Collaborative Research: Nexus of Simulation, Sensing and Actuation for Aerodynamic Vibration Reduction of Wind Turbine Blades

合作研究：风力涡轮机叶片气动减振仿真、传感和驱动的结合

• 批准号: 1300970
• 负责人: Qingli Dai
• 金额: $ 26.9万
• 依托单位: Michigan Technological University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1300970&HistoricalAwards=false
• 关键词: Collaborative; Research; Nexus; Simulation; Sensing

The objective of this collaborative research project is to advance the smart blade system through innovations in areas of advanced computational models of fluid-structure interactions, sensors and actuators. Wind energy, an important source of clean and renewable energy, is becoming a major component of the U.S. energy portfolio. The interest in large capacity wind turbines as an economical way to harvest wind energy has significantly increased in recent years. Wind turbine blades are over 100m in length and the trend of increasing the size of the blades continues. However, increases in the size of wind turbine blades means that aerodynamic vibrations need to be managed to prevent catastrophic failures. The collaborative project team takes an innovative perspective to advance the smart turbine blade technology. The hypothesis of this research is that aerodynamic vibrations in wind turbine blades can be effectively mitigated with bio-inspired strategies for flow sensing, surface morphological change and fluid-structure interactions. The specific goals of this research project are 1) to understand blade vibration dynamics with advanced modeling of fluid-structure interactions; 2) to study the mechanism of bio-sensing for flow turbulence determination and to implement a feasible sensor design strategy; and 3) to understand and emulate the functions of "smart fins" and "smart denticles" for aerodynamic vibration reductions. A systematic approach will be undertaken by combining modeling, sensing and actuation strategies. The smart blade system performance will also be validated via simulation-based virtual testing and reduced-scale model experiments. All of these aim to advance the state of art in the smart wind turbine blades.This project presents a great opportunity to advance smart blade technologies, which include intelligent components for blade vibration reduction. A unique bio-inspired strategy will be pursued to prevent catastrophic failures of wind turbine blades by effectively mitigating the aerodynamic vibrations. The strategy will also improve the operational efficiency of the wind energy system. All of these advances will have important impacts on the safe and efficient production of wind energy.

这个合作研究项目的目标是通过在流固耦合、传感器和执行器的先进计算模型领域的创新来推进智能叶片系统。风能是清洁和可再生能源的重要来源，正在成为美国能源组合的重要组成部分。近年来，人们对大容量风力涡轮机作为一种经济的风能收集方式的兴趣显著增加。风力机叶片长度超过100米，并且叶片尺寸的增加趋势仍在继续。然而，风力涡轮机叶片尺寸的增加意味着需要控制空气动力学振动以防止灾难性故障。合作项目团队以创新的视角推进智能涡轮叶片技术。本研究的假设是，风力涡轮机叶片的气动振动可以通过流动传感、表面形态变化和流固耦合等仿生策略有效减轻。本研究项目的具体目标是：1)通过先进的流固耦合建模来了解叶片振动动力学；2)研究生物传感对湍流度测定的机理，实现可行的传感器设计策略；3)了解和模拟“智能鳍”和“智能齿”在气动减振方面的功能。将采用系统的方法，结合建模、传感和驱动策略。智能叶片系统的性能也将通过基于仿真的虚拟测试和缩小比例的模型实验进行验证。所有这些都旨在提高智能风力涡轮机叶片的技术水平。该项目为推进智能叶片技术提供了一个很好的机会，其中包括用于叶片减振的智能组件。一种独特的仿生策略将通过有效减轻空气动力学振动来防止风力涡轮机叶片的灾难性故障。该战略还将提高风能系统的运行效率。所有这些进步都将对风能的安全和高效生产产生重要影响。