Data-driven RANS for simulations of large wind farms
Data-driven RANS for simulations of large wind farms
复制标题
用于大型风电场模拟的数据驱动 RANS
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
S. Leonardi
中科院分区：
文献类型：
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作者：
G. Iungo;F. Viola;U. Ciri;M. Rotea;S. Leonardi
在风能工业中，对风力涡轮机尾流的实时预测的需求日益增长，以便优化发电厂控制并抑制有害的尾流相互作用。为此，提出了一种数据驱动的RANS方法，以便通过数据同化过程实现非常低的计算成本和足够的精度。RANS模拟采用经典的Boussinesq假设和混合长度湍流闭合模型，并通过现有数据进行了校准。以不同叶尖速比运行的实用规模风力涡轮机的高保真LES模拟作为数据库。结果表明，利用轴向和径向速度分量的雷诺应力以及轴向速度在径向上的梯度，可以对RANS模拟的混合长度模型进行准确的标定。结果表明，在很近的尾迹区，混合长度基本不变，在扩散区，混合长度随下游距离的增加而线性增加。提出了以下游方向混合长度的变化率作为判断风力机涡轮机尾迹过渡区与近尾迹过渡区的判据。最后，RANS模拟进行了校准的混合长度模型，并观察到一个很好的协议与LES模拟。
In the wind energy industry there is a growing need for real-time predictions of wind turbine wake flows in order to optimize power plant control and inhibit detrimental wake interactions. To this aim, a data-driven RANS approach is proposed in order to achieve very low computational costs and adequate accuracy through the data assimilation procedure. The RANS simulations are implemented with a classical Boussinesq hypothesis and a mixing length turbulence closure model, which is calibrated through the available data. High-fidelity LES simulations of a utility-scale wind turbine operating with different tip speed ratios are used as database. It is shown that the mixing length model for the RANS simulations can be calibrated accurately through the Reynolds stress of the axial and radial velocity components, and the gradient of the axial velocity in the radial direction. It is found that the mixing length is roughly invariant in the very near wake, then it increases linearly with the downstream distance in the diffusive region. The variation rate of the mixing length in the downstream direction is proposed as a criterion to detect the transition between near wake and transition region of a wind turbine wake. Finally, RANS simulations were performed with the calibrated mixing length model, and a good agreement with the LES simulations is observed.