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Phononic crystals (PnCs) have garnered significant interest owing to their ability to manipulate wave propagation, particularly through phononic band gaps and defect modes. However, conventional defective PnCs are limited by their fixed defect-band frequencies, which restricts their adaptability to dynamic environments. This study introduces a novel approach for temperature-controlled tunability of defective PnCs by integrating shape memory alloys (SMAs) into defect regions. The reversible phase transformations of SMAs, driven by temperature variations, induce significant changes in their mechanical properties, enabling real-time adjustment of defect-band frequencies. An analytical model is developed to predict the relationship between the temperature-modulated material properties and defect-band shifts, which is validated through numerical simulations. The results demonstrate that defect-band frequencies can be dynamically controlled within a specified range, thereby enhancing the operational bandwidth of the ultrasonic sensors. Additionally, sensing-performance analysis confirms that while defect-band frequencies shift with temperature, the output voltage of the sensors remains stable, ensuring reliable sensitivity across varying conditions. This study represents a significant advancement in tunable PnC technology, paving the way for next-generation ultrasonic sensors with enhanced adaptability and reliability in complex environments.

Phononic crystals (PnCs) have garnered significant attention due to their unique ability to control elastic waves in unconventional ways. One area of research focuses on utilizing defects within PnCs. Defects create new pass bands within band gaps, leading to concentrated wave energy within the defects. However, defect-mode-enabled wave localization is effective only at specific frequencies, limiting its usefulness when the frequencies of incident waves vary. Existing methods to mechanically tune defect bands involve changing the geometries of unit cells or defects or attaching elastic foundations, which necessitates the detachment and reattachment of certain structures depending on the engineering situation. Considering these challenges, this study introduces a novel approach that utilizes the reconfigurable PnC design, incorporating permanent magnets and ferromagnetic materials. The case study involves a one-dimensional PnC consisting of a long metal beam with rectangular block-shaped permanent magnets periodically arranged and attached to the beam by magnetic forces. A defect is created by shifting a subset of these block-shaped permanent magnets in parallel. The extent of this parallel movement alters the vibrating characteristics of the defect, facilitating the mechanical control of the defect bands in the defective PnC. The effectiveness of this approach is experimentally validated.

Defective phononic crystals (PnCs) have garnered significant attention for their ability to localize and amplify elastic wave energy within defect sites or to perform narrowband filtering at defect-band frequencies. The necessity for continuously tunable defect characteristics is driven by the variable excitation frequencies encountered in rotating machinery. Conventional tuning methodologies, including synthetic negative capacitors or inductors integrated with piezoelectric defects, are constrained to fixed, offline, and incremental adjustments. To address these limitations, the present study proposes an active feedback approach that facilitates online, wide-range steering of defect bands in a one-dimensional PnC. Each defect is equipped with a pair of piezoelectric sensors and actuators, governed by three independently tunable feedback gains: displacement, velocity, and acceleration. Real-time sensor signals are transmitted to a multivariable proportional controller, which dynamically modulates local electroelastic stiffness via the actuators. This results in continuous defect-band frequency shifts across the entire band gap, along with on-demand sensitivity modulation. The analytical model that incorporates these feedback gains has been demonstrated to achieve a level of agreement with COMSOL benchmarks that exceeds 99%, while concurrently reducing computation time from hours to seconds. Displacement- and acceleration-controlled gains yield predictable, monotonic up- or down-shifts in defect-band frequency, whereas the velocity-controlled gain permits sensitivity adjustment without frequency drifts. Furthermore, the combined-gain operation enables the concurrent tuning of both the center frequency and the filtering sensitivity, thereby facilitating an instantaneous remote reconfiguration of bandpass filters. This framework establishes a new class of agile, adaptive ultrasonic devices with applications in ultrasonic imaging, structural health monitoring, and prognostics and health management.

Defect modes in phononic crystals (PnCs) provide strongly localized resonances that are essential for frequency-dependent wave filtering and highly sensitive sensing. Their functionality increases greatly when their spectral characteristics can be externally tuned without altering the structural configuration. However, existing feedback control strategies rely on laminated piezoelectric defects, which have uniform electromechanical loading that causes voltage cancellation for even-symmetric defect modes. Consequently, only odd-symmetric defect bands can be manipulated effectively, which limits multi-band tunability. To overcome this constraint, we propose a segmented piezoelectric sensor–actuator design that enables symmetry-dependent feedback at the defect site. We develop a transfer-matrix analytical framework to incorporate complex-valued feedback gains directly into dispersion and transmission calculations. Analytical predictions demonstrate that real-valued feedback yields opposite stiffness modifications for odd- and even-symmetric modes. This enables the simultaneous tuning of both defect bands and induces an exceptional-point-like coalescence. In contrast, imaginary feedback preserves stiffness but modulates effective damping, generating a parity-dependent amplification-suppression response. The analytical results closely match those of fully coupled finite-element simulations, reducing computation time by more than two orders of magnitude. These findings demonstrate that segmentation-enabled feedback provides an efficient and scalable approach to tunable, multi-band, non-Hermitian wave control in piezoelectric PnCs.

Several previous studies have been dedicated to incorporating double defect modes of a phononic crystal (PnC) into piezoelectric energy harvesting (PEH) systems to broaden the bandwidth. However, these prior studies are limited to examining an identical configuration of the double defects. Therefore, this paper aims to propose a new design concept for PnCs that examines differently configured double defects for broadband elastic wave energy localization and harvesting. For example, a square-pillar-type unit cell is considered and a defect is considered to be a structure where one piezoelectric patch is bonded to a host square lattice in the absence of a pillar. When the double defects introduced in a PnC are sufficiently distant from each other to implement decoupling behaviors, each defect oscillates like a single independent defect. Here, by differentiating the geometric dimensions of two piezoelectric patches, the defects’ dissimilar equivalent inertia and stiffness contribute to individually manipulating defect bands that correspond to each defect. Hence, with adequately designed piezoelectric patches that consider both the piezoelectric effects on shift patterns of defect bands and the characteristics for the output electric power obtained from a single-defect case, we can successfully localize and harvest the elastic wave energy transferred in broadband frequencies.

This study proposes a strategy for decomposing the computational domain to solve differential equations using physics-informed neural networks (PINNs) and progressively saving the trained model in each subdomain. The proposed progressive domain decomposition (PDD) method segments the domain based on the dynamics of residual loss, thereby indicating the complexity of different sections within the entire domain. By analyzing residual loss pointwise and aggregating it over specific intervals, we identify critical regions requiring focused attention. This strategic segmentation allows for the application of tailored neural networks in identified subdomains, each characterized by varying levels of complexity. Additionally, the proposed method trains and saves the model progressively based on performance metrics, thereby conserving computational resources in sections where satisfactory results are achieved during the training process. The effectiveness of PDD is demonstrated through its application to complex PDEs, where it significantly enhances accuracy and conserves computational power by strategically simplifying the computational tasks into manageable segments.

This study examines the energy-localization performance of a one-dimensional phononic crystal (PnC) with a defect when exposed to burst waves of different cycle numbers under longitudinal waves. Using the finite element method, band structures of the defect-introduced PnC were calculated, revealing a phononic band-gap range, defect-band frequencies, and corresponding defect-mode shapes. The transient analysis examined the longitudinal displacement at the center of this defect in the time domain for various burst-wave scenarios. The results indicate that energy-localization performance inside the defect highly depended on the number of cycles. Energy-localization performance was better with larger cycles or continuous waves, although burst waves with a small number of cycles also showed some improvement, albeit limited. Moreover, burst waves with a small number of cycles did not clearly induce fixed-like boundary conditions (in other words, nodal points in standing waves) within the defect-introduced PnC, leading to obscure energy-localized behaviors. Key messages from this work can be summarized as follows. First, comparing the energy-localization performance under incident burst waves with different cycle numbers for different systems might not be appropriate. Second, the physically reasonable formation of defect-mode-enabled energy localization requires burst waves with a large (in the case study, over 500) number of cycles.

This study aims to investigate elastic wave localization that leverages defect band splitting in a phononic crystal with double defects through in-depth analysis of comparison of numerical and experimental results. When more than one defect is created inside a phononic crystal, these defects can interact with each other, resulting in a distinctive physical phenomenon from a single defect case: defect band splitting. For a phononic crystal consisting of circular-hole type unit cells in a thin aluminum plate, under A0 (the lowest antisymmetric) Lamb waves, both numerical simulations and experiments successfully confirm the defect band splitting phenomenon via frequency response functions for the out-of-plane displacement calculated/measured at the double defects within a finite distance. Furthermore, experimental visualization of in-phase and out-of-phase defect mode shapes at each frequency of the split defect bands is achieved and found to be in excellent agreement with the simulated results. Different inter-distance combinations of the double defects reveal that the degree of the defect band splitting decreases with the increasing distance due to weaker coupling between the defects. This work may shed light on engineering applications of a multiple-defect-introduced phononic crystal, including broadband energy harvesting, frequency detectors, and elastic wireless power transfer.

Prognostics and health management (PHM) has developed into a crucial discipline because of its never-ending pursuit of safety, effectiveness, and dependability. The aircraft Landing gear (LG) is one of the most significant components during takeoff and landing. Consequently, the PHM of LG is essential for the aircraft to operate safely and reliably. This paper provides an in-depth exploration of the developments, difficulties, and prospects in PHM for aircraft LG. The study begins by providing an overview of the LG parts and related faults, emphasizing their importance for the flight safety. The insights of PHM are presented based on various artificial intelligence (AI) techniques. Various approaches are discussed for fault detection and isolation (FDI) and remaining useful life (RUL). These efforts help to improve the maintenance and decision-making (MDM) process, which improves the overall effectiveness of PHM. With the aim of giving researchers a useful resource, this review addresses to fill the research gaps based on the available literature so far. It lays the foundations for future advancements by highlighting the challenges in this field.

This review paper addresses the critical need for structural prognostics and health management (SPHM) in aircraft maintenance, highlighting its role in identifying potential structural issues and proactively managing aircraft health. With a comprehensive assessment of various SPHM techniques, the paper contributes by comparing traditional and modern approaches, evaluating their limitations, and showcasing advancements in data-driven and model-based methodologies. It explores the implementation of machine learning and deep learning algorithms, emphasizing their effectiveness in improving prognostic capabilities. Furthermore, it explores model-based approaches, including finite element analysis and damage mechanics, illuminating their potential in the diagnosis and prediction of structural health issues. The impact of digital twin technology in SPHM is also examined, presenting real-life case studies that demonstrate its practical implications and benefits. Overall, this review paper will inform and guide researchers, engineers, and maintenance professionals in developing effective strategies to ensure aircraft safety and structural integrity.