Search Results (3,341)

Addressing the issues of brittle spalling and debris scattering commonly observed in Normal Concrete (NC) under high-velocity impact loading, this study investigates the resistance of polyurea-reinforced concrete targets against high-velocity bullet penetration. High-velocity projectile penetration tests were conducted at approximately 510 m/s to comparatively analyze the failure modes of plain concrete targets and targets reinforced with polyurea coatings of varying thicknesses. Furthermore, a three-dimensional numerical model based on the coupled Finite Element-Smoothed Particle Hydrodynamics (FE-SPH) algorithm was constructed to overcome the numerical instabilities inherent in traditional finite element methods when handling large material deformations and debris flows. The experimental results indicate that while the polyurea coating has a limited direct effect on reducing the depth of penetration (DOP)—showing marginal reductions of 1.8% and 2.3% for 2 mm and 5 mm coatings, respectively—it demonstrates a significant physical confinement effect. Notably, the 5 mm polyurea coating effectively suppresses brittle spalling on the impact face, reducing the crater diameter by 15.5% compared to the plain concrete target and restricting the propagation of radial cracks. Energy analysis and interface pressure monitoring reveal that the polyurea coating employs a “peak-shaving and valley-filling” mechanism driven by mechanical impedance mismatch, transforming transient impacts into steady-state compression with lower energy density. Consequently, this significantly enhances the overall impact toughness and secondary protection capability of the structure. These findings provide critical references for the refined reinforcement design of existing defensive structures. Full article

To investigate the underlying mechanisms of talent mobility in higher-education institutions influenced by factors such as the development environment, macroeconomic policies, and evaluation mechanisms, this paper proposes a nonlinear stochastic differential equation (SDE) dynamical model that incorporates time delays and multiplicative noise. We analyze the dynamic processes of talent mobility under varying conditions regarding the number of nodes, policy implementation cycles, and noise intensity. First, we employ central manifold theory and stochastic averaging methods to reduce the system to a one-dimensional averaged $It\widehat{o}$ equation. Subsequently, with $\tau$ as a parameter, we conduct an in-depth study of the system’s stochastic bifurcation behavior using the corresponding Fok–Planck–Kolmogorov equations. Finally, we validate the theoretical conclusions through numerical simulations. The results indicate that the number of nodes, policy delay, and noise intensity all have significant effects on system stability; an increasing delay induces random P-bifurcation in the system, and when $N\le 3$ and $N>3$, the system exhibits distinctly different steady-state behaviors. We also found that excessively high noise intensity disrupts system stability, whereas moderate noise intensity has a positive effect on stability. This study not only provides theoretical insights into the dynamic evolution mechanisms of talent mobility in regional universities but also offers valuable guidance for universities in formulating talent recruitment and evaluation policies. The methodology employed in this study opens up a promising avenue for analyzing complex dynamic problems in the field of sociology. Full article

This paper investigates the adaptive prescribed performance fixed-time control problem for uncertain strict-feedback nonlinear systems in the presence of unknown control coefficients and actuator faults. A switching-based control strategy is developed to address the uncertainty in control coefficients, where adaptive parameters are adjusted online according to design requirements. To regulate the transient and steady-state behavior, a fixed-time prescribed performance function is incorporated into the control design, ensuring that the tracking error evolves within predefined bounds. The command filter technique is employed to simplify the backstepping procedure and avoid the issue of complexity growth, while filter-induced errors are compensated using auxiliary signals. Rigorous Lyapunov analysis establishes that all closed-loop signals remain bounded and that the tracking error converges to a small neighborhood of zero within a fixed time, independent of initial conditions. The effectiveness of the proposed method is demonstrated through numerical simulations and a practical example. Full article

Large-aperture steerable reflector antennas are pivotal for deep-space exploration and satellite communication, but their high-frequency performance is often compromised by wind-induced structural deformations. This study employs a high-fidelity fluid–structure interaction (FSI) framework, coupling Computational Fluid Dynamics (CFD) and the Finite Element Method (FEM), to investigate the dynamic response of an 18 m Square Kilometre Array (SKA) antenna under transient wind loads. The structural FEM is validated against experimental modal data, ensuring the capture of essential vibration characteristics. We evaluate steady-state wind pressure coefficients ($C_p$) and transient responses under a simulated Davenport wind spectrum across the antenna’s full operational elevation range. Surface accuracy degradation is rigorously quantified using the Root Mean Square Error (RMSE) of the best-fit paraboloid. The results demonstrate a significant correlation between peak deformation and surface error, pinpointing 15° and 90° pitch angles as the most critical configurations for profile degradation due to the “air pocket effect” and asymmetric pressure distributions, respectively. These insights establish a robust theoretical basis for structural optimization and the development of active surface control strategies for next-generation aerospace signal acquisition infrastructure. Full article

To promote the green and low-carbon transition and achieve sustainable development in the transportation sector, virtual calibration technology was employed for the efficient and precise control of emissions from heavy-duty diesel engines and aftertreatment systems. A data-driven, semi-empirical and semi-physical simulation modeling method was proposed. By constructing core modules based on physical mechanisms and refining empirical parameters using experimental data, the method improves computational efficiency while maintaining the prediction accuracy of key parameters. Additionally, a collaborative architecture combining physical actuators and virtual sensor signals was introduced, laying the foundation for the validity of virtual calibration. By innovatively introducing a closed-loop system with real actuators and virtual sensors, the dynamic response characteristics of the control system are faithfully reproduced, providing a reliable environment for validating the results of virtual calibration. Under steady-state conditions, the results demonstrated an average relative error of 1.7% for brake-specific fuel consumption (BSFC) and 6.1% for NOx emissions. An open-loop system for the virtual calibration testing platform was constructed for steady-state calibration. Using the main injection timing and common rail pressure as independent variables, a D-optimal design was utilized to generate 43 sets of experimental points, from which a polynomial regression model was established (R² ≥ 98%). Under the constraints of NOx and pre-turbine temperature, fuel consumption in the low-load range is reduced by 0.5–3 g/kW·h, aftertreatment NOx emissions are reduced by 0.5–3 g/kW·h, and exhaust temperature is increased by 10 °C. This study establishes a complete development workflow consisting of “operating condition design-virtual optimization-bench validation,” significantly enhancing calibration efficiency and engineering applicability. This method shortens the calibration cycle and reduces the number of physical bench tests, providing the industry with a comprehensive calibration methodology tailored to engine operating conditions that is both reproducible and scalable. Full article

Multiple-fault events can initiate overload propagation and cascading outages, resulting in severe load loss and reduced system resilience. Therefore, beyond conventional protection concepts based on the (n − 1) criterion, there is also a need to address multiple-fault events to minimize loss of load. This paper presents an optimized overload tripping scheme to mitigate cascading outages in high-voltage grids under multiple-fault conditions, where selected line switches or circuit breakers are opened in a controlled manner to isolate limited grid sections, minimize interrupted load, and prevent further overload propagation. The method combines inverse definite minimum time relay modeling with a heuristic graph-search algorithm implemented in pandapower to identify feasible switching actions that minimize load loss while preventing overload propagation. The approach is demonstrated on SimBench high-voltage urban and mixed benchmark grids under double-line fault scenarios. In the urban grid, the proposed scheme reduces the maximum load loss from 34.0% to 2.4%, while in the mixed grid, the reduction is from 50.3% to 5.2%. A SAIFI-inspired resilience proxy is introduced to quantify the reduction in customer/load interruptions, showing a resilience improvement factor of about 3.6 for cascading scenarios. In addition, thermal inertia analysis indicates that corrective switching must be completed within approximately 5 min to remain within line-temperature limits. The analysis is based on quasi-steady-state power-flow and relay simulations; transient stability effects are outside the scope of this study. The results demonstrate that the optimized overload tripping scheme is a promising adaptive protection strategy for improving grid resilience under severe contingency conditions. Full article

Post-traumatic auditory and vestibular complaints are frequent after traumatic brain injury (TBI) and temporal bone trauma. They create particular difficulty in medico-legal practice because the evaluator must distinguish diagnosis, functional impact, plausibility of traumatic causation, and the credibility of reported deficits and/or symptoms. This manuscript is a narrative expert review, not a systematic review or a validated forensic prediction rule. It aims to synthesize clinically relevant evidence and propose a practical cross-check framework for structured audio-vestibular assessment in post-traumatic and medico-legal contexts. Pure-tone audiometry remains the functional entry point, but it should be interpreted in conjunction with speech audiometry, tympanometry, acoustic reflexes, transient-evoked and distortion-product otoacoustic emissions, auditory brainstem responses, and auditory steady-state responses. Vestibular evaluation should combine videonystagmography, video head impulse testing, cervical and ocular vestibular evoked myogenic potentials, and computerized dynamic posturography, recognizing that each method interrogates different physiological domains and frequencies. Particular emphasis is placed on the separation between clinical diagnosis, physiological localization, functional impairment, and medico-legal attribution. The article also discusses safeguards against false-positive attribution of malingering, the time course after TBI, inter-rater variability, and the role of specialist expertise in medico-legal reporting. The proposed framework does not eliminate uncertainty; rather, it is intended to make expert reasoning transparent, cautious, internally consistent, and defensible. Full article

Accurate prediction of steady-state relative permeability via pore-scale modeling is fundamental to understanding multiphase flow processes in diverse engineering applications. However, the stochastic nature of the initial fluid distribution (IFD) in simulations is frequently overlooked, creating uncertainties that may obscure the physical influence of critical parameters on transport behavior. In this study, a color-gradient lattice Boltzmann method was employed to conduct extensive steady-state simulations across two porous media of varying geometric complexity. The investigation focused on evaluating three representative IFD patterns across different capillary numbers (Ca) and viscosity ratios (M). By introducing the coefficient of variation (CV) and distribution interval overlap analysis, the IFD-induced uncertainty was systematically quantified. The results demonstrate that the IFD is a primary source of statistical variance in relative permeability, exhibiting a strong nonlinear coupling with Ca, M, and structural complexity. CV analysis reveals that uncertainty peaks within specific saturation windows, which shift according to the pore geometry. Specifically, the peak uncertainty window for total relative permeability shifts from S_w$\in$ [0.5, 0.7] in the simple model to S_w$\in$ [0.3, 0.5] in the heterogeneous model. Notably, the wetting phase exhibits pronounced instability in the low-saturation regime, with the wetting-phase CV reaching its maximum at S_w = 0.3 in the simple model. At low Ca conditions, IFD-induced errors can entirely mask the physical sensitivity of relative permeability to Ca and M within certain saturation intervals. Furthermore, variations in initial configurations lead to divergent evolutions of the fluid-fluid interfacial area relative to wetting saturation, highlighting the role of microscopic topological memory in governing flow behavior. This research provides a quantitative foundation for IFD sensitivity in pore-scale modeling and proposes the integration of a CV-based uncertainty framework into macro-scale models to enhance the robustness and reliability of multiphase flow predictions. Full article

In response to the problems of high energy consumption and difficulty in precise regulation of electric tracing anti-icing systems for polar marine engineering equipment in low-temperature, strong-wind, and high-humidity environments, this paper conducts experimental measurement and predictive modeling research on the convective heat transfer characteristics of electric heat-traced circular cylinders in cross-flow. First, a controllable environmental experimental system was set up to obtain 144 sets of steady-state convective heat transfer data under different combinations of wind speed, temperature, humidity, and heat flux density. Based on this, a Nusselt number (Nu) prediction model using a fully connected Deep Neural Network (DNN) was constructed, and its performance was evaluated through five-fold cross-validation. The results show that the DNN model can effectively capture nonlinear mapping relationships among multiple factors, and its prediction accuracy ($R^2$ = 0.9828) is superior to that of traditional machine learning models. Furthermore, the Shapley Additive Explanations (SHAP) method was introduced to analyze the multi-factor coupling mechanisms, quantify the contribution of each input variable to the Nu prediction, and provide a data-driven reference for the optimization of engineering parameters under extreme polar conditions. Full article

Hydraulic oscillator tools (HOTs) are effective solutions for mitigating excessive drag encountered during sliding drilling in horizontal wells. However, their field performance remains unpredictable due to theoretical limitations in modeling nonlinear friction behavior under axial vibration. To address this gap, a series of friction tests was conducted on sandstone–steel pairs under water-based mud lubrication. Experimental results demonstrate that steady-state sliding friction follows the velocity-dependent Dieterich–Ruina model, while vibration–sliding coupled friction is accurately described by the Dahl model. Integrating these findings, a comprehensive drillstring dynamic model was developed. The model was solved using an explicit central difference method and validated against field hook load data from Well XX-1, with prediction errors below 9%. Parametric studies further quantified HOT performance, revealing that excitation force amplitude and HOT placement significantly impact drag reduction, whereas vibration frequency exerts a relatively modest influence. Meanwhile, the effective propagation distance induced by the hydraulic oscillator is relatively limited, resulting in a drag reduction rate of no more than 30% even under optimal parameter conditions. This work establishes a validated theoretical framework for optimizing hydraulic oscillator parameters in horizontal drilling. Full article

The responses of a very large floating structure (VLFS), which is modeled as a thin viscoelastic plate floating on a fluid of finite depth, are analytically studied within the framework of the nonlinear potential flow theory. We use the Laplace equation with the dynamical boundary condition to express a balance among the hydrodynamic, inertial, and viscoelastic forces. For the case of steady-state incident waves, we obtain convergent series solutions for plate deflection and velocity potential by choosing the optimal convergence-control parameter $C_0$ and proper auxiliary linear operators in the homotopy analysis method (HAM). The strain relaxation time for the viscoelastic plate is studied, and the result shows that the plate deflection decreases when the retardation time increases. The influences of other physical parameters on the viscoelastic plate are also discussed. The nonlinearity of dispersion relation and the retardation time of the plate have important and non-negligible effects on the responses of the VLFS. The results obtained here may be helpful in understanding the different physical parameters to model hydroelastic responses of a VLFS in the real ocean. Full article

This research paper investigates the solution of diffusion equations characterized by Third-Kind (Robin) boundary conditions within n-dimensional complex domains. The analysis is conducted in the $L_2$ Hilbert space, which facilitates the substantiation of both the existence and uniqueness of solutions through variational methods. Analytical solutions are derived for multidimensional domains by employing the Fourier method and spectral analysis techniques. Complementing this theoretical framework, a high-accuracy numerical approach based on the Associated Legendre Polynomials Collocation Spectral Method (ALP-CSM) with Chebyshev–Gauss–Lobatto nodes is developed. Rigorous convergence analysis confirms spectral accuracy, with numerical examples in one, two, and three dimensions demonstrating error decay from $\mathcal{O}\left({10}^{-3}\right)$ to machine precision $\mathcal{O}\left({10}^{-15}\right)$. The mathematical impact of Third-Kind boundary conditions on the diffusion rate and the steady state of the system is demonstrated. The obtained results provide a robust tool for modeling physical processes, particularly in systems involving heat exchange on the surfaces of complex-structured domains, offering both theoretical insight and computational efficiency. Full article

This study investigated the effects of dietary nano-antimicrobial peptides (NAP) on the microbial communities and metabolic profiles in Tibetan sheep. Using 16S rRNA gene high-throughput sequencing and non-targeted metabolomics, the contents of the small intestine, rumen, and rectum were systematically analyzed in a control group (Group A) and a NAP-supplemented group (Group B). Multi-omics integration methods, including O2PLS and Pearson correlation analysis, were employed to explore the association between microbial communities and metabolites. Alpha and beta diversity analyses revealed significant differences (p < 0.05) in the microbial community structure of the small intestine between the two groups. In contrast, the rumen and rectal microbiota remained relatively stable, indicating that the regulatory effects of NAP on the intestinal microecology are site-specific. In the small intestine, NAP altered the composition of dominant functional microbiota and the abundance of taxa related to energy metabolism. Metabolomic analysis identified significant shifts in metabolic profiles, specifically within the bile acid, fatty acid, and phospholipid pathways (p < 0.05). Group A exhibited baseline steady-state characteristics (e.g., cholic acids and phospholipids), whereas Group B showed activation of unsaturated fatty acids and related metabolites. Multi-omics integration revealed a stable systematic association between intestinal microbial genera and metabolites. Specifically, bile acid and prostaglandin metabolites were negatively correlated with Firmicutes-related taxa. These findings suggest that NAP supplementation may contribute to maintaining host energy metabolism and intestinal homeostasis by regulating intestinal microecology. Full article

The dual-active-bridge (DAB) converter is the core component of the DC micro-grid system; it has the advantages of topological structure symmetry, high efficiency, and high-power density. Model predictive control (MPC) is often employed to improve the dynamic response characteristics of the system, but its strong parameter dependence is a key factor limiting the development of MPC. Therefore, a model-free predictive control (MFPC) method combining an ultra-local model with model predictive control is proposed to solve the problem of strong dependence of traditional MPC on system model parameters. Firstly, establish the ultra-local mathematical model of the DAB converter. The system’s lumped disturbances are identified using the residual prediction method and substituted into the discrete model of the system at the next time step to achieve model-free prediction. Secondly, a minimum back-flow power constraint is added to the cost function to improve the steady-state performance of the converter. Thirdly, in the extended phase shift modulation, the Lagrange multiplier method (LMM) is proposed to reduce the current stress, ultimately achieving the collaborative optimization of the comprehensive performance of the DAB. Finally, a simulation model is built using MATLAB/Simulink, and compared with traditional control methods, the voltage ripple has been reduced by 51.3%, 89.1%, and 85.1%, respectively; the current stress significantly decreases both when the output voltage reference value changes and when the load resistance changes abruptly, and both can basically achieve zero back-flow power operation. The validity and superiority of the proposed strategy have been verified. Full article

In this study, carboxyl groups were introduced onto CNT surfaces via acid oxidation, and Ag nanoparticles were successfully deposited onto the CNTs through an in situ chemical reduction method. At an Ag-to-CNTs100 mass ratio of 3:1, the as-prepared composite achieved a thermal conductivity of 1.45 W/(m·K) in dimethyl silicone oil, representing enhancements of 187.5% and 76.9% relative to pure Ag nanoparticles and pristine CNTs100, respectively, at equivalent loadings. Concurrently, tribological tests revealed that the AgHTs-3 at a 3:1 mass ratio and 25 wt% loading exhibited a steady-state friction coefficient of 0.08–0.12, reflecting an approximately 72% reduction compared with pure dimethyl silicone oil. Electrical conductivity measurements demonstrated that CO-CNTs100 attained saturation at 30 wt% with a resistivity of 36.5 Ω·m, whereas the AgHTs-3 nanocomposite achieved a resistivity of 4.7 Ω·m at 35 wt%. The incorporation of Ag nanoparticles effectively enhanced the overall performance of the nanocomposites. Through the formation of a synergistic heterostructure with carboxyl-functionalized carbon nanotubes, the composite not only significantly improved the thermal conductivity of dimethyl silicone oil but also effectively optimized the interfacial lubricating film while substantially reducing the friction coefficient and wear volume. Moreover, the introduction of silver promoted the dispersion stability of the composites in dimethyl silicone oil, enabling higher filler loadings and thereby effectively boosting electrical conductivity. Full article