This PDF file contains the front matter associated with SPIE Proceedings Volume 12946, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Metamaterials are artificially engineered structures that can exhibit unconventional properties not easily observed in nature. These unique architectures offer a robust platform for manipulating acoustic or elastic wave properties for energy localization and focusing, thus broadening their potential applications in energy harvesting and sensing. I will provide a comprehensive overview of the latest breakthroughs in the design of phononic crystals and acoustic metamaterials. These advancements promise a substantial enhancement in energy harvesting performance when these materials are integrated with piezoelectric components. In addition to wave-based metamaterials, I will summarize our recent advances in vibration energy harvesting, sensing, and energy absorption applications, facilitated by the exciting field of mechanical metamaterials. These materials feature meta-atoms that respond to external stimuli, collectively displaying extraordinary material properties like negative stiffness, Poisson's ratio, and multistability. Notably, these architectures are not constrained by dynamic wavelength dependence, offering broader design opportunities across multiple scales.

In recent years, the concept of reconfigurable matter engineered based on nature-inspired modular architectures has been explored to create advanced structures. The modules are designed to be reconfigurable, so to produce synergistic and intriguing dynamic functionalities, such as programmable phononic bandgap and nontraditional wave steering. More recently, with the rapid advances in high-performance intelligent systems, we are witnessing a prominent demand for the next generation of mechanical matter to have much more built-in intelligence and autonomy. An emerging direction is to pioneer the structures’ high dimensionality and nonlinearity for mechano-intelligence via physical computing. That is, we aim to concurrently embody computing power and functional intelligence, such as perception, learning, memorizing, decision-making and execution, directly in the mechanical domain, advancing from conventional systems that solely rely on add-on digital computer to achieve intelligence. This presentation will highlight some of these advancements in harnessing engineered matter for structural dynamics tailoring, from adaptive wave controls to self-learning-self-tuning intelligence.

The main drawbacks of conventional acoustic imaging systems are low resolution images and high power consumption. This presentation will outline a new imaging paradigm that addresses these limitations. In this approach the environment is probed with an omnidirectional acoustic source and the back-scattered sound captured on an acoustic aperture is converted into a coherent optical field. The latter is focused into an image with off-the-shelf commercial cameras. The acoustic-to-light conversion is performed by a metasurface consisting of independent unit cells requiring no electronic synchronization thus facilitating scalability of the metasurface to large numbers of unit cells. The main advantages of this method are low power requirements and the ability to provide much higher resolution images than possible with conventional methods especially when low frequency sound is used to probe the environment.

Reconfigurability and tunability of the tessellation of Tachi-Miura Polyhedron (TMP), an origami-based cellular structure composed of bellows-like unit cells after its fabrication was investigated. By combining origami-based tessellation design, analytical method with rigid-origami modeling, efficient manufacturing of a prototype, and experimental verification, our research results show that a significant range of in-situ tunability in effective density, Young's modulus, and Poisson's ratio can be obtained within the elastic regime with orders of magnitude changes.

Passive acoustic metamaterials are constrained in their achievable properties and ability to integrate into realizable devices by unwanted property coupling, narrowband resonance, high loss, and challenges of fabrication. These limitations can be overcome by instead employing active components, such as speakers and microphones, to generate the desired acoustic response. We have developed an approach to designing scalable active metamaterials that allows for each unit cell to be of identical hardware and independent programming, but interact together as a bulk medium with desired effective properties. Unlike in a controls approach, the programming of these unit cells is a simple gain between local sensor and driver components. Here, we demonstrate an active metamaterial with individually programmable bulk modulus and mass density tensor. We validate its effective properties by comparing its experimental scattering behavior to that of simulated continuous media.

Under ideal assumptions of infinite lattices where the infinite wave attenuation intensity is achievable, the bandgap estimation considers the bandgap bounds to achieve broadened band width. However, for practical applications in which finite or limited numbers of unit cells are allowed, the induced bandgap region actually includes frequencies with poor wave attenuation intensity. Therefore, for realizing true wave attenuation applications at targeted operating frequencies, it is of critical importance to locate the operating frequency not only within the bandgap region but also at which the wave attenuation intensity is strongest. To address this issue, we explore a tool for estimating the operating frequency with strong wave attenuation intensity from local resonances of scattering unit cells. Since the implicit correlation between the local resonance and the frequency location of strong wave attenuation intensity is determined by multiple parameters and cannot be analytically expressed by the complicated modeling, we suggest a physics-informed machine leaning approach. By introducing analytical modeling physics into the machine learning models, both the operating frequency and t

In elastic metamaterials, local microstructural differences lead to non-local macroscopic interactions between stress-strain and momentum-velocity, known as Willis coupling, resulting in exotic wave phenomena such as asymmetric wave reflection and unidirectional transmission. Recently, it was discovered that the structural asymmetry induces additional macroscopic cross-coupling between the electrical field and momentum in piezoelectric metamaterials, known as electro-momentum coupling. This new form of coupling offers external control over elastic wave propagation in metamaterials via external electrical stimulus. In this work, we introduce a mechanism for tuning Willis and electro-momentum couplings in 1D piezoelectric metamaterials using a resistor-inductor-capacitor (RLC) shunt circuit by simply tuning the shunt resistance and inductance. Using analytical derivations, we demonstrate a considerable variation in cross-coupling coefficients near the resonant bandgap frequencies, which is controlled by the RLC circuit resonance and electrical damping. We then showcase tunable asymmetric wave propagation in 1D piezoelectric metamaterials with resonant shunts.

This talk will investigate the inverse design of the dispersion curves of a class of non-local piezoelectric metamaterials. The use of non-local circuit connections between unit cells introduces both frequency and wavenumber-dependence to the dispersion curves, greatly expanding the possibilities for wave manipulation in non-local metamaterials. Furthermore, the use of non-local interaction enables symmetry-breaking, such that non-reciprocal behavior can be obtained without nonlinearity or time-varying material properties. To explore the full design space of non-local piezoelectric metamaterials, we present a general analytical model and inverse design methods to calculate the required shunt circuitry for a given set of non-reciprocal dispersion curves. Numerical case studies are given for non-local resistive, inductive, and capacitive shunt circuits, which provide insight into the uses of each of these circuits in non-local metamaterial systems. We then experimentally demonstrate this approach using a piezoelectric bimorph beam with local and non-local circuit interactions, highlighting non-reciprocity and the tunability of the dispersion relation of the system.

Mechanical computing has gained prominence in recent years, presenting a secondary yet viable computation mechanism in off-grid and low access environments. In this work, we present a first attempt to perform parallel mechanical computing within a prescribed acoustic domain, by exploiting time-modulated metasurfaces which enable simultaneous multi-wave beaming in distinct frequency channels. The realization of parallel processing in analog computing lays the foundation for substantial advancements in both in acoustic and optical domains, and unlocks several features which this far have been elusive in physical neuromorphic and reservoir computing.

Electromagnetic interactions hold promise in the realms of dynamics and control, especially when eddy currents induced through coils combine with permanent magnets to produce a Lorentz force. A coil's interaction with eddy currents, stemming from two-way coupling via mutual inductance, enables non-contact sensing and actuation of nearby metallic structures. When combined with vibrating structures, this electromagnetic interaction paves the way for innovative applications for structural health monitoring and vibration control. In this investigation, a locally resonant electromagnetic unit cell is proposed to impose damped vibration absorption on the dynamics of an oscillating beam. This approach of noncontact vibration absorption preserves the resonant characteristics of the host structure, while facilitating precise calibration and tuning via an electrical shunt. This induced local resonance is pivotal for adaptive vibration control and suppression, serving as a novel mechanism to mitigate potential damage or fatigue in vital structural components. In addition, locally resonant structures have been demonstrated in the implementation of elastic waveguiding and related applications. In this investigation, the non-contact vibration absorber is examined and modeled via a lumped parameter approach combining the fundamental laws of elastic mechanics and electromagnetics. The resulting displacement-force transmissibility is examined and compared to the more traditional approach of a LC-shunted piezoelectric absorber. These investigations illustrate the expected challenges stemming from high electrical resistivity in the electromagnetic coil and sensitivity to the lift-off distance. A mitigation technique for these challenges is proposed, involving the integration of negative impedance converters in the electrical shunt which allows the absorber to be optimally tuned.

The exploration of intelligent machines has recently spurred the development of physical neural networks, a class of intelligent metamaterials capable of learning, whether in silico or in situ, from observed data. In this letter, we introduce ’equilibrium learning’, a novel physical learning rule designed for lattice-based mechanical neural networks (MNNs) to achieve target performance. This approach leverages the steady states of nodes for back-propagation, efficiently updating the learning degrees of freedom. One-dimensional MNNs, trained with equilibrium learning in silico, can exhibit the desired behaviors on demand function as intelligent mechanical machines. The approach is then employed for the precise morphing control of two-dimensional MNNs subjected to shear or uniaxial loads. Moreover, the MNN is trained to execute classical machine learning tasks such as regression, and preprogrammed bandgap control, establishing it as a versatile platform for physical learning. Our approach presents an efficient pathway for the design of lattice-based mechanical metamaterials for a wide range of static and dynamic target functionalities, positioning them as powerful engines for physical learning.

We observe maxon-like dispersion of ultrasonic guided waves in elastic metamaterial consisting of pillars periodically bonded to one side of a beam. The pillars act as asymmetric resonators and induce strong hybridization between longitudinal and bending wave modes. This creates a local maximum (i.e., maxon) on the dispersion curve at the interior of the first Brillouin zone. We observe localized maxon mode with zero group velocity (ZGV) through numerical and experimental investigation. In contrast to the roton mode, our measurements also demonstrate a unique maxon feature of peak-frequency down-shift in space.

Highly precise space structures are sensitive to vibration during on-orbit operation. This work develops metamaterials that can be integrated into space structures, namely dissipative metamaterial beams for vibration mitigation. The proposed dissipative phononic crystal beam with twisted viscoelastic inclusions presents high structural damping over a large space-level temperature range. The developed novel metamaterials can be implemented in the next-generation space structures, which can be manufactured and assembled in space to enhance their spatiotemporal precision.

Phononic materials act as mechanical filters of incident acoustic and vibrational loads. Generally speaking, they attenuate wave propagation within bandgaps and resonate outside them. Nonetheless, elastic periodic lattices often exhibit “truncation resonances” inside bandgaps when certain conditions are met. This study provides a generalized roadmap for the design and selective placement of truncation resonances in such lattices, integrating all factors that play a role in the onset of truncation resonances and shape its dynamic response. This framework is then experimentally validated using a canonical physical realization of differently-truncated finite phononic lattices.

In high-precision refractive optical systems, e.g. telescopes and spectrometers for space applications, temperature differences in influence the optical performance in the form of aberrations, especially thermal defocus. By temperature changes not only the radii of the optical elements itself, but also the distances between the optical elements are changing in dependency of the used mount materials. This causes a thermal defocus of the system and needs to be compensated. There are multiple approaches for compensation, generally divided into active and passive compensation. Active compensation techniques, e.g. piezo actuators, are highly accurate but require a control system, which is a cost and complexity driver. Passive compensation techniques often use combinations of several materials with different coeffcients of thermal expansion. This increases the overall complexity and mass. The focus of this research is to develop a mechanical structure that passively compensates for thermal defocus over a wide temperature range. Auxetic unit cells are the basis of this structure. Auxetic structures exhibit a negative Poisson´s ratio. By three-dimensional combination of auxetic unit cells, these can be arranged in a way that a transformation from radial deformation into longitudinal deformation is realized. The geometrical complexity requires additive manufacturing as manufacturing process. This concept is used to develop a mounting structure that passively compensates for thermal defocus by opposing longitudinal deformation, which is triggered by the thermally induced radial deformation of the structure itself under temperature changes. By analytical and numerical investigations, the mechanical behavior of the auxetic cells is studied in dependency of geometrical parameters. With these results, an auxetic mounting structure is developed. The aim is to compensate 5 μmK^-1 axial spacing over a temperature range of 80 K. For proof of concept, a demonstrator system is numerically investigated under thermal loads and on its structural-dynamic properties.

This paper examines the stability implications of integrating piezoelectric actuators into metamaterial beams, focusing on the compensation of structural damping and its effect on the system’s dynamic performance. Metamaterials, characterized by their unique bandgap properties, offer potential in various engineering applications, including vibration control and energy harvesting. However, structural damping inherent in such systems can degrade these properties, prompting the use of piezoelectric actuators as a compensatory mechanism. Through a distributed parameter model and modal analysis, this study explores the temporal and spatial dynamics of the metamaterial beam and investigates how piezoelectric actuation influences the natural frequencies and mode shapes, with a particular emphasis on stability thresholds. Employing root locus analysis, the paper visualizes the transition of system stability across different levels of actuation voltage, highlighting the delicate balance between enhanced performance and stability. The findings delineate a clear operational voltage range, within which piezoelectric actuation improves bandgap properties without compromising system stability.

Recently, researchers have incorporated topological phases into mechanical metamaterials to facilitate defect-immune elastic wave and vibration manipulation. The topological mechanical metamaterials developed thus far have achieved extraordinary wave control capabilities through the construction of robust elastic waveguides at the boundaries and interfaces of 1D, 2D, and 3D periodic mechanical lattices. Given the overwhelming focus of previous research on traditional integer-dimensional mechanical architectures, an unexplored opportunity exists to investigate the emergence of topological phases in fractal mechanical metamaterials, which have a non-integer dimension and exhibit self-similarity across multiple scales. This research addresses the unexplored opportunity and advances the state of the art through the synthesis of a 1.89D fractal mechanical metamaterial that harnesses higher-order topological phases to enable multifaceted elastic wave and vibration control. The proposed fractal topological mechanical metamaterial is a thin plate with embedded torsional spring-mass resonators that are arranged into the pattern of a 1.89D Sierpiński carpet. A numerical eigenfrequency analysis uncovers coexisting topological corner and edge states that trap wave energy at the myriad corner and edge interfaces available in the 1.89D fractal. The outcomes from this study provide insight into the attainment of higher-order topological states in fractal metamaterials that localize elastic waves and vibrations across various locations and frequencies, opening the door for future research of topological phases in mechanical metamaterials with fractal architectures.

Programmable piezoelectric metamaterials are attractive due to their ability to precisely tune band structures and thereby control waves in unprecedented ways. By connecting piezoelectric unit cells to shunt circuits, the effective stiffness of the piezoelectric metamaterial can be tuned locally, offering a higher degree of design freedom over mechanical metamaterials. On the other hand, with advances in topological insulators, and the introduction of topological properties such as quantum Hall, quantum spin Hall, quantum Valley-Hall, and Weyl physics in acoustic and elastic systems have opened new avenues to manipulate waves. Topological interface modes are of interest due to their resistance to backscattering, defects, and energy leakage. In this study, a 1D Su–Schrieffer–Heeger (SSH) type programmable piezoelectric metamaterial beam model is proposed to explore topological interface modes. Specifically, we use the band folding mechanism, by doubling the primitive unit cells we close the bands in the high symmetric points and develop two band folding points near the locally resonant bandgap. Furthermore, the effective stiffness of the two-unit cells is varied using inductive shunt circuits to break the space inversion symmetry and lift the degeneracies. We show that by tuning the inductive shunt resonant frequency of the two-unit cells near the locally resonant bandgap frequency, the two band foldinginduced bandgaps can be merged along with the locally resonant bandgap, and two topological interface modes are realized numerically. Our future work involves experimental demonstration of merging topological bandgaps to realize multiple interface states.

In this study, we explore the influence of the Non-Hermitian Skin Effect (NHSE) on the morphing behavior of topological structures, thereby transitioning topological edge state to a extended state and a converted edge state. The NHSE is induced through a non-local sensor-actuator circuit, while the electro-mechanical coupling of piezoelectric patches facilitates the interplay between electrical and mechanical energy. Reconfigurable morphing is observed under different degrees of non-Hermiticity, causing the topological mode to transition from a localized state to an extended state, and subsequently to a converted-localized state. Remarkably, the frequency of the topological mode experiences only slight variations, owing to the protective effects of the bandgap, while other bulk modes undergo noticeable frequency shifts. Additionally, by strategically designing the non-Hermiticity distribution in 2D topological plates, the topological mode can assume a diverse array of shapes.

Investigating the vibro-elastic dispersion relations of architected materials presents a significant challenge due to the complex interplay between the material’s microstructure, inherent physical properties, and wave propagation characteristics. Leveraging on the rapidly growing power of quantum computing, we employ quantum algorithms that are tailored to Noisy Intermediate-scale Quantum (NISQ) specifications to calculate band structures of elastic waves. We demonstrate predictions of dispersion bands with the Variational Quantum Eigensolver (VQE) algorithm for selected architected materials. This research prepares the metamaterial research community for the upcoming era of quantum computing.

The manipulation of low- and mid-frequency sounds using artificially engineered structures has gathered significant research interest as a promising approach to mitigate noise. With this intention, the investigation of acoustic metamaterials has emerged as a considerable field. These particular structural forms possess exceptional qualities and hold the potential for further progress. Specifically, membrane-type acoustic metamaterials (M-AMMs) have gathered interest due to their ability to yield desired sound insulation properties through the local resonance mechanism of a mass-spring system. This resonance phenomenon can be tuned within the aforementioned frequency range, offering a viable solution for achieving enhanced sound attenuation. So far, there have been several efforts to design and simulate such structures using conventional hand-made techniques. However, to minimize the challenges arising from manufacturing and to ensure consistent results, it is worth investigating the possibility of creating these unit cells through additive fabrication approaches. In order to investigate the dynamic behavior of M-AMM unit cells manufactured by a multi-material printing approach, finite element analyses are conducted in Ansys® Workbench. As a primary point, the pre-stress in the membrane induced by a secondary structure is realized and investigated. The effect of printed mass configurations and pre-stress of the membrane are analyzed via different modeling approaches. Whereas the magnitude of the mass remains equal for all mass-attached unit cells, the shape and distribution are varied. The outputs from the simulations and results obtained from the experiment are compared and discussed with respect to further investigations to realize M-AMMs.

We reveal the unique and fundamental advantage of inerter-based elastic metamaterials by a comparative study among different configurations. When the embedded inerter is connected to the matrix material on both ends, the metamaterial shows definite superiority in forming a band gap in the ultralow frequency - equivalently the ultra-long wavelength - regime, where the unit cell size can be four or more orders of magnitude smaller than the operating wavelength. In addition, our parametric studies in both one and two dimensions pave the way towards designing next-generation metamaterials for structural vibration mitigation.

In recent years, seismic metamaterials (SMs) have attracted considerable attention for their capacity to manipulate seismic wave propagation. Unlike conventional seismic mitigation methods, SMs offer broader applicability and enhanced seismic energy absorption capabilities. Despite the burgeoning interest in SM research, the focus has predominantly been on the design of SM unit cells. The introduction of SM layout pattern design, a novel strategy to achieve superior wave shielding performance, has proven effective in enhancing the seismic protection of infrastructures. However, existing research on SM pattern design primarily centers on graded SMs with linear layouts, neglecting the exploration of more intricate layout patterns. To bridge this research gap, this study proposes a hierarchical design strategy that integrates a novel embedded SM unit cell design with the exploration of three graded SM layout patterns including one linear and two polynomial patterns. Subsequently, the frequency domain analysis (FDA) method is developed to assess the wave attenuation performances of the proposed SMs. The analysis results demonstrate that compared to the non-graded SM, the proposed graded SMs exhibit elevated seismic wave attenuation capabilities, suggesting potentials for advanced wave reduction.

Galloping energy harvesting has received great attention due to its simplicity in capturing flow energy. Shaping bluff bodies with different surface protrusions has been proven to obviously alter the amplitude and mode of oscillations, and only leeward protrusions can enhance the galloping vibration amplitude. This paper mainly focuses on determining the optimal lengths of rectangular, triangular, and elliptical leeward protrusions. An electromechanical mathematical model was established based on the extended Hamilton’s principle and Euler- Bernoulli beam theory to predict the dynamic responses of the galloping energy harvester. Based on the developed model and corresponding wind tunnel simulations using Computational Fluid Dynamics (CFD), it is shown that the optimal lengths of rectangular, triangular, and elliptical protrusions are 14 mm, 17 mm, and 17 mm, respectively. Further impedance matching simulations also reveal that the optimal load resistances for the different protruded bluff bodies are all around 300 kΩ, as long as the wind speed is greater than the galloping cut-in speed. Furthermore, the output power of the three optimally designed protruded bluff bodies outperforms that of the original bluff body by 118.94%, 178.07%, and 187.38%, respectively, at the wind speed of 5 m/s. This also indicates that elliptical surface protrusions have a great potential to enhance the galloping energy harvesting performance.

In recent years, vibration energy harvesting has emerged as a promising solution as sustainable power supply for Internet-of-Things (IoT) sensing and monitoring devices in various sectors. Nevertheless, the practical implementation of vibration energy harvesting encounters challenges, primarily stemming from the unpredictability and low-frequency nature of real-world vibrations. In this work, we introduce a magnetic gear-based electromagnetic energy harvester (MG-EMEH). The MG-EMEH leverages a distinctive magnetic arrangement, enabling it to perform the up-conversion of low-frequency excitations into high-frequency contra-rotating motions for efficient energy harvesting. The magnets are fully utilized for two purposes: formation of magnetic gear and inducing current in coils that are attached to and rotate with one magnet layer. The innovative structural design is based on finite element simulation to provide initial confirmation of its feasibility. An experimental setup is then established to evaluate the performance of the proposed MG-EMEH. The outcomes obtained from both simulation and experiment demonstrate exciting potential of the MG-EMEH for harvesting energy from low-frequency vibrations below 1.6 Hz. The MG-EMEH from this work represents a step forward in vibration energy harvesting by offering a viable solution to the persisting challenge associated with the conversion of unpredictable and low-frequency vibrations, such as human motions and ocean waves, into useable energy source for wearables, medical applications and offshore environmental monitoring.

In this paper, we present designing vibration energy harvesters based on the heart motion data achieved through image processing. Energy harvesting is intended as a means to continuously produce energy by converting heart motion to electricity. This energy can be used to power pacemakers. Currently, pacemakers are powered by non-rechargeable batteries that run out in about 10 years. The pacemakers need to be surgically replaced when the batteries run out. Using energy harvesters could pave the way for permanent pacemakers that do not require periodic surgical replacement. We have developed low-frequency energy harvesting designs that have small size and at the same time can be tuned to the heart motion frequencies. One important aspect of the design process is having a reasonable estimate of the heart motion. We use image processing and specifically optical flow tracking techniques to track the motion of individual points in Echo, MRI, and ultrasonic images. We experimentally test the developed harvesters to examine their power generation.

We applied our previously developed wireless sensor node (WSN), which is powered by a piezoelectric vibration energy harvester (VEH), to structural health monitoring and verified its classification performance. The self-powered WSN can measure structural vibrations and transmit the three-axis acceleration waveform data wirelessly. A normal intact beam and four beams that had a single notch cut at four different locations were prepared for classification testing. The WSN, which was connected to the piezoelectric VEH, was mounted on the tip of an aluminum alloy beam, and a random vibration was then applied to the clamped end of the beam. First, the power of the piezoelectric VEH under the random vibration condition was investigated by introducing the probability of harvesting. The beam’s vibrational modes were identified within the 0–1,600 Hz frequency range using the transmitted waveform data. Second, based on an analogy to 2D image classification, we created an input image by arranging the x, y, z-axis acceleration spectrum data vertically and then classifying the arranged data using 2D convolutional neural network (2D-CNN) layers. The Bayesian optimization technique was used to maximize classification accuracy by optimizing the hyperparameters. We confirmed that the self-powered WSN can provide high classification accuracy of 99.9%. We also revealed the basis of this high classification accuracy using gradient-weighted class activation mapping (Grad-CAM) and the continuous wavelet transform (CWT). Our self-powered WSN, which can provide additional features by transmitting the three-axis acceleration waveforms, represents a useful tool for structural health monitoring or predictive maintenance applications.

Quasi-static-toggling (QST) electromagnetic energy harvesters are the most successful commercialized small-scale mechanical energy harvesters for self-powered wireless electronic gadgets, such as motion-powered wireless switches. However, the working principle was not seriously investigated and theoretically analyzed until recently. Given the belated attention from academia, there is still a large gap in fully understanding the QST operation and grasping its optimal energy harvesting strategy. This paper provides a parametric analysis for optimizing the mechanical input energy of a bistable QST harvester. The input energy is calculated as the stored potential energy in the energy-buffering spring before toggling or being released. Such an amount of energy correlates with the magnetic force, the distance between two magnetic poles, and the stiffness of the energy-buffering spring. On the other hand, the most considerate design as a self-powered wireless press button must also take the human factor, i.e., comfortable fingertip force, into consideration. All these factors are taken into consideration in this paper, based on a lumped model. A multi-field coupled numeric simulation is carried out using Matlab Simscape to validate the theoretical analysis. The result shows that the optimal energy input is realized when the product of magnetic pole distance and energy-buffering spring stiffness equals the magnetic attaching force. An experimental prototype is manufactured based on this guidance. Such a new insight is valuable for making effective and full use of a QST electromagnetic energy harvester.

Kinetic energy from vibrations emerging from mechanical systems such as machines and vehicles has been thoroughly studied as a power source in the last two decades. Numerous kinetic energy harvesters have been built to convert human locomotion into electrical power but haven’t been implemented on a wide commercial scale. On the other hand, energy harvesters for farm animals haven’t been studied as much. In this paper, we present a three-dimensional electromagnetic induction based kinetic energy harvester optimized specifically for cattle wearable applications. All the device parameters are obtained with an empirical optimization procedure by considering specific cattle locomotion characteristics. The prototype is 3-D printed with low friction and impact resistant materials. Finally, the device is tested in a real free grazing scenario with live cattle. The kinetic energy harvester performed well and was able to power the load and transmit animal body temperature data over long distances for up to 7 times/h.

Monitoring of aquatic life is important for assessing long-term impacts on activities associated with fish stock and migration. One promising approach for long-term monitoring involves the development of self-powered telemetry devices capable of powering themselves by harnessing energy from the fish body undulations using implanted devices or from fluid motions generated by fish swimming using external devices. One of the latter devices is a broadband low frequency nonlinear bistable energy harvester. This cost-effective harvester has been inspired from the doubly curved leaf blades of a Venus-fly trap. This work examines the static behavior of such a bio-inspired bistable energy harvester by analyzing its force-displacement characteristics. The objective is to identify crucial design parameters to optimize the harvester’s performance for potential application in self-powered fish telemetry tags. The unique characteristics of hysteresis loop and snap-through discontinuity of the harvester are investigated using finite element analysis. The finite element model is found to qualitatively replicate experimental observations. Additionally, geometrical and assembly parameters that affect the force-displacement behavior of the harvester are identified. A sensitivity analysis is performed to determine the effect of the aspect ratio, buckling displacement and thickness of the proposed harvester on the static force-displacement curve.

This paper explores using electrically induced damping (electrical damping) as a dual indicator to power output and harvesting bandwidth in vibration-based piezoelectric energy harvesting. It evaluates electrical damping's impact on bandwidth and power using an impedance-based unified model. Analytical formulas are systematically developed to quantify the electrical damping and change in system stiffness for harvesters interfaced with various circuit topologies. The relationship between the electrical damping and power is elucidated through both analytical analysis and numerical examples. This electrical damping approach allows for analyzing power and bandwidth simultaneously.

The demand for renewable energy has increased in recent years, including the market for micro wind turbines. However, low-cost products lack a proper control system and advanced features. This paper improves on an integrated control system with real-time monitoring and braking capabilities. The prototype uses off-the-shelf components, reducing costs and not needing constant maintenance. The control electronics include a three-phase rectifier and a DC-DC converter to regulate the output voltage. To allow real-time control, a microcontroller measures the voltage, current, and turbine speed. An emergency stop button is also implemented to stop the turbine with an electronic brake. Primary system performance data is displayed in a user interface by a Raspberry Pi and stored for future analysis. Experimental results demonstrate the feasibility of the proposed control system for commercial micro wind turbines of 200 W and 400 W.

Conventional bi-stable piezoelectric cantilever-based vibration energy harvesters must jump over the potential barrier for the large amplitude oscillation. In this research, we develop a harvester with annular potential energy function. The harvester can circumambulate around the potential barrier rather than jumping over it over a wide spectrum under small amplitude excitations. The proposed harvester contains two conventional piezoelectric cantilevers placed orthogonal to each other which are coupled by repulsive magnetic force. We demonstrate the features of the proposed harvester analytically and experimentally. Case studies were carried out, and it is proved that the proposed annular stable harvester has a bandwidth of 3.9 Hz and a voltage output performance 3.01 times better than that of a conventional bistable one under excitations of 3 m/s².

The article aims to develop a physics-informed neural network (PINN) for parameter identification in a piezoelectric harvester using experimental sampling data. The advantage of PINN lies in its efficient inverse calculation of parameters with minimal sampled signals. For instance, with a single piezoelectric oscillator, the data collection process requires only two sets of piezoelectric voltage waveforms acquired at different electric loads and excitation frequencies. The training process involves minimizing the loss function, which comprises the model-based differential equations and the sampled time-domain voltage signals. The results successfully achieve inverse parameter identification, covering mechanical damping ratio, capacitance, and voltage source (force magnitude divided by the piezoelectric constant). In addition, the voltage frequency response, based on the inverse parameters, agrees well with experimental observations, confirming the model’s reliability.

The energy-harvesting efficiency of a magnetostrictive energy harvester feeding a load with a resistive input impedance is described with four non-dimensional parameters: electromagnetic-mechanical spring constant ratio, primary damping ratio, coil damping ratio, and damping ratio of input resistance. Among the parameters, the damping ratio of input resistance has an optimal value while the energy-harvesting efficiency becomes high as the electromagnetic-mechanical spring constant ratio increases or as the primary damping ratio or the coil damping ratio decreases. This paper presents experimental investigation methods to find and validate the optimal damping ratio of a magnetostrictive energy harvester under free vibration. It was found that the unimorph magnetostrictive energy harvester investigated in this study can harvest 10.1% of given mechanical energy with the optimally tuned damping ratio of input resistance.

Metamaterials have marked notable advancements in vibration damping and energy harvesting. However, the specific impact of internal coupled resonators, encompassing both linear and nonlinear types, has received limited attention from researchers. This study aims to delve into this underexplored area. Utilizing a distributed parameter model grounded in modal analysis, our research investigates the effects of these coupled resonators on metamaterial functionality. We particularly emphasize the influence of varying the position of attached masses and its consequent impact on the bandgap properties. Through developing theoretical and mathematical models for metastructural beams with internally coupled resonators, our approach facilitates future simulations and analyses. This investigation not only provides pivotal insights for the design and optimization of metamaterials but also underscores the possible potential of manipulating resonator properties to broaden their practical applications.

Mechanical metamaterials are engineered structures with complex geometric arrangements that display unconventional mechanical properties which are uncommon in traditional materials. They possess the ability to manipulate and control mechanical wave propagation across specific frequency ranges known as frequency bandgaps. This feature makes them extremely useful for a variety of applications in structural control including passive vibration isolation. These materials can be engineered to selectively block or redirect the input motion at their dominant frequency while allowing the transmission of motion at other frequencies. This paper aims to study the dynamic performance of an innovative mechanical metamaterial designed for seismic isolation in multi-story buildings. This seismic isolator, which is termed the meta-isolator (MI), utilizes solid friction to enhance energy dissipation in addition to its natural viscous damping. The proposed MI comprises multiple interconnected cells linked in series via a network of springs and dampers, where each cell includes a lumped mass, spring, damper, and sliding surface, providing both vibration isolation and energy dissipation functionalities. A dynamic model is developed to characterize the nonlinear hysteresis behavior of the proposed MI. This model is implemented on a one-story building model to assess its seismic performance under specific ground motion. A parametric analysis is conducted to optimize the key parameters of both the MI and the building model aiming to reduce drift and absolute acceleration responses. These parameters include mass and frequency ratios, the magnitude of normal force acting on the sliding surface within each cell, and the number of cells. Finally, the optimized dynamic model of the MI is utilized to evaluate its efficacy in seismic isolation of a finite element (FE) model of a one-story 2D frame building subjected to the same ground motion. The FE model is developed using the OpenSEESPy package which is a Python 3 interpreter for OpenSEES. Insights from this study indicate significant promise for the performance of the proposed MI offering hope for its use as a potent passive control system for seismic isolation. In particular, we have shown that lightweight MIs with low frequency ratios (less than 0.5) can outperform the conventional seismic isolators.

A lightweight structure endowed with broadband wave absorption capabilities is highly sought in the development of vibration and noise reduction applications. In this work, we present a type of absorbing meta-barrier via topological optimization which totally absorbs the waves near the boundaries at an extremely broadband frequency range. In order to elucidate the perfect absorption mechanism, we developed a theoretical model that incorporates a lumped mass-spring-damper system coupled to a semi-infinite thin beam. The sample is fabricated through 3D printing. The absorption performance is experimentally characterized, and a maximum absorption of more 95% is achieved over a broad frequency range from 700 Hz to over 3000 Hz in the experiment. Our approach provides an effective solution to the design of lightweight broadband noise and vibration isolation/suppression devices for practical engineering applications.

Traditional vibration control dampers are sensitive to frequency modulation and very difficult to achieve long-period frequency modulation. The control effects of traditional dampers are not ideal in the face of complex and variable loads, and their robustness is low. Latching control is a phase optimization method applied in wave energy converters (WECs). Based on the latching control of WEC, this paper proposed the latched mass damper (LMD) and three different control strategies for the optimization of vibration control. The LMD can directly extend the periods to match well with the long-period vibrations by introducing the latching forces. First, the theoretical model of LMD is established and analyzed. Then, the dynamic performances of LMDs with different strategies are compared. The results indicate that the proposed LMD and strategies for vibration control are feasible. The proposed latching control mechanism and strategies can provide an innovative solution for improving the efficiency of high-frequency detuned dampers, designing of long-period dampers with limited stroke spaces, and adaptive semi-active dampers in the future.

3D printing technology is widely researched and applied to industry nowadays, and the range of printing materials has expanded from polymers and metals to composites. Since 3D printed CFRP exhibits changing elastic properties depending on a path of printing or fiber rate, a method for measuring elastic properties is necessary to predict the performance of the printed result. This study proposes a non-contact, non-destructive measurement method of effective elastic properties of 3D printed CFRP plate using laser-ultrasonic technique. To generate and detect Lamb waves on the plate without contact, Q-Switched pulsed laser and a laser doppler vibrometer were used, respectively. As a first step to calculate the effective elastic properties from Lamb wave measurements, a dispersion curve calculation method based on Lamb wave signals was suggested. Subsequently, an inverse calculation of elastic properties from the dispersion curve was proposed. Effective elastic properties of 3D printed CFRP were measured using developed method, and the error was estimated.

This article presents an innovative method for monitoring rotating instruments using piezoelectric array sensors and multi-task CNN (convolutional neural network) learning. The setup involves connecting piezoelectric patches, enabling spatial condition monitoring with a single voltage signal. The sensor array simultaneously tracks four bearing conditions and three gear conditions. However, a single-task CNN faces challenges, especially when trying to distinguish weak gear faults while simultaneously considering different bearing conditions. To address this, the article employs multi-task learning, simplifying the classification task by utilizing shared convolutional layers and two distinct fully connected layers dedicated to gear and bearing health states. This approach is also adaptable for extending fault detection to additional locations. Experiment demonstrates that multi-task learning achieved a 90% accuracy in identifying missing tooth defects in gears, outperforming the 78% accuracy of single-task learning. It confirms multi-task learning’s effectiveness in detecting weak faults during multi-location defect monitoring using piezoelectric arrays.

Shape memory alloy (SMA) shows promise as a powerful actuator for soft robots, offering high force and a simple mechatronic setup. However, effectively controlling SMA-actuated soft robots remains challenging. This study presents a modular robot design that leverages a curved beam's snap-through mechanics and an integrated ``mechanical'' circuit. This design enables robotic locomotion with only a single DC power supply, representing a notable advancement toward SMA-actuated, controller-free locomotion.

This study aims to develop a new type of piezoelectric actuator that can generate surface acoustic waves (SAWs) on a piezoelectric ceramic and non-metallic composite. In the structural design, this study employs a composite SAW device coupling a piezoelectric plate with uniform electrodes and attaches it to an 84mm by 53mm rectangular non-metallic substrate. The piezoelectric actuator and the non-metallic substrate are coupled with a solid coupler to transfer acoustic vibrations to the substrate, thus exciting antisymmetric Lamb waves. Lamb waves refer to elastic propagating in a solid plate with doubly free boundaries, for which displacements occur both in parallel and perpendicular to the direction of wave propagation. In the driving method, we design a SAW composite that can generate antisymmetric Lamb waves in the frequency range between 200 kHz and 400 kHz. Finally, experimental studies are conducted to verify this SAW device's design excited the surface acoustic waves and the resonate frequency's wavelength.

Pulse-echo (PE) laser ultrasonic testing (LUT) is a reliable non-destructive testing (NDT) method used to inspect subsurface damages in composite materials. Accurate detection and localization of damages require a perpendicular impingement angle and a constant stand-off distance between the sensing laser and the inspection surface. A 6-degree-offreedom robot arm can carry the specimen along a raster path to maintain these requirements throughout the LUT process. However, the movement requires precise control over the position and orientation of the robot arm's tool center point (TCP), which depends on the shape of the inspection target. In real-world applications, mathematical representations or 3D CAD models of the inspection target are not always available. This study proposes the use of 3D scan-generated models to create raster scan paths for robotic PE LUT. Geometries of the region of interest (ROI) are utilized to generate the necessary scan grids for controlling the robot arm's movement.

Pressure monitoring in a high-temperature condition stands as a primary objective in development and optimization of pressure sensors. Langasite, which are known to be useful in high temperatures, is a great candidate for gauging pressure in such challenging environments, particularly in Surface Acoustic Wave (SAW) devices. Employing a new FEM approach, we provide calculating the frequency shift of the resonator when subjected to varying force angles. Our analysis extends to consider nonlinear material constants, including second and third-order piezoelectric, electrostrictive, and third-order dielectric constants. Within this study, we computed the sensitivity of stress-induced velocity shifts, incorporating the effects of nonlinear parameters. Validation of our numerical findings is conducted against experimental data obtained from a SAW quartz circular resonator, where the force application rotates around the SAW disk at specific angles relative to the wave propagation direction. Presenting the results concluded from our findings, we present a comprehensive force-velocity effect graph, describing the interplay between force angle rotation and frequency shift across various Euler angles. Our numerical results are consistent with experimental measurements, demonstrating alignment both in overall trends and the span of frequency shifts observed. The validation process agrees with our numerical approach, as evidenced by its applicability to diverse configurations of SAW resonators with varying Euler angle arrangements. Notably, our analysis reveals that Y-cut Langasite (LGS) SAW resonators exhibit lower sensitivity to force-velocity effects compared to their Y-cut quartz SAW sensor counterparts.

Fluid depth measurement finds practical applications across various industries, including petroleum, water treatment processes, nuclear technology, and household appliances. Traditional methods include magnetic, radiation, linear variable differential transformers, LASER, pressure, capacitive, float, conductivity probe, fiber optics, ultrasonic, and triple bubbler sensors. Despite its promising attributes, such as suitability for low to very high-temperature environments, wireless interrogation, battery-less operation, and compact size, the application of piezoelectric Surface Acoustic Wave (SAW) sensors in fluid depth measurement has been comparatively unexplored in the current body of literature. This research presents a depth gauge engineered for fluid depth assessment employing a Surface Acoustic Wave (SAW) resonator. The apparatus comprises a 20 μm thick aluminum diaphragm, with a quartz SAW resonator affixed to it. Employing an 868 MHz quartz SAW resonator enables the precise measurement of minute depth variations, with a remarkable sensitivity of 532 Hz/mm (equivalent to 0.61 ppm/mm), allowing for accurate detection of changes as small as 1 mm.

Electronic assemblies are subjected to damaging impact and shock loadings in various scenarios, including aerospace, automotive, and military applications. In safety-critical situations, the online detection, quantification, and localization of damage within the electronic assembly would enable intelligent systems to take corrective actions to mitigate or circumvent the effects of damage within the electronic assemblies. This preliminary work investigates a reduced-order model-based method for online damage detection, quantification, and localization of printed circuit boards (PCBs). The local eigenvalue modification procedure (LEMP) is used to accelerate the computational processing time of the model, thereby enabling its use in online damage detection during an impact or shock event. The proposed method tracks changes in the model’s state using an error minimization technique in the frequency domain. A baseline state is established by creating and simulating a numerical model that accurately represents a healthy PCB response. Potential reduced-order models with varying stiffness matrices are developed online and compared to the system’s current state. These reduced-order models introduce a single change in stiffness to the system. LEMP calculates the overall change in the system to obtain the new system-level dynamic response. Incorporating LEMP within the frequency-based analysis demonstrates the potential for effective damage detection on PCBs. This work validates the proposed methodology using a rectangular PCB with induced damage. The PCB is modeled pinned at each corner, and its dynamic response is simulated using ABAQUS and processed with the generalized eigenvalue procedure. LEMP is used to update a single change in the system while obtaining a 587 times speed up when compared to the generalized eigenvalue approach. The LEMP algorithm performance and reliability for updating the model state are discussed in the paper.

Origami, an ancient Japanese paper art, has inspired diverse fields, leading to modern origami-inspired engineering, notably origami robots known for outstanding versatility across scales. Recent advancements in the field of origami robots have ushered in improved performance through the implementation of innovative actuation methods and the incorporation of non-rigid origami. The latter, known for its energy-efficient approach utilizing multi-stabilities, facilitates shape-changing and shape-locking. Nevertheless, non-rigid origami in robotics remains in its formative stages, marked by persistent challenges, notably the intricate task of achieving precise actuation while managing the bistability-reconfiguration trade-off. This article aims to develop a multifunctional mesoscale reconfigurable robot based on the Kresling tower pattern, characterized by advanced mechanical properties such as rapid shape-changing, shape-blocking capabilities, and adjustable structural behavior. To this end, we present the development of a polypropylene (PP)-based origami robot based on the ‘Kresling’ pattern. An in-body actuation mechanism is developed to facilitate the robot’s bistable shape-changing capabilities. Furthermore, we provide a comprehensive mechanical performance assessment of the origami robot.

This paper proposes a novel approach of multiple non-continuous cascade-connected one-port Surface Acoustic Wave Resonators (SAWRs) at operating frequency of 433.42 MHz to improve the resolution of temperature sensing. Three different sets of SAWR-based temperature sensors have been experimented using one, two, and three cascade connected commercial SAWR(s) to evaluate performance, including sensitivity and sensing resolution, of the temperature sensor with respect to increasing the number of SAWRs in cascade connection. The frequency shifts of all three different configurations were measured at the same time w.r.t. the change in the temperature. The sensitivity is calculated as the ratio of the total frequency shift to the total temperature change. Results demonstrated that not only the sensitivity of the sensor but also the resolution of the sensor to sense the temperature is increased with increasing the number of SAWRs in the cascade connection. Maximum sensitivity on average of 681.81 Hz/°C was noted with 3 SAWRs which is 33.3% higher than the sensitivity of 1 SAWR which was on average 454.54 Hz/°C. Additionally, 1 SAWR could detect on average 9.46 °C of change in the temperature while 3 SAWRs could detected 6.74 °C of change in the temperature, proving the higher resolution of temperature sensing with a greater number of SAWRs. This unique approach of increasing resolution and sensitivity of the sensor can help to develop sensor array using SAWRs which can be operated using single device.

In the aim of attenuating noise transmission through air-ducts, research is prompted for overcoming the limitations of classical acoustic liners, especially in the aero-engines applications. The new generation of Ultra- High-By-Pass-Ratio (UHBR) turbofans while considerably reducing fuel consumption, increases noise pollution especially at lower frequencies because of their larger diameter, lower number of blades and rotational speed. Moreover, they present a shorter nacelle, leaving less available space for acoustic treatments. In case of simplified one-dimensional propagation, integral constraints exist which analytically define the limits of the scattering performances of reciprocal systems, such they are the local impedance liners, for a fixed length of the acoustic treatment along the duct. In this contribution, we analyse a special boundary condition breaking the reciprocity principle, and overcoming the limitations of locally reacting liners. We call it Advection Boundary Law as it introduces a convection on the boundary, responsible of non-reciprocal behaviour at grazing incidence, and of the enhancement of transmission loss with respect to pure locally-reacting resonators. Performances and passivity of such boundary law are numerically analysed first in grazing-incidence problems. The grazing-incidence problem is experimentally studied in a plane-wave acoustic waveguide lined by electroacoustic resonators which can be programmed to reproduce such advection boundary law.

In the attempt to reduce fuel consumption, a new generation of Ultra-High-By-Pass-Ratio (UHBR) turbofans have been introduced in the aeronautic industry which are structurally noisier especially at lower frequencies, because of their larger diameter, lower number of blades and rotational speed. Moreover, they present a shorter nacelle, leaving less available space for acoustic treatments. For this reason, innovation in the liner technology is highly demanded. In this contribution, we analyse the performances of an electroacoustic liner, made up of microphones (sensors) and small loudspeakers (actuators). Such array of electroacoustic resonators can feature an interesting boundary operator, called Advection Boundary Law. Such boundary law has been analysed in grazing-incident acoustic fields without air-flow and in case of plane waves. Here, we adapt such boundary condition to attenuate spinning modes. Numerical simulations in case of spinning-modes, shows the potentiality and the passivity issues of such innovative boundary law. Finally, a reproduction of a turbofan engine (scale 1:3) accomplishing real-life rotational speeds, allows to assess the performances of the Advection Boundary Law in presence of mean-flow and spinning-modes.

In this paper we report on a new method to generate radial surface acoustic waves (SAW) on a thick ringdisk. We set up a 0.25mm thick ring-type piezoelectric actuator to generate surface acoustic waves on a surface of a 1mm long stainless-steel pipe with a 5.5mm wall thickness. The boundaries are set to be free at all the edges and surfaces. Finite element simulations were employed to identify corresponding resonance modes and driving frequencies. Radial-type Rayleigh waves were selected and used to design interdigital electrodes on the piezoelectric actuator surface. Based on our design, it is found that a radial Rayleigh-wave-type surface acoustic wave will be generated at 7.42MHz with a substantial amplitude enhancement at the inner surface, which converges from the outer ring to the inner ring on the opposite surface of the disk. Applying this radial Rayleigh wave, we demonstrated that this surface acoustic wave can be used to create acoustic streaming in a droplet.

To address the limited payload space within small satellites, there is growing interest in deployable structures within the field of space engineering. This study proposes a design concept of a deployable reflector based on the compact folding method known as the “Flasher Pattern”. The design ensures that the length of the crease line in stowed and deployed state are equal, and points forming an origami pattern meet an elliptic paraboloid equation formed by the folding pattern. In general, the reflector structure has design requirements related to size and shape. To verify the suitability of the origami-based structure as a reflector design, performance analysis, including packing efficiency, surface error, and focal error, is conducted by varying the design parameters of the structural configuration. To confirm the feasibility of the deployable origami-based structure, a prototype was manufactured using thin material. To ensure self-deployment, torsional-induced hinges that operate within the elastic range of the material were used as a deployment mechanism. The fabricated structure was confirmed to unfold as intended from the folded state to the deployed state.

Origami is a technique of folding a sheet along patterns, allowing the shape, size, or mechanical properties to be changed. Its ability to compactly fold and then deploy into a large size is widely exploited in space structures, including solar panels, space architecture, and so on. Especially, for 3D foldable structures like space shelters, thick materials are essential for structural robustness and excellent radiation shielding. However, interference due to the thickness of panels restricts the folding mechanism, and avoiding the interference makes the pattern and folding sequence complicated. In this study, we propose a Recessed Pyramid Origami (RPO) module which can be folded into a pyramidal shape with a recessed center from a single flat plane. Although it has an open-loop pattern with a separation in a partial sector, it can achieve a simple one degree-of-freedom (DOF) folding motion by additional internal patterning. Its final shape is defined by panel-to-panel contact, eliminating the need for extra end-stoppers. Moreover, the RPO is rigid-foldable, enabling the use of thicker panels, and it can be designed in various shapes according to design parameters. The RPO also can create various 3D structures including approximated dome, prism, arch, corrugated panel, tube, and polyhedron by tessellating. It is anticipated that a wide range of foldable structures can be designed by utilization of the proposed RPO and its derivatives.

Shape memory polymers (SMPs) are smart materials capable of “remembering” and reverting to a pre-set shape upon exposure to specific stimuli like heat or light, even after substantial deformation. When combined with flexible electronics, SMPs enable innovative morphing structures for space applications. These structures, such as adaptive satellite antennas and solar arrays, possess the capability to reconfigure themselves dynamically, thereby optimizing signal reception, energy harvesting, and spacecraft aerodynamics in accordance with the unique demands of each mission. This research merges material extrusion (MEX) and fused filament deposition techniques to advance the additive manufacturing of SMP-based morphing structures, which feature conductive silver traces encased in a shape memory polylactic acid/thermoplastic matrix. Thermo-mechanical material properties of the printed SMP, including the maximum recoverable strain and glass transition temperature, were first characterized. Subsequently, a series of SMP-based structures were prototyped to demonstrate their morphing capabilities in response to light, thermal, or electrical stimuli. These results confirmed the feasibility of on-demand manufacturing of morphing structures, reducing the constraints of Earth-based pre-launch design limits and ensuring sustainability for future deep-space exploration missions.

The concept of an electric vertical take-off and landing (eVTOL) aircraft emerged recently with the advanced technological developments in electric propulsion systems and the demand for urban air mobility which can be a countermeasure for traffic congestion in urban and suburban areas. Even though significant research has been done on eVTOL designs, due to the early era of the concept and the wide variety of designs available, there are no specific ways/standards to approach an eVTOL design, unlike the conventional helicopter and aircraft. This research aims to explore the performance evaluation of an eVTOL propeller from the views of both fluid and structural simulations by conducting Computational Fluid Dynamics (CFD) and Fluid-Structure Interaction (FSI) on a wide range of advance ratios. The CFD model used in this research is Reynolds- Averaged Navier-Stokes (RANS) equations with a k-ω Shear Stress Transport (SST) turbulent model. The results provide optimized eVTOL design for the next generation of aircraft concepts to fulfill the aerodynamic performance demands of the world.

This paper introduces an innovative approach featuring a reinforced concrete beam that combines smart Fiber Reinforced Concrete beam integrated with Shape Memory Alloys (SMA-FRC) and Fiber Bragg Grating (FBG) sensors. Embedded FBGs serve as strain sensors for real-time structural monitoring while the SMA wires serve as integrated actuators to recover the excessive deflective and ensure the safety of the structure. The primary objective of the study is to assess the structural integrity and reliability of the SMA-FRC beam, particularly in terms of crack detection and crack width estimation. The remarkable properties of shape memory alloy wires, encompassing shape memory effect (SME) and superelasticity effect (SE), play a pivotal role in the system's performance and functionality. The SME and SE properties empower the beam to autonomously recover its deformation and effectively reduce crack width. Real-time measurements of strain, stress, and crack width are continuously collected by an integrated processing unit, providing valuable insights into the beam's reliability, and generating risk assessment reports. Upon detecting excessive strain and thus crack width by Embedded FBG sensors, the SMA wires will be activated to recover the excessive deformation. For low induced strain, the strain recovery will be performed using SE, while for high strain, the SME will be activated by induing electric energy. To assess crack development and propagation within the beams, a combination of ACI codes and analytical modelling techniques have been utilized. Additionally, regression methods are applied in conjunction with beam theories to derive continuous deflection profiles based on the optical signals of the FBG sensors. A proof-of-the-concept experimental test has been conducted to illustrate the performance and functionality of the smart SMA-FRC beam in crack monitoring and control. The research outcomes demonstrate that the incorporation of FBG sensors is an efficient means of crack and strain detection for FRC beams. Furthermore, the embedded SMA wires potentially enhances structural integrity and extends the service life of the beam by substantially reducing crack formation.

This work presents a novel actuator system consisting of a combination of shape memory alloy (SMA) wires with electrostatic clutches. The proposed combination of smart material systems stems from the need to reduce the energy consumption of SMA based actuators while leveraging their high energy densities in compact actuation systems. To this end, large actuation forces provided by SMA wires are combined with the highly energy-efficient actuation of electrostatic clutches. The actuation of the SMA wire generates the desired motion after which the activation of the clutch and deactivation of the SMA wire ensures that the desired position is held in an energyefficient manner. A retractable electro-adhesive tubular clutch was chosen for our testing due to its enclosed nature and ease of fabrication. A characterization setup is also developed and used to demonstrate the feasibility of our new actuator concept. The tests show that a 254 μm SMA wire has an energy consumption rate of about 850.7 mJs⁻¹ as compared to less than 31.3 mJs⁻¹ for the clutch, which results in an energy savings rate of over 800 mJs⁻¹ for position holding tasks.

In times of increasingly complex overall technical systems and the associated component integration, there is a need to downsize existing components. Due to their high energy density, shape memory alloy (SMA) actuators offer the possibility of developing lightweight and compact systems. These actuators are available in a variety of shapes, e.g., wires. A challenge for shape memory alloy wire actuators arises in the design. SMA actuator wires can typically generate a stroke of 2-4%. One possibility to integrate a certain length for the required actuator stroke without additional elements (e.g., levers) is the deflection of the wire. It can be observed, however, that the lifetime of the deflected wires is significantly shorter than in the non-deflected state, despite the same external mechanical stress and actuator stroke. It is also expected that cracks occur at the deflected points where the mechanical stress is high. Therefore, investigations on the influence of the deflections on the lifetime of SMA wires are necessary. In this paper, a test set-up for deflected SMA wires is developed, which is a modular extension of an existing lifetime test rig for SMA actuator wires. The focus of the setup is the ability of parameter variations to determine their influence on lifetime behavior. Potential influencing factors which are investigated are the deflection radius, the actuator elongation, the deflection path, and the wire diameter. The setup is validated in terms of friction and bending stiffness.

Smart materials that can change their stiffness and damping properties in response to external changes are being utilized in a variety of applications, including structural control and vibration dampening. Magnetorheological elastomers (MREs), a type of smart material, exhibit changes in stiffness or damping performance when exposed to an external magnetic field. This unique property of MR materials is an advantage and can be utilized in damping system. In order to utilize MR materials in vibration dampening devices, it is important to maximize the dynamic properties of the device. To enhance the change in dynamic properties when a magnetic field is applied, topology optimization was used to find the design that maximizes the change in natural frequency, which is one of the important parameters indicating dynamic properties. The material properties of MR were reproduced using data from existing literature, and analysis was performed. The volume fraction was changed in order to optimize the topology. It was observed that the natural frequency increased by over 40% compared to the original when the volume fraction was set to 50%. The volume fraction with the largest difference was identified. This study can serve as a fundamental research for producing devices that reduce vibration.

The goal of this investigation is to determine how the sedimentation of the magnetorheological (MR) fluid within an MR sandwich beam affects the beam’s dynamics. To discover this phenomenon, the authors measured the natural frequency and damping of a beam under several conditions, such as outer-layer stiffness, magnet location, and magnetic field strength, for beams whose MR fluid had sedimented and beams that were shaken to redistribute the fluid within the beam. The collected data was compared to previously measured modal data of the same beams when they were first built. The results show that the beams’ natural frequency lowered when the MR fluid sedimented and was shaken, but the damping rose for sedimented and shaken beams.

The use of magnetorheological dampers for structure vibration mitigation has been largely considered due to the semiactive nature of their control. While the ability to deliver high-performance with a limited power supply is attractive, it is imperative that the controls are properly developed under various excitation frequencies. However, because the desired control force may not directly be commanded to the magnetorheological damper, the employment of a secondary control to dictate the voltage applied to the damper is necessary. This proves further challenging due to the inability to invert the most accurate damper mathematical models. This research focuses on the assessment of multiple controller designs, including LQR and Skyhook, to determine their effectiveness under a wide range of excitation frequencies. The influence of limited sensing information is also analyzed through case studies.

Real-time hybrid testing is a method frequency utilized for assessing systems with nonlinear and challenging-to-model elements. This approach integrates numerical and physical components, allowing for swift and cost-effective refinement of system iterations. In this research, a magnetorheological (MR) damper is used as the physical substructure due to its inherent nonlinear, hysteretic behavior, while a simple four-story structure serves as the numerical substructure. The effectiveness of the proposed real-time hybrid testing framework is validated by comparing its results with both analytical and physical recreations of the experimental system. This comparison aims to confirm the framework’s reliability and its potential as a testing method for systems with complex, nonlinear components.

Magnetorheological elastomers (MREs) are smart composite materials whose viscoelastic properties can be varied upon the application of an external magnetic field. MRE-based systems can provide field dependent variable stiffness and damping simultaneously due to viscoelastic properties of MREs. This unique behavior of MREs enables them to be effectively utilized in the development of adaptive isolators or absorbers to suppress vibrations in wide range of frequencies. The present study aims to investigate the capability of different viscoelastic models to predict the response behavior of MREs under varying dynamic loading conditions. To this end, MRE samples with a 25% volume fraction of ferromagnetic particles were fabricated and tested under shear mode. The field dependent viscoelastic models based on Kelvin–Voigt, Maxwell, Standard Linear Solid (SLS), and Generalized Maxwell (GM) are then formulated to predict the variation of storage and loss moduli under different driving frequencies and applied magnetic flux densities. Results indicate an inability of Kelvin-Voigt, Maxwell and SLS models to reasonably estimate the MRE’s mechanical behaviour under different magnetic flux densities and excitation frequencies, whereas the GM model with seven parameters was able to accurately predict the field-dependent and frequency-dependent characteristics of MREs.

This study aimed to investigate the accuracy and reliability of near-infrared (NIR) spectroscopy combined with terahertz (THz) spectroscopy for quantitative analysis of the chemical composition of food samples. Traditionally, NIR and THz spectroscopy have been used separately in food analysis. While NIR spectroscopy has advantages in terms of nondestructive and speed, THz spectroscopy is prized for its ability to penetrate deep into the sample, making it suitable for detecting internal quality. A detailed comparison of NIR and THz spectral data from commercially available biscuit and sunflower seed samples was conducted to evaluate the effectiveness of the combined use of the two techniques in improving the accuracy of ingredient detection and to explore how to effectively integrate the two spectral data to maximize their benefits in food analysis. The experimental results showed that NIR spectra showed high prediction accuracy in biscuit samples but were relatively weak in sunflower seed samples. Considering the characteristics of THz spectroscopy and recent research progress, THz spectroscopy may provide more accurate predictions than NIR spectroscopy in the chemical composition of sunflower seed analysis. This study demonstrates a new food detection strategy and provides the food industry with an effective method to ensure the quality and safety of food for the public. It also provides new perspectives and tools for analyzing other materials and compounds.

Extension modes have proven to provide sufficient ice-interface shear stresses, exceeding the adhesion strength of ice to the substrate and ensuring instantaneous ice delamination. A proposed proof of concept involves the utilization of lightweight piezoelectric actuators for deicing the leading edges of aircraft with minimal power requirements. This deicing method induces vibrations into the structure by activating its inherent extension resonant frequencies, thereby creating sufficient stress levels to fracture the ice and delaminate it from the surface. The deicing mechanism depends strongly on the chosen excitation mode, which depends on the actuator placement. Hard and soft piezoelectric actuators were used to excite extensional modes to generate shear stresses at the interface leading edge/ice great enough to delaminate the ice. Deicing was demonstrated with a power input density of 0.074 W/cm² for a surface ratio of 0.07 hard piezoelectric actuators per cm².

This contribution discusses a design rule for the piezoelectric actuators positioning for the excitation of extensional modes supported with experimentations.

This study uses the data-driven machine-learning technique called ridge regression to address an innovative method of designing hourglass lattice-structured metamaterials. Metamaterials are engineered materials with properties derived from their intricate structural arrangements, holding promise for various applications. The hourglass lattice arrangement is exciting because of its unique mechanical characteristics and possible advantages in stiffness modulation. Designing such complex structures often involves manual iterations and simulations, which can be time-consuming and limited in exploring the vast design space. In this paper, we suggest an innovative approach that uses data-driven machine learning to speed up and improve the design process. By training models on a dataset of metamaterial behaviors, we enable the prediction of optimal hourglass lattice configurations for desired mechanical properties. This prediction uses a machine learning algorithm to analyze the data obtained from existing design simulations. This predictive capacity empowers researchers and engineers to explore an extensive design space efficiently, thus uncovering optimal configurations that might remain undiscovered using traditional methods like manual adjustments and iteration or physical prototyping and testing, which are time-consuming and labor-intensive. Engineers iteratively refine designs based on simulation or test results, limiting design space exploration and potentially missing optimal configurations. The core innovation lies in the ability of these models to predict the mechanical properties and behaviors of hourglass lattice metamaterials based on their structural characteristics, such as radius of curvature and thickness. This methodology can potentially revolutionize metamaterial design by efficiently using data-driven machine-learning models.

Wave propagation in periodic media is crucial for energy transmission, enabling control over energy direction and isolation. This involves breaking the inversion symmetry of a spring-mass chain by introducing mirrored copies of periodic combinations, creating a unique, irreversible structure with distinct dispersion properties. The customizability and linearity of the interface lattice present the main challenge in controlling the interface mode. Our study focuses on designing a controllable on-demand interface mode using shape memory alloy-based smart material actuation mechanism. We included shape memory alloy-type springs along with the conventional springs in the analysis. SMA has the unique ability to change its phase in response to a temperature change due to the phase transition between martensite and austenite crystal structures. As a result, the modulus of elasticity also varies resulting in a change in stiffness. Through this system, the voltage-dependent stiffness can be tuned. This, in turn, enables the existence of an interface mode to be adjusted to the desired frequency and amplitude. The proposed system also allows us to obtain a range of split bandgaps that are dependent on controlling parameters. It is observed that the variation in the actuation voltage is to be controlled for the interface mode tuning and adjust stiffness accordingly. A generalized theoretical scheme is developed for several types of spring combinations at the interface and its effects are compared. Such system can significantly change the wave propagation in periodic media, leading to advancements in energy transmission systems with enhanced efficiency and versatility. Thus, the obtained interface mode within the bandgap can be tuned by the applied voltage enabling future application in wave focusing, wave guiding, and energy harvesting.

Recent advancements in the field of material science and robotics have resulted in smart, adaptive, and intelligent systems for in-field applications. Conventional electromagnetism-based actuators contribute significantly to the size and weight of these systems, and hence, they are not suitable for mobile robots. Shape memory alloys (SMA) have emerged as better alternatives due to their unique characteristics, such as high force-to-weight ratio, noiseless operation, and muscle-like motion, with the potential to develop novel actuation for biomedical, space, and robotic applications. SMAs regain their shape at higher temperatures through the shape memory effect. This effect causes the alloy to transform its shape and then fully recover during phase transition. SMA actuators have been thoroughly examined for their potential integration into robotic hands, arms, and manipulators. However, relatively long cooling times to retransform from austenite to martensite state make SMAs unsuitable for fast and rapid cyclic applications. The current research aims to examine the effect of an evaporative (spray) cooling technique using acetone, methanol, and deionized water as cooling agents on the cooling time of SMA. Comparative studies are performed to study the effect of different coolants on a 1-DOF SMA coil actuator. Furthermore, a SMA-based rotary actuator has been developed, demonstrating the feasibility of implementing an acetone-based spray cooling technique. A control circuit is designed to regulate the spraying process over the SMA coils. This novel evaporative technique offers a significant improvement (154%) in the actuation frequency of the SMA-based actuation system compared to free convection. The findings underscore the potential of evaporative cooling methods to enhance the performance of SMA-based actuators, with implications for fast cyclic applications such as robotic systems.

Advancements in robotic manipulation have led to the development of Variable Stiffness Actuators (VSAs), which have the potential to revolutionize the field by endowing manipulators with high levels of compliant actuation. VSAs are known to provide robustness and flexibility, and hence, they are ideal for tasks requiring variable stiffness, especially in soft robotics and shock-absorbing applications by efficiently harnessing potential energy for repetitive movements. The current research focuses on developing an SMA-based agonist-antagonistic VSA, which follows a non-linear force-displacement relationship. The prototype has been developed with SMA coils in bipenniform configuration with a rotary end effector coupled with an optical encoder to measure the angular displacement. The experiments were conducted on an SMA coil with bias weights, and a regression model was trained for temperature variation with input voltage. ANN (Artificial neural network) was deployed for training the model, achieving an accuracy of 89.12%. Further, an LSTM (Long short-term memory)-based RL (reinforcement learning) model is proposed, that can be integrated with the SMA-based VSA. This architecture defines the change in the state of the current angular displacement depending upon the history of actions. The actions signify the input voltages sampled at regular time intervals during the experiment. Thus, the developed SMA-based VSA system promises to elevate the degree of automation and broaden robotics applications in compliance, adaptability, and efficient energy utilization.