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

University Of Maryland, College Park, College Park MD

Investigators

Abstract

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.