Search Results (2,294)

To reduce the adverse effects of stern-shaft system vibration on ship performance, this work combined hydrodynamic excitations calculated for a semi-submerged propeller and established a multibody dynamics (MBDs) model of the stern shaft system that included a flexible shaft, propeller, and elastically damped support bearings. The MBDs model's accuracy was verified through comparison between experimentally identified modal parameters and those computed by the model. It was found that the bearing stiffness and the hydrodynamic excitation frequency collectively determine the vibration amplitude and modal shape of the shaft system, based on an analysis of varied bearing stiffness and damping. Bearing displacement had a significant impact on shafting vibration. And the tie rod with a stiffness of 2.5 × 10⁷ N/m provided a noticeable vibration damping effect. The findings offered theoretical support for mitigating stern-shaft vibration in high-speed vessels subjected to hydrodynamic excitation from semi-submerged propellers. Full article

The output of MEMS gyroscopes is highly vulnerable to ambient temperature variations, which induce temperature drift errors and degrade navigation precision. Consequently, temperature compensation for MEMS gyroscope outputs is of critical importance. To address this issue, this study proposes a novel temperature compensation model for the dual-mass vibration MEMS gyroscope (DMVMG), which integrates the TTAO-VMD, 1D-CNN-Bi-GRU-Attention, and SHAKF algorithms. The implementation process of the proposed model is as follows: firstly, the structural configuration and fundamental operating principle of the DMVMG are elaborated. Secondly, the temperature error compensation model is constructed based on the fusion of the TTAO-VMD, 1D-CNN-Bi-GRU-Attention, and SHAKF algorithms. Thirdly, the raw output signal of the DMVMG is preprocessed using the TTAO-VMD algorithm, which decomposes the signal into four distinct components, namely high-frequency noise, white noise, mixed noise, and temperature-induced noise. Subsequently, the high-frequency and white noise components are eliminated, while the mixed noise component is filtered via the SHAKF algorithm. On this basis, the 1D-CNN-Bi-GRU-Attention algorithm is adopted to establish the temperature error compensation model, with the temperature, temperature change rate, time, and temperature-induced noise as input variables. Finally, the optimized signal components are reconstructed to yield the temperature-compensated output of the DMVMG. The experimental results based on the Allan variance method demonstrate that the angle random walk (N) is reduced from 18.56 $°/\sqrt{h}$ to 0.17 $°/\sqrt{h}$, and the bias instability (B) is decreased from 32.76 °/h to 0.82 °/h, verifying the effectiveness of the proposed method. Full article

This review explores the variety of diamond-based sensing applications, emphasizing their material properties, such as high Young’s modulus, thermal conductivity, wide bandgap, chemical stability, and radiation hardness. These diamond properties give excellent performance in mechanical, pressure, thermal, magnetic, optoelectronic, radiation, biosensing, quantum, and other applications. In vibration sensing, nano/poly/single-crystal diamond resonators operate from MHz to GHz frequencies, with high quality factor via CVD growth, diamond-on-insulator techniques, and ICP etching. Pressure sensing uses boron-doped piezoresistive, as well as capacitive and Fabry–Pérot readouts. Thermal sensing merges NV nanothermometry, single-crystal resonant thermometers, and resistive/diode sensors. Magnetic detection offers FeGa/Ti/diamond heterostructures, complementing NV. Optoelectronic applications utilize DUV photodiodes and color centers. Radiation detectors benefit from diamond’s neutron conversion capability. Biosensing leverages boron-doped diamond and hydrogen-terminated SGFETs, as well as gas targets such as NO₂/NH₃/H₂ via surface transfer doping and Pd Schottky/MIS. Imaging uses AFM/NV probes and boron-doped diamond tips. Persistent challenges, such as grain boundary losses in nanocrystalline diamond, limited diamond-on-insulator bonding yield, high temperature interface degradation, humidity-dependent gas transduction, stabilization of hydrogen termination, near-surface nitrogen-vacancy noise, and the cost of high-quality single-crystal diamond, are being addressed through interface and surface chemistry control, catalytic/dielectric stack engineering, photonic integration, and scalable chemical vapor deposition routes. These advances are enabling integrated, high-reliability diamond sensors for extreme and quantum-enhanced applications. Full article

Background: Semi-occluded vocal tract exercises (SOVTE) improve vocal quality and capacity. Water resistance therapy (WRT), a specific form of SOVTE with a tube submerged under water, generates increased and oscillating oral pressure through bubble formation during phonation, thereby influencing transglottal pressure and vocal fold dynamics. While the physiological effects of WRT using tube-based systems have been extensively studied, the influence of vowel-specific vocal tract configurations during WRT remains unclarified. This study examined how different vowel qualities during WRT affect vocal fold oscillation using the DoctorVox^® mask, which allows near-natural mouth opening and vowel articulation. Methods: Ten vocally healthy, untrained adults (25–50 years) performed a continuous vowel glide (/i/–/a/–/u/-/i/) at constant fundamental frequency and habitual loudness during WRT using the DoctorVox^® mask, with the tube submerged 2 cm in water. Simultaneous recordings included transnasal high-speed videoendoscopy (20,000 fps), electroglottography (EGG), acoustic signals and intra-tube oral pressure measurements. Glottal area waveforms (GAW) were derived to calculate the open quotient (OQ_GAW) and closing quotient (ClQ_GAW). Analyses were conducted separately for intra-tube pressure maxima, minima and intermediate phases within the bubble cycle during WRT. Statistical analysis used Wilcoxon signed-rank tests with Bonferroni correction. Results: In the baseline condition without WRT, significant vowel-related differences were found: /u/ showed a higher open quotient than /i/ and /a/ (p < 0.05) and a higher closing quotient than /a/ (p < 0.05). During WRT, these vowel-specific differences were no longer statistically significant. A non-significant trend toward reduced OQ_GAW during WRT was observed, most notably for /u/, while differences between pressure phases within the bubble cycle were minimal. Conclusions: WRT using the DoctorVox^® mask reduces vowel-specific differences in vocal fold vibration patterns, suggesting that for voice therapy, vowel quality modifications during WRT have little impact on vocal outcomes. Full article

The free vibration of stiffened plates analyzed using classical plate–beam theoretical theory (PBM) simplified the vibrations of stiffeners parallel to the plane of the stiffened plate as the first-order torsional vibration of the stiffener cross-section. This simplification introduces errors in both the natural frequencies and mode shapes of the structure for stiffened plates with relatively tall stiffeners. To mitigate the issue previously described, this paper proposes an enhanced plate–beam theoretical model (EPBM). The EBPM decouples stiffener deformation into two components: (1) bending deformation along the transverse direction of the stiffened plate, governed by Euler–Bernoulli beam theory, and (2) transverse deformation of the stiffeners, modeled using thin plate theory. Virtual torsional springs are introduced at the stiffener–plate and stiffener–stiffener interfaces via penalty function method to enforce rotational continuity. These constraints are transformed into energy functionals and integrated into the system’s total energy. Displacement trial functions constructed from Chebyshev polynomials of the first kind are solved using the Ritz method. Numerical validation demonstrates that the EBPM significantly improves accuracy over the BPM: errors in free-vibration frequency decrease from 2.42% to 0.63% for the first mode and from 9.79% to 1.34% for the second mode. For constrained vibration, the second-mode error is reduced from 4.22% to 0.03%. This approach provides an effective theoretical framework for the vibration analysis of structures with high stiffeners. Full article

Brake squeal is an undesirable high-frequency noise caused by vibrations induced by friction in disc brake systems. The noise is strongly affected by temperature, as this influences the material properties of the friction pair and the dynamic behaviour of the brake components. This study investigates the effect of temperature changes on the squeal characteristics of a disc brake system under different operating conditions. Experiments are carried out using a laboratory-scale test setup comprising a rotating disc, pneumatically actuated callipers, and precise measurement equipment. A series of test combinations is performed by systematically varying three parameters: disc surface temperature (40, 55, 70, 85, 100 °C), brake pressure (4.0 bar), and disc rotational speed (50, 100, 150, 200 rpm). Acceleration data are acquired using an accelerometer mounted directly on the calliper, while sound pressure data are measured with a fixed-position microphone located 0.5 m from the disc surface. The collected data are analyzed in the time and frequency domain to identify squeal events and their dominant frequencies. The effect of temperature on brake squeal noise and vibration varies with operating conditions, showing different patterns at low and high disc speed at constant brake pressure. This highlights the importance of considering both thermal and mechanical factors together when addressing brake squeal. Full article

To address the computational redundancy, inadequate multi-scale feature capture, and poor noise robustness of traditional deep networks used for bearing vibration and acoustic signal feature extraction, this paper proposes a fault diagnosis method based on Depthwise Separable Atrous Convolution (DSAC) and Acoustic Spatial Pyramid Pooling (ASPP). First, the Continuous Wavelet Transform (CWT) is applied to the vibration and acoustic signals to convert them into time–frequency representations. The vibration CWT is then fed into a multi-scale feature extraction module to obtain preliminary vibration features, whereas the acoustic CWT is processed by a Deep Residual Shrinkage Network (DRSN). The two feature streams are concatenated in a feature fusion module and subsequently fed into the DSAC and ASPP modules, which together expand the effective receptive field and aggregate multi-scale contextual information. Finally, global pooling followed by a classifier outputs the bearing fault category, enabling high-precision bearing fault identification. Experimental results show that, under both clean data and multiple low signal-to-noise ratio (SNR) noise conditions, the proposed DSAC-ASPP method achieves higher accuracy and lower variance than baselines such as ResNet, VGG, and MobileNet, while requiring fewer parameters and FLOPs and exhibiting superior robustness and deployability. Full article

The flexible positioning platform is a critical structural component in the ultra-high acceleration macro–micro motion platform, enabling precise positioning across multiple scales. However, under high-frequency start–stop cycles and prolonged multi-condition operation, it is prone to fatigue damage induced by random vibrations, which poses a threat to system reliability. This study proposes a method for evaluating and optimizing the platform’s performance under random vibration based on power spectral density (PSD) analysis. In accordance with the IEC 60068-2-64 standard, representative load spectra from Tables A.8 and A.6 were selected as excitation inputs. Frequency-domain analyses of stress, strain, and displacement were conducted using ANSYS Workbench 2022R1 in conjunction with the nCode platform, incorporating the Gaussian three-sigma probability interval. The results reveal that stress and deformation are highly concentrated in the hinge region, indicating a structural vulnerability. Fatigue life predictions were carried out using the Dirlik method and Miner’s linear damage rule under various PSD loading conditions. The findings demonstrate that hinge stiffness is a key factor influencing vibration resistance and service life. This research provides theoretical support for the design optimization of flexible structures operating in complex random vibration environments. Full article

Spherical caps exploit their intrinsic curvature to achieve efficient stress distribution, delivering exceptional strength-to-weight ratios. This advantage renders them indispensable for aerospace systems, pressurized containers, architectural domes, and structures operating in extreme environments, such as deep-sea or nuclear containment. Their superior load-bearing capacity enables diverse applications, including satellite casings and high-pressure vessels. Meticulous optimization of geometric parameters and material selection ensures robustness in demanding scenarios. Given their significance, this study examines the natural frequency and static response of bio-inspired helicoidally laminated carbon fiber–reinforced polymer matrix composite spherical panels surrounded by Winkler elastic foundation support. Utilizing a 3D elasticity approach and the finite element method (FEM), the governing equations of motion are derived via Hamilton’s Principle. The study compares five helicoidal stacking configurations—recursive, exponential, linear, semicircular, and Fibonacci—with traditional laminate designs, including cross-ply, quasi-isotropic, and unidirectional arrangements. Parametric analyses explore the influence of lamination patterns, number of plies, panel thickness, support rigidity, polar angles, and edge constraints on natural frequencies, static deflections, and stress distributions. The analysis reveals that the quasi-isotropic (QI) laminate configuration yields optimal vibrational performance, attaining the highest fundamental frequency. In contrast, the cross-ply (CP) laminate demonstrates marginally best static performance, exhibiting minimal deflection. The unidirectional (UD) laminate consistently shows the poorest performance across both static and dynamic metrics. These investigations reveal stress transfer mechanisms across layers and elucidate vibration and bending behaviors in laminated spherical shells. Crucially, the results underscore the ability of helicoidal arrangements in augmenting mechanical and structural performance in engineering applications. Full article

The vibration compaction behavior of fully graded fresh concrete differs fundamentally from that of conventional two-graded concrete. Based on measured vibration responses of an internal vibrator and sinking-ball tests, an energy transfer model for fully graded concrete was established by incorporating the effects of aggregate-specific surface area, paste–aggregate ratio, dynamic damping, and natural frequency, and the spatiotemporal attenuation of vibration energy in fresh concrete was systematically analyzed. Experimental results indicate that fully graded concrete exhibits a higher energy absorption capacity during the early stage of vibration, with a maximum energy absorption rate of 423 W and a peak energy transfer efficiency of 76.3%, both of which are significantly higher than those of two-graded concrete at the same slump. However, as a dense aggregate skeleton rapidly forms, the energy absorption efficiency of fully graded concrete decreases more rapidly during the middle and later stages of vibration, showing a characteristic pattern of “high initial absorption followed by rapid attenuation.” Through segregation assessment and porosity analysis, a safe vibration energy range for fully graded concrete was quantitatively determined, with lower and upper energy thresholds of 159.7 J·kg⁻¹ and 538.5 J·kg⁻¹, respectively. In addition, the experiments identified recommended vibration durations of 30–65 s and effective vibration influence radii of 22–85 mm for fully graded concrete under different slump conditions. These findings provide a quantitative basis for the control of vibration parameters and energy-oriented construction of fully graded concrete. Full article

The Survey Camera (SC) is the key instrument of the China Space Station Telescope (CSST), with its imaging performance significantly constrained by microvibrations from internal sources such as the shutter and cryocoolers. This paper proposes a systematic microvibration suppression scheme integrating disturbance source control, payload isolation, and transfer path optimization to meet the stringent requirements. The Cryocooler Assembly (CCA) compressor adopts a symmetric piston layout and a real-time vibration cancellation algorithm to reduce the vibration. Coupled with a vibration isolator designed by combining hydraulic damping and a flexible structure, it achieves a vibration isolation efficiency of 95%. The shutter adopts dual-blade symmetric design with sinusoidal angular acceleration control, ensuring its vibrations fall within the compensable range of the Fast Steering Mirror (FSM). And the finite element optimization method is used to optimize the dynamic characteristics of the Support Structure (SST) made of M55J carbon fiber composite material, to avoid resonance in the critical frequency bands. System-level tests on the integrated SC show that the RMS values of vibration force and torque within 8–300 Hz are 0.25 N and 0.08 N·m, respectively, meeting design specifications. This scheme validates effective microvibration control, guaranteeing the SC’s high-resolution imaging capability for the CSST mission. Full article

We numerically investigate vibrational resonance (VR) and vibrational energy harvesting (VEH) in a mechanical system driven by a low-frequency periodic force, using time-periodic phase modulation of the potential function. We focus on how the characteristics of high-frequency excitations influence frequency response, power output, and harvesting efficiency. We uncover two modulation-induced phenomena—resonant induction and resonant amplification—that together produce a double VR effect. We demonstrate that in the weak low-frequency regime ($\omega \le 0.3$), the power output can exceed that of the moderate regime ($\omega \approx 1$). Among the modulating waveforms, square waveform (SQW) demonstrated superior efficiency over other waveforms, which corresponds to higher response amplitude. In addition, the frequency ratio $K=6.7$ yielded optimal performance compared to other frequency ratios, thereby providing both maximum power output and efficiency. These findings suggest a new design strategy for energy harvesters, leveraging both primary and induced VR to enhance performance. Full article

With the rapid development of high-voltage DC (HVDC) power systems, accurate measurement of surface electrostatic potential on insulating components has become critical for electric field assessment and insulation reliability. This paper proposes an electrostatic potential sensor based on cantilever micro-vibration modulation, which employs piezoelectric actuators to drive high-frequency micro-vibration of cantilever-type shielding electrodes, converting the static electrostatic potential into an alternating induced charge signal. An electrostatic induction model is established to describe the sensing principle, and the influence of structural and operating parameters on sensitivity is analyzed. Multi-physics coupled simulations are conducted to optimize the cantilever geometry and modulation frequency, aiming to enhance modulation efficiency while maintaining a compact sensor structure. To validate the effectiveness of the proposed sensor, an electrostatic potential measurement platform for insulating components is constructed, obtaining response curves of the sensor at different potentials and establishing a compensation model for the working distance correction coefficient. The experimental results demonstrate that the sensor achieves a maximum measurement error of 0.92% and a linearity of 0.47% within the 1–10 kV range. Surface potential distribution measurements of a post insulator under DC voltage agreed well with simulation results, demonstrating the effectiveness and applicability of the proposed sensor for HVDC insulation monitoring. Full article

During orbital service, precision aerospace equipment is frequently subjected to harsh vibration environments that can significantly affect reliability and service life. Consequently, the development of effective vibration isolation technologies has become a crucial aspect of aerospace structural design. In this study, random vibration theory and frequency-domain analysis methods were employed to investigate the dynamic response characteristics of a symmetrical frustum-shaped metal rubber (FSMR) isolation device under complex operating conditions. The influence of metal rubber density, spring stiffness, and input vibration level on its isolation performance was systematically examined. This work presents the first systematic experimental investigation into the nonlinear dependencies of the performance of a symmetrical frustum-shaped metal rubber isolator on multiple parameters (density, stiffness, excitation level) under random vibration. The test results show that under identical excitation conditions, the device achieves optimal damping ratio and isolation efficiency (59.71%) when the metal rubber density is 2.0 g/cm³. A moderate increase in spring stiffness reduces the resonance peak and improves stability, with a stiffness of 100 kN/m exhibiting the best overall performance. In addition, higher input vibration levels markedly elevate the acceleration response and the resonant peak amplification factor of the isolator, demonstrating that high-intensity excitation magnifies the vibration response and degrades the isolation efficiency. Full article

This study investigates the application of machine learning (ML) techniques for predicting vibration frequencies of thin rectangular plates with variable thickness. Traditional optimization methods, such as genetic algorithms, require repeated solutions of the plate vibration eigenproblem using finite element (FE) analysis, which is computationally expensive. To reduce this cost, a surrogate model based on artificial neural networks (ANNs) is proposed as an efficient alternative. The dataset includes variations in plate geometry, boundary conditions, and thickness distribution, encoded numerically for model training. ANN architecture and hyperparameters—such as the number of hidden layers, neurons per layer, and activation functions—were systematically tuned to achieve high prediction accuracy while avoiding overfitting. Data preprocessing steps, including standardization and scaling, were applied to improve model stability. Performance was evaluated using metrics such as RMSE and R². The results demonstrate that ANNs can accurately predict eigenvalues with significantly reduced computational effort compared to FE analysis. This approach offers a practical solution for integrating machine learning into structural optimization workflows. Full article