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。

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.