Investigating Wind Farm Wake Interactions by Leveraging a Viscous Vortex Particle Method

风能领域的一个主要障碍是管理由于尾流干扰而发生在风电场中的功率损失（10-30%）。即使将这些损失减少几个百分点，也会对我们大量生产清洁能源和减少温室气体排放的能力产生重大影响。减少这些损失需要解开风电场流动行为的复杂性。风力发电场通常由10到100台涡轮机组成，旋转的叶片产生混合和相互作用的尾流，受地形和大气行为的影响。涡旋粒子方法已经被证明是一种有效的方法来模拟相邻领域（例如，旋翼飞机）的尾流主导流，并且与传统方法相比，它可以以更快的计算速度提供对风电场流场的洞察。然而，在粘性壁面（如地形、其他涡轮机）周围有效传播涡旋粒子仍然是一个挑战，这也是本提案的焦点。这种基本的方法可能在其他尾流主导的流场中有潜在的用处，比如模拟飞机、水下航行器、水或烟雾在其他物体周围的运动等。该项目还将促进一个边做边学的平台的发展，向学生介绍计算空气动力学，就像空气动力学的Codecademy®。粘性涡旋粒子法基于求解Navier-Stokes方程的涡度形式，采用无网格拉格朗日格式，可以准确地保留涡旋结构，并通过只在需要的地方放置粒子来提高计算效率。第一个目标是扩展方法，以允许粘性壁周围的粒子有效传播。第二个目标是利用所提出的方法的速度来创建适合混合高度风电场的新的分析尾流模型。最近的研究表明，混合高度风力发电场具有显著增加发电量的潜力。现有的分析尾流模型往往不适合这些情况，因为它们不包括重要的耦合效应，如混合和夹带。因此，第三个目标是进行广泛的敏感性研究，以确定最相关的参数和策略，以减轻部分清醒的负面影响。风力涡轮机经常在转子盘的一部分上遇到传入尾迹，造成不对称载荷，并可能增加疲劳损伤和噪音。所提出的方法在捕获流动物理中的保真度与预测速度之间提供了良好的平衡，从而能够对尾迹相互作用进行稳健的探索。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

One major impediment in the wind energy field is managing the power losses (10-30%) that occur in a wind farm because of wake interference. Mitigating these losses by even a few percent would have a major impact on our ability to abundantly produce clean energy and reduce greenhouse gas emissions. Reducing these losses requires untangling the complexities of wind farm flow behavior. Wind farms typically consist of 10s or 100s of turbines, with rotating blades creating wakes that mix and interact, affected by terrain and atmospheric behavior across many scales. Vortex particle methods have been demonstrated to be an effective approach for simulating wake-dominant flows in adjacent fields (e.g., rotorcraft) and can potentially offer insight into wind farm flow fields at much faster computational speeds compared to traditional methods. However, efficiently propagating vortex particles around viscous walls (e.g., terrain, other turbines) remains a challenge that is a focal point of this proposal. The fundamental methodology could potentially be useful in other wake-dominant flow fields like simulating aircraft, underwater vehicles, the motion of water or smoke around other objects, etc. The project will also facilitate the development of a learn-by-doing platform to introduce students to computational aerodynamics—like a Codecademy® for aerodynamics.The viscous vortex particle method is based on solving the vorticity form of the Navier-Stokes equations, and, using a meshless Lagrangian scheme, which can accurately preserve vortical structures and improve computational efficiency by placing particles only where needed. The first objective is to extend the methodology to allow for efficient propagation of particles around viscous walls. The second objective is to leverage the speed of the proposed methodology to create a new analytical wake model appropriate for mixed height wind farms. Recent work has demonstrated that mixed height wind farms have the potential for a significant increase in power production. Existing analytical wake models are often not appropriate for these scenarios as they do not include important coupling effects such as mixing and entrainment. So, the third objective is to conduct broad sensitivity studies to identify the most relevant parameters and strategies to mitigate the negative effects of partial waking. Wind turbines often encounter incoming wakes over just a portion of the rotor disk causing asymmetric loading and potentially increased fatigue damage and noise. The proposed methodology provides a good balance between capturing the fidelity in flow physics with prediction speed to enable a robust exploration of wake interactions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.