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

风力涡轮机（WT）的尾流效应与WT旋转叶片下游的速度降低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%，WT叶片疲劳载荷增加5%~15%。此外，WT尾流还影响靠近居民区的WT群中的航空声学噪声产生源（特别是，在约20 - 160 Hz的频率范围内的低频噪声和频率小于约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"风力涡轮机综合征"。正是在这种背景下，提出了一个研究计划，其中涉及一个新的“气动弹性致动器线”（AAL）尾流模型的发展，并随后将该模型集成到内部计算流体动力学（CFD）代码流由PI的小组开发。由于AAL的可预测性对湍流模型的选择很敏感，因此将提出一种基于部分分辨数值模拟（PRNS）方法的新湍流模型。通过自适应网格加密（AMR）算法，以正确地解决每个WT叶片的解决方案的精度和计算效率将得到提高。为了估计孤立和集群WT的疲劳损伤和寿命，基于M-N和S-N曲线（其中M、S和N分别表示力矩、应力和载荷循环次数）的疲劳模型将与AAL中的结构组件连接。最后，将开发基于流体动力学/声学分裂技术的声学代码，并将其耦合到STREAM以预测WT噪声。 该计划的新颖之处在于开发了一种名为WATWind的综合风能工具。这种独特的工具将STREAM与新提出的AAL尾流模型相结合，后者又可以与（1）用于噪声预测的声学模块，（2）用于WT叶片疲劳损伤和总寿命估计的疲劳模块，以及（3）用于风力发电预测的人工神经网络（ANN）工具箱相连接。预计WATWind可以帮助西门子加拿大等风力发电机叶片制造商预测风力发电机叶片的寿命，并帮助安大略的独立电力系统运营商（IESO）提高其风力发电预测模型的性能。此外，WATWind还可用于帮助健康研究人员和环境与气候变化部（MOECC）回答与WT低频噪声和次声相关的生理影响（焦虑，耳鸣或听力损失）等问题。根据该提案培训的博士和硕士学生将获得并为风能作为新兴可持续能源之一的设计和技术开发所需的关键知识基础做出贡献。

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.