| 风电场湍流尾迹的机理与模型 | |
| Alternative Title | The Mechanisms and Models of Turbulent Wakes in Wind Farms |
王泽伟
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| Thesis Advisor | 杨晓雷 |
| 2024-05-20 | |
| Degree Grantor | 中国科学院大学 |
| Place of Conferral | 北京 |
| Subtype | 硕士 |
| Degree Discipline | 流体力学 |
| Keyword | 风电场，风力机，尾迹 |
| Abstract | 随着风能的快速发展，风电场中复杂的尾迹流动对于风电场性能的影响变得至关重要。深入研究风电场布局对流场的影响及尾迹的物理机制，同时开发可迅速预测尾迹速度剖面的工程模型对于风电场的选址和控制优化具有重要意义。本研究旨在揭示风电场中以尾迹为对象的复杂流动的物理机制，包括百米量级的风力机尾迹与万米量级的风电场尾迹。具体而言，我们将专注于风电场中风力机尾迹的空间分布特征与风电场湍流尾迹的恢复机理，并发展能够准确预测风力机尾迹速度剖面与风电场尾迹速度分布的参数化模型。为了实现这些目标，我们通过高精度的大涡模拟对风电场进行了模拟，并根据研究问题的尺度将其分为两个部分的内容进行研究：1. 风电场中风力机尾迹的复杂物理特征探究；2.风电场湍流尾迹恢复机理的研究。
在第一部分的内容中，我们研究了在上游多台风力机尾迹相互作用下，下游风力机尾迹物理量的空间分布与尾迹发展演化的物理机理。我们发现在风电场深处排列的风力机尾迹在垂向发展不均匀，并且尾迹中心有向上偏移的现象。不同流向风力机间距对于尾迹中心的偏移几乎没有影响，但表面粗糙度越高，其尾迹中心偏移的幅度越大。我们还基于现有尾迹解析模型，开发了考虑尾迹中心向上偏移的适用于风电场情景的风力机尾迹解析模型。该解析模型能够准确预测风电场中风力机尾迹的速度剖面在垂向分布的不对称性，并且揭示了尾迹中心的垂向偏移对于解析模型预测的重要影响。
在第二部分的内容中，我们研究了不同流向风力机间距、地表粗糙度以及流向风力机排列数目对风电场湍流尾迹的影响。我们发现不同流向风力机间距仅在风电场近尾迹区域（即，距离风电场末端小于 20 倍风力机转子直径的位置）对湍流强度有显著影响。地表粗糙度与流向风力机排列数目通过影响风电场的能量摄取幅值和湍流水平显著影响了风电场尾迹的速度亏损。随后，通过平均动能方程，我们进一步探究了风电场湍流尾迹的恢复机理。我们发现与风电场内部流动的恢复机理不同的是，在风电场尾迹中，除了垂向的湍流剪切，垂向平均对流也主导了风电场尾迹的恢复。最后，基于风电场尾迹的恢复机理发展了适用于多种不同风电场布局的尾迹解析模型，其预测结果与大涡模拟数据具有高度的一致性。
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| Other Abstract | With the rapid development of wind energy, the influence of complex wake flow in wind farms on their performance has become increasingly crucial. In-depth research into the impact of wind farm layout on flow fields and their physical mechanisms, along with the development of engineering models capable of rapidly predicting wake velocity deficit profiles, holds significant importance for wind farm siting and control optimization.
This study aims to elucidate the physical mechanisms of complex flow focusing on wakes in wind farms, including wakes from individual wind turbines at the scale of hundreds of meters and wind farm wakes at the scale of thousands of meters. Specifically, we will focus on the spatial distribution characteristics of wind turbine wakes within wind farms and the recovery mechanisms of turbulence wakes in wind farms, while developing parameterized models capable of accurately predicting velocity profiles of wind turbine wakes and velocity deficits in wind farm wakes.
To achieve these objectives, we conducted high-precision large eddy simulations (LES) of wind farms and divided the study into two main parts based on the scale of the research questions: 1. Investigation of the complex physical characteristics of wind turbine wakes in wind farms; 2. Study of the recovery mechanisms of turbulence wakes in wind farms.
In the first part, we investigated the spatial distribution of physical quantities of downstream wind turbine wakes under the interaction of wakes from multiple upstream turbines and the physical mechanism of wake expansion. We found that wind turbine wakes located deep within the wind farm expand non-uniformly in the vertical direction, with a phenomenon of upward deviation in wake centers. The spacing between wind turbines in the downwind direction had almost no effect on the deviation of wake centers, but higher surface roughness led to larger deviations in wake centers. Furthermore, we developed a wind turbine wake analytical model suitable for wind farm scenarios, which accurately predicts the asymmetry in the vertical distribution of velocity profiles in wind turbine wakes and reveals the significant influence of the vertical position of wake centers on the predictions of the analytical model.
In the second part, we investigated the effects of different spacing between wind turbines in the downwind direction, surface roughness, and the number of wind turbines in the downwind direction on turbulence wakes in wind farms. We found that different spacing between wind turbines in the downwind direction only significantly affected turbulence intensity in the near wake region of the wind farm (i.e., locations within 20 wind turbine rotor diameters from the end of the wind farm). Surface roughness and the number of wind turbines in the downwind direction significantly affected velocity deficits in wind farm wakes by influencing the energy extraction amplitude and turbulence level of wind farms. Subsequently, using the turbulent kinetic energy equation, we further investigated the recovery mechanism of turbulence wakes in wind farms. We found that, unlike the recovery mechanism of internal flow within wind farms, vertical advection, in addition to vertical turbulent shear, dominated the recovery of wind farm wakes.
Finally, based on the recovery mechanism of wind farm wakes, we developed a wake analytical model applicable to various wind farm layouts, with predictive results highly consistent with LES data.
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| Language | 中文 |
| Document Type | 学位论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/95225 |
| Collection | 非线性力学国家重点实验室 |
| Recommended Citation GB/T 7714 | 王泽伟. 风电场湍流尾迹的机理与模型[D]. 北京. 中国科学院大学,2024. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| 84503.pdf（16703KB） | 学位论文 | 开放获取 | CC BY-NC-SA | Application Full Text | ||
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