Machine learning for flow-informed aerodynamic control in turbulent wind conditions

Abstract

Control of aerodynamic forces in gusty, turbulent conditions is critical for the safety and performance of technologies such as unmanned aerial vehicles and wind turbines. The presence and severity of extreme flow conditions are difficult to predict, and explicit modeling of fluid dynamics for control is not feasible in real time. Model-free reinforcement learning methods present an end-to-end control solution for nonlinear systems as they require no prior knowledge, can easily integrate different types of measurements, and can adapt to varying conditions through interaction. Here, we show that reinforcement learning methods can achieve effective aerodynamic control in a highly turbulent environment. Algorithms are trained with different neural network structures, and we find that reinforcement learning agents with recurrent neural networks can effectively learn the nonlinear dynamics involved in turbulent flows and strongly outperform conventional linear control techniques. We also find that augmenting state observations with measurements from a set of bioinspired flow sensors can improve learning stability and control performance in aerodynamic systems. These results can serve to inform future gust mitigation systems for unmanned aerial vehicles and wind turbines, enabling operation in previously prohibitively dangerous conditions.

Bayesian inference of vorticity in unbounded flow from limited pressure measurements

“…This has implications for RL-based flow control approaches (Verma et al 2018), which treat the fluid flow as a partially observable Markov decision process. Pressure sensor data potentially make the process more Markovian, and therefore more amenable to learning a control policy (Renn & Gharib 2022). In short, there is less risk that control decisions made from measured pressures data are working on a mistaken belief about the flow state.…”