Pseudo-2D RANS: a LiDAR-driven mid-fidelity model for simulations of wind farm flows
Pseudo-2D RANS: a LiDAR-driven mid-fidelity model for simulations of wind farm flows
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DOI：
10.1063/5.0076739
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发表时间：
2021-11
影响因子：
2.5
通讯作者：
S. Letizia;G. Iungo
中科院分区：
文献类型：
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作者：
S. Letizia;G. Iungo
下一代风电场流模型越来越需要帮助现代风力发电厂的设计，操作和性能诊断。风力发电场空气动力学的描述的准确性，包括大气稳定性的影响，合并尾流，和由涡轮机转子引起的压力场，以及低计算成本是这些工具的必要属性。伪二维RANS模型的制定提供了一个有效的解决方案的Navier-Stokes方程的风力发电场流安装在平坦的地形和海上。湍流闭合和致动器盘模型的校准基于风力激光雷达测量的风力涡轮机尾流收集在不同的操作和大气条件下。实施浅水配方，以实现收敛的速度和压力场的解决方案，整个农场的计算成本可比的中保真度工程尾流模型。提供了伪二维RANS模型的理论基础和数值方案，并详细描述了验证和确认过程。该模型进行了评估，对一个大型数据集的电力生产的陆上风电场位于德克萨斯州北部，显示了归一化的平均绝对误差为5.6%的10分钟平均有功功率和3%的集群风电场的效率，这代表8%和24%，分别改善相对于最好的表现在这项工作中测试的工程尾流模型。
Next-generation models of wind farm flows are increasingly needed to assist the design, operation, and performance diagnostic of modern wind power plants. Accuracy in the descriptions of the wind farm aerodynamics, including the effects of atmospheric stability, coalescing wakes, and the pressure field induced by the turbine rotors, and low computational costs are necessary attributes for such tools. The Pseudo-2D RANS model is formulated to provide an efficient solution of the Navier-Stokes equations governing wind-farm flows installed in flat terrain and offshore. The turbulence closure and actuator disk model are calibrated based on wind LiDAR measurements of wind turbine wakes collected under different operative and atmospheric conditions. A shallow-water formulation is implemented to achieve a converged solution for the velocity and pressure fields across a farm with computational costs comparable to those of mid-fidelity engineering wake models. The theoretical foundations and numerical scheme of the Pseudo-2D RANS model are provided, together with a detailed description of the verification and validation processes. The model is assessed against a large dataset of power production for an onshore wind farm located in North Texas showing a normalized mean absolute error of 5.6\% on the 10-minute-averaged active power and 3\% on the clustered wind farm efficiency, which represent 8\% and 24\%, respectively, improvements with respect to the best-performing engineering wake model tested in this work.