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.

该学院早期职业发展（CAREER）资助将开创世界上第一个快速原型系统，用于实验优化飞行动力学和空中风能系统的控制。空中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高空强风。这些系统的成功实现预计将产生低于0.25美元每千瓦时的平准化电力成本，为偏远社区，岛屿，军事基地和深水离岸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，进一步加剧了高原型开发成本。这项研究将使用1/100比例的模型将控制系统原型设计成本降低几个数量级，这些模型是在水槽实验室测试设施中3D打印，系留和“飞行”的。水通道为优化控制系统设计提供了理想的机制，同时复制了全尺寸系统的关键动态特性。在整个项目中，学生将通过与领先的早期空中风能公司的互动，发展工业和小企业的观点。推广活动包括为高中工程夏令营开发风筝设计模块，为经济困难的学生共同设计一个能源丰富的科学课程，为早期的大学高中。空中风能飞行性能的优化是一个耦合的工厂和控制器优化问题，实验是必不可少的，但在全尺寸昂贵。这项研究通过一个独特的框架解决了工厂/控制器耦合和实验的必要性，该框架将数值优化与实验室规模的实验结合在一起，对3D打印模型进行实验室规模的实验，这些模型被拴在水槽中并“飞行”。该水槽平台将用于系留系统的闭环控制，已被证明可产生与全尺寸系统类似的动态性能。在建议的工厂和控制器优化过程中，实验数据将用于执行参数识别，并生成后续数值优化迭代的校正。在每次数值优化迭代完成时，将使用最佳实验设计技术来确定一组待测试的配置，同时考虑到每次重新配置的成本。该研究将集中在所提出的算法的收敛性和效率结果的推导，利用系统识别和实验的最优设计的工具。此外，本研究所提出的最佳化方法，将在一固定式与迎风式的空中风力发电系统上进行验证。