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.

该奖项是根据2021年《美国救援计划法》（公法117-2）全部或部分资助的。大气表面层中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主要的挑战，以解释和模拟风电场中的湍流机制。了解潜在行为可以降低功率生产变异性，从而不利地影响电力水平的电力成本，并增强风能与其他形式的发电相比，增强了风能的竞争力。具体而言，分别假设大气波动和小型涡轮尺度动力学启动醒目的曲折现象，这是风力涡轮机的远距离唤醒的连贯振荡。唤醒曲折会影响唤醒恢复，唤醒相互作用和风电场发电的不确定性的不稳定动态。该项目将阐明其潜在的地层行为，这对于使设计和模型降低风能的成本至关重要。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动荡的交集提高公共科学意识和教育。拟议的项目推进了对风力涡轮机中主要不稳定性的基本见解。该项目将开发一系列高保真大型模拟，以测量上风大气边界层和风力涡轮机唤醒中动能和涡度的光谱和演变。研究和建模大型相干结构量表动能的传递和传输将使用数据驱动的分析和从模拟尾流的启用计算的发现开发。该方法和仿真将用于研究风力涡轮机唤醒并利用以开发多分辨率风电场模型的形成机制。（1）通过量化大气表面层和唤醒曲折的向上特征之间的能量转移来开发和评估唤醒蜿蜒的创造机制的建议；（2）开发风电场模型，以捕获风力涡轮机，风电场和气氛的不同长度。这项研究的预期结果包括对唤醒曲折和多分辨率，多规模风电场建模的时空演化的基本了解。通过解决风力涡轮机中大型相干结构的形成和持久性的不确定性，可以减轻时间准确的风电场模型的不确定性和模型不足，并且可以减轻唤醒唤醒在电力波动中的作用的设计，可以引入该奖项，以反映了NSF的智力范围，并反映了NSF的构建范围。审查标准。