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Abstract Bird flight is often characterized by outstanding aerodynamic efficiency, agility and adaptivity in dynamic conditions. Feathers play an integral role in facilitating these aspects of performance, and the benefits feathers provide largely derive from their intricate and hierarchical structures. Although research has been attempted on developing membrane-type artificial feathers for bio-inspired aircraft and micro air vehicles (MAVs), fabricating anatomically accurate artificial feathers to fully exploit the advantages of feathers has not been achieved. Here, we present our 3D printed artificial feathers consisting of hierarchical vane structures with feature dimensions spanning from 10⁻²to 10²mm, which have remarkable structural, mechanical and aerodynamic resemblance to natural feathers. The multi-step, multi-scale 3D printing process used in this work can provide scalability for the fabrication of artificial feathers tailored to the specific size requirements of aircraft wings. Moreover, we provide the printed feathers with embedded aerodynamic sensing ability through the integration of customized piezoresistive and piezoelectric transducers for strain and vibration measurements, respectively. Hence, the 3D printed feather transducers combine the aerodynamic advantages from the hierarchical feather structure design with additional aerodynamic sensing capabilities, which can be utilized in future biomechanical studies on birds and can contribute to advancements in high-performance adaptive MAVs. 

Abstract Uncrewed aerial vehicles are integral to a smart city framework, but the dynamic environments above and within urban settings are dangerous for autonomous flight. Wind gusts caused by the uneven landscape jeopardize safe and effective aircraft operation. Birds rapidly reject gusts by changing their wing shape, but current gust alleviation methods for aircraft still use discrete control surfaces. Additionally, modern gust alleviation controllers challenge small uncrewed aerial vehicle power constraints by relying on extensive sensing networks and computationally expensive modeling. Here we show end-to-end deep reinforcement learning forgoing state inference to efficiently alleviate gusts on a smart material camber-morphing wing. In a series of wind tunnel gust experiments at the University of Michigan, trained controllers reduced gust impact by 84% from on-board pressure signals. Notably, gust alleviation using signals from only three pressure taps was statistically indistinguishable from using six pressure tap signals. By efficiently rejecting environmental perturbations, reduced-sensor fly-by-feel controllers open the door to small uncrewed aerial vehicle missions in cities. 

Abstract Some bird species fly inverted, or whiffle, to lose altitude. Inverted flight twists the primary flight feathers, creating gaps along the wing’s trailing edge and decreasing lift. It is speculated that feather rotation-inspired gaps could be used as control surfaces on uncrewed aerial vehicles (UAVs). When implemented on one semi-span of a UAV wing, the gaps produce roll due to the asymmetric lift distribution. However, the understanding of the fluid mechanics and actuation requirements of this novel gapped wing were rudimentary. Here, we use a commercial computational fluid dynamics solver to model a gapped wing, compare its analytically estimated work requirements to an aileron, and identify the impacts of key aerodynamic mechanisms. An experimental validation shows that the results agree well with previous findings. We also find that the gaps re-energize the boundary layer over the suction side of the trailing edge, delaying stall of the gapped wing. Further, the gaps produce vortices distributed along the wingspan. This vortex behavior creates a beneficial lift distribution that produces comparable roll and less yaw than the aileron. The gap vortices also inform the change in the control surface’s roll effectiveness across angle of attack. Finally, the flow within a gap recirculates and creates negative pressure coefficients on the majority of the gap face. The result is a suction force on the gap face that increases with angle of attack and requires work to hold the gaps open. Overall, the gapped wing requires higher actuation work than the aileron at low rolling moment coefficients. However, above rolling moment coefficients of 0.0182, the gapped wing requires less work and ultimately produces a higher maximum rolling moment coefficient. Despite the variable control effectiveness, the data suggest that the gapped wing could be a useful roll control surface for energy-constrained UAVs at high lift coefficients. 

Abstract Diving birds are regarded as a classic example of morphological convergence. Divers tend to have small wings extending from rotund bodies, requiring many volant species to fly with rapid wingbeats, and rendering others flightless. The high wing-loading of diving birds is frequently associated with the challenge of using forelimbs adapted for flight for locomotion in a “draggier” fluid, but this does not explain why species that rely exclusively on their feet to dive should have relatively small wings, as well. Therefore, others have hypothesized that ecological factors shared by wing-propelled and foot-propelled diving birds drive the evolution of high wing-loading. Following a reexamination of the aquatic habits of birds, we tested between hypotheses seeking to explain high wing-loading in divers using new comparative data and phylogenetically informed analyses. We found little evidence that wing-propelled diving selects for small wings, as wing-propelled and foot-propelled species share similar wing-loadings. Instead, our results suggest that selection to reduce buoyancy has driven high wing-loading in divers, offering insights for the development of bird-like aquatic robots. 

Architected materials can be synthesized to effectively control elastic wave propagation via bandgap engineering, which is essential for vibration mitigation and sound attenuation applications. Mechanisms for controlling elastic waves include Bragg scattering resulting from structural periodicity and local resonance effects, both of which were employed to create bandgaps. It is also shown that with careful design, the Bragg- and resonance-type bandgaps can be combined to achieve broader stopbands. Structures that use these bandgap mechanisms, especially those utilizing local resonators, add mass to the system, which could be highly undesirable for many engineering applications where mass considerations directly impact the system efficiency. To address this shortfall and advance the state of the art, this research proposes a mass-conserved design concept for metastructures that utilize both Bragg-type and local resonance bandgap mechanisms to create enhanced bandgap characteristics within the structure. The mass-conserved design is achieved by employing the existing system mass to create geometries that allow for bandgap generation, rather than adding mass to the structure as seen in traditional metastructure systems. The developed methodology shows that broader stopbands can be created without adding mass to the overall system, and this research identifies critical design parameters in order to achieve effective mass-conserved bandgap engineering compared to traditional metastructures designs. This method of mass-conserved metastructure design is shown to be effective when applied to various means of tuning that further enhance the bandgap regions, including the use of multiple-degree-of-freedom resonators and hybrid unit cells. These advancements provide a mass-constrained approach to bandgap engineering methods, expanding our ability to create lighter and more efficient structures with effective vibration control. 

Despite its prevalence in neurosensory systems for pattern recognition, event detection, and learning, the effects of sensory adaptation (SA) are not explored in reservoir computing (RC). Monazomycin‐based biomolecular synapse (MzBS) devices that exhibit volatile memristance and short‐term plasticity with two strength‐dependent modes of response are studied: facilitation and facilitation‐then‐depression (i.e., SA). Their ability to perform RC tasks including digit recognition, nonlinear function learning, and aerodynamic gust classification via combination of model‐based device simulations and physical experiments where SA presence is controlled is studied. Simulations exhibiting moderate SA achieve significantly higher accuracy classifying a custom 5 × 5 binary digit set, with experimental validation achieving maximum testing accuracies of 90%. Classifications of the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset achieve a maximum testing accuracy of 94.34% in devices with SA. Fitting error of the Mackey–Glass time series is also significantly reduced by SA. Experimentally obtained pressure distributions representing gusts on an airfoil in a wind tunnel are classified by MzBS reservoirs. Reservoirs exhibiting SA achieve 100% accuracy, unlike MzBS reservoirs without SA and comparable static neural networks. 

Smooth camber morphing aircraft offer increased control authority and improved aerodynamic efficiency. Smart material actuators have become a popular driving force for shape changes, capable of adhering to weight and size constraints and allowing for simplicity in mechanical design. As a step towards creating uncrewed aerial vehicles (UAVs) capable of autonomously responding to flow conditions, this work examines a multifunctional morphing airfoil’s ability to follow commands in various flows. We integrated an airfoil with a morphing trailing edge consisting of an antagonistic pair of macro fiber composites (MFCs), serving as both skin and actuator, and internal piezoelectric flex sensors to form a closed loop composite system. Closed loop feedback control is necessary to accurately follow deflection commands due to the hysteretic behavior of MFCs. Here we used a deep reinforcement learning algorithm, Proximal Policy Optimization, to control the morphing airfoil. Two neural controllers were trained in a simulation developed through time series modeling on long short-term memory recurrent neural networks. The learned controllers were then tested on the composite wing using two state inference methods in still air and in a wind tunnel at various flow speeds. We compared the performance of our neural controllers to one using traditional position-derivative feedback control methods. Our experimental results validate that the autonomous neural controllers were faster and more accurate than traditional methods. This research shows that deep learning methods can overcome common obstacles for achieving sufficient modeling and control when implementing smart composite actuators in an autonomous aerospace environment. 

Birds perform astounding aerial maneuvers by actuating their shoulder, elbow, and wrist joints to morph their wing shape. This maneuverability is desirable for similar-sized uncrewed aerial vehicles (UAVs) and can be analyzed through the lens of dynamic flight stability. Quantifying avian dynamic stability is challenging as it is dictated by aerodynamics and inertia, which must both account for birds’ complex and variable morphology. To date, avian dynamic stability across flight conditions remains largely unknown. Here, we fill this gap by quantifying how a gull can use wing morphing to adjust its longitudinal dynamic response. We found that it was necessary to adjust the shoulder angle to achieve trimmed flight and that most trimmed configurations were longitudinally stable except for configurations with high wrist angles. Our results showed that as flight speed increases, the gull could fold or sweep its wings backward to trim. Further, a trimmed gull can use its wing joints to control the frequencies and damping ratios of the longitudinal oscillatory modes. We found a more damped phugoid mode than similar-sized UAVs, possibly reducing speed sensitivity to perturbations, such as gusts. Although most configurations had controllable short-period flying qualities, the heavily damped phugoid mode indicates a sluggish response to control inputs, which may be overcome while maneuvering by morphing into an unstable flight configuration. Our study shows that gulls use their shoulder, wrist, and elbow joints to negotiate trade-offs in stability and control and points the way forward for designing UAVs with avian-like maneuverability.