Dynamic Feature-based Deep Reinforcement Learning for Flow Control of Circular Cylinder with Sparse Surface Pressure Sensing

TL;DR

This paper introduces a dynamic feature-based deep reinforcement learning approach for flow control around a circular cylinder, achieving significant drag reduction and lift fluctuation mitigation using sparse surface pressure sensors, applicable across various Reynolds numbers.

Contribution

The study presents a novel DF-DRL method that enhances DRL performance with dynamic features, enabling effective flow control with minimal sensor data without requiring a dynamic flow model.

Findings

Drag coefficient reduced by 25% compared to vanilla DRL.

Achieved about 8% drag reduction with only one pressure sensor at Re=100.

Reduced drag by over 30% at Re=500 and 1000, demonstrating robustness.

Abstract

This study proposes a self-learning algorithm for closed-loop cylinder wake control targeting lower drag and lower lift fluctuations with the additional challenge of sparse sensor information, taking deep reinforcement learning as the starting point. DRL performance is significantly improved by lifting the sensor signals to dynamic features (DF), which predict future flow states. The resulting dynamic feature-based DRL (DF-DRL) automatically learns a feedback control in the plant without a dynamic model. Results show that the drag coefficient of the DF-DRL model is 25% less than the vanilla model based on direct sensor feedback. More importantly, using only one surface pressure sensor, DF-DRL can reduce the drag coefficient to a state-of-the-art performance of about 8% at Re = 100 and significantly mitigate lift coefficient fluctuations. Hence, DF-DRL allows the deployment of sparse sensing of the flow without degrading the control performance. This method also shows good robustness in controlling flow under higher Reynolds numbers, which reduces the drag coefficient by 32.2% and 46.55% at Re = 500 and 1000, respectively, indicating the broad applicability of the method. Since surface pressure information is more straightforward to measure in realistic scenarios than flow velocity information, this study provides a valuable reference for experimentally designing the active flow control of a circular cylinder based on wall pressure signals, which is an essential step toward further developing intelligent control in realistic multi-input multi-output (MIMO) system.