Search Results (283)

Given the widespread application of multi-story modular container building structures, this article proposes a new seismic isolation system called the “sliding friction isolation system (IS)” that utilizes friction energy dissipation between containers. Firstly, lateral stiffness tests were conducted on a 20 ft container, a 40 ft container, and 20 ft connected containers. The constraint consists of four fixed-bottom corner pieces, and the load is achieved using a symmetrical longitudinal concentrated loading method. Their stiffness values were 58.07 kN/mm, 33.41 kN/mm, and 60.03 kN/mm, respectively, providing the necessary parameters for IS. Secondly, an IS model was established, and based on the theory of random vibration, the relationship between c_ei (the equivalent damping of i layer of the structure) and μ (the inter-layer friction coefficient) of the system was obtained. Thirdly, a nonlinear finite element model of a six-story container building was established. Namely, the non-isolation system with standard damping ratios (NIS-sdr), the non-isolation system with equivalent damping ratio (NIS-edr), and the IS. Elastic-plastic nonlinear time-history analyses were then conducted to study the dynamic responses of three systems under strong earthquakes. The analyses yielded the top displacement of the structure, each structural layer’s maximum displacement and displacement angle, the slip of each layer, the hysteresis loops, and the cumulative dissipated energy of IS. The results show that compared to NIS sdr and NIS edr, IS can effectively reduce the maximum interlayer displacement. The largest angular displacement between the structural layer of IS and NIS-edr is far less than that of NIS-sdr. The spectral characteristics of seismic waves (the EL-Centro wave, Taft wave, and artificial wave) can significantly affect the dynamic response of IS. Additionally, the length of the sliding hole on the corner piece can be set to 35 mm based on the displacement of each layer under the Taft wave to meet the standards for container houses (T/CECS 1932-2025). Full article

This study investigates the reliability of power-terminal solder joints in intelligent power modules (IPMs) subjected to thermal cycling, random vibration, and packaging/assembly-induced deformation. Fifty IPMs were tested under temperature cycling from −55 °C to 125 °C and random vibration from 20 to 2000 Hz, and the experimental observations were combined with finite element simulations of thermal, vibration, and deformation loads. The modules survived 200 temperature cycles in the free state, whereas functional abnormalities occurred after board-level assembly and subsequent environmental loading. Simulation results showed that random vibration produced limited solder-layer stress because the first structural mode was above the excitation range, while packaging and PCB deformation markedly increased the initial stress of the power-terminal solder joints. When local deformation reached approximately 0.5 mm, the calculated solder-pad stress reached or exceeded the shear-strength risk range, consistent with the failure tendency observed in highly deformed modules. Weibull analysis further indicated a fatigue-dominated failure process with an increasing failure rate. These findings suggest that deformation control, package stiffness improvement, and assembly flatness management are critical for improving the reliability of IPM power-terminal solder joints. Full article

Large-amplitude and long-term vibration deformation under external environmental loads often occurs on tensile membrane structures. Proper sensor placement plays a vital role in effectively achieving the objectives of a structural health monitoring system. In order to optimize the sensor placement to identify the modal vibration parameters for tensile membrane structures, this paper proposes an optimal sensor placement method based on the Fisher information matrix (FIM) and improved random strategy discrete particle swarm optimization algorithm (IRSDPSO). Firstly, the structural modal order is selected by using the two-norm difference and trace change rate of FIM, and the number of sensors is determined based on the QR decomposition and MAC criterion. Secondly, an improved particle swarm optimization algorithm named IRSDPSO, which has the discrete characteristic, is proposed to arrange the placement of sensors. Finally, the convergence, stability and sensitivity are used to evaluate the effectiveness of optimal sensor placement results using a numerical modal test example of the plane bidirectional tensile membrane structure. The results show that the first nineteen modal frequencies can be accurately identified. This indicates that the proposed optimal sensor placement method can determine the number of sensors and arrange the placement of the sensors. The work is reasonable and feasible in the optimal sensor placement for the tensile membrane structure. Full article

This study investigates broadband vibration and mechanical shock mitigation for an aluminum (AlSi10Mg) battery pack casing by integrating mechanical metamaterial wall modifications and add-on damping structures. A 12.432 kWh underbody-type casing is designed. Two wall architectures, i.e., the star-triangular honeycomb (STH) and a novel hybrid auxetic (NHA), are implemented on three walls (top, front, and rear) of the battery pack casing. A mechanical damping (DSMS) and three biomimetic damping concepts (BWBIS, BPPIS and BBIGPS) are further compared. All designs are evaluated through simulation using random vibration analysis based on ISO 12405-2 standard, followed by shaker-based shock and random vibration experiments. Simulations show that both modified casings suppress the casing vibration by approximately ${10}^2$–${10}^6$ relative to the solid casing, and their dominant peaks shift to above 150 Hz. The NHA casing provides higher overall vibration mitigation than the STH casing (98.07% longitudinal, 95.09% vertical, and 93.60% transverse versus 97.64%, 94.00%, and 91.51%). Thus, the NHA casing is selected for fabrication. In addition, BPPIS and BBIGPS outperform BWBIS and DSMS, and thus, BPPIS is selected for fabrication due to its simpler geometry and lower mass. Experimentally, the solid-BPPIS configuration achieves the most robust random vibration attenuation across all measurement points, with average root mean square (RMS) reductions of 26.82% (vertical), 87.34% (longitudinal), and 83.60% (transverse). Shock tests reveal strong direction dependence; adding damping structures improves longitudinal and transverse shock mitigation, while vertical shock mitigation remains limited. The results provide design-level guidance on selecting wall architectures and damping layouts for practical vibration and shock protection of electric vehicle (EV) battery pack casings. Full article

In this work, we develop machine-learned moment tensor potentials (MTPs) to simulate the static and dynamic structural properties in Al_xGa_1−xN and related materials. The potentials are trained on DFT-calculated data for forces, stresses, and energies obtained from random atomic displacements and cell deformations. MTP-calculated physical properties, including lattice parameters and elastic constants, thermal expansion, harmonic and anharmonic vibrational properties, and the thermal conductivity, are benchmarked against first-principles results and experimental data. The comparisons testify to the very high accuracy achieved by the machine-learned potentials despite the massively reduced computational effort. Additionally, the impact of various aspects of the MTP training procedure is examined. Full article

The vibration serviceability of large-span footbridges under crowd loading has become a governing design criterion. However, the significant divergence among existing load models introduces substantial uncertainty into response prediction. This study presents a comparative evaluation of ten representative crowd walking load models from international codes and the relevant literature. It objectively evaluates their theoretical mechanisms regarding crowd synchronization and structural damping. Initial parametric sensitivity analyses are conducted utilizing single-degree-of-freedom systems. Subsequently, the predictive capabilities of these models are evaluated against field measurements from four footbridges under resonant and off-resonant conditions. The investigation reveals that response-based amplification models (e.g., M1–M3) assume high synchronization and thus overestimate accelerations under natural unrestricted resonant flows. However, these models perform reasonably well under off-resonant high-frequency conditions. In contrast, load-based models that incorporate the square-root growth law and explicit damping terms (e.g., M8–M10) better represent uncorrelated crowd flows under resonant conditions. These observations, while derived from a limited set of validation cases, provide indicative guidance and illustrate that accounting for phase randomness and structural damping is important for serviceability assessment. Full article

Membrane distillation (MD) is a promising technology for high-salinity water treatment, but scaling still remains a critical limitation to stable operation. This study introduces a novel approach by exploring vibration signal design as a control variable for scaling mitigation in hollow-fiber DCMD, shifting from the conventional treatment of vibration as a fixed-frequency mechanical input. The influence of different vibration modes, including fixed, random, and patterned (music-derived structured non-stationary excitation) vibrations, on CaSO₄ scaling in hollow-fiber direct contact membrane distillation (DCMD) was systematically investigated. Bench-scale experiments were conducted using synthetic saline feed (35,000 mg/L NaCl and 2000 mg/L CaSO₄) under both outside-in and inside-out configurations. The results reveal that vibration modifies flux decline behavior by delaying the critical volume concentration factor (VCF_cr) and reducing post-critical scaling kinetics. In the outside-in mode, patterned vibration achieved the highest critical VCF (3.39) and lowest scale formation rate, indicating effective suppression of nucleation and crystal growth. In contrast, fixed-frequency vibration (100 Hz) was more effective in the inside-out mode, owing to resonance-induced amplification of vibration transmissibility (>140%), which enhanced local shear at the membrane surface. Spectral analysis shows that patterned vibration provides broadband and non-stationary excitation with multiple dominant frequencies, enabling continuous disruption of scaling processes, whereas random vibration lacks structured energy distribution. Furthermore, patterned vibration reduced energy consumption by 16–23% compared to fixed and random modes while maintaining comparable or superior fouling mitigation. These findings demonstrate that vibration pattern design, coupled with system resonance characteristics, is a key factor in optimizing MD performance and energy efficiency. Full article

The electro-hydraulic track tensioning system of a tracked vehicle directly affects track engagement stability, vibration response, and energy utilization efficiency under complex terrain and time-varying loads. Accurate and robust control is therefore of great engineering significance. This paper focuses on an electro-hydraulic tensioning system with a composite actuation structure consisting of a proportional main valve and two 2/2 on–off valves and proposes a learning-enhanced nonlinear model predictive control (L-NMPC) method. Residual learning, adaptive weight/constraint scheduling, and execution-layer mode coordination are integrated into a unified predictive control framework. The study is carried out on a strongly coupled Simulink–AMESim–RecurDyn co-simulation model and an LF1352 prototype-vehicle test platform. Comparative evaluations are conducted under steady step-and-ramp tracking, random rough terrain, sudden steering/braking pulses, supply-pressure limitation, and parameter drift/sudden-change conditions. The evaluation indices include track-tension tracking error, peak overshoot, settling time, energy consumption, and stability under parameter mismatch. Compared with conventional nonlinear model predictive control (NMPC), the proposed L-NMPC reduces the root-mean-square error of track tension by 42–58%, decreases peak overshoot by 30–40%, shortens settling time by 25–35%, and achieves a 12–17% reduction in energy consumption at the simulation level. Under ±20% parameter perturbation, the fluctuation in track tension can be constrained within ±1.1 kN. The simulation and real-vehicle results remain consistent in terms of the dominant dynamic trends and performance ranking. This study provides a verifiable implementation path for model–data-fusion control of strongly coupled electro-hydraulic actuation systems and offers an engineering reference for intelligent, energy-efficient, and highly reliable control of tracked-vehicle chassis systems. Full article

This study examines the potential of sustainable, biobased paper-based structures as panel/membrane sound absorbers. Although intact paper is naturally impermeable and a poor sound absorber, transforming it into complex three-dimensional origami geometries, specifically the Miura-ori pattern, could produce effective panel/membrane absorbers. Three distinct Miura-ori samples (A, B, and C) were fabricated with increasing geometric complexity, ranging from a simple triangular prism to a complex labyrinthine waveguide. The random incidence sound absorption coefficients of these samples were measured in a validated small-scale reverberation room. The underlying absorption mechanisms were further investigated through modal analysis and non-contact vibration velocity measurements. The results indicate that increased geometric complexity enhances acoustic performance. Sample C, the most complex structure, demonstrated the most consistent broadband absorption. The analysis confirmed a significant positive correlation between acoustic pressure modes, surface vibration velocity, and sound absorption peaks, indicating that acoustic energy dissipation is driven by the vibrational response of the paper membrane coupled with resonant modes in the air gap. This research demonstrates that tunable origami folding techniques using intact paper can be used to design lightweight acoustic treatments for diffuse sound fields in the mid-frequency range. Full article

This study experimentally investigates the aerodynamic mechanisms and dynamic responses of slender high-rise buildings subjected to oblique-downstream interference effects. Using a simulated open-terrain atmospheric boundary layer, a square prismatic principal building (aspect ratio 8.0) was evaluated alongside an identical interfering building. High-frequency force balance and aeroelastic vibration tests were conducted across four Scruton numbers ($S_{cr}$). Aerodynamic damping was quantified using the random decrement technique and a trial-and-error approximation. Results show pronounced resonant amplification under strict conditions. Specifically, at a low $S_{cr}$ (1.12), a reduced velocity ($U_r$) of 5.5, and an interference location of $\left(x/B,y/B\right)=\left(-1.5, 1.5\right)$, the principal building exhibits an inclined elliptical trajectory, driven by a negative aerodynamic damping effect of approximately −2%. Higher $S_{cr}$ values attenuate displacement, but rooftop acceleration amplifications persist, reaching an interference factor of 2.0. Ultimately, the synchronized rhythmic channeling required to excite the principal building necessitates a minimum wake width from the interfering structure (breadth-to-depth ratio > 0.5), highlighting critical aeroelastic instabilities in dense high-rise clusters. Full article

Small satellites provide cost-effective platforms for environmental monitoring. Open-source commercial off-the-shelf (COTS) hyperspectral payloads, such as those launched with HYPSO-1 and -2, have a ground sampling distance (GSD) of 100 m. However, detecting smaller features, such as water quality in lakes, requires a GSD below 10 m and a high signal-to-noise ratio. Terrestrial COTS Schmidt–Cassegrain telescopes lack launch-load stiffness and in-orbit refocus capability. This study presents a deployable modified COTS (MCOTS) Schmidt–Cassegrain telescope that uses the original optical COTS components, a 3D-printed high-performance polymer (HPP) structure, and a dual-lead-screw deployment and focusing mechanism. The telescope has a stowed length of 280 mm and deploys to an additional 110 mm, making integration into a 16U platform with a payload length of 290 mm feasible. The modified structure is evaluated using shock and sine-sweep vibration testing, with collimation and focus verified before and after testing. Collimation remained concentric within measurement uncertainty. Complementary random-vibration finite-element simulations predicted a $3\sigma$ von Mises stress of 26.5 MPa, yielding a safety factor of 2.8. The results demonstrate a feasible pathway for adapting COTS telescopes toward space-grade COTS (SCOTS) payloads, bridging the gap between rapid production, cost efficiency, and performance for small Earth observation missions. Full article

Early detection of bearing faults in rotating machinery is essential for ensuring system reliability and effective maintenance planning. Vibration signals inherently contain characteristic fault-related frequency components, providing rich information for both physically interpretable and data-driven analyses. In this study, a multi-representation and correlation-aware feature extraction framework is proposed for automatic classification of bearing faults from vibration signals. Experimental evaluations are conducted using the Case Western Reserve University (CWRU) Bearing Dataset. The dataset includes vibration recordings corresponding to inner race, outer race, ball faults, and healthy conditions under different damage severities. The proposed approach first applies Variational Mode Decomposition (VMD) to suppress noise and enhance frequency-related characteristics. Three different feature representations are then constructed: analytical spectral descriptors, raw Transformer-based deep representations, and a hybrid feature vector obtained by combining these two representations. The hybrid structure is further enhanced through regularized Canonical Correlation Analysis (rCCA), which models the relationship between Transformer representations and spectral descriptors, enabling correlation-aware feature fusion. Spectral, raw Transformer, and rCCA-enhanced hybrid feature vectors are evaluated separately using SVM, Random Forest, and XGBoost classifiers. The results demonstrate that both spectral and Transformer-based representations provide strong performance individually; however, integrating these complementary information sources while modeling their correlations leads to superior and more balanced classification performance. In particular, the rCCA-enhanced hybrid feature vector achieves the best results across all performance metrics. The findings indicate that combining physically meaningful frequency-domain information with data-driven deep representations yields a more robust and generalizable solution for bearing fault diagnosis. Full article

Accurate color classification plays a critical role across diverse fields, from textile manufacturing and environmental monitoring to biomedical diagnostics. This study introduces a machine-learning-driven approach to spectral color sensing using SENSIPATCH, a compact, wearable sensor system; while SENSIPATCH integrates multiple sensing modalities, including bioimpedance, electrochemical, thermal, humidity, and vibrational sensors, this work specifically utilizes its spectrometer module, which comprises multi-wavelength LEDs and photodiodes. Targeting the classification of 100 standardized PANTONE colors, the proposed framework is evaluated under controlled lighting conditions to ensure repeatable spectral acquisition. The experimental design includes both firm and loose contact scenarios to emulate variability in wearable placement. A structured data-preprocessing pipeline involving baseline correction, bootstrapping, and Z-score normalization was employed to enhance signal quality and improve model generalization. Five machine learning models were evaluated: Random Forest, SVM, MLP, CNN, and LSTM. The MLP demonstrated the strongest classification performance. Notably, the MLP achieved consistent accuracy across both contact conditions, indicating robustness against sensor placement variations. These results highlight the feasibility of compact LED-based wearable spectroscopy for reliable color classification under controlled measurement conditions, providing a baseline for future extensions to more diverse lighting conditions. Full article

Tuned mass dampers (TMDs) are commonly used to reduce excessive vibrations in engineering structures. Although their vibration control performance has been widely studied, the reliability of TMD-equipped structures under stochastic excitations has not been sufficiently investigated. In practical applications, random loads and system uncertainties may significantly affect structural safety, and an efficient evaluation of failure probability remains a challenging task. Thus, the applications of these methods are greatly limited in vibration control. In this work, the structural reliability of systems equipped with TMDs is analyzed by adopting the first-passage time (FPT) as the failure criterion. Numerical investigations are performed on continuous beam models with TMDs under different types of stochastic excitation. In addition, an experimental study on a two-story steel frame structure is conducted to further examine the reliability performance of TMD-controlled systems. To reduce the computational cost associated with Monte Carlo simulation, a data-driven classification method is employed to approximate the failure domain based on a limited number of samples. The results indicate that the proposed approach enables accurate reliability estimation with a substantial reduction in computational cost, making it suitable for large-scale reliability analysis of vibration-controlled structures under stochastic excitation. The experimental results further demonstrate the applicability of the proposed reliability assessment method for practical vibration control problems. Full article

Reliable multi-sensor bearing fault diagnosis is challenged by temporal leakage caused by window-level random splitting, limited modeling of cross-sensor dependencies, and inadequate integration of raw temporal dynamics with time-frequency representations. To address these issues, this study proposes a leakage-resistant multi-sensor diagnosis framework that combines a partition-before-windowing evaluation protocol with adaptive time-frequency graph learning and reliability-aware fusion. Continuous vibration records are first divided into disjoint temporal regions with guard intervals and overlap auditing to suppress time-neighbor leakage. The model then extracts complementary features from a raw-signal branch and a dual-resolution log-STFT branch, while adaptive graph learning captures sample-dependent inter-sensor couplings and sensor reliability weighting highlights informative channels. A cross-gated fusion module further integrates temporal and graph-domain representations in a sample-adaptive manner for final classification. Experiments on a reconstructed nine-class benchmark derived from the HUSTbearing dataset show that the proposed method achieves a Macro-Accuracy of 0.973, a Macro-Recall of 0.964, and a Macro-F1 of 0.954, outperforming representative raw-signal and STFT-based baselines under the same leakage-resistant protocol. These results demonstrate that jointly modeling multi-scale time-frequency structure, dynamic sensor relationships, and reliable evaluation yields an effective and interpretable solution for intelligent bearing fault diagnosis under complex operating conditions. Full article