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

风力机的尾迹效应与风力机旋转叶片下游速度的降低和湍流强度的增加有关。这通常会导致风电场总发电量下降10%~20%，风电机组叶片疲劳负荷增加5%~15%。此外，WT尾迹也会影响邻近住宅区的WTS群的空气声学噪音来源(特别是频率范围约为20-160赫兹的低频噪音和频率低于约20赫兹的次声)。这已成为一个日益重要的问题，因为它可能会影响人类健康。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)尾迹模型，并随后将该模型集成到由PI小组开发的内部计算流体动力学(CFD)代码流中。针对AAL的预报能力对湍流模型的选择非常敏感的问题，提出了一种基于部分分辨数值模拟(PRNS)方法的新型湍流模型。通过实施自适应网格加密(AMR)算法来适当地求解每个WT叶片，从而提高了解的精度和计算效率。为了估计孤立和集群WTS的疲劳损伤和寿命，基于M-N和S-N曲线的疲劳模型(M、S和N分别表示弯矩、应力和载荷循环次数)将与AAL中的结构部件对接。最后，开发了一个基于流体力学/声学分裂技术的声学程序，并将其耦合到流中以预测WT噪声。 该计划的新奇之处在于开发了一种名为WATWind的综合风能工具。这一独特的工具将STREAM与新提出的AAL尾迹模型相结合，该模型又可以与(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)用于风功率预测的人工神经网络(ANN)工具箱相连接。预计WATWind可以帮助风电叶片制造商，如西门子加拿大公司，预测风电叶片的寿命，以及安大略省的独立电力系统运营商(IESO)，以改进他们的风能预测模型的性能。此外，WATWind还可用于帮助卫生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。根据这一提议培训的博士和MASC学生将获得风能作为新兴的可持续能源之一的设计和技术开发所需的关键知识库，并为其做出贡献。

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.