论文标题
风力涡轮机唤醒模型的领先效果
Leading effect for wind turbine wake models
论文作者
论文摘要
随着风能在全球范围内的扩展，可靠，快速，成本效益的风力涡轮机唤醒模型的需求正在增长。这是一个重大的挑战，因为风力涡轮机面临各种流入条件，包括湍流，不均匀性/机构和上游唤醒。因此，已经提出了大量的工程模型，每个工程模型基于不同的物理概念。大多数人集中在平均速度恢复和湍流构建后恢复和湍流衰减的遥远唤醒上。我们认为，唤醒建模的最重要或领先的参数是虚拟起源的长度尺度。从文献中测试不同模型，从Lidar获得的实验室风力涡轮机和多兆瓦涡轮机的数据集中，我们发现当添加这种虚拟起源时，所有模型的性能都明显更好。因此，我们的结果可用于近唤醒区的尚未定义。
As wind energy expands worldwide, the demand of reliable, fast, cost-efficient wind turbine wake models is growing. This is a significant challenge as wind turbines face various inflow conditions, that include turbulence, inhomogeneities/instationarities and upstream wakes. In consequence, an enormous number of engineering models, each one based on different physical concepts, has been proposed. The majority focuses on the far wake where the mean velocity recovers and turbulence decays after it built up. We argue that the most important, or the leading, parameter for wake modeling is the length scale of a virtual origin. Testing different models from the literature for data sets from laboratory wind turbines and multi-megawatt turbines obtained by LiDAR, we find that all models perform significantly better when such a virtual origin is added. Our results can therefore be used for a yet missing definition of a near wake zone.
