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

这一小型企业技术转让(STTR)第一阶段项目的更广泛影响/商业潜力正在扩大风力涡轮机的全球部署，增加产量是目前固定叶片无法实现的。随着风力发电项目加速向海外转移，具有先进控制能力的可适应叶片有助于解决技术和科学挑战，扩展了未来风力发电场所需的更大涡轮机的物理特性。开发一种高保真建模工具来设计变形叶片，能够有效地提高能量产生、减少磨损、抑制振动、提高稳定性和降低载荷，从而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，以负担得起的电力高效、经济地从风能中提取电力，并改善必要的负载和稳定性，以开发能够在具有挑战性的水深和极端天气中安装的浮动风力发电场。拟议中的技术使大型涡轮机在更多地点更好地捕捉风能，以防止能源价格波动，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，这项研究产生的叶片技术为新的制造技术和其他行业的商业应用提供了机会，如航空、汽车和海洋可再生能源。这个STTR第一阶段项目建议研究一个高保真模型，以支持具有自适应扭角分布(TAD)的先进风力涡轮机叶片配置的设计和控制。常规控制通常应用于转子扭矩，以最大限度地捕获风能，从而使产量低于额定风速。超过这个速度，控制转移到叶片的俯仰角，以保持最大功率。然而，现有设计中的限制导致了为了减少负载、减轻振动和提高稳定性而放弃风能捕获或发电的权衡。主动自适应TAD提供了更大的控制能力，并在不进行权衡的情况下满足了这些目标。这项研究的一个关键目标是了解与TAD有关的复杂的气动弹性和气动关系。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。这项拟议工作的技术成果是创建了一对数字孪生兄弟，使具有自适应TAD的高度复杂的叶片能够进行有效的设计和强大的控制。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.