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

风力涡轮机的尾迹效应与风力涡轮机旋转叶片下游的速度下降和湍流强度增加有关。这通常会导致风电场总发电量下降10%~20%，风力涡轮机叶片疲劳负荷增加5%~15%。此外，小波尾迹还会影响靠近居民区的小波尾迹群的航空声噪声源（特别是频率约为20-160 Hz的低频噪声和频率小于20 Hz的次声）。这已成为一个日益重要的问题，因为它可能影响人类健康。这通常被称为“风力涡轮机综合症”。正是在这种背景下，提出了一项研究计划，该计划涉及开发一种新的“气动弹性致动器线”（AAL）尾流模型，并随后将该模型集成到PI小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比较敏感，本文提出了一种基于部分解析数值模拟（PRNS）方法的湍流模型。通过引入自适应网格细化（AMR）算法，对小波变换叶片进行正确解析，提高求解精度和计算效率。在AAL中，将基于M-N和S-N曲线的疲劳模型（其中M、S和N分别表示弯矩、应力和载荷循环次数）与结构构件相结合，以估计孤立和集群WTs的疲劳损伤和寿命。最后，将开发一种基于水动力/声分裂技术的声学代码，并将其与STREAM相结合以预测小波噪声。

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. The novelty of the program is the development of an integrated wind energy tool called WATWind. This unique tool will couple STREAM with the newly proposed AAL wake model, which in turn can be interfaced with (1) an acoustic module for noise prediction, (2) a fatigue module for fatigue damage and total lifetime estimation of a WT blade, and (3) an artificial neural network (ANN) toolbox for wind power forecasting. It is expected that WATWind can assist WT blade manufacturers, such as Siemens Canada, to predict the lifespan of a WT blade, and the Independent Electricity System Operator (IESO) in Ontario to improve the performance of their wind power forecasting models. In addition, WATWind can also be used to assist health researchers and the Ministry of the Environment and Climate Change (MOECC) in answering questions such as what are the physiological effects (anxiety, tinnitus or hearing loss) associated with WT low-frequency noise and infrasound. The PhD and MASc students trained under this proposal will have acquired and contributed to the critical knowledge base that is needed for design and technology development of wind energy as one of the emerging sustainable energy sources.