The area localized coupled model for analytical mean flow prediction in arbitrary wind farm geometries
The area localized coupled model for analytical mean flow prediction in arbitrary wind farm geometries
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DOI：
10.1063/5.0042573
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发表时间：
2020-09
影响因子：
2.5
通讯作者：
Genevieve M. Starke;C. Meneveau;J. King;D. Gayme
中科院分区：
文献类型：
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作者：
Genevieve M. Starke;C. Meneveau;J. King;D. Gayme
这项工作引入了区域局域耦合(ALC)模式，它扩展了早期耦合经典尾迹叠加和大气边界层模式的方法，以便能够适用于任意风电场布局。耦合尾迹和自上而下的边界层模型特别具有挑战性，因为后者需要对与某些涡轮机特有的流动区域相关联的平面区进行平均。ALC模型使用Voronoi细分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环流上，对发展中的内部边界层进行自上而下的描述，以估计局部平均速度分布。基于这种局部化自上而下模型的轮毂高度速度与尾迹模型之间的耦合是通过在每个单元中实施平均速度的最小二乘误差来实现的。ALC模型是使用一个尾迹模型实现的，该尾迹模型的轮廓从高帽函数过渡到高斯函数，并通过线性叠加来考虑尾迹相互作用。与大涡模拟(LES)数据的详细比较表明，该模型在准确预测复杂风电场几何形状的功率和轮毂高度速度方面是有效的。对于一半涡轮机排列成阵列，另一半随机分布的混合阵列-随机农场，进一步用LES进行验证，表明该模型在捕获不同风电场配置的结果方面具有多功能性。在这两种情况下，ALC模型都表明，相对于一系列风向的主流方法，ALC模型对农场和单个涡轮机的功率预测都有所改善。
This work introduces the Area Localized Coupled (ALC) model, which extends earlier approaches to coupling classical wake superposition and atmospheric boundary layer models in order to enable applicability to arbitrary wind-farm layouts. Coupling wake and top-down boundary layer models is particularly challenging since the latter requires averaging over planform areas associated with certain turbine-specific regions of the flow. The ALC model uses Voronoi tesselation to define a local area around each turbine. A top-down description of a developing internal boundary layers 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 ALC model is implemented using a wake model with a profile that transitions from a top-hat to Gaussian function and accounts for wake interactions through linear superposition. Detailed comparisons to large-eddy simulation (LES) data demonstrate the efficacy of the model in accurate predictions of both power and hub height velocity for complex wind farm geometries. Further validation with LES for a hybrid array-random farm that has half of the turbines arranged in an array and the other half randomly distributed indicates the model's versatility with respect to capturing results from different wind farm configurations. In both cases, the ALC model is shown to produce improved power predictions for both the farm and individual turbines over prevailing approaches for a range of wind inflow directions.