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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.

 

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.

 

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. 

Abstract Some bird species exhibit a flight behavior known as whiffling, in which the bird flies upside-down during landing, predator evasion, or courtship displays. Flying inverted causes the flight feathers to twist, creating gaps in the wing’s trailing edge. It has been suggested that these gaps decrease lift at a potentially lower energy cost, enabling the bird to maneuver and rapidly descend. Thus, avian whiffling has parallels to an uncrewed aerial vehicle (UAV) using spoilers for rapid descent and ailerons for roll control. However, while whiffling has been previously described in the biological literature, it has yet to directly inspire aerodynamic design. In the current research, we investigated if gaps in a wing’s trailing edge, similar to those caused by feather rotation during whiffling, could provide an effective mechanism for UAV control, particularly rapid descent and banking. To address this question, we performed a wind tunnel test of 3D printed wings with a varying amount of trailing edge gaps and compared the lift and rolling moment coefficients generated by the gapped wings to a traditional spoiler and aileron. Next, we used an analytical analysis to estimate the force and work required to actuate gaps, spoiler, and aileron. Our results showed that gapped wings did not reduce lift as much as a spoiler and required more work. However, we found that at high angles of attack, the gapped wings produced rolling moment coefficients equivalent to upwards aileron deflections of up to 32.7° while requiring substantially less actuation force and work. Thus, while the gapped wings did not provide a noticeable benefit over spoilers for rapid descent, a whiffling-inspired control surface could provide an effective alternative to ailerons for roll control. These findings suggest a novel control mechanism that may be advantageous for small fixed-wing UAVs, particularly energy-constrained aircraft. 

Abstract Birds morph their wing shape to accomplish extraordinary manoeuvres 1–4 , which are governed by avian-specific equations of motion. Solving these equations requires information about a bird’s aerodynamic and inertial characteristics 5 . Avian flight research to date has focused on resolving aerodynamic features, whereas inertial properties including centre of gravity and moment of inertia are seldom addressed. Here we use an analytical method to determine the inertial characteristics of 22 species across the full range of elbow and wrist flexion and extension. We find that wing morphing allows birds to substantially change their roll and yaw inertia but has a minimal effect on the position of the centre of gravity. With the addition of inertial characteristics, we derived a novel metric of pitch agility and estimated the static pitch stability, revealing that the agility and static margin ranges are reduced as body mass increases. These results provide quantitative evidence that evolution selects for both stable and unstable flight, in contrast to the prevailing narrative that birds are evolving away from stability 6 . This comprehensive analysis of avian inertial characteristics provides the key features required to establish a theoretical model of avian manoeuvrability.