Search Results (38,526)

The mechanical behavior of deep rock masses under high-stress conditions exhibits significant nonlinear characteristics. However, current in situ stress field inversion methods typically rely on linear elastic constitutive models and multiple linear regression analysis. By analyzing the results of triaxial stress–strain tests and confining pressure calibration experiments on rocks, and drawing on the nonlinear concepts from the Duncan-Zhang model, a nonlinear characterization function was developed, represented by mean stress p, bulk modulus K, and shear modulus G. The nonlinear elastic constitutive model was integrated into a numerical simulation framework, and a new in situ stress field inversion fitting method based on nonlinear elastic constitutive modeling was proposed. This method uses initial linear iterations followed by multiple nonlinear iterations until convergence is achieved. Applied to the inversion of the deep in situ stress field at the Xishan Iron Mine, the results demonstrate that compared to traditional linear regression-based methods, the errors in mean stress, deviatoric stress, and the Lode parameter were reduced by 58%, 50%, and 22%, respectively, confirming the effectiveness of this method in in situ stress field inversion in rock mechanics. Full article

Because dust-emission processes driven by local, small-scale winds (e.g., terrain-induced winds) are difficult to accurately capture with mesoscale or larger-scale predictive models, this study employed a CFD-Lagrangian particle-tracking approach to numerically simulate near-surface dust release and transport under different atmospheric stability conditions in the same local flow field. The novelty of this work was the integration of MOST-based stable/neutral/unstable inflow construction with Lagrangian particle tracking, enabling a consistent comparison of stability effects within one framework. This framework is useful for assessing local blowing-sand impacts on short-range receptors. A near-surface source term was specified for PM10-class mineral dust, and particles were emitted using a vertically exponential allocation. Simulations were conducted over a kilometer-scale flow domain containing an idealized cosine hill, and the low-level concentration patterns and dispersion-height variations in the resulting dust cloud were analyzed. Compared with neutral conditions, stable stratification produced higher near-surface concentrations and a lower dispersion height, whereas unstable stratification yielded lower near-surface concentrations and a higher dispersion height; as the $\left|L\right|$ increased, the unstable cases gradually approached the neutral state. The influence of reference wind speed exhibited clear stability dependence: under stable conditions, stronger winds intensified the buoyancy-related suppression of dust dispersion, while under unstable conditions, stronger winds inhibited the vertical spreading of the dust cloud. In addition, reduced air density representative of plateau environments resulted in lower dust-cloud concentrations and higher dispersion heights. These findings highlight the coupled effects of stratification and wind speed on near-field dust dispersion and provide a reference for assessing local dust emissions over complex terrain. Full article

This article mainly researches the model dimension reduction in the finite volume element (FVE) method based on proper orthogonal decomposition (POD) for the two-dimensional (2D) hyperbolic equation. For this objective, an FVE method with unconditional stability and second-order temporal accuracy, and the existence, stability, and error estimates of the FVE solutions are first reviewed. Thereafter, most importantly, a new FVF model dimension reduction (FVEMDR) formulation is established by applying POD technology to lower the dimension of the vectors composed of unknown coefficients for the FVE solutions. The greatest contribution of this article is the theoretical analysis of the existence, unconditional stability, and error estimations for the FVEMDR solutions. Moreover, in computation, two sets of numerical simulations are provided to confirm the validity of the theoretical results and show the effectiveness of the FVEMRD formulation. Full article

In this study, we explore the stability of shield tunnel faces excavated entirely within confined aquifers through a combined physical investigation. A series of orthogonally designed model tests were performed to examine how the hydraulic head difference (Δh) and aquitard thickness (M) jointly influence face stability and seepage behavior. Our results reveal a distinct concave-downward pore-pressure profile and a steep hydraulic gradient immediately ahead of the excavation face. Excavation-induced stress redistribution was largely restricted to the aquifer, whereas the overlying aquitard exhibited negligible disturbance due to its low permeability and higher strength. The evolution of stress disturbance followed a three-stage process encompassing initial disturbance, progressive development, and large-scale destabilization. Deformation contours exhibited a conical failure zone with normalized width and height ranging from 0.7D to 1.0D and 1.7D to 1.86D. Surface settlements remained within ±1 mm, confirming that deformation was effectively confined below the aquitard. Numerical simulations reproduced the overall hydro-mechanical response, validating the experimental observations but slightly overpredicting support pressures due to the absence of arching effects. The findings highlight Δh/M as the dominant control parameter, with aquitard thickness exerting a moderating influence. Full article

Parameter estimation for biochemical reaction networks is computationally demanding, especially for systems with oscillatory nonlinear dynamics, where standard iterative optimization strategies, including genetic algorithms, often struggle with prohibitive computational costs. We introduce an efficient parameter estimation framework that combines a real-coded genetic algorithm with a novel adaptive simulation termination strategy. This strategy defines a time-dependent termination boundary based on population quantiles, which is permissive during early transients and becomes progressively stricter as simulations advance, explicitly accounting for the temporal structure of oscillatory behavior. Crucially, this mechanism facilitates the efficient identification and early simulation termination of poor parameter candidates, thus avoiding the computational expense of full-horizon simulations. The framework further integrates global exploration with the modified Powell method for rapid local refinement. Numerical experiments on two benchmark oscillatory models—the Lotka–Volterra and Goodwin oscillators—demonstrate that the framework reduces computational cost by approximately 30–50% compared to a baseline GA without this strategy. For the parameter-sensitive Goodwin model, the framework efficiently identifies candidates evolving toward damped oscillations caused by subtle parameter variations. Sensitivity analysis also confirms robustness across diverse hyperparameter settings, indicating that adaptive simulation termination provides a practical acceleration mechanism for inverse problems in systems biology where iterative objective function evaluation dominates runtime. Full article

Offshore reinforced concrete (RC) structures, such as bridges and high-piled wharves, are frequently subjected to the coupled action of steel corrosion and ship collision loads. However, existing studies lack systematic quantification and in-depth revelation of the synergistic degradation mechanism under this coupling effect, resulting in an insufficient scientific basis for engineering design and reinforcement. To address this gap, this study established a refined three-dimensional numerical model of drop hammer-reinforced concrete beams based on ABAQUS, comprehensively considering the strain rate effects of steel and concrete, steel–concrete bond–slip behavior, and the trilinear constitutive model of corroded steel. After validating the model’s reliability against experimental data from the existing literature, parametric simulations were conducted to investigate the coupled effects of different corrosion rates and drop heights (0.25–1.5 m). Key findings include: (1) corrosion reduces the peak impact force by 9.7–58.9% and increases the maximum mid-span displacement by 6.6–35.7%, with this effect amplified by higher drop heights; (2) shear performance degradation (16.14–35.19%) is significantly more severe than flexural performance degradation (13.28–28.93%), confirming that shear performance is more sensitive to corrosion; (3) corrosion causes cracks to propagate from a localized distribution to a global distribution, while higher drop heights accelerate structural evolution toward brittle failure; (4) the synergistic degradation law of “corrosion exacerbates impact damage, and impact amplifies corrosion defects” is revealed. By quantifying the corrosion–impact coupling effect, this study advances research in the field and provides critical technical support for damage assessment and service life prediction for offshore RC structures. In engineering practice, it is recommended that offshore structures in high-corrosion environments prioritize shear resistance enhancement and adopt targeted protective measures for high-impact-risk areas to mitigate the risk of brittle failure. Full article

This study investigates the fuel discrimination capability of the flame volume mixing method (FV mixing method) in predicting the lean blowout (LBO) limits of different fuels. Conventional FV-based models exhibit limited sensitivity to variations in fuel properties, especially under lean conditions and for sustainable aviation fuels. In this work, an improved FV mixing method is proposed by replacing the classical droplet evaporation treatment with the Abramzon–Sirignano droplet evaporation model, which accounts for fuel-dependent liquid properties, Stefan flow, and coupled convective heat and mass transfer between the gas phase and droplets. As a result, the proposed method shows enhanced sensitivity to fuel variability and improves the prediction accuracy of the LBO limit for the sustainable aviation fuel Cat-C1. The model performance is validated through numerical simulations and compared with experimental data. The results indicate that, compared with the baseline FV mixing method, the proposed approach reduces the LBO prediction error by 5.7%. The improved FV mixing method provides a more robust framework for LBO prediction, with potential applications in fuel characterization and combustion optimization. Full article

At present, the global logistics sector faces critical challenges, including rising energy costs and pressure to reduce CO₂ emissions. Traditional linear supply chains are becoming inefficient, necessitating a transition toward shared logistics based on the principles of the sharing economy. This paper presents a progressive three-layer architecture that transforms conventional reactive data collection into an autonomous, proactive management system for the distribution of consumable materials. While previous research established foundations in IoT connectivity for smart vending machines, this study advances the process by integrating an intelligent layer of artificial intelligence (AI) algorithms. The framework utilizes Long Short-Term Memory (LSTM) neural networks for demand forecasting, dynamic route optimization (VRP/ACO) for replenishment, and Isolation Forest/DBSCAN algorithms for real-time anomaly detection. To evaluate the framework, a numerical simulation was conducted using representative pilot scenarios. The results indicate that within the simulated environment, the system achieves over 95% accuracy in inventory depletion prediction (MAPE = 4.02%). In these analyzed instances, this leads to a 25–30% reduction in stock-out risks and a 25% reduction in replenishment distance. These findings demonstrate the significant potential for reducing operational costs and carbon footprints in green logistics. The study confirms that the synergy between IoT infrastructure and AI-driven analysis provides a robust foundation for transitioning from static methodologies to resilient, collaborative logistics ecosystems. Full article

Additive manufacturing, particularly 3D printing, is increasingly shaping the production of polymer-based components, enabling complex geometries and tailored functional performance. Yet, predicting their mechanical behavior remains challenging due to material anisotropy and sensitivity to processing conditions. This work presents an exploratory study designed to provide the experimental basis for the development and calibration of predictive models of mechanical properties in 3D-printed components. Standard ISO 527-2 Type 1A specimens were fabricated using thermoplastic PLA (polylactic acid) with systematic variations in layer orientation, infill overlap, and printing velocity. Mechanical characterization was carried out through uniaxial tensile testing to determine tensile strength and stiffness of the material specimens, while scanning electron microscopy (SEM) provided complementary insights into interlayer bonding, filament alignment, porosity, and fracture morphology. Results showed that material type and processing strategies strongly influenced mechanical response, with SEM highlighting microstructural features that govern interlayer adhesion and failure mechanisms. These findings contribute to a deeper understanding of process–structure–property relationships in additive manufacturing and establish the groundwork for predictive model development. Ongoing efforts will integrate these experimental insights into numerical simulations employing homogenized material models, thereby enhancing design optimization and reliability of 3D-printed structural components. Full article

Motivated by the recent progress in Finite-Time Fault Estimation (FTFE) and its application to very few classes of Nonlinear Dynamical Systems (NDSs), this paper aims to drive further advancements in the field. In this research direction, a review of the literature reveals that most studies integrate the Linear Matrix Inequality (LMI) approach with the Takagi–Sugeno fuzzy (TSF) models to approximate nonlinear dynamics. However, the Sum Of Squares (SOS) approach offers numerous advancements and improvements over the LMI approach for TSF models. As an initial effort, by applying the SOS approach, this paper proposes two design procedures to ensure the finite-time boundedness of the state and actuator estimation errors for a class of polynomial fuzzy (PF) models. The first result relies on a polynomial integral observer. The second result is derived using a polynomial proportional-integral observer. Simulation results are provided to compare the two design procedures. Full article

For a deep-buried complex cavern with complex geological conditions, it is difficult to determine the critical warning deformation of surrounding rock. A determination method for warning deformations based on rock strength is proposed to study the warning status of the surrounding rock of the Baihetan left-bank underground powerhouse. Three warning levels—blue, yellow, and red—are numerically established based on crack initiation stress, dilatancy stress, and uniaxial compressive strength of the rock mass. These warning deformations are influenced not only by the critical stresses but also by the cavern shape, rock position, deformation properties and in situ stresses. The in situ stresses were inversely analyzed by a three-dimensional geological model orthogonal to the principal stresses and present a high determination coefficient of 0.834 with the measured results. However, the complex geological conditions could bring great uncertainties to the simulation results and significantly reduce the warning deformations. Thus, the monitored deformations that reflect these uncertainties, instead of the simulated stresses or deformations, were used to predict the warning state of the surrounding rock, which was analyzed by comparing the on-site monitored deformations with the critical warning deformations. The warning results demonstrated that the proposed methodology enables prediction of warning location, timing and grades, and 85.9% of monitoring points obtained correct warning signals. Full article

Overtaking maneuvers can induce significant changes in the airflow field between vehicles, potentially compromising the stability and safety of the overtaken vehicle. This study investigates the aerodynamic characteristics during overtaking in a platoon of vehicles using the 1:2.5 DrivAer fastback model as the subject of analysis. To simulate the external flow during overtaking within a vehicle platoon, the Reynolds-Averaged Navier–Stokes (RANS) equations are employed under steady-state, incompressible flow assumptions. A baseline simulation is first performed for a single vehicle, and the results are validated against experimental data to ensure the reliability of the numerical method. The simulation is subsequently extended to a two-vehicle platoon configuration with a longitudinal spacing of half a vehicle length. Under steady platoon driving conditions, no significant lateral aerodynamic disturbances are observed between adjacent vehicles, and a two-vehicle platoon is subjected to relatively small lateral forces. However, during the overtaking process, notable variations in aerodynamic forces and moments occur. In particular, the lateral force coefficient and yaw moment coefficient of two-vehicle platoons reach their peak values at about two vehicle lengths ahead of the critical overtaking position. Furthermore, during the overtaking maneuver, the aerodynamic characteristics of the overtaken vehicle exhibit continuous fluctuations. The resulting variations in the lateral force coefficient and cornering stiffness have a sustained impact on vehicle handling stability, providing crucial insights for enhancing vehicle maneuverability. Full article

The mechanical method has become a new construction method for connected aisles in metro tunnels due to its advantages of fast construction speed, high safety, and minimal ground disturbance. During the tunneling process, the interaction mechanism between the composite segment and the shield cutterhead is complex. Taking Shenzhen Metro Line 8 No. 1 Connected Aisle as the research object, a 3D refined model of the shield cutterhead, composite segments and bolt system were built with Abaqus to investigate their dynamic response under cutting. The Drucker–Prager damage model and contact algorithm were introduced to describe the nonlinear behavior of the cutting process. The reliability of the numerical model was verified by concrete cutting tests and on-site Fiber Bragg Grating monitoring, and good agreements were observed. Results show cutterhead cutting first induces circumferential squeezing, then extends longitudinally with a notable time lag, and longitudinal dynamic response is much stronger than transverse. Affected by cutterhead thrust–rotation coupling, cuttable segments have larger displacement with maximum 0.07 mm, forming an asymmetric deformation zone. Ring joint opening follows “a distal attenuation of the opening amount” rule with maximum 0.018 mm, while bolt stress and displacement show “near-end concentration with gradient attenuation”, with longitudinal bolts being more responsive. Mechanical disturbance from small-shield cutting is minimal, with tunnel segment deformation, joint openings, and bolt stress all remaining well below code-specified allowable values. Numerical results show good agreement with field monitoring data of ring joint openings obtained using Fiber Bragg Grating (FBG) sensors, confirming the reliability of the simulation. The results can provide references for structural design and construction parameter optimization of composite segments in a connected aisle. Full article

To address the challenges of excessive local thinning, poor surface quality, and low production efficiency in traditional multi-pass deep-drawn aluminum alloy fairings, this study investigates the effects of process parameters—including liquid chamber pressure, holding force, and differentiated lubrication schemes—on the liquid-filled forming performance and wall thickness distribution of a 460 × 280 × 1.5 mm thin-walled 2A12 aluminum alloy fairing. Employing an integrated liquid-filled forming technique combining a flexible punch with a rigid die, the research combines numerical simulation with experimental validation. The study demonstrates good consistency between experimental results and numerical simulations. The optimal forming process parameters are liquid chamber pressure of 10 MPa, holding force of 1100 kN, and a lubrication scheme (friction coefficients of 0.01 for the flange and forming zones and 0.06 for the transition radius zone). Under these parameters, part wrinkling and cracking are effectively suppressed, achieving optimal wall thickness uniformity in the formed parts, with a maximum thinning rate of only 6.6%. The proposed liquid-assisted forming process and differentiated lubrication scheme provide a new technical pathway for high-precision manufacturing of thin-walled complex curved components made of 2A12 aluminum alloy. Compared to traditional multi-stage drawing processes, both forming efficiency and quality are significantly improved. Full article

Offshore wind energy is an attractive renewable energy source due to its advantages. However, the chaotic marine environment makes the analysis of offshore wind energy extremely difficult. Furthermore, studying the behavior of wind turbines under rare and hazardous natural events such as rogue waves is crucial for the safety and operation of wind turbines and the platforms mounted on them. Therefore, this study numerically investigates the aerodynamic, hydrodynamic, and structural properties of the National Renewable Energy Laboratory (NREL) 5 MW wind turbines under the effect of the Draupner wave, the first marine rogue wave ever recorded. To this end, the geometric and structural information of the NREL 5 MW wind turbines mounted on monopile and jacket platforms is explained. The characteristics of the Draupner wave and the variations in its wave height time series are investigated. The recorded wave height time series values are imported into the QBlade program, and the dynamics of NREL 5MW monopile and jacket wind turbines are simulated. Based on the simulation data, the aerodynamic, hydrodynamic, and structural properties of these structures are examined and analyzed. The results demonstrate that Draupner waves have a significant effect on the aerodynamic, hydrodynamic, and structural parameters of the wind turbines. These parameters are observed to reach their highest values, particularly between the 250th and 280th seconds, when the Draupner wave height reaches its peak. Our findings indicate that the jacket structure experienced higher total forces due to its larger wetted surface area and geometric complexity, while the monopile foundation showed higher inertial loading in the X-direction because of its larger added mass. Additionally, we observed that total aerodynamic power generation is significantly affected by the passage of the Draupner rogue wave. We discuss our findings and their limitations. This numerical study is intended to be a milestone for researchers working on the structural health of offshore wind turbines and platforms under the effect of rogue waves. Full article