Modeling of Wake Effects on Power Loss, Fatigue Damage and Noise on a Cluster of Wind Turbines

风力发电机组功率损耗、疲劳损坏和噪声的尾流效应建模

• 批准号: RGPIN-2017-03935
• 负责人: Lien, FueSang
• 金额: $ 2.7万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=638734
• 关键词: Modeling; Wake; Effects; Power; Loss

Wake effects of a wind turbine (WT) are related to a decreasing velocity and an increasing turbulence intensity downstream of the rotating blades of a WT. This typically results in a 10%~20% decline in the total wind farm power production and a 5%~15% increase in the WT blade fatigue load. In addition, WT wakes also influence the sources of aeroacoustic noise generation (particularly, low-frequency noise in the frequency range from about 20-160 Hz and infrasound with frequencies less than about 20 Hz) in a cluster of WTs close to residential areas. This has become an issue of growing importance as it can potentially affect human health. This is commonly referred to as the “wind turbine syndrome”. It is against this background that a research program is proposed which involves the development of a new “aeroelastic actuator line” (AAL) wake model and the subsequent integration of this model into an in-house Computational Fluid Dynamics (CFD) code STREAM developed by the PI’s group. As the predictability of AAL is sensitive to the choice of turbulence models, a new turbulence model based on the Partially Resolved Numerical Simulation (PRNS) approach will be proposed. The solution accuracy and computational efficiency will be enhanced by implementing the adaptive mesh refinement (AMR) algorithm in order to properly resolve each WT blade. In order to estimate fatigue damage and lifespan of isolated and clusters of WTs, fatigue models based on M-N and S-N curves (where M, S and N denote the moment, stress and number of load cycles, respectively) will be interfaced with the structural component in AAL. Finally, an acoustic code based on the hydrodynamic/acoustic splitting technique will be developed and coupled to STREAM to predict WT noise.

风力涡轮机（WT）的唤醒作用与速度的降低以及wt旋转叶片下游的湍流强度的增加有关。这通常导致风电场电源产量下降10％〜20％，而WT叶片疲劳负荷增加了5％〜15％。此外，WT唤醒还会影响空气噪声产生的来源（部分地，频率范围内的低频噪声在约20-160 Hz的频率范围内，频率低于20 Hz的频率）在接近居民的WT群中。由于它可能影响人类健康，因此这已成为越来越重要的问题。这通常称为“风力涡轮综合征”。正是在这种背景下，提出了一项研究计划，其中涉及开发新的“弹性执行器线”（AAL）唤醒模型，并将该模型的随后集成到PI组开发的内部计算流体动力学（CFD）代码流中。由于AAL的可预测性对湍流模型的选择敏感，因此将提出基于部分分辨的数值模拟（PRN）方法的新湍流模型。通过实现自适应网格改进（AMR）算法，可以提高解决方案的准确性和计算效率，以正确解决每个WT刀片。为了估计WTS的疲劳损伤和寿命，基于M-N和S-N曲线的疲劳模型（分别表示M，S和N分别表示负载循环的时刻，压力和负载周期的数量）将与AAL中的结构分量进行交互。最后，将开发基于流体动力/声学分裂技术的声学代码，并耦合以预测WT噪声。