Search Results (144)

In this study, nonlinear Lamb wave-based higher harmonic detection is employed to assess the tensile-induced microdamage in patch-repaired carbon fiber-reinforced polymer (CFRP) structures. With respect to the external repair design optimization model based on proxy technology, the minimum nonlinear coefficients are obtained from the optimal patch design parameters, thereby improving the tensile performance of the repaired structure and capturing the repair effect of the patch. First, the nonlinear Lamb wave propagation behaviors of patch-repaired CFRP laminates are assessed under different tensile displacements, and the accuracy of the finite-element model strategy is confirmed by experimental results. Second, on the basis of the tensile displacement induced under the highest nonlinear response, the effects of the radius, thickness and rotation angle of the patch on the secondary and tertiary nonlinear coefficients of the composite glued repair structure and the tensile damage area of the matrix are discussed. After the effects of individual parameters on the patch repair structure are analyzed, the effect of multiple target parameters on the quadratic relative acoustic nonlinearity coefficient of the patch repair structure is investigated via a Latin hypercube experimental design and the Diffuse Approximation method, and the optimal solutions for the mesh parameters of the patch repair structure are successfully obtained, which provides a reference for the multiparameter optimization of patch repair structures in engineering cases. Full article

Key detection performance metrics, particularly resolution, are largely determined by the design parameters of ultrasonic arrays. The structural design of the transducer strongly influences critical indicators, including side lobe levels, beam directivity, and focal spot size. To improve parameter selection, this study proposes a multi-objective optimization strategy specifically tailored for cylindrical surface ultrasonic transducers. The geometric parameters of the array and the variables influencing resolution performance are mapped in a nonlinear manner. The NSGA-II algorithm is employed to perform extremum seeking optimization on a trained BPNN, generating a Pareto-optimal solution set by specifying main-lobe width, side-lobe intensity, and sound-pressure uniformity as optimization objectives. For validation, the geometric configurations derived from this solution set are applied in acoustic field simulations. Simulation results demonstrate that the dynamic aperture exhibits clear regularity when the array settings meet millimeter-level resolution requirements. These findings support real-world engineering applications and provide valuable insights for enhancing the geometric design of cylindrical ultrasonic arrays. Full article

Transcranial focused ultrasound is a promising noninvasive technique for neuromodulation in neurological and psychiatric disorders, but accurate prediction of acoustic transmission through the skull remains a major challenge. In this study, we present a five-layer numerical human head model that integrates frequency-dependent acoustic parameters with nonlinear time-explicit dynamics to realistically capture ultrasound propagation. The model explicitly represents skin, trabecular bone, cortical bone, and brain, each assigned experimentally derived acoustic properties across a clinically relevant frequency range (0.5–5 MHz). Numerical simulations were performed in the frequency domain and time-explicit to quantify sound transmission loss and focal depth under high-intensity and high-frequency stimulation. The results show the effect of frequency, radius of curvature, and skull thickness on maximum pressure ratio, focal depth, and focus zone inside the brain tissue. Findings indicate that skull geometry, particularly radius of curvature and thickness, strongly influences the focal zone, with thinner skull regions allowing deeper penetration and reduced transmission loss. Comparison of the frequency-domain model with the time-explicit model demonstrated broadly similar trends; however, the frequency-domain approach consistently underestimated transmission loss and was unable to capture nonlinear effects such as frequency harmonics. These findings highlight the importance of nonlinear, time-explicit modeling for accurate transcranial ultrasound planning and suggest that the proposed framework provides a robust tool for optimizing stimulation parameters and identifying ideal target zones, supporting the development of safer and more effective neuromodulation strategies. Full article

Traditional cooperative navigation algorithms for multiple AUVs are typically designed for a single specific configuration, such as parallel or leader-slave. This paper proposes a novel cooperative navigation algorithm based on factor graph and Lie group to address the multi-AUV localization problem, which is applicable to various multi-AUV configurations. First, the motion state of an AUV is represented within the two-dimensional special Euclidean group (SE(2)) space from Lie theory. Second, the motion of the AUV and acoustic-based range and bearing measurements are modeled to derive the motion error function and the range and bearing error function, respectively. Depending on the formulation of the motion error function, the proposed approach comprises two methods: Method 1 and Method 2. Third, the Gauss-Newton method is employed for nonlinear optimization to obtain the optimal estimates of the motion states for all AUVs. Finally, a parameter-level simulation system for AUV cooperative navigation is established to evaluate the algorithm’s performance under two different multi-AUV configurations. Method 1 is designed for parallel configurations, reducing the average RMSE of position and orientation errors by 29% compared to the EKF. Method 2 is tailored for leader-slave configurations, reducing the average RMSE of position and orientation errors by 38% compared to the EKF. Simulation results demonstrate that the proposed algorithm achieves superior performance across different AUV configurations compared to conventional EKF-based approaches. Full article

In this paper, we investigate the generalized coupled Zakharov system (GCZS), a fundamental model in plasma physics that describes the nonlinear interaction between high-frequency Langmuir waves and low-frequency ion-acoustic waves, including the influence of magnetic fields on weak ion-acoustic wave propagation. This research aims to achieve three main objectives. First, we uncover soliton solutions of the coupled system in hyperbolic, trigonometric, and rational forms, both in single and combined expressions. These results are obtained using the extended rational sinh-Gordon expansion method and the $\left({\displaystyle \frac{G^{\prime }}{G}},{\displaystyle \frac{1}{G}}\right)$-expansion method. Second, we analyze the dynamic characteristics of the model by performing bifurcation and sensitivity analyses and identifying the corresponding Hamiltonian function. To understand the mechanisms of intricate physical phenomena and dynamical processes, we plot 2D, 3D, and contour diagrams for appropriate parameter values. We also analyze the bifurcation of phase portraits of the ordinary differential equations corresponding to the investigated partial differential equation. The novelty of this study lies in the fact that the proposed model has not been previously explored using these advanced methods and comprehensive dynamical analyses. Full article

This study addresses autonomous underwater vehicle (UUV) recovery onto dynamic docking stations by proposing a fork-column recovery control system with a segmented docking strategy (long-distance approach + guided descent). To enhance model fidelity, transmission lag of actuators is captured by a specified transfer function, and nonlinear dynamics are characterized as an improved quasi-linear parameter-varying (QLPV) model. An adaptive variable–prediction–step mechanism was designed to accommodate different phases of acoustic–optical guided recovery. A model predictive controller (MPC) was developed based on an improved dynamic model to effectively handle complex constraints during the recovery process. Simulation and physical experiments demonstrated that the proposed system significantly reduces errors, among which the control accuracy (tracking error under disturbance < 0.3 m) and docking success rate (>95%) are notably superior to traditional methods, providing a reliable solution for the dynamic recovery of unmanned underwater vehicles (UUVs). Full article

Effective management of Micropterus salmoides resources requires accurate assessment of their abundance and distribution. Fisheries acoustics is a key method for such evaluations, yet its application is limited by insufficient target strength (TS) data. This study combines the Sobel edge detection technique with the Kirchhoff ray-mode model to estimate the TS of Micropterus salmoides cultured in Guangdong, China, and validates the results through in situ measurements. The relationships between TS and fish body length were established at 38 kHz, 70 kHz, 120 kHz, and 200 kHz. At 200 kHz, the average in situ TS was –42.41 dB, with a fitted formula of TS = 32.00 lgL − 88.24. Further validation was performed using time- and frequency-domain analyses of echo signals. The results show that TS increases with swim bladder volume, indicating its dominant influence. The relationship between TS and frequency is nonlinear and affected by the swim bladder angle, swimming posture, and behavioral patterns. This study also improves the computational efficiency of the Kirchhoff ray-mode model. Overall, it provides essential parameters for acoustic stock assessment of Micropterus salmoides, providing a scientific basis for their sustainable management and conservation. Full article

Shale oil and gas reservoirs are characterized by low porosity and low permeability. The development of ultra-long horizontal wells can significantly increase reservoir contact area and enhance single-well production. Shale formations exhibit distinct bedding structures, high formation pressure, high rock hardness, and strong anisotropy. These characteristics result in poor drillability, slow drilling rates, and high costs when drilling horizontally, severely restricting efficient development. Therefore, accurately predicting the drillability of shale gas wells has become a major challenge. Currently, most scholars rely on a single parameter to predict drillability, which overlooks the coupled effects of multiple factors and reduces prediction accuracy. To address this issue, this study employs drillability experiments, mineral composition analysis, positional analysis, and acoustic transit-time tests to evaluate the effects of mineral composition, acoustic transit time, bottom-hole confining pressure, and formation drilling angle on the drillability of horizontal well reservoirs, innovatively integrating multiple parameters to construct a nonlinear model and introducing three intelligent optimization algorithms (PSO, AOA-GA, and EBPSO) for the first time to improve prediction accuracy, thus breaking through the limitations of traditional single-parameter prediction. Based on these findings, a nonlinear regression prediction model integrating multiple parameters is developed and validated using field data. To further enhance prediction accuracy, the model is optimized using three intelligent optimization algorithms: PSO, AOA-GA, and EBPSO. The results indicate that the EBPSO algorithm performs the best, followed by AOA-GA, while the PSO algorithm shows the lowest performance. Furthermore, the model is applied to predict the drillability of Well D4, and the results exhibit a high degree of agreement with actual measurements, confirming the model’s effectiveness. The findings support optimization of drilling parameters and bit selection in shale oil and gas reservoirs, thereby improving drilling efficiency and mechanical penetration rates. Full article

In modern industry, the spring full-lift safety valve is a key device for safe pressure relief of pressure-bearing systems. Its valve seat sealing surface is easily damaged after long-term use, causing internal leakage, resulting in safety hazards and economic losses. Therefore, it is of great significance to quickly and accurately diagnose its internal leakage state. Among the current methods for identifying fluid machinery faults, model-based methods have difficulties in parameter determination. Although the data-driven convolutional neural network (CNN) has great potential in the field of fault diagnosis, it has problems such as hyperparameter selection relying on experience, insufficient capture of time series and multi-scale features, and lack of research on valve internal leakage type identification. To this end, this study proposes a safety valve internal leakage identification method based on high-frequency FPGA data acquisition and improved CNN. The acoustic emission signals of different internal leakage states are obtained through the high-frequency FPGA acquisition system, and the two-dimensional time–frequency diagram is obtained by short-time Fourier transform and input into the improved model. The model uses the leaky rectified linear unit (LReLU) activation function to enhance nonlinear expression, introduces random pooling to prevent overfitting, optimizes hyperparameters with the help of horned lizard optimization algorithm (HLOA), and integrates the bidirectional gated recurrent unit (BiGRU) and selective kernel attention module (SKAM) to enhance temporal feature extraction and multi-scale feature capture. Experiments show that the average recognition accuracy of the model for the internal leakage state of the safety valve is 99.7%, which is better than the comparison model such as ResNet-18. This method provides an effective solution for the diagnosis of internal leakage of safety valves, and the signal conversion method can be extended to the fault diagnosis of other mechanical equipment. In the future, we will explore the fusion of lightweight networks and multi-source data to improve real-time and robustness. Full article

Ultrasonic wave attenuation in biological tissues arises from complex interactions between mechanical, structural, and fluidic properties, making it essential to identify dominant mechanisms for accurate simulation and device design. This work introduces a novel integration of experimentally measured tissue parameters into time-explicit nonlinear acoustic wave simulations, in which the equations are directly solved in the time domain using an explicit solver. This approach captures the full transient waveform without relying on frequency-domain simplifications, offering a more realistic representation of ultrasound propagation in heterogeneous media. The study estimates both sound diffusivity and viscous damping parameters (dynamic and bulk viscosity) for a broad range of ex vivo tissues (skin, adipose tissue, skeletal muscle, trabecular/cortical bone, liver, myocardium, kidney, tendon, ligament, cartilage, and gray/white brain matter). Four regression models (power law, linear, exponential, logarithmic) were applied to characterize their frequency dependence between 0.5 and 5 MHz. Results show that attenuation is more strongly driven by bulk viscosity than dynamic viscosity, particularly in fluid-rich tissues such as liver and myocardium, where compressional damping dominates. The power-law model consistently provided the best fit for all attenuation metrics, revealing a scale-invariant frequency relationship. Tissues such as cartilage and brain showed weaker viscous responses, suggesting the need for alternative modeling approaches. These findings not only advance fundamental understanding of attenuation mechanisms but also provide validated parameters and modeling strategies to improve predictive accuracy in therapeutic ultrasound planning and the design of non-invasive, tissue-specific acoustic devices. Full article

Inverting seawater sound speed profiles (SSPs) using Bayesian methods enables optimal parameter estimation and provides a quantitative assessment of uncertainty by analyzing the posterior distribution of target parameters. However, in nonlinear geophysical inversion problems like acoustic tomography, calculating the posterior distribution remains challenging. In this study, a Bayesian framework is used to construct the posterior distribution of target parameters based on acoustic travel-time data and prior information. A Hamiltonian Monte Carlo (HMC) approach is developed for SSP inversion, offering an effective solution to the computational issues associated with complex posterior distributions. The HMC algorithm has a strong physical basis in exploring distributions, allowing for accurate characterization of physical correlations among target parameters. It also achieves sufficient sampling of heavy-tailed probabilities, enabling a thorough analysis of the target distribution characteristics and overcoming the low efficiency often seen in traditional methods. The SSP dataset was created using temperature–salinity profile data from the Hybrid Coordinate Ocean Model (HYCOM) and empirical formulas for SSP. Experiments with acoustic propagation time data from the Kuroshio Extension System Study (KESS) confirmed the feasibility of the HMC method in SSP inversion. Full article

To address the challenges of low diagnostic accuracy arising from the non-stationary and nonlinear time-varying characteristics of acoustic signals in rolling bearing fault diagnosis, as well as their susceptibility to noise interference, this paper proposes a fault diagnosis method based on a Weighted Average Algorithm–Feature Mode Decomposition (WAA-FMD) and a Local–Global Adaptive Multi-scale Attention Mechanism (LGAF)–Swin Transformer. First, the WAA is utilized to optimize the key parameters of FMD, thereby enhancing its signal decomposition performance while minimizing noise interference. Next, a bilateral expansion strategy is implemented to extend both the time window and frequency band of the signal, which improves the temporal locality and frequency globality of the time–frequency diagram, significantly enhancing the ability to capture signal features. Ultimately, the introduction of depthwise separable convolution optimizes the receptive field and improves the computational efficiency of shallow networks. When combined with the Swin Transformer, which incorporates LGAF and adaptive feature selection modules, the model further enhances its perceptual capabilities and feature extraction accuracy through dynamic kernel adjustment and deep feature aggregation strategies. The experimental results indicate that the signal denoising performance of WAA-FMD significantly outperforms traditional denoising techniques. In the KAIST dataset (NSK 6205: inner raceway fault and outer raceway fault) and the experimental dataset (FAG 30205: inner raceway fault, outer raceway fault, and rolling element fault), the accuracies of the proposed model reach 100% and 98.62%, respectively, both exceeding that of other deep learning models. In summary, the proposed method demonstrates substantial advantages in noise reduction performance and fault diagnosis accuracy, providing valuable theoretical insights for practical applications. Full article

The rock strata traversed by frozen shafts in coal mines located in western regions are predominantly composed of weakly cemented, water-rich sandstones of the Cretaceous system. Investigating the rheological damage behavior of saturated sandstone under frozen conditions is essential for evaluating the safety and stability of these frozen shafts. To explore the damage evolution and creep characteristics of Cretaceous sandstone under the coupled influence of low temperature and in situ stress, a series of triaxial creep tests were conducted at a constant temperature of −10 °C, under varying confining pressures (0, 2, 4, and 6 MPa). Simultaneously, acoustic emission (AE) energy monitoring was employed to characterize the damage behavior of saturated frozen sandstone under stepwise loading conditions. Based on the experimental findings, a fractional-order creep constitutive model incorporating damage evolution was developed to capture the time-dependent deformation behavior. The sensitivity of model parameters to temperature and confining pressure was also analyzed. The main findings are as follows: (1) Creep deformation progressively increases with higher confining pressure, and nonlinear accelerated creep is observed during the final loading stage. (2) A fractional-order nonlinear creep model accounting for the coupled effects of low temperature, stress, and damage was successfully established based on the test data. (3) Model parameters were identified using the least squares fitting method across different temperature and pressure conditions. The predicted curves closely match the experimental results, validating the accuracy and applicability of the proposed model. These findings provide a theoretical foundation for understanding deformation mechanisms and ensuring the structural integrity of frozen shafts in Cretaceous sandstone formations of western coal mines. Full article

The growing demand for low-frequency, broadband vibration and noise suppression technologies in next-generation mechanical equipment has become increasingly urgent. Effective negative mass locally resonant structures represent one of the most paradigmatic classes of acoustic metamaterials. Their unique elastic wave bandgaps enable efficient suppression of low-frequency vibrations, while inherent nonlinear effects provide significant potential for the design and tunability of these bandgaps. To achieve ultra-low-frequency and ultra-broadband vibration attenuation, this study employs Duffing oscillators exhibiting negative-stiffness characteristics as structural elements, establishing a bistable nonlinear acoustic-metamaterial mechanical model. Subsequently, based on the effective negative mass local resonance theory, the perturbation solution for the dispersion curves is derived using the perturbation method. Finally, the effects of mass ratio, stiffness ratio, and nonlinear term on the starting and cutoff frequencies of the bandgap are analyzed, and key geometric parameters influencing the design of ultra-low vibration reduction bandgaps are comprehensively investigated. Subsequently, the influence of external excitation amplitude and the nonlinear term on bandgap formation is analyzed using numerical computation methods. Finally, effective positive mass, negative mass, and zero-mass phenomena within distinct frequency ranges of the bandgap and passband are examined to validate the theoretically derived results. The findings demonstrate that, compared to a positive-stiffness system, the bandgap of the bistable nonlinear acoustic metamaterial incorporating negative-stiffness Duffing oscillators shifts to higher frequencies and widens by a factor of $\sqrt{2}$. The external excitation amplitude F changes the bandgap starting frequency and cutoff frequency. As F increases, the starting frequency rises while the cutoff frequency decreases, resulting in a narrowing of the bandgap width. Within the frequency range bounded by the bandgap starting frequency and cutoff frequency, the region between the resonance frequency and cutoff frequency corresponds to an effective negative mass state, whereas the region between the bandgap starting frequency and resonance frequency exhibits an effective positive mass state. Critically, the bandgap encompasses both effective positive mass and negative mass regions, wherein vibration propagation is suppressed. Concurrently, a zero-mass state emerges within this structure, with its frequency precisely coinciding with the bandgap cutoff frequency. This study provides a theoretical foundation and practical guidelines for designing nonlinear acoustic metamaterials targeting ultra-low-frequency and ultra-broadband vibration and noise mitigation. Full article

Moisture-induced instability in rock masses presents a significant threat to the safety and sustainability of underground infrastructure. This study proposes a nonlinear energy signal fusion framework to predict failure in moisture-affected limestone by integrating acoustic emission data with energy dissipation metrics. Uniaxial compression tests were carried out under controlled moisture conditions, with real-time monitoring of AE signals and strain energy evolution. The results reveal that increasing moisture content reduces the compressive strength and elastic modulus, prolongs the compaction phase, and induces a transition in failure mode from brittle shear to ductile tensile–shear behavior. An energy partitioning analysis shows a clear shift from storage-dominated to dissipation-dominated failure. A dissipation factor (η) is introduced to characterize the failure process, with critical thresholds η_min and η_f identified. A nonlinear AE-energy coupling model incorporating water-sensitive parameters is proposed. Furthermore, an energy-based instability criterion integrating multiple indicators is established to quantify failure transitions. The proposed method offers a robust tool for intelligent monitoring and predictive stability assessment. By integrating data-driven indicators with environmental sensitivity, the study provides engineering insights that support adaptive support design, long-term resilience, and sustainable decision making in groundwater-rich rock environments. Full article