Collaborative Research: Learning to estimate and control gust-induced aerodynamics

合作研究：学习估计和控制阵风引起的空气动力学

• 批准号: 2247006
• 负责人: Anya Jones
• 金额: $ 37.12万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-15 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2247006&HistoricalAwards=false
• 关键词: Collaborative; Research; Learning; estimate; control

Large-amplitude fluid dynamic disturbances, or “gusts”, are a pervasive challenge for many energy and propulsion systems involving lifting surfaces, such as wind turbines, fixed and rotary-wing aircraft, and turbomachinery. Flow disturbances are often atmospheric, caused by terrain or weather, or introduced by the aerodynamics of other systems, as in a wind farm or a swarm of air vehicles. They become relatively stronger as the system's weight and size decrease or as weather events become more extreme. Gust encounters can significantly undermine the desired performance of the system, or at worst, cause catastrophic failure. Devising an automated strategy for large-amplitude gust mitigation is exceptionally challenging because the aerodynamic responses of the system to the gust and to actuation are highly dependent upon each other. Reinforcement learning (RL) is a promising approach for control of such complex fluid flows that circumvents many of the obstacles to previous approaches, but it is challenged by the burden of training: in a naïve application of RL, the algorithm must see a suitably large range of gust conditions and actuation responses during training to determine the best response for each encounter.It is very likely that RL training can be accelerated if the algorithm incorporates flow state information and a prediction of flow physics. The augmentation of RL with flow state information remains largely unexplored, primarily because of the challenges of practically inferring this information in real time with a small number of on-board sensors. Sensors provide a limited footprint of the flow around them, but this footprint can reveal most of the essential flow information. This program will leverage prior work in computational and experimental investigations of unsteady aerodynamics to advance the state of the art of flow state estimation from limited sensors and to close the gap on practical use of RL in fluid dynamics. The program will deploy experiments and computations to estimate coherent vortex structures in a flow during encounters of a fixed wing or rotating blade with a large-amplitude disturbance. With use of both computations and experiments with detailed flow measurements, the program will explore a wide range of crucial flow physics in gust encounters, including scaling effects across a wide range of Reynolds numbers, and to study the influence of wing/blade pitching on these encounters during RL training. This program will demonstrate, for the first time, reinforcement learning control of gust interactions in a laboratory setting.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.

大振幅流体动力扰动或“阵风”对于许多涉及升力面的能源和推进系统（例如风力涡轮机、固定翼和旋转翼飞机以及涡轮机械）来说是一个普遍的挑战。流动扰动通常是由地形或天气引起的大气扰动，或者是由其他系统的空气动力学引入的，例如在风电场或飞行器群中。随着系统重量和尺寸的减小或者天气事件变得更加极端，它们变得相对更强。阵风会严重破坏系统的预期性能，或者在最坏的情况下，导致灾难性故障。设计用于大幅阵风缓解的自动化策略非常具有挑战性，因为系统对阵风和驱动的空气动力响应高度依赖于彼此。强化学习 (RL) 是一种很有前途的控制此类复杂流体流动的方法，它克服了以前方法的许多障碍，但它受到训练负担的挑战：在 RL 的简单应用中，算法必须在训练期间看到适当大范围的阵风条件和驱动响应，以确定每次遭遇的最佳响应。如果算法包含流动状态，则 RL 训练很可能会加速 流动物理的信息和预测。强化学习与流状态信息的增强在很大程度上仍未得到探索，主要是因为使用少量机载传感器实际实时推断此信息存在挑战。传感器提供其周围流量的有限足迹，但该足迹可以揭示大多数基本流量信息。 该项目将利用之前在非定常空气动力学的计算和实验研究方面的工作，推进通过有限传感器进行流动状态估计的最新技术，并缩小强化学习在流体动力学中实际应用的差距。该计划将部署实验和计算，以估计固定翼或旋转叶片遇到大振幅扰动时流动中的相干涡流结构。通过使用详细的流量测量的计算和实验，该程序将探索阵风遭遇中的各种关键流动物理，包括各种雷诺数的缩放效应，并研究强化学习训练期间机翼/叶片俯仰对这些遭遇的影响。 该项目将首次展示实验室环境中阵风相互作用的强化学习控制。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。