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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 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-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 flapless 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 analysed in detail. 

Covert feathers are a set of self-actuating, passively deployable feathers located on the upper surfaces of wings that augment lift at post-stall angles of attack. Due to these benefits, the study of covert-inspired passive flow control devices is becoming an increasingly active area of research. In this work, we numerically investigate the aerodynamic benefits of torsionally mounting five covert-inspired flaps on the upper surface of a NACA0012 airfoil. Two-dimensional high-fidelity simulations of the flow past the airfoil–flap system at low Re=1000 and a high angle of attack of 20∘ were performed. A parametric study was conducted by varying the flap moment of inertia and torsional hinge stiffness to characterize the aerodynamic performance of this system. Lift improvements as high as 25% were attained. Two regimes of flap dynamics were identified that provided considerable aerodynamic benefits. A detailed investigation of the flow physics of both these regimes was conducted to understand the physical mechanisms by which the passively deployed flaps augmented the lift of the airfoil. In both regimes, the flap was found to act as a barrier in preventing the upstream propagation of reverse flow due to flow separation and trailing edge vortex. The torsional spring and flap inertia yielded additional flap dynamics that further modulated the surrounding flow and associated performance metrics. We discuss some of these fluid–structure interaction effects in this article. 

State estimation is key to both analysing physical mechanisms and enabling real-time control of fluid flows. A common estimation approach is to relate sensor measurements to a reduced state governed by a reduced-order model (ROM). (When desired, the full state can be recovered via the ROM.) Current methods in this category nearly always use a linear model to relate the sensor data to the reduced state, which often leads to restrictions on sensor locations and has inherent limitations in representing the generally nonlinear relationship between the measurements and reduced state. We propose an alternative methodology whereby a neural network architecture is used to learn this nonlinear relationship. A neural network is a natural choice for this estimation problem, as a physical interpretation of the reduced state–sensor measurement relationship is rarely obvious. The proposed estimation framework is agnostic to the ROM employed, and can be incorporated into any choice of ROMs derived on a linear subspace (e.g. proper orthogonal decomposition) or a nonlinear manifold. The proposed approach is demonstrated on a two-dimensional model problem of separated flow around a flat plate, and is found to outperform common linear estimation alternatives.