Search Results (8,784)

This paper deals with the problem of constructing optimal difference formulas in the Hilbert space $H_2^{\left(m\right)}\left(0,1\right)$ through Sobolev’s method. Firstly, Sobolev’s method of construction of optimal difference formulas in the Hilbert space $H_2^{\left(m\right)}\left(0,1\right)$, which is based on the discrete analogue $L_h\left[\beta \right]$, is described. Secondly, a discrete analogue $L_h\left[\beta \right]$ of differential operator $\left({\displaystyle \frac{d^2}{d{x}^2}}+2\mathrm{sgn}x{\displaystyle \frac{d}{dx}}+1\right){\left[1-{\displaystyle \frac{d^2}{d{x}^2}}\right]}^{\left[m-2\right]}$ having variable coefficients is contructed. Thirdly, for $m=2$ the optimal difference formula is obtained. Finally, at the end of the paper, we present some numerical results, which serves to confirm the numerical convergence of the optimal difference formula. Full article

Hopper scales are critical dynamic metering equipment in industrial production, yet their metrological performance is often compromised by wear on weighing units over long-term service. This study proposes a wear state assessment method based on the evolution of vibration features. Focusing on the rocker-column weighing unit, we analyzed the mechanism by which geometric changes in the spherical indenter—caused by fretting wear—alter the system’s constraint state. A global-to-local coupled Discrete Element Method and Finite Element Method (DEM–FEM) model was constructed to account for material-structure interactions, alongside a dynamic simulation model considering wear evolution. The simulation accuracy was validated through a dedicated experimental platform. The results indicate that as spherical wear intensifies, the low-frequency swaying of the indenter is suppressed, causing the system’s vibration mode to transition from a flexible, swaying-dominated state to a high-frequency, rigid-impact-dominated state. In the frequency domain, this manifests as energy migration, characterized by attenuation of the low-frequency main peak and an elevation of the high-frequency broadband noise floor. Crucially, as a key innovation for wear diagnosis, this study reveals the directional sensitivity of statistical indicators. While the Root Mean Square (RMS) exhibits a non-monotonic V-shaped trend, the Kurtosis and Margin factors of the tangential vibration demonstrate superior monotonic sensitivity. Under severe wear conditions, these two indicators increase by 14 and 11 times, respectively. These findings provide highly effective diagnostic criteria and hold significant engineering application value for the predictive maintenance of industrial dynamic weighing systems. Full article

Addressing the challenge of tracing hidden and transient cross-connections in urban drainage networks, this study develops a SWMM–Bayesian coupled model based on the Py SWMM interface using the Daming Lake area in Jinan as a case study. By employing a Markov Chain Monte Carlo (MCMC) algorithm to drive the interaction between dynamic simulation and statistical inference, the model achieves multidimensional joint posterior estimation of pollution source location (J_x), discharge intensity (M), and discharge timing (T). The results indicate: (1) Model accuracy: The coupled model demonstrates strong source tracing capability, with mean absolute errors below 0.6% in single-parameter inversion. Under multi-parameter joint inversion, the true values of all parameters consistently fall within the 95% confidence intervals. (2) Parameter sensitivity: The influence of MCMC step size on the uncertainty of pollution tracing results is systematically clarified. Discrete source location estimates (J_x) exhibit high robustness to step size variation due to spatial heterogeneity in hydraulic responses, whereas continuous physical parameters (M and T) show strong dependence on the selected step size scale. (3) Practical application: The impact of spatial monitoring network configuration on pollution tracing performance is examined. By deploying a complementary monitoring system integrating trunk and branch pipelines, the inversion accuracy for mass (M) and time (T) parameters is significantly improved by 84.2% and 88.5%, respectively. Overall, the proposed pollution source tracing method for urban drainage networks effectively overcomes the multi-solution challenge in complex network inversion, providing critical technical support for refined urban water environment management. Full article

Modified discrete transfer matrix and transmission line models were applied to nonlinear refractive index fiber Bragg gratings (FBG). The methods were validated against analytical solutions for Kerr-type uniform FBG, evaluating accuracy, convergence, and computational time. Spectral reflectivity, bistability, index distribution, and group delay were computed for various FBG types, with results discussed for each grating, particularly regarding reflectivity and bistability. Full article

Hydraulic fracturing is a critical technology for developing shale gas reservoirs, which are typical natural nanoporous media. However, the complex two−phase flow induced by fracturing fluid retention and the strong interference among hydraulic fractures introduce significant uncertainties to productivity forecasting. To address these challenges, this study proposes a transient productivity forecasting method to characterize fluid transport in fractured nanoporous media. This method introduces a gas−water two−phase pseudo−pressure function to reconstruct the flow equations, utilizing micro−segment discretization and the principle of superposition to accurately characterize pressure drop interference among fractures, enabling rapid dynamic productivity forecasting under realistic well trajectory conditions. The investigation reveals that while increasing fracture count, half−length, and permeability enhances productivity, these improvements exhibit significant diminishing marginal returns, indicating the existence of optimal economic thresholds for these engineering parameters. Conversely, elevated water saturation, skin factor, and stress sensitivity lead to a decline in productivity. Analysis of flow interference demonstrates that fractures at the wellbore extremities contribute significantly higher production than those in the central section due to reduced interference, while deviations in the wellbore trajectory further exacerbate production heterogeneity. Field application confirms that the proposed method achieves reliable production history matching under realistic well trajectories and accurately captures the typical three−stage production characteristics of shale gas wells, providing a robust basis for Estimated Ultimate Recovery (EUR) assessment and fracturing design optimization. Full article

Sweeping jet actuators (SJAs) are promising for active flow control in aerospace systems, but integrating actuator-resolved unsteady CFD into full-configuration simulations is often impractical due to small geometric scales and $\mathcal{O}({10}^2)$ Hz oscillations that demand fine grids and small time steps. This work develops a reduced-order modeling (ROM) framework to generate time-resolved boundary conditions at the actuator exit from SJA flow data. Dynamic mode decomposition (DMD) is particularly attractive for this purpose because it provides a linear, data-driven input–output representation of the actuator effect, even though it does not explicitly model the underlying nonlinear switching mechanism. We introduce an eigenvalue-sorted dynamic mode decomposition (ES-DMD) method that performs stability-aware mode ranking based on the discrete-time DMD eigenvalues, prioritizing modes with $\Re (\lambda )$ closest to unity to retain near-neutrally stable oscillatory dynamics, improving robustness relative to conventional amplitude-based selections for high-frequency oscillatory flows. The method is evaluated across multiple operating conditions, with detailed analysis performed for the highest mass-flow case ($\dot{m}=0.01$ lb/s), representing the most dynamically demanding condition considered. Across multiple operating conditions, ES-DMD yields consistent reconstructions of the dominant switching dynamics. For one-dimensional exit-plane profiles, combining ES-DMD with time-delay embedding enables accurate reconstruction and multi-period prediction using only 20 modes (7.6% of the full system rank). The proposed approach provides a practical pathway to incorporate unsteady SJA effects into large-scale aerospace CFD through compact, predictive boundary-condition models. Full article

This study presents the development of a computational tool designed to help automate decision-making in excavation and foundation construction in rock, aiming to minimize risks to adjacent historical structures in an urban context. The tool uses a graphical interface and focuses on estimating the propagation of vibrations generated by these construction processes. A working methodology has been proposed, and a computational tool has been developed to predict the feasibility and safety of specific construction techniques in different areas of study. Using field-collected data, a three-dimensional survey of adjacent buildings is conducted in a 3D CAD model, converting the continuous terrain into a discrete point mesh. This mesh enables the tracing of vibrational wave trajectories from their origin to potentially affected structures. The tool then calculates the peak particle velocities (PPV) at the foundations of these structures. By comparing these PPV values with predefined thresholds—selected from different excavation procedures with heavy equipment—excavation zones where equipment can be safely used are visually represented using a color-coded scheme. To validate the applicability of the proposed method and developed approach, the tool was tested on a case study: The Rehabilitation Project of the Cervantes Theater in Segovia, promoted by the Ministry of Transport, Mobility, and Urban Agenda. This project is currently halted due to damage sustained by adjacent buildings during the excavation process. Full article

The hanging net shielding system, employing a suspended cage-type enclosed structure to restrict the high-voltage transmission wire, has seen increasingly widespread application in transmission line crossing construction. However, the lack of a comprehensive dynamic analysis methodology has limited the standardization of its design and usage. In this investigation, a systematical dynamic modeling and analysis procedure of the hanging net shielding system is proposed based on the absolute nodal coordinate formulation (ANCF). The carrier cable, slings and transmission wire are discretized by the ANCF cable element. The spatial flexible beam–beam contact model and the assumption of a single contact area are adopted to perform the contact searching between the transmission wire and the horizontal pulley. The system dynamics analysis equation is assembled and solved by generalized alpha method. A full-scale model is simulated for the transmission wire impact condition and the variation history of the tension in carrier cable and the sling cable are given. The peak value of the tension in carrier cable could be 110 kN, while the largest tension in sling cable is 9 kN. Results could help to ensure construction safety, shorten the design cycle of the protection system and reduce the development cost at the same time. Full article

Secure compressive sensing (SCS) mostly benefits scenes such as IoT with finite computer resources, the fields of spaceflight and medicine, etc. Recently, research on SCS has aroused widespread interest. Nevertheless, existing work on embedding security of CS usually requires an extra cryptographic routine applied to the measurement vectors. In this paper, we proposed an SCS scheme boosted by the hyper-chaotic system, which outperforms state-of-the-art methods and endows the SCS with a high level of inherent security. Encryption and sampling processing are accomplished simultaneously in our scheme, i.e., security is achieved when sampling with a measurement matrix, which is generated by an initial-value (secret key)-driven discrete hyper-chaotic (HC) system. Moreover, the application of the HC matrix decreases both the computing and bandwidth consumption costs of secret key streams transmission compared with traditional CS-based encryption methods. Experimentally, the HC-based matrix demonstrates excellent reconstruction performance, achieving an average SSIM of 0.91 and PSNR of 29.09 dB on the Set5 dataset at a sampling ratio of 0.5, outperforming conventional matrices such as Bernoulli and Hadamard. Security analysis confirms that the system exhibits asymptotic spherical secrecy and high key sensitivity—a deviation of ${10}^{-16}$ in the initial value results in complete decryption failure. Furthermore, the scheme shows strong robustness against additive Gaussian white noise and cropping attacks, maintaining a PSNR above 15 dB even under 50% cropping. Compared to existing methods, the proposed approach reduces bandwidth consumption by transmitting only the HC initial parameters rather than the entire measurement matrix. These results demonstrate that the HC-driven SCS framework provides inherent security, high reconstruction fidelity, and practical efficiency, making it suitable for secure sensing in constrained environments. Full article

In signal processing and information analysis, the detection and identification of anomalies present in signals constitute a critical research focus. Accurately discerning these deviations using probabilistic, statistical, and information-theoretic methods is essential for ensuring data integrity and supporting reliable downstream analysis. Outlier detection in functional data aims to identify curves or trajectories that deviate significantly from the dominant pattern—a process vital for data cleaning and the discovery of anomalous events. This task is challenging due to the intrinsic infinite dimensionality of functional data, where outliers often appear as subtle shape deformations that are difficult to detect. Moving beyond conventional approaches that discretize curves into multivariate vectors, we introduce a novel framework that projects functional data into a low-dimensional space of meaningful features. This is achieved via a tailored weighting scheme designed to preserve essential curve variations. We then incorporate the Mahalanobis distance to detect directional outlyingness under non-Gaussian assumptions through a robustified bootstrap resampling method with data-driven threshold determination. Simulation studies validated its superior performance, demonstrating higher true positive and lower false positive rates across diverse anomaly types, including magnitude, shape-isolated, shape-persistent, and mixed outliers. The practical utility of our approach was further confirmed through applications in environmental monitoring using seawater spectral data, character trajectory analysis, and population data underscoring its cross-domain versatility. Full article

This paper investigates the contact stress induced by a rigid sphere sliding on a coating-ceramic system. A discrete element model incorporating a ceramic substrate, a surface coating, and a rigid sphere is developed. The influences of the coating grain elastic modulus, coating surface friction coefficient, coating thickness, and interface microcrack defects on the stress distribution within the system are analyzed. The results indicate that a higher coating-to-substrate elastic modulus ratio increases the overall stress but reduces the interfacial shear stress. A lower surface friction coefficient is more beneficial for hard coatings. The relatively optimal coating thickness (h/a) is approximately 0.5. When interface microcrack defects are present, stress concentrations occur at their locations. Longer interface microcracks lead to greater stress concentration, and the interfacial concentrated stress increases with crack length. Full article

To elucidate how pre-crack inclination affects the dynamic mechanical response, failure modes, and energy evolution of rocks, uniaxial impact compression tests were conducted on Φ50 mm Baima Iron Mine magnetite specimens with varying pre-crack angles using a split Hopkinson pressure bar (SHPB) system. The experiments were integrated with PFC2D discrete element simulations to investigate crack propagation and stress field characteristics. The results demonstrate that all specimens maintained dynamic stress equilibrium under impact loading. Crack inclination significantly influenced the dynamic stress–strain response: specimens with 0°~30°cracks exhibited gradual post-peak stress decay, indicating ductile behavior, while specimens with larger inclinations (≥45°) displayed pronounced brittle failure. Dynamic compressive strength followed a “U-shaped” trend with crack angle, reaching a minimum at 45°, whereas 0°and 90°specimens exhibited similar strength. Failure modes transitioned from axial splitting to wing-crack dominance, while anti-wing and shear cracks decreased significantly with increasing crack angle. Energy analysis indicated that reflected energy decreased and transmitted energy increased with increasing crack angle. Numerical simulations reproduced the experimental macroscopic failure patterns accurately, revealing the underlying mechanisms of crack-tip coalescence and stress concentration shifts as a function of crack inclination. These findings offer insights into the dynamic failure mechanisms of jointed rocks and provide guidance for engineering safety assessments. Full article

Reliable state-of-charge (SoC) estimation is crucial for safe and efficient battery management. However, it is challenging in practice. Terminal-voltage sensitivity becomes weak in open-circuit-voltage (OCV) plateau regions. Model uncertainty also persists at practical sampling periods. To tackle this issue, this paper proposes a discrete-time, model-based SoC estimation framework. This framework combines a dual-polarization equivalent-circuit model with a tuning-light sliding-mode observer. It is specifically designed for digitally sampled battery management systems. The modeling stage includes: (i) a discrete-time DP representation suitable for embedded use, (ii) a shape-preserving PCHIP reconstruction of the OCV–SoC curve and its derivative, and (iii) an effective-slope regularization mechanism that maintains non-vanishing output sensitivity even in flat OCV regions. On top of this structure, a boundary-layer SMO is developed with output-error shaping, model-driven gain scaling, and simple bias-compensation terms based on integral correction and leaky Coulomb counting. A discrete-time Lyapunov analysis is conducted directly on the surface dynamics. This analysis shows finite-time reaching to the boundary layer and a practical limit on the steady-state error that depends on the sampling period, disturbance level, and boundary-layer width. Numerical tests on a DP model identified from experimental data indicate that the proposed method achieves SoC accuracy similar to a switching-gain adaptive SMO. The results confirm the benefits of a model-centric design. The discrete-time formulation and convergence proof, which do not depend on high sampling rates, provide robustness advantages over traditional sliding-mode methods. The proposed method also performs better than a tuned EKF in plateau regions, requiring much less tuning effort. Full article

This study introduces a hybrid point cloud compression method that transfers from octree-nodes to voxel occupancy estimation to find its lower-bound bitrate by using a Binary Arithmetic Range Coder. In previous attempts, we demonstrated that our entropy compression model based on index convolution achieves promising performance while maintaining low complexity. However, our previous model lacks an autoregressive approach, which is apparently indispensable to compete with the current state-of-the-art of compression performance. Therefore, we adapt an autoregressive grouping method that iteratively populates, explores, and estimates the occupancy of 1-bit voxel candidates in a more discrete fashion. Furthermore, we refactored our backbone architecture by adding a distiller layer on each convolution, forcing every hidden feature to contribute to the final output. Our proposed model extracts local features using lightweight 1D convolution applied in varied ordering and analyzes causal relationships by optimizing the cross-entropy. This approach efficiently replaces the voxel convolution techniques and attention models used in previous works, providing significant improvements in both time and memory consumption. The effectiveness of our model is demonstrated on three datasets, where it outperforms recent deep learning-based compression models in this field. Full article

We conduct a numerical study of integral algebraic equations (IAEs) with singular points, which pose significant challenges for standard computational methods. The presence of singular points often renders classical discretization schemes unstable and inaccurate. This work explores the reformulation of such problems using a least squares framework to restore numerical stability. By recasting the singular IAE as a minimization problem, the least squares method effectively handles the non-integrability and ill-conditioning inherent in direct approaches. We provide a numerical analysis of the proposed scheme and present results from several test cases, demonstrating its superior performance in terms of the convergence rate and solution quality compared to conventional methods. Our findings establish the least squares method as a viable and effective tool for solving singular IAEs. Full article