风力涡轮机尾流中的速度和湍流分解 - 第 2 部分:分析模型

Erwan Jézéquel, Frederic Blondel, Valery Masson
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引用次数: 0

摘要

摘要这项工作的目的是为风力涡轮机尾流中的流向速度和湍流建立一个分析模型,其中独立考虑了膨胀和蜿蜒。通过使用在移动参照系中选择的形状函数,简化并分析解决了配套论文中提出的速度和湍流分解方程。通过这种方法,我们可以提出一个基于物理的附加湍流模型,从而更好地解释相关物理现象,特别是非中性大气中的湍流。使用了五个输入参数:非蜿蜒唤醒的宽度(垂直和水平方向)、唤醒蜿蜒的标准偏差(两个方向)和修正的混合长度。对这些参数提出了两种校准方法:一种是在用户可以获得速度时间序列的情况下,另一种是在用户无法获得速度时间序列的情况下。结果在中性和不稳定大涡流模拟(LES)上进行了测试,这两种模拟都是用 Meso-NH 计算的。该模型对两个方向的流向速度都显示出良好的结果,并能准确预测大气不稳定性引起的变化。对于轴向湍流,模型在中性情况下错过了顶端的最大湍流,而在不稳定情况下,建议的校准导致了高估。不过,该模型的表现还是令人鼓舞的,因为它可以预测随着不稳定性和蜿蜒度的增加,形状函数会发生变化(从双峰变为单峰)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Breakdown of the velocity and turbulence in the wake of a wind turbine – Part 2: Analytical modelling
Abstract. This work aims to develop an analytical model for the streamwise velocity and turbulence in the wake of a wind turbine where the expansion and the meandering are taken into account independently. The velocity and turbulence breakdown equations presented in the companion paper are simplified and resolved analytically, using shape functions chosen in the moving frame of reference. This methodology allows us to propose a physically based model for the added turbulence and thus to have a better interpretation of the physical phenomena at stake, in particular when it comes to wakes in a non-neutral atmosphere. Five input parameters are used: the widths (in vertical and horizontal directions) of the non-meandering wake, the standard deviation of wake meandering (in both directions) and a modified mixing length. Two calibrations for these parameters are proposed: one if the users have access to velocity time series and the other if they do not. The results are tested on a neutral and an unstable large-eddy simulation (LES) that were both computed with Meso-NH. The model shows good results for the streamwise velocity in both directions and can accurately predict modifications due to atmospheric instability. For the axial turbulence, the model misses the maximum turbulence at the top tip in the neutral case, and the proposed calibrations lead to an overestimation in the unstable case. However, the model shows encouraging behaviour as it can predict a modification of the shape function (from bimodal to unimodal) as instability and thus meandering increases.
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