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

大振幅流体动力扰动或“阵风”是涉及升力面的许多能量和推进系统（例如风力涡轮机、固定翼和旋转翼飞机以及涡轮机）的普遍挑战。气流扰动通常是由大气、地形或天气引起的，或者是由其他系统的空气动力学引起的，如风力发电场或一群飞行器。随着系统重量和体积的减小或天气事件变得更加极端，它们变得相对更强。阵风的遭遇会严重破坏系统的预期性能，或者在最坏的情况下，导致灾难性的故障。设计一个自动化的大振幅阵风缓解策略是非常具有挑战性的，因为系统的气动响应阵风和驱动是高度依赖于彼此。强化学习（RL）是控制这种复杂流体流动的一种有前途的方法，可以绕过以前方法的许多障碍，但它受到训练负担的挑战：在RL的一个简单应用中，在训练过程中，为了确定每次遭遇的最佳响应，算法必须能够看到一个适当大范围的阵风条件和激励响应，如果满足以下条件，那么RL训练就很有可能加速该算法结合了流状态信息和流物理学的预测。增强RL与流状态信息仍然在很大程度上未被探索，主要是因为在真实的时间与少量的板载传感器实际推断此信息的挑战。传感器提供了它们周围的流动的有限足迹，但这种足迹可以揭示大多数基本的流动信息。 该计划将利用非定常空气动力学的计算和实验研究中的先前工作，以推进从有限传感器进行流态估计的最新技术水平，并缩小RL在流体动力学中实际使用的差距。该计划将部署实验和计算，以估计在遇到固定翼或旋转叶片与大振幅扰动的流动中的相干涡结构。通过使用计算和详细的流动测量实验，该计划将探索阵风遭遇中广泛的关键流动物理学，包括在广泛的雷诺数范围内的缩放效应，并研究在RL训练期间机翼/叶片俯仰对这些遭遇的影响。 该计划将首次在实验室环境中演示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知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.