CAREER:Tackling Fluid Dynamics at Full Scale for Wind Energy Applications

职业：全面解决风能应用的流体动力学问题

• 批准号: 1454259
• 负责人: Jiarong Hong
• 金额: $ 50.47万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1454259&HistoricalAwards=false
• 关键词: CAREER; Tackling; Fluid; Dynamics; Full

1454259HongThe goal of this proposal is to explore the structure of the atmospheric boundary layer (ABL) directly at field scales by tracking the trajectories of snowflakes and using them as flow tracers. This approach will remove scaling and modeling adjustments currently made in scaled down wind tunnel experiments. Findings from this research will impact directly the wind energy industry. The proposed research activities are integrated into the educational plan of the proposal, which is centered on the use of the field laboratory and the wind turbine as a hands-on learning facility. Teachers, graduate and undergraduate students will participate in this project. The PI proposes to develop a super-large-scale particle image velocimetry (SLPIV) technique that will be complemented by laboratory scale experiments to understand full-scale wake dynamics around wind turbines. The innovation of this proposal is that SLPIV will be implemented by using snowflakes as natural tracers to quantify incoming turbulence and wake flow fields around a fully-instrumented wind turbine at the University of Minnesota. The field data will then inform the design of laboratory scale experiments that will aim to reproduce the key full-scale wake phenomena by tuning controllable experimental parameters. This proposal's goal to decipher the dynamics of the atmospheric layer behind a full scale wind turbine has both fundamental and practical importance. Better understanding of the turbine wake behavior and the fluid-solid interaction can lead to 10-20% improvements in the efficiency of wind turbines, while the establishment of the SLPIV as a ubiquitous experimental method can allow its use to study large scale ABL turbulence. Our climate, agriculture, and commerce are affected by the turbulence within the ABL.

1454259 Hong该提案的目标是通过跟踪雪花的轨迹并将其用作流动示踪剂来直接在场尺度上探索大气边界层（ABL）的结构。这种方法将消除目前在按比例缩小的风洞实验中所做的缩放和建模调整。这项研究的结果将直接影响风能行业。拟议的研究活动被纳入该提案的教育计划，该计划以使用现场实验室和风力涡轮机作为实践学习设施为中心。教师、研究生和本科生将参加这个项目。PI建议开发超大规模粒子图像测速（SLPIV）技术，该技术将通过实验室规模的实验来补充，以了解风力涡轮机周围的全尺寸尾流动力学。该提案的创新之处在于，SLPIV将通过使用雪花作为自然示踪剂来实现，以量化明尼苏达大学全仪表化风力涡轮机周围的湍流和尾流场。然后，现场数据将为实验室规模实验的设计提供信息，这些实验旨在通过调整可控实验参数来重现关键的全尺寸尾流现象。 该建议的目标是破译全尺寸风力涡轮机后面的大气层的动力学，具有基本和实际的重要性。更好地了解涡轮机尾流特性和流固相互作用可以使风力涡轮机的效率提高10-20%，而SLPIV作为一种普遍存在的实验方法的建立可以使其用于研究大尺度ABL湍流。我们的气候、农业和商业都受到ABL内部动荡的影响。