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.

这位教师早期职业发展（职业）格兰特将开创一种第一个世界中的快速原型制度，用于实验优化空中风能系统的飞行动态和控制。机载风能系统用系tethers和举起车身代替塔楼，减少部署时间和固定的基础设施成本，并使涡轮机能够利用强大的高空风。预计这些系统的成功实现将产生低于每千瓦时0.25美元的电力成本，从而为偏远社区，岛屿，军事基地和深水近海地点提供成本竞争力的能源解决方案。在恶劣的大气条件下稳定空气中风能系统的控制系统的综合仍然是其广泛接受的瓶颈，这进一步加剧了高原型开发成本。这项研究将使用1/100尺度的模型在水道实验室测试设施中使用3D打印，束缚和“飞行”的1/100尺度模型，将控制系统的原型成本降低。水通道提供了优化控制系统设计的理想机制，同时复制了全尺度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降风能公司的互动来发展工业和小型企业的观点。外展活动包括开发用于高中工程夏令营的风筝设计模块，以及为早期的大学高中提供经济弱势群体学生的能源丰富的科学课程的共同设计。机载风能飞行绩效的优化代表了一个耦合的植物和控制器优化问题，实验是可观的，但在成熟的情况下是昂贵的。这项研究涉及植物/控制器耦合以及通过一个独特的框架进行实验的必要性，该框架将数值优化与在水通道中束缚和“飞行”的3D印刷模型上的实验室规模实验相结合。该水通道平台将被仪器进行链接系统的闭环控制，已显示出与全尺度系统相似的动态性能。在拟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为随后的数值优化迭代生成校正。在完成每个数值优化迭代时，考虑到每个重新配置的成本，将使用实验技术的最佳设计来确定要测试的配置。该研究将重点介绍提出的算法的收敛性和效率结果的推导，从系统识别中利用工具以及实验的最佳设计。此外，源自这项研究的优化方法将在静止和横风空降风能系统上进行验证。