CAREER: Efficient Experimental Optimization for High-Performance Airborne Wind Energy Systems

职业：高性能机载风能系统的高效实验优化

• 批准号: 1453912
• 负责人: Christopher Vermillion
• 金额: $ 50万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1453912&HistoricalAwards=false
• 关键词: CAREER; Efficient; Experimental; Optimization; Performance

This Faculty Early Career Development (CAREER) grant will pioneer a first-in-world rapid prototyping system for experimentally optimizing the flight dynamics and control of airborne wind energy systems. Airborne wind energy systems replace towers with tethers and a lifting body, reducing deployment time and fixed infrastructure costs, and enabling turbines to take advantage of strong, high-altitude winds. Successful realization of these systems is projected to yield levelized costs of electricity below $0.25 per kW-h, providing cost-competitive energy solutions to remote communities, islands, military bases, and deep-water offshore locations. The synthesis of control systems to stabilize airborne wind energy systems in harsh atmospheric conditions remains a bottleneck for their widespread acceptance, further exacerbated by high prototype development costs. This research will reduce control system prototyping costs by multiple orders of magnitude, using 1/100-scale models that are 3D printed, tethered, and "flown" in a water channel laboratory test facility. The water channel provides an ideal mechanism for optimizing the control system design while replicating key dynamic properties of the full-scale system. Throughout the project, students will develop an industrial and small-business perspective through interactions with a leading early-stage airborne wind energy company. Outreach activities include the development of kite design modules for a high school engineering summer camp and co-design of an energy-rich science curriculum for an early college high school for economically disadvantaged students.Optimization of airborne wind energy flight performance represents a coupled plant and controller optimization problem, where experiments are indispensable but expensive at full-scale. This research addresses the plant/controller coupling and the necessity of experiments through the a unique framework that combines numerical optimization with lab-scale experiments on 3D printed models that are tethered and "flown" in a water channel. This water channel platform, which will be instrumented for closed-loop control of tethered systems, has been shown to yield provably similar dynamic performance to full-scale systems. In the proposed plant and controller optimization process, experimental data will be used to perform parameter identification and generate corrections to subsequent numerical optimization iterations. At the completion of each numerical optimization iteration, optimal design of experiments techniques will be used to determine a set of configurations to be tested, taking into account the cost of each reconfiguration. The research will focus on the derivation of convergence and efficiency results for the proposed algorithms, leveraging tools from system identification and optimal design of experiments. Furthermore, the optimization methods originating from this research will be validated on both a stationary and crosswind airborne wind energy system.

该学院的早期职业发展（CAREER）资助将开创世界首创的快速原型系统，用于实验性地优化飞行动力学和机载风能系统的控制。机载风能系统用系绳和提升体取代了塔架，减少了部署时间和固定基础设施成本，并使涡轮机能够利用强劲的高空风。这些系统的成功实现预计将使平均电力成本低于每千瓦时 0.25 美元，为偏远社区、岛屿、军事基地和深水近海地点提供具有成本竞争力的能源解决方案。在恶劣的大气条件下稳定机载风能系统的控制系统的综合仍然是其广泛接受的瓶颈，而高昂的原型开发成本进一步加剧了这一瓶颈。这项研究将使用 3D 打印、系绳并在水道实验室测试设施中“飞行”的 1/100 比例模型，将控制系统原型制作成本降低多个数量级。水道为优化控制系统设计同时复制全尺寸系统的关键动态特性提供了理想的机制。在整个项目中，学生将通过与一家领先的早期机载风能公司的互动，培养工业和小型企业的视角。外展活动包括为高中工程夏令营开发风筝设计模块，以及为经济困难的学生为早期大学高中共同设计富含能源的科学课程。机载风能飞行性能的优化是一个耦合装置和控制器优化问题，其中实验是必不可少的，但全面而言成本高昂。这项研究通过一个独特的框架解决了设备/控制器的耦合和实验的必要性，该框架将数值优化与对拴在水道中并“飞行”的 3D 打印模型进行实验室规模的实验相结合。该水道平台将配备用于系留系统闭环控制的仪器，已被证明可产生与全尺寸系统相似的动态性能。在所提出的设备和控制器优化过程中，实验数据将用于执行参数识别并生成对后续数值优化迭代的修正。在每次数值优化迭代完成时，将使用实验技术的优化设计来确定一组要测试的配置，同时考虑每次重新配置的成本。该研究将重点关注所提出算法的收敛性和效率结果的推导，利用系统识别和实验优化设计的工具。此外，源自这项研究的优化方法将在固定和侧风机载风能系统上得到验证。