Bio-inspired variable-stiffness flaps for hybrid flow control, tuned via reinforcement learning

TL;DR

This paper introduces a hybrid active-passive flow control method using bio-inspired variable-stiffness flaps, optimized via reinforcement learning, achieving significant lift improvements through adaptive fluid-structure interactions.

Contribution

It presents a novel hybrid control strategy that actively varies hinge stiffness with reinforcement learning, surpassing fixed-stiffness and passive approaches in aerodynamic performance.

Findings

Lift improvements up to 136% over flap-less airfoil.

Hybrid controller enables large-amplitude flap oscillations.

Reinforcement learning accelerates training of adaptive stiffness control.

Abstract

A bio-inspired, passively deployable flap attached to an airfoil by a torsional spring of fixed stiffness can provide significant lift improvements at post-stall angles of attack. In this work, we describe a hybrid active-passive variant to this purely passive flow control paradigm, where the stiffness of the hinge is actively varied in time to yield passive fluid-structure interaction (FSI) of greater aerodynamic benefit than the fixed-stiffness case. This hybrid active-passive flow control strategy could potentially be implemented using variable stiffness actuators with less expense compared with actively prescribing the flap motion. The hinge stiffness is varied via a reinforcement learning (RL)-trained closed-loop feedback controller. A physics-based penalty and a long-short-term training strategy for enabling fast training of the hybrid controller are introduced. The hybrid controller is shown to provide lift improvements as high as 136\% and 85\% with respect to the flap-less airfoil and the best fixed-stiffness case, respectively. These lift improvements are achieved due to large-amplitude flap oscillations as the stiffness varies over four orders of magnitude, whose interplay with the flow is analyzed in detail.