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ERI: Formation Mechanisms and Modeling of Wake Meandering in Wind Farms

ERI：风电场尾流曲流的形成机制和建模

基本信息

批准号：
2136371
负责人：
Daniel Foti
金额：
$ 19.88万
依托单位：
University of Memphis
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-02-01 至 2025-01-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2136371&HistoricalAwards=false
关键词：
ERI Formation Mechanisms Modeling Wake

项目摘要

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Interactions between stochastic fluctuations in the atmospheric surface layer and turbulent features produced by wind turbines drive major challenges to explain and model turbulence mechanisms in wind farms. Understanding the underlying behaviors can reduce power production variability, which adversely influences the levelized cost of electricity, and enhance wind energy’s competitiveness compared to other forms of power production. Specifically, large atmospheric fluctuations and the small turbine-scale dynamics are separately hypothesized to initiate the wake meandering phenomenon, a coherent oscillation of the far wake of wind turbines. Wake meandering affects the unsteady dynamics of wake recovery, wake interactions, and uncertainty of power production in wind farms. This project will elucidate its underlying formation behavior, which is crucial to enable designs and models to lower the levelized cost of wind energy. The project will also include significant educational activities including outreach programs with Memphis non-profit organizations and increased public science awareness and education through art and its intersection with turbulence. The proposed project advances fundamental insights into dominant instabilities in wind turbines. The project will develop a series of high-fidelity large-eddy simulations to measure the spectra and evolution of kinetic energy and vorticity in the upwind atmospheric boundary layer and wind turbine wake. An approach to investigate and model the transfer and transport of kinetic energy of large coherent structure scales will be developed using data-driven analysis and computational-enabled discovery from the simulated wake flows. The methodology and simulations will be employed to investigate the formation mechanism of meandering of a wind turbine wake and leveraged to develop multi-resolution wind farm models. The project proposes to (1) develop and evaluate wake meandering genesis mechanisms by quantifying the energy transfer between upwind features in the atmospheric surface layer and wake meandering; and (2) develop wind farm models to capture disparate length scales of the wind turbine, wind farm, and atmosphere. Expected outcomes of this research include fundamental understanding of the spatio-temporal evolution of wake meandering and multi-resolution, multi-scale wind farm modeling. By addressing the uncertainty of the formation and persistence of large coherent structures in wind turbine wakes, the uncertainty and model inadequacy of time-accurate wind farm models can be mitigated, and designs to modify the role of wake meandering in power fluctuations can be introduced.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.

该奖项全部或部分根据2021年美国救援计划法案（公法117 - 2）资助。大气表面层中的随机波动与风力涡轮机产生的湍流特征之间的相互作用驱动了解释和建模风电场中湍流机制的重大挑战。了解潜在的行为可以减少电力生产的变化，这对电力的平准化成本产生不利影响，并提高风能与其他形式的电力生产相比的竞争力。具体而言，大的大气波动和小的涡轮机规模的动态分别假设启动尾流蜿蜒现象，相干振荡的远尾风力涡轮机。尾流弯曲影响尾流恢复的非定常动力学、尾流相互作用和风电场发电的不确定性。该项目将阐明其潜在的形成行为，这对于使设计和模型能够降低风能的平准化成本至关重要。该项目还将包括重要的教育活动，包括与孟菲斯非营利组织的外展计划，以及通过艺术及其与湍流的交叉提高公众的科学意识和教育。该项目提出了对风力涡轮机主要不稳定性的基本见解。该项目将开发一系列高保真大涡模拟，以测量逆风大气边界层和风涡轮机尾流中动能和涡度的频谱和演变。将利用数据驱动的分析和模拟尾流的计算发现，开发一种研究和模拟大相干结构尺度动能转移和输送的方法。该方法和模拟将用于研究风力涡轮机尾流弯曲的形成机制，并用于开发多分辨率风电场模型。该项目建议：（1）通过量化大气表面层中逆风特征与尾流弯曲之间的能量转移，开发和评估尾流弯曲成因机制;（2）开发风电场模型，以捕获风力涡轮机、风电场和大气的不同长度尺度。本研究的预期成果包括对尾流弯曲时空演变的基本理解和多分辨率、多尺度风电场建模。通过解决风力涡轮机尾流中大相干结构的形成和持续的不确定性，可以减轻时间精确的风电场模型的不确定性和模型不足，该奖项反映了NSF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进行评估，被认为值得支持审查标准。