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

大范围流体动力扰动，或“阵风”，对许多涉及升力面的能源和推进系统，如风力涡轮机、固定翼和旋转翼飞机，以及涡轮机械，是普遍存在的挑战。气流扰动通常是由地形或天气引起的大气扰动，或由其他系统的空气动力学引起的，如在风力发电场或一群飞行器中。随着系统重量和大小的减少或天气事件变得更加极端，它们会变得相对较强。遇到阵风可能会显著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。设计一种大幅度阵风缓解的自动化策略是非常具有挑战性的，因为系统对阵风和激励的气动响应高度依赖于彼此。强化学习(RL)是一种很有前途的方法，用于控制这种复杂的流体流动，它绕过了以前方法的许多障碍，但它受到了训练负担的挑战：在RL的幼稚应用中，算法必须在训练过程中看到合适的大范围阵风条件和激励响应，以确定每个遇到的最佳响应。如果该算法结合了流动状态信息和流动物理预测，则很有可能加速RL训练。用流动状态信息来增强RL在很大程度上还没有被探索，主要是因为用少量的车载传感器来实际实时推断这种信息是有挑战的。传感器提供了其周围流动的有限足迹，但这种足迹可以揭示大多数基本的流动信息。该计划将利用以前在非定常空气动力学计算和实验研究方面的工作，推进从有限传感器估计流动状态的最新技术，并缩小RL在流体动力学实际应用方面的差距。该计划将部署实验和计算，以估计在固定翼或旋转叶片遇到大幅度扰动时流动中的相干涡旋结构。通过使用详细的流动测量的计算和实验，该程序将探索阵风遭遇的广泛的关键流动物理，包括在广泛的雷诺数范围内的标度效应，并研究在RL训练期间机翼/叶片俯仰对这些遭遇的影响。该项目将首次在实验室环境中展示强化学习对阵风相互作用的控制。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.