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风电场中因尾流干扰而发生的电力损失(10%-30%)。减少这些损失即使只有几个百分点，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。减少这些损失需要理清风电场流动行为的复杂性。风力发电场通常由10个或100个涡轮机组成，旋转的叶片产生的尾流混合并相互作用，受到许多尺度上的地形和大气行为的影响。涡旋粒子方法已被证明是模拟邻近区域(如旋翼飞行器)中以尾流为主的流动的一种有效方法，并且与传统方法相比，它可以更快的计算速度提供对风电场流场的洞察。然而，有效地在粘性壁面(例如地形、其他涡轮机)周围传播涡旋颗粒仍然是一项挑战，这也是本提案的重点。该基本方法可能对其他以尾流为主的流场有潜在的帮助，如模拟飞机、水下航行器、水或烟雾围绕其他物体的运动等。该项目还将促进一个边做边学平台的开发，向学生介绍计算空气动力学-如空气动力学的Codecademy®。粘性涡旋质点方法基于求解Navier-Stokes方程的涡度形式，并使用无网格拉格朗日格式，通过只在需要的地方放置粒子可以准确地保留涡流结构并提高计算效率。第一个目标是扩展该方法，以允许颗粒在粘性壁周围高效传播。第二个目标是利用所提议的方法的速度来创建适合于混合高度风电场的新的分析尾迹模型。最近的研究表明，混合高度风电场具有显著提高发电量的潜力。现有的分析尾迹模型通常不适合这些场景，因为它们不包括重要的耦合效应，如混合和卷吸。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，以减轻部分清醒带来的负面影响。风力涡轮机经常在转子盘的一部分上遇到来袭的尾迹，造成不对称的负载，并可能增加疲劳损伤和噪音。拟议的方法在捕捉流动物理的保真度和预测速度之间提供了良好的平衡，以实现对尾流相互作用的强有力的探索。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.