Engineering models for merging wakes in wind farm optimization applications
Engineering models for merging wakes in wind farm optimization applications
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风电场优化应用中合并尾流的工程模型
DOI：
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发表时间：
2015
期刊：
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通讯作者：
Juan Pablo
中科院分区：
文献类型：
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作者：
Gunner Chr;Murcia Leon;Juan Pablo
本文针对详细的CFD模拟，并涵盖不同的涡轮机间距、环境湍流强度和平均风速，对4种不同的工程尾流叠加方法进行了验证。第一个工程模型是一个简单的线性叠加的尾流赤字，如应用在例如Fuga。第二种方法是平方和的平方根方法，它被广泛应用于PARK程序。第三种方法目前与动态尾流弯曲（DWM）模型一起使用，该方法假设尾流影响下游流场由环境流场和所有上游涡轮机在任何空间位置和任何时间的贡献中的主导尾流的叠加确定。G.C.开发的最后一种方法。Larsen模型是一种新发展起来的基于抛物线型方法的模型，它连续地结合了尾流亏损。研究表明，尾流相互作用强烈依赖于相对尾流亏损幅度，即相对于环境平均风速归一化的亏损幅度，并且DWM框架内的主导尾流假设是最准确的。
The present paper deals with validation of 4 different engineering wake superposition approaches against detailed CFD simulations and covering different turbine interspacing, ambient turbulence intensities and mean wind speeds. The first engineering model is a simple linear superposition of wake deficits as applied in e.g. Fuga. The second approach is the square root of sums of squares approach, which is applied in the widely used PARK program. The third approach, which is presently used with the Dynamic Wake Meandering (DWM) model, assumes that the wake affected downstream flow field to be determined by a superposition of the ambient flow field and the dominating wake among contributions from all upstream turbines at any spatial position and at any time. The last approach developed by G.C. Larsen is a newly developed model based on a parabolic type of approach, which combines wake deficits successively. The study indicates that wake interaction depends strongly on the relative wake deficit magnitude, i.e. the deficit magnitude normalized with respect to the ambient mean wind speed, and that the dominant wake assumption within the DWM framework is the most accurate.