This work introduces the area localized coupled (ALC) model, which extends the applicability of approaches that couple classical wake superposition models and atmospheric boundary layer models to wind farms with arbitrary layouts. Coupling wake and top–down boundary layer models is particularly challenging since the latter requires averaging over planform areas associated with turbine-specific regions of the flow that need to be specified. The ALC model uses Voronoi tessellation to define this local area around each turbine. A top–down description of a developing internal boundary layer is then applied over Voronoi cells upstream of each turbine to estimate the local mean velocity profile. Coupling between the velocity at hub-height based on this localized top–down model and a wake model is achieved by enforcing a minimum least-square-error in mean velocity in each cell. The wake model in the present implementation takes into account variations in wind farm inflow velocity and represents the wake profile behind each turbine as a super-Gaussian function that smoothly transitions between a top-hat shape in the region immediately following the turbine to a Gaussian profile downstream. Detailed comparisons to large-eddy simulation (LES) data from two different wind farms demonstrate the efficacy of the model in accurately predicting both wind farm power output and local turbine hub-height velocity for different wind farm geometries. These validations using data generated from two different LES codes demonstrate the model's versatility with respect to capturing results from different simulation setups and wind farm configurations.

中文翻译：

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用于任意风电场几何结构中分析平均流预测的区域局部耦合模型

这项工作引入了区域局部耦合 (ALC) 模型，该模型将经典尾流叠加模型和大气边界层模型耦合到具有任意布局的风电场的方法的适用性扩展。耦合尾流和自上而下的边界层模型特别具有挑战性，因为后者需要对与需要指定的涡轮机特定区域相关联的平面区域进行平均。ALC 模型使用 Voronoi 镶嵌来定义每个涡轮机周围的局部区域。然后在每个涡轮机上游的 Voronoi 单元上应用对发展中的内部边界层的自上而下的描述，以估计局部平均速度剖面。基于这种局部自上而下模型的轮毂高度处的速度与尾流模型之间的耦合是通过强制每个单元中平均速度的最小最小二乘误差来实现的。本实施中的尾流模型考虑了风电场流入速度的变化，并将每个涡轮机后面的尾流剖面表示为超高斯函数，该函数在涡轮机紧随其后的区域中的礼帽形状与高斯剖面之间平滑过渡下游。与来自两个不同风电场的大涡模拟 (LES) 数据的详细比较证明了该模型在准确预测不同风电场几何形状的风电场功率输出和局部涡轮轮毂高度速度方面的有效性。这些使用从两个不同 LES 代码生成的数据进行的验证演示了该模型