Collaborative Research: Empowering Next Generation Offshore Wind Farms Through Systematic Characterization of Floating Wind Turbine Array Dynamics

更高、更一致的风速，沿着丰富的可用区域，使海上风电成为提高美国可再生能源（例如公用事业规模风电场）供电比例的一条有希望的途径。许多地方的水深，例如，在美国西海岸的海上，以及能够将装置放置在远离海岸的地方，在那里它们不那么有争议，使得浮动平台成为未来装置的吸引人的选择。虽然陆上风电场已经建立，但对海洋环境（例如，波浪）和涡轮机运动以及它们的尾流影响功率输出效率和漂浮风力发电场中的涡轮机磨损。这笔赠款将支持采用实验室实验和高保真计算机模拟的研究，这将使风力发电厂功率输出的预测更加稳健和现实。该项目中生成的数据可用于设计和控制更改，以提高浮动风电场的效率和弹性。这些进步将有助于为更多的风力发电铺平道路，增加清洁，可再生技术的使用，从而减少我们能源系统的碳足迹。大型发电厂的建设和运营也将有助于创造和维持一个充满活力的新的美国风能经济。该项目将培训研究生掌握风能科学广泛的跨学科工具。将通过为公共实体（例如，该项目将开发最先进的实验室实验和数值模拟工具，以测量、分析和表征影响浮动风电场功率输出和局部涡轮机特性的耦合风、波浪、尾流和平台动力学。通过这些研究开发的新知识和数据将被用来创建一套动态系统建模，估计和分析工具，这些工具将提供更好的预测关键风电场特性，如功率输出和涡轮机负载。这些数据将使用动力系统工具进行查询，例如适当的正交分解和动态（Koopman）模式分解，使我们能够推断出重要的系统特性，以进行进一步的分析，模型验证和开发估计技术，作为未来实时估计和控制算法的构建模块。研究成果包括：（1）新的系统表征方法，将实验室和仿真工具相结合，系统地探索了耦合的风、波浪、涡轮机尾流和平台动力学如何在广泛的相互作用的空间和时间尺度上影响风电场特性;（2）一套物理模型，描述与浮动风电场的操作、设计和控制最相关的相互作用的动态;以及（3）利用数据和建模工具的估算方法，同时考虑到可以在实时控制范例中实施的传感和驱动方法。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Higher and more consistent wind speeds, along with the abundance of available area, make offshore wind a promising pathway to increasing the percentage of US electricity supplied by renewable energy sources, such as utility scale wind farms. The depth of the water in many locations, e.g., off the west coast of the US, and the ability to place installations further from shore, where they are less controversial, make floating platforms an appealing choice for future installations. While onshore wind farms are well established, far less is known about how interactions between the ocean environment (e.g., waves) and turbine motions and their wakes affect power output efficiency and turbine wear in floating wind farms. This grant will support research that employs laboratory experiments and high-fidelity computer simulations that will enable more robust and realistic predictions of wind plant power output. The data to be generated in this project can be leveraged to make design and control changes to increase efficiency and resilience of floating wind farms. These advances will help pave the way to more installed wind power that increases the use of clean, renewable technologies, thereby decreasing the carbon footprint of our energy system. The construction and operation of large-scale power plants will also help create and sustain a vibrant new US wind energy economy. The project will train graduate students in the broad interdisciplinary tools of wind energy science. Broader community outreach will be achieved through curriculum developed for public entities (e.g., museums) and summer programs for K-12 students.This project will develop state-of-the art laboratory experiments and numerical simulation tools to measure, analyze and characterize the coupled wind, wave, wake and platform dynamics affecting the power output and local turbine properties in floating wind farms. The new knowledge and data developed through these studies will be exploited to create a suite of dynamical systems modeling, estimation and analysis tools that will provide better predictions of critical wind farm properties such as power output and turbine loading. The data will be interrogated using dynamical systems tools such as proper orthogonal decomposition and dynamic (Koopman) mode decomposition that enable us to infer important system properties for further analysis, model validation and to develop estimation techniques as the building blocks for future real-time estimation and control algorithms. The outcomes of this work include: (1) New system characterization approaches that couple laboratory and simulation tools to systematically explore how the coupled wind, wave, turbine wake and platform dynamics affect the wind farm properties over a broad range of interacting spatial and temporal scales; (2) A suite of physics-informed models that describe the dynamics of the interactions most relevant to operation, design and control of floating wind farms; and (3) Estimation approaches that exploit the data and modeling tools, while taking into account the sensing and actuation approaches that can be implemented within real-time control paradigms.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.