Wind Turbine Wake Characterization from Temporally Disjunct 3-D Measurements
Wind Turbine Wake Characterization from Temporally Disjunct 3-D Measurements
复制标题
时间分离 3D 测量的风力涡轮机尾流表征
DOI：
10.3390/rs8110939
复制
发表时间：
2016
期刊：
影响因子：
5
通讯作者：
M. Churchfield
中科院分区：
文献类型：
--
作者：
P. Doubrawa;R. Barthelmie;Hui Wang;S. Pryor;M. Churchfield
扫描激光雷达可用于获取大气表层内外的三维风测量。在这项工作中，表征风力涡轮机尾流的指标来自 LiDAR 观测和大涡模拟 (LES) 数据，这些数据用于重新创建 LiDAR 扫描几何形状。这些指标是在单尾流条件下涡轮机下游离散距离处的垂直和横流方向上的二维平面计算的。模拟数据用于估计通过扫描 LiDAR 测量值量化平均尾流特征时的不确定性，由于仪器探测大量空气所需的时间，这些测量值在时间上是分离的。根据 LES 输出，我们确定使用合成 LiDAR 采样的风速在实际平均值的 10% 以内，并且扫描的分离性质不会影响平面内风速的空间变化。我们提出了扫描几何密度和覆盖指数，它们量化了感兴趣区域中采样点的空间分布，对于设计用于尾流表征的 LiDAR 测量活动很有价值。我们发现扫描几何覆盖范围对于尾流中心、方向和长度尺度的估计很重要，而在寻求表征速度赤字分布时密度更重要。
Scanning LiDARs can be used to obtain three-dimensional wind measurements in and beyond the atmospheric surface layer. In this work, metrics characterizing wind turbine wakes are derived from LiDAR observations and from large-eddy simulation (LES) data, which are used to recreate the LiDAR scanning geometry. The metrics are calculated for two-dimensional planes in the vertical and cross-stream directions at discrete distances downstream of a turbine under single-wake conditions. The simulation data are used to estimate the uncertainty when mean wake characteristics are quantified from scanning LiDAR measurements, which are temporally disjunct due to the time that the instrument takes to probe a large volume of air. Based on LES output, we determine that wind speeds sampled with the synthetic LiDAR are within 10% of the actual mean values and that the disjunct nature of the scan does not compromise the spatial variation of wind speeds within the planes. We propose scanning geometry density and coverage indices, which quantify the spatial distribution of the sampled points in the area of interest and are valuable to design LiDAR measurement campaigns for wake characterization. We find that scanning geometry coverage is important for estimates of the wake center, orientation and length scales, while density is more important when seeking to characterize the velocity deficit distribution.