Collaborative Research: Structured Wakes Behind Oscillating Foils: Characterization, Control, and Cooperative Behavior

合作研究：振荡翼片背后的结构化尾流：表征、控制和合作行为

• 批准号: 1921594
• 负责人: Jennifer Franck
• 金额: $ 22.5万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1921594&HistoricalAwards=false
• 关键词: Collaborative; Research; Structured; Wakes; Behind

Energy harvesting using oscillating foils has the potential to tap large hydrokinetic (tidal and riverine) energy resources and provide renewable and sustainable energy to water-front communities. Oscillating foils are less harmful to marine flora and fauna, can operate in many more locations and at lower water speeds than is possible for conventional rotary hydrokinetic turbines. Oscillating foils can also be placed in close proximity to each other, increasing the power density - the total power that can be extracted from a given area. However, optimizing this placement depends on improved understanding of the interactions between adjacent hydrofoils, and in particular, the way in which structured wakes form, advect downstream and impact the performance of a neighboring hydrofoil. This research project will address these issues, and lead to the development of predictive tools that can aid in the development of commercially feasible hydrokinetic energy systems, Using a tightly-integrated combination of experimental and computational approaches, a systematic framework will be developed for systematically characterizing and controlling the wake interactions between oscillating foils. The primary goal is to categorize vortex topology & convective paths based on the kinematics of the flapping motion. These tools will be developed with aid of machine learning classification and regression models. Once the wake structure is well-predicted, control algorithms will be developed, and the vortex-foil interactions will be characterized in terms of beneficial and detrimental interactions for the purpose of energy harvesting. Simulations of single and multiple hydrofoils will be performed using a combination of two-dimensional direct numerical simulation (DNS) and three-dimensional Large Eddy Simulations (LES). A computer-controlled three-hydrofoil system each equipped with force and torque sensors will be used in a large open surface water flume in conjunction with unsteady wake velocity surveys using Particle Image Velocimetry (PIV).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.

利用振荡箔收集能量有可能利用大型流体动力（潮汐和河流）能源资源，并为滨水社区提供可再生和可持续的能源。振荡翼片对海洋植物群和动物群的危害较小，可以在比常规旋转流体动力涡轮机可能的位置更多的位置和更低的水速下操作。振荡箔也可以彼此靠近放置，增加功率密度-可以从给定区域提取的总功率。 然而，优化这种布置取决于对相邻水翼之间的相互作用的更好理解，特别是结构化尾流形成、向下游平流并影响相邻水翼性能的方式。 该研究项目将解决这些问题，并导致预测工具的开发，可以帮助开发商业上可行的流体动能系统，使用实验和计算方法的紧密集成组合，将开发一个系统的框架，用于系统地表征和控制振荡翼之间的尾流相互作用。主要目标是根据扑翼运动的运动学对涡流拓扑对流路径进行分类。 这些工具将在机器学习分类和回归模型的帮助下开发。一旦尾流结构被很好地预测，控制算法将被开发，并且为了能量收集的目的，涡-翼相互作用将根据有益和有害的相互作用来表征。将使用二维直接数值模拟（DNS）和三维大涡模拟（LES）相结合的方式对单个和多个水翼进行模拟。 一个计算机控制的三个水翼系统，每个都配备了力和扭矩传感器，将用于一个大型的开放式表面水槽，并结合使用粒子图像测速法（PIV）进行的非定常尾流速度调查。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Can we harness the power of the oceans?

我们能利用海洋的力量吗？

• DOI: 10.33424/futurum307
• 发表时间: 2022
• 期刊: Futurum Careers
• 作者: Franck, Jennifer

Machine Learning to Classify Vortex Wakes of Energy Harvesting Oscillating Foils

• DOI: 10.2514/1.j062091
• 发表时间: 2022-05
• 期刊: AIAA Journal
• 影响因子: 2.5
• 作者: B. L. R. Ribeiro;Jennifer A. Franck

Wake-foil interactions and energy harvesting efficiency in tandem oscillating foils

• DOI: 10.1103/physrevfluids.6.074703
• 发表时间: 2021-03
• 期刊: Physical Review Fluids
• 影响因子: 2.7
• 作者: B. L. R. Ribeiro;Yunxing Su;Quentin Guillaumin;K. Breuer;Jennifer A. Franck

Vortex dynamics and Reynolds number effects of an oscillating hydrofoil in energy harvesting mode

• DOI: 10.1016/j.jfluidstructs.2020.102888
• 发表时间: 2018-02
• 期刊: Journal of Fluids and Structures
• 影响因子: 3.6
• 作者: B. L. R. Ribeiro;S. Frank;Jennifer A. Franck

Leading edge vortex formation and wake trajectory: Synthesizing measurements, analysis, and machine learning

前缘涡流形成和尾流轨迹：综合测量、分析和机器学习

• DOI: 10.1103/physrevfluids.7.074704
• 发表时间: 2022
• 期刊: Physical Review Fluids
• 影响因子: 2.7
• 作者: Lee, Howon;Simone, Nicholas;Su, Yunxing;Zhu, Yuanhang;Ribeiro, Bernardo Luiz;Franck, Jennifer A.;Breuer, Kenneth
• 通讯作者: Breuer, Kenneth