STTR Phase I: Active Blade Morphing Control to Improve Efficiency and Reduce Loading for Wind Turbines

这个小企业技术转让（STTR）第一阶段项目的更广泛的影响/商业潜力正在扩大风力涡轮机的全球部署，增加产量是今天的固定叶片无法实现的。具有先进控制能力的适应性叶片有助于解决技术和科学挑战，因为风力项目加速了离岸移动，扩展了未来风电场所需的大型涡轮机的物理特性。开发一种高保真建模工具来设计变形叶片，能够有效地提高能源生产，减少磨损，阻尼振动，提高稳定性和减少负载，实现了两个关键目标。 这些目标包括加快可再生能源的部署，以经济高效的方式从风力中提取可负担得起的电力，并提高开发能够在具有挑战性的水深和极端天气下安装的浮动风电场所需的负载和稳定性。该技术使大型涡轮机能够更好地在更多地点捕获风能，以抵御能源价格波动，创造就业机会，并促进发达国家和发展中国家更多地参与全球能源转型。此外，从这项研究中产生的叶片技术提供了新的制造技术和其他行业，如航空，汽车和海洋可再生能源的商业应用的机会。这个STTR第一阶段项目提出了一个高保真模型，以支持设计和控制先进的风力涡轮机叶片配置与自适应扭转角分布（角）。常规控制通常应用于转子扭矩以使风捕获最大化，并且因此使产量最大化，低于额定风速。高于该速度时，控制切换到铲刀桨距角以保持全功率。然而，现有设计中的限制导致了权衡，其中放弃风力捕获或电力生产以减少负载、减轻振动并提高稳定性。主动自适应控制器提供了更大的控制能力，并满足这些目标，没有权衡。这项研究的一个重要目标是了解复杂的气动弹性和空气动力学关系的方法。该技术是一种高保真模型，可以在合理的时间内模拟这些动力学和气动弹性性能。该模型涉及开发一个结合这些动态的框架，并使用利用数据分析和机器学习的计算工具。拟议工作的技术成果是创建一个数字孪生模型，能够有效设计和鲁棒控制具有自适应能力的高度复杂的叶片。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。

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is expanding global deployment of wind turbines with increased production not obtainable with today’s fixed blades. An adaptable blade with advanced control capabilities helps solve technical and scientific challenges as wind projects accelerate their move offshore, extending the physics of the larger turbines needed for future wind farms. Developing a high-fidelity modeling tool to design morphing blades capable of boosting energy production, reducing wear and tear, dampening vibration, improving stability, and reducing load effectively achieves two crucial goals. These goals include accelerating the deployment of renewable energy with affordable electricity that is efficiently and economically extracted from wind and improving the loading and stability necessary for the development of floating wind farms capable of installation in challenging water depths and extreme weather. The technology proposed makes large turbines better at capturing wind in more locations to protect against volatile energy prices, generate jobs, and promote greater participation in the global energy transition for developed and developing countries alike. Furthermore, the blade technology resulting from this research provides opportunities for new manufacturing techniques and commercial applications in other industries such as aviation, automotive, and marine renewable energy.This STTR Phase I project proposes to examine a high-fidelity model to support the design and control of an advanced wind turbine blade configuration with an adaptive twist angle distribution (TAD). Conventional control is generally applied to rotor torque to maximize wind capture, and thus production, below the rated wind speed. Above this speed, control shifts to the blade pitch angle to maintain full power. However, limitations in existing designs lead to trade-offs where wind capture or power production is relinquished to reduce loads, mitigate vibration, and improve stability. The actively adaptive TAD provides greater control capabilities and satisfies these objectives without trade-offs. A crucial goal of this research is the means to understand the complex aeroelastic and aerodynamic relationships with respect to the TAD. The technology is a high-fidelity model that simulates these dynamics and the aeroelastic performance in a reasonable amount of time. This model involves the development of a framework combining these dynamics and uses computational tools that leverage data analytics and machine learning. The technical result of the proposed work is the creation of a digital twin that enables effective design and robust control of highly sophisticated blades with adaptive TAD.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.