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

利用粘性涡旋粒子法研究风电场尾流相互作用

• 批准号: 2006219
• 负责人: Andrew Ning
• 金额: $ 30万
• 依托单位: Brigham Young University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006219&HistoricalAwards=false
• 关键词: Investigating; Wind; Farm; Wake; Interactions

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.

风能领域的一个主要障碍是管理风电场因尾流干扰而产生的功率损失（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的能力产生重大影响。减少这些损失需要解决风电场流动行为的复杂性风电场通常由数十或数百个涡轮机组成，旋转叶片产生混合和相互作用的尾流，受地形影响。涡粒子方法已被证明是模拟邻近场（例如旋翼机）中尾流主导流的有效方法，并且与传统方法相比，可以以更快的计算速度深入了解风电场流场。然而，在粘性壁（例如地形、其他涡轮机）周围有效传播涡流粒子仍然是该提案的焦点，其基本方法可能在其他尾流主导流场中有用。模拟飞机、水下航行器、水或烟雾在其他物体周围的运动等。该项目还将促进边做边学平台的开发，向学生介绍计算空气动力学，例如空气动力学的 Codecademy®。粒子法基于求解纳维-斯托克斯方程的涡量形式，并采用无网格拉格朗日方案，通过仅将粒子放置在以下位置，可以准确地保留涡结构并提高计算效率第一个目标是扩展该方法，以允许粒子在粘性壁周围有效传播。最近的工作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分析尾流模型。证明了混合高度风电场具有显着增加发电量的潜力，现有的分析尾流模型通常不适合这些场景，因为它们不包括混合和夹带等重要的耦合效应。广泛的敏感性研究以确定最相关的参数风力涡轮机经常会在转子盘的一部分上遇到传入的尾流，从而导致不对称负载并可能增加疲劳损坏和噪声。所提出的方法在捕捉流动物理的保真度之间提供了良好的平衡。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。