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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. 

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 Forpractical considerations reinforcement learning has proven to be a difficult task outside of simulation when applied to a physical experiment. Here we derive an optional approach to model free reinforcement learning, achieved entirely online, through careful experimental design and algorithmic decision making. We design a reinforcement learning scheme to implement traditionally episodic algorithms for an unstable 1-dimensional mechanical environment. The training scheme is completely autonomous, requiring no human to be present throughout the learning process. We show that the pseudo-episodic technique allows for additional learning updates with off-policy actor-critic and experience replay methods. We show that including these additional updates between periods of traditional training episodes can improve speed and consistency of learning. Furthermore, we validate the procedure in experimental hardware. In the physical environment, several algorithm variants learned rapidly, each surpassing baseline maximum reward. The algorithms in this research are model free and use only information obtained by an onboard sensor during training. 

Structural health monitoring of fiber reinforced composites is an extensive field of research that aims to reduce maintenance costs through in-situ damage detection. However, the need for externally bonded sensor systems and complicated fabrication processes limit the widespread application of most current structural health monitoring techniques. This work introduces a novel multifunctional fiber reinforced composite that relies on a ferroelectric prepreg fabricated using dehydrofluorinated (DHF) polyvinylidene fluoride (PVDF), which exhibits a thermally stable piezoelectric response. The self-sensing material presented in this work requires minimal external components, as the piezoelectric sensing mechanism is fully contained within the composite. This is accomplished by fabricating a ferroelectric prepreg consisting of DHF PVDF infused woven fiberglass, which is sandwiched between woven carbon fabric layers that act as electrodes, thus forming a piezoelectric sensor fabricated with entirely structural composite materials. Notably, the sensing material is a fully distributed prepreg rather than discretely embedded sensors which enables simplified monitoring of complex structures. As the composite experiences damage under flexural and tensile loading, the internal change in strain results in a charge separation that is detectable as a voltage emission across the sample electrodes. The self-sensing capabilities of this material are explored using traditional mechanical testing techniques, showing comparable performance to common damage detection methods, all while eliminating the need for external bonding of sensors to the structure. 

Gliding birds perform feats that challenge even our most advanced similar-sized unmanned aerial vehicles (UAV). Their ostensible skill supports a pervasive belief that birds are highly efficient gliders that outperform modern UAVs. which led us to ask: are gliding birds truly more efficient than UAVs? Avian flight efficiency a well-studied topic and often cited as the inspiration for novel UAV designs. Despite the multitude of studies that have quantified the aerodynamic efficiency of gliding birds, there is no comprehensive summary of their findings. This is problematic because differing methodologies have inconsistent levels of uncertainty. As such, the lack of consolidated information on gliding bird efficiency inhibits a true comparison to UAVs. To fill this gap, we surveyed published theoretical and experimental estimates of avian aerodynamic efficiency and investigated the uncertainty of each method. We found that there was substantial variation in the aerodynamic efficiency predicted by different methods, which can lead to disparate conclusions on gliding bird efficiency. Our survey showed that measurements on live birds gliding in wind tunnels provide a reliable minimum estimate on a birds’ aerodynamic efficiency and allows the quantification of the wing configurations used in flight. Next, we surveyed the aeronautical literature to collect all published aerodynamic efficiencies of similar-sized, non-copter UAVs. The consolidated information allowed a direct comparison of modern UAVs and gliding birds. Contrary to our expectation, we found that there is no definitive evidence that any gliding bird species is either more or less efficient than a similar-sized UAV. This non-result highlights a critical need for new technology and analytical advances that can reduce the uncertainty associated with estimating a gliding bird’s aerodynamic efficiency. Nevertheless, our survey indicated that UAV designs informed by species flying within subcritical regimes may extend their operational range into lower Reynolds number regimes.