Genesis and Dynamics of very-large-scale Motions in the Atmospheric Boundary Layer and their Interactions with Utility-scale Wind Turbines

大气边界层超大规模运动的成因和动力学及其与公用事业规模风力涡轮机的相互作用

• 批准号: 1705837
• 负责人: Giacomo Valerio Iungo
• 金额: $ 24.12万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1705837&HistoricalAwards=false
• 关键词: Genesis; Dynamics; very; large; scale

In wind energy technology, predicting the evolution of wind turbine wakes under different atmospheric conditions is important for optimizing power production from a wind farm and mitigating damaging loads on the turbines due to detrimental wake interactions. This project aims to investigate the origin, evolution and regeneration of what are called very-large-scale motions (VLSMs) that evolve in the layer of air near the blade surface, termed the atmospheric boundary layer (ABL), and their interactions with utility-scale wind turbines. As the scale of wind farms continues to grow and as wind turbines increase in size, VLSMs are expected to profoundly influence wind power production and the downstream evolution of wind turbine wakes. However, the origin and dynamics of VLSMs are not well understood in some cases, and therefore their effects and interactions with utility-scale wind turbines are difficult to predict. This project advances numerical simulations and proof-of-concept laboratory experiments on this topic. Additionally, undergraduate students are being trained to measure wind velocities at the University of Texas Dallas mobile LIDAR (Light Imaging, Detection, And Ranging) station, which is a unique facility for education and outreach activities. This training impact students from a wide range of backgrounds and makes them aware of important topics, such as meteorology, extreme weather phenomena, anthropogenic effects on the environment and renewable energy. The goals of this research project are to: 1) better understand the physical mechanisms generating VLSMs and their variability as a consequence of the daily cycle of atmospheric stability, different topography and land cover; 2) explore how VLSMs affect downstream evolution of wakes produced by utility-scale wind turbines; 3) develop numerical tools that will enable thorough and timely predictions of wind turbine wakes by reproducing modulations of the Reynolds stresses induced by VLSMs. This research project is comprised of three interrelated tasks. First, there are two LIDAR measurement campaigns, the first one for a site over a relatively flat terrain in North Texas and a second one over a complex terrain. The third task uses the resulting experimental data for modeling VLSM-induced modulations on wind turbine wakes through optimal tuning of turbulence closure models within an adjoint Reynolds-averaged Navier-Stokes framework. This research project is helping to answer a number of key questions related to the morphology and energy content of VLSMs and how variable are they under different regimes of the atmospheric stability, the physical mechanisms governing generation of VLSMs and energy transport among coherent structures with different length-scales for ABL flows, and the role of land cover and topography in VLSM genesis and dynamics. The research project also aims to quantify the effects of the amplitude modulations induced by VLSMs on aerodynamic performance of utility-scale wind turbines and downstream evolution of wind turbine wakes.

在风能技术中，预测不同大气条件下风力涡轮机尾流的演变对于优化风电场的发电量和减轻由于有害尾流相互作用而对涡轮机造成的破坏性负载非常重要。 该项目旨在研究超大规模运动 (VLSM) 的起源、演化和再生，这些运动在叶片表面附近的空气层（称为大气边界层 (ABL)）中演化，以及它们与效用的相互作用规模的风力涡轮机。随着风电场规模的持续增长和风力涡轮机尺寸的增加，VLSM预计将深刻影响风电生产和风力涡轮机尾流的下游演变。然而，在某些情况下，VLSM 的起源和动力学尚不清楚，因此它们的影响以及与公用事业规模风力涡轮机的相互作用很难预测。该项目推进了该主题的数值模拟和概念验证实验室实验。 此外，本科生正在德克萨斯大学达拉斯分校移动激光雷达（光成像、探测和测距）站接受测量风速的培训，该站是一个独特的教育和外展活动设施。 这种培训对来自不同背景的学生产生影响，使他们了解重要的主题，例如气象学、极端天气现象、人为对环境的影响和可再生能源。该研究项目的目标是：1）更好地了解产生 VLSM 的物理机制及其由于大气稳定性、不同地形和土地覆盖的日常循环而导致的变化； 2) 探索VLSM如何影响公用事业规模风力涡轮机产生的尾流的下游演化； 3) 开发数值工具，通过再现 VLSM 引起的雷诺应力调制，能够全面、及时地预测风力涡轮机尾流。该研究项目由三个相互关联的任务组成。 首先，有两次激光雷达测量活动，第一次是在德克萨斯州北部相对平坦的地形上进行的，第二次是在复杂地形上进行的。 第三项任务使用所得实验数据，通过在伴随雷诺平均纳维-斯托克斯框架内对湍流闭合模型进行优化调整，对 VLSM 引起的风力涡轮机尾流调制进行建模。 该研究项目正在帮助回答一些与VLSM的形态和能量含量相关的关键问题，以及它们在不同大气稳定性条件下的变化程度、控制VLSM产生的物理机制以及不同长度的相干结构之间的能量传输-ABL 流量的尺度，以及土地覆盖和地形在 VLSM 发生和动力学中的作用。该研究项目还旨在量化 VLSM 引起的振幅调制对公用事业规模风力涡轮机空气动力性能和风力涡轮机尾流下游演变的影响。

项目成果

Wind LiDAR Measurements of Wind Turbine Wakes Evolving over Flat and Complex Terrains: Ensemble Statistics of the Velocity Field

风力激光雷达测量平坦和复杂地形上演变的风力涡轮机尾流：速度场的集合统计

• DOI: 10.1088/1742-6596/1452/1/012077
• 发表时间: 2020-01
• 期刊: Journal of Physics: Conference Series
• 作者: Zhan, Lu;Letizia, Stefano;Iungo, Giacomo Valerio
• 通讯作者: Iungo, Giacomo Valerio

Coupling of mesoscale Weather Research and Forecasting model to a high fidelity Large Eddy Simulation

中尺度天气研究和预报模型与高保真大涡模拟的耦合

• DOI: 10.1088/1742-6596/1037/6/062010
• 发表时间: 2018-06
• 期刊: Journal of Physics: Conference Series
• 作者: Santoni, C;García;Ciri, U;Iungo, GV;Leonardi, S
• 通讯作者: Leonardi, S

Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows

伪二维 RANS：用于模拟风电场流量的 LiDAR 驱动的中保真度模型

• DOI: 10.1063/5.0076739
• 发表时间: 2022-03
• 期刊: Journal of Renewable and Sustainable Energy
• 影响因子: 2.5
• 作者: Letizia, S.;Iungo, G. V.
• 通讯作者: Iungo, G. V.

Profitability optimization of a wind power plant performed through different optimization algorithms and a data-driven RANS solver

通过不同的优化算法和数据驱动的 RANS 求解器对风力发电厂进行盈利优化

• DOI: 10.2514/6.2018-2018
• 发表时间: 2018-01
• 期刊: 2018 Wind Energy Symposium
• 作者: Santhanagopalan, Vignesh;Letizia, Stefano;Zhan, Lu;Al;Iungo, Giacomo Valerio
• 通讯作者: Iungo, Giacomo Valerio

Cluster analysis of wind turbine wakes measured through a scanning Doppler wind LiDAR

通过扫描多普勒测风激光雷达测量风力涡轮机尾流的聚类分析

• DOI: 10.2514/6.2021-1181
• 发表时间: 2021-01
• 期刊: AIAA Scitech 2021 Forum
• 作者: Maulik, Romit;Rao, Vishwas;Renganathan, S. Ashwin;Letizia, Stefano;Iungo, Giacomo Valerio
• 通讯作者: Iungo, Giacomo Valerio