence on the wake of the NREL-5MW wind turbine. Sparsity-Promoting Dynamic Mode Decomposition (SP-DMD) is performed on snapshots extracted from large-eddy simulations of the turbine wake, for detecting the most dynamically-relevant flow structures in the presence or absence of inflow turbulence. We demonstrate that inflow turbulence generated by a precursor simulation radically changes the most dynamically-relevant flow structures. For the laminar-inflow case the DMD modes selected by the SP algorithm have high wavenumbers and are spatially localized. When turbulence is added at the inflow, these high-frequency modes are superseded by low-frequency modes lying in the frequency range of the wake meandering and filling the whole domain, mostly corresponding to those dynamically relevant for the precursor simulation. These results show that, in the presence of inflow turbulence, coherent structures linked to endogenous mechanisms such as tip and root vortices loose their dynamical relevance in favour of those exogenously excited by turbulence, indicating that low-dimensional models of turbine wakes should take into account atmospheric turbulence. (c) 2021 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据