论文标题
基于涡流拉伸的大型涡流模拟框架
Vortex-Stretching based Large Eddy Simulation Framework for Wind Farms
论文作者
论文摘要
在大型风电场中，风力涡轮机后面的唤醒分布导致下游风力涡轮机的风速大大降低，导致大量功率损失。因此，有效预测风力涡轮机非常重要。因此，我们提出了一个大涡模拟（LES）方法，该方法将涡度拉伸以建模风力涡轮机中的瞬变。此外，我们提出了改进的执行器磁盘模型，该模型解释了大气和唤醒之间的双向反馈。首先，我们表明，新模型预测的平均风的垂直轮廓与实验测量具有极好的一致性。接下来，我们验证了预测的雷诺对风洞数据的应力，并表明分散应力约占雷诺应力的40％。最后，我们表明所提出的LES方法准确预测了风力涡轮机的特征。将LES结果与先前报道的数据进行比较，我们发现新的LES框架准确地预测了近访问和远效率区域的流量统计。
In large wind farms, wake distribution behind a wind turbine causes a considerable reduction of wind velocity for downstream wind turbines, resulting in a significant amount of power loss. Therefore, it is very crucial to predict wind turbine wakes efficiently. Thus, we propose a large-eddy simulation (LES) methodology, which takes the vorticity stretching to model transients in wind turbine wakes. In addition, we present an improved actuator disk model, which accounts for two-way feedback between the atmosphere and the wakes. First, we show that the vertical profile of the mean wind predicted with the new model has an excellent agreement with experimental measurements. Next, we validate the predicted Reynolds stresses against wind tunnel data and show that the dispersive stresses account for about 40% of Reynolds stresses. Finally, we show that the proposed LES method accurately predicts the characteristics of wind turbine wakes. Comparing the LES results with previously reported data, we have found that the new LES framework accurately predicts the flow statistics in both the near-wake and the far-wake regions.
