A point vortex transportation model for yawed wind turbine wakes
A point vortex transportation model for yawed wind turbine wakes
复制标题
偏航风力机尾流的点涡传输模型
DOI：
--
复制
发表时间：
2020
影响因子：
3.7
通讯作者：
F. Porté
中科院分区：
文献类型：
--
作者：
H. Zong;F. Porté
本研究通过在偏转风力机后的多个流向位置进行立体粒子成像测速，揭示了反向旋转涡对（CVP）的形成机制，并提出了一个点涡输运（PVT）模型来再现自上而下的不对称肾状尾迹（也称为卷曲尾迹）。结果表明，在偏航风力机后形成的CVP来源于轮毂涡与叶尖涡向流分量的复杂相互作用，这与没有轮毂涡的偏航阻力盘有本质区别。具体来说，当偏航角超过临界值时，一小部分从转子盘边缘脱落的顺流涡量由负转正，随后在相互感应下与集中的轮毂涡合并，形成一个正涡量区；同时，沿转子边缘分布的剩余流向涡量卷曲并演变为另一片负涡量。这两个流向涡度的斑块基本上构成了CVP。基于实验得到的物理知识，首先利用分布在旋翼边缘的点涡云和位于旋翼中心的轮毂涡重建非均匀横流速度场，然后对尾迹速度亏缺的简化输运扩散方程进行数值求解，共同构成了PVT模型。这种基于物理的降阶模型是第一个能够准确再现偏转风力涡轮机后尾流变形的模型。
In this study, stereo particle imaging velocimetry measurements are performed at multiple streamwise locations behind a yawed wind turbine to reveal the formation mechanisms of the counter-rotating vortex pair (CVP), and a point vortex transportation (PVT) model is proposed to reproduce the top–down asymmetric kidney-shaped wake (also referred to as a curled wake). Results indicate that the CVP formed behind a yawed wind turbine originates from the complex interactions between the hub vortex and the streamwise components of the blade tip vortices, which is fundamentally different from the case of a yawed drag disk where the hub vortex is absent. Specifically, when the yaw angle exceeds a critical value, a small part of the streamwise vorticity shed from the rotor disk edge switches its sign from negative to positive and subsequently merges with the concentrated hub vortex under mutual induction, creating a patch of positive vorticity; meanwhile, the remaining streamwise vorticity distributed along the rotor edge curls and evolves into another patch of negative vorticity. These two patches of streamwise vorticity essentially constitute the CVP. Based on the physics learnt from the experiments, the non-uniform cross-stream velocity fields are first reconstructed by a cloud of point vortices distributed along the rotor edge and a hub vortex located in the rotor centre, and subsequently used to numerically solve a simplified transportation–diffusion equation of the wake velocity deficit, which altogether constitute the PVT model. This physics-based reduced-order model is the first model capable of accurately reproducing the wake deformation behind a yawed wind turbine.