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.

风能场中的一个主要障碍是管理由于唤醒干扰而在风电场发生的功率损失（10-30％）。减轻这些损失甚至百分之几，将对我们绝对产生清洁能源和减少温室气体排放的能力产生重大影响。减少这些损失需要弄清风电流流动行为的复杂性。风电场通常由10秒或100台涡轮机组成，旋转叶片会产生混合和相互作用的唤醒，并受许多尺度上的地形和大气行为的影响。涡流粒子方法已被证明是一种有效的方法，用于模拟相邻场中的唤醒优势流动（例如旋翼法船），并且与传统方法相比，可以以更快的计算速度以更快的计算速度提供对风电场流动场的见解。但是，有效地在粘性壁（例如，地形，其他涡轮机）周围有效地传播涡流颗粒仍然是该建议的焦点。基本方法论可能有可能在其他唤醒的流动场中有用并且，使用无网状的拉格朗日方案，该方案可以通过仅在需要的地方放置颗粒来准确地保留涡流结构并提高计算效率。第一个目标是扩展方法，以使粘膜周围的颗粒有效地传播。第二个目标是利用所提出的方法的速度创建适合混合高度风电场的新分析唤醒模型。最近的工作表明，混合高度风电场有可能大幅增加发电。现有的分析唤醒模型通常不适合这些情况，因为它们不包含重要的耦合效果，例如混合和入口。因此，第三个目标是进行广泛的灵敏度研究，以确定最相关的参数和策略，以减轻部分唤醒的负面影响。风力涡轮机经常在转子磁盘的一部分上遇到传入的唤醒，从而导致不对称的负载，并可能增加疲劳损伤和噪音。拟议的方法在捕获流动物理学的忠诚度以预测速度以实现对尾流相互作用的良好探索之间提供了良好的平衡。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子的智力优点和更广泛的影响来评估NSF的法定任务。