Search Results (29,455)

Monopiles are the predominant foundation type for offshore wind turbines (OWTs). Their diameters have increased substantially to accommodate larger structures, while current design approaches primarily rely on the API “p-y” model to simulate soil–pile interaction (SPI), which significantly underestimates the ultimate lateral pile capacity of large-diameter monopiles. Further, the API model accounts only for lateral soil resistance, neglecting mechanisms that substantially influence the lateral response of piles with low length-to-diameter (L/D) ratios, including pile toe shear, toe moment, and axial interfacial shaft friction. To address these problems, this study proposes a complete set of mechanism-aligned, spring-based SPI models capable of accurately simulating lateral pile response in sand across the full L/D spectrum typical of OWTs. The models include: a one-spring “p-y” model for flexible piles, capturing distributed lateral soil resistance; a two-spring “p-y + M_R-θ_R” model for semi-rigid piles, which additionally accounts for pile toe shear and bending moment resistance against rigid-body rotations; and a three-spring “p-y + M_R-θ_R + M_p-θ_p” model for rigid piles, which further includes rotational springs to account for distributed moment resistance due to rotation-induced shaft friction effects in sand. The derived spring parameter formulas have been calibrated using readily available engineering parameters, such as soil modulus, friction angle, and pile geometry. The three mechanism-aligned SPI models were validated against full-scale offshore monopile tests, centrifuge tests, and small-scale laboratory experiments, achieving less than 10% error in predicted pile capacities and less than 15% error in soil–pile coupled stiffness evolution. Full article

This study develops a two-dimensional computational fluid dynamics model for the reactive zone of an industrial-scale shaft furnace operated under ENERGIRON ZR (zero-reforming) conditions, where carbon in direct reduced iron (DRI) is explicitly distinguished into combined carbon in Fe₃C and free carbon in graphitic form. The model is validated against available plant data and then applied to investigate the effects of reducing gas temperature, gas composition, and gas feed rate on reduction, carburization, and carbon partitioning. The results show that in situ reforming, iron oxide reduction, and carburization are strongly coupled near the gas inlet. Increasing the reducing gas temperature from 1273 K to 1373 K raises the metallization degree from 0.9426 to 1.000 and the total carbon mass fraction from 0.01722 to 0.04938, while decreasing the combined carbon fraction from 97.6% to 86.3% because of enhanced Fe₃C decomposition. The effect of CH₄ content is temperature-dependent: at 1273 K and 1323 K, increasing CH₄ from 15% to 25% decreases both metallization and total carbon because intensified endothermic reforming lowers the in-furnace thermal level, whereas at 1373 K the total carbon changes from 0.04849 to 0.04938 and then to 0.04179, reflecting a shift in the controlling factor from CH₄ availability to thermal limitation. Increasing gas feed rate from 1400 Nm³/t-pellet to 1600 Nm³/t-pellet improves both reduction and beneficial carburization, with the total carbon mass fraction increasing from 0.02360 to 0.04047, while the combined carbon fraction decreases slightly from 93.9% to 92.5%. The predicted carbon partitioning results also show qualitative agreement with the limited industrial data, particularly the decreasing combined carbon fraction with increasing total carbon content in DRI. Full article

Background: Neuroinflammation contributes to the etiopathology and symptom severity of neurodegenerative and neuropsychiatric disorders. Glial cells, especially microglia and astrocytes, play a crucial role in neuroinflammation. It has been reported that ginseng (Panax ginseng) and its bioactive component ginsenoside Rg1 exhibit anti-inflammatory effects and can improve cognitive performance in various models. However, the exact underlying mechanisms remain unclear. Methods: Astrocyte–microglia co-culture models simulating physiological (M5, 5–10% microglia) and pathological/inflammatory (M30, 30–40% microglia) conditions were treated with different concentrations of ginsenoside Rg1 (15, 30, 45 µM) or ginseng extract (derived from Korean red ginseng) at low (12.5, 25, 37.5 µg/mL) or high doses (125, 250, 375 µg/mL) for 24 h. Cell viability was assessed using the MTT assay while microglial reactivity was examined using immunocytochemistry. Astrocytic gap-junctional coupling was investigated using the scrape-loading method, and connexin 43 (Cx43) expression was analyzed using immunocytochemistry and Western blot. Results: Both Rg1 and low-dose ginseng extract reduced microglial activation under inflammatory conditions by promoting a shift in microglia from an activated to homeostatic (resting) phenotype. Rg1 preserved astrocytic gap-junctional function by preventing the inflammation-induced downregulation of Cx43 expression and enhancing Cx43-mediated gap-junctional intercellular communication. Rg1 caused a significant reduction in glial cell viability, but only at high concentrations (30 and 45 µM), under inflammatory conditions. High-dose ginseng extract showed a significant concentration-dependent reduction in glial cell viability under physiological and pathological conditions, without comparable anti-inflammatory benefits. Conclusions: This study demonstrates that low-dose ginseng and its active compound Rg1 exert anti-inflammatory effects by modulating astrocytic coupling and microglial reactivity. These results provide a novel therapeutic perspective for the use of ginseng in the treatment of neurodegenerative and neuropsychiatric diseases related to neuroinflammation. Full article

Accurate prediction of wear in lubricated metal-to-metal contacts remains a critical challenge, as calibration parameters derived from laboratory tests often lack transferability to finite element method (FEM) simulations. While classical linear Archard models are widely applied, they fail to capture the nonlinear load-dependent wear behavior observed under varying operating conditions. This study addresses this limitation by developing and validating a nonlinear wear formulation based on the Archard–Bayer law within a coupled experimental–numerical framework. A comprehensive Pin-on-Disc test matrix was conducted under lubricated conditions using carbide–steel contacts across varying loads and cycle counts. Wear progression was quantified and analysed using outlier-corrected weighted regression, yielding a force exponent $m_{exp}=1.58\pm 0.34$ and cycle exponent $n_{exp}= 0.41 \pm 0.17$. The calibrated nonlinear model was implemented in a FEM environment and systematically evaluated across multiple loading scenarios. The nonlinear formulation demonstrates improved predictive capability compared to the classical linear Archard model, particularly under higher load conditions (15 N–20 N), where deviations between simulation and experiment remain below 11%. The FEM-calibrated exponent (m = 1.35) lies within the 95% confidence interval of the experimental value, indicating that numerical adjustments required for stability are statistically non-significant. The results show that nonlinear wear models provide a more accurate representation of load-dependent wear behavior but require constrained calibration ranges for reliable application. The proposed methodology enables robust transfer of experimentally derived wear parameters into FEM simulations and provides a practical basis for tool-life prediction, parameter tuning, and model deployment in sheet metal forming processes. Full article

Achieving the co-combustion of biomass and coal in utility boilers while reducing carbon dioxide emissions poses significant challenges owing to the divergent physicochemical properties of the fuels. These differences can induce high-temperature corrosion and erosion of heating surfaces, threatening boiler safety. Despite this, integrated CFD-based assessments of sulfidic corrosion and particle erosion risks remain insufficiently addressed under realistic biomass–coal cofiring conditions. In this study, an integrated CFD-based risk assessment framework was established for biomass–coal cofiring boilers. The main novelty lies in the combined evaluation of high-temperature sulfidic corrosion and particle erosion risks under different biomass injection strategies. Specifically, user-defined functions were developed to classify high-temperature sulfidic corrosion risks based on local O₂, CO, and H₂S concentrations; the effects of biomass injection layers were quantitatively compared; the Oka erosion model was coupled with CFD particle tracking to predict wall wear; and an entropy-weighted multi-indicator method was used to rank the overall safety of different cofiring strategies. This study found that sufficiently high near-wall H₂S concentrations in the main combustion zone indicate an increased risk of sulfidic corrosion under reducing-atmosphere conditions. Compared with pure coal combustion, biomass injection through layer A exacerbates wall corrosion, whereas biomass injection through layer AB mitigates it. Erosion is primarily localized near burner nozzles. Notably, biomass cofiring reduces the average erosion rate by 7.9–30.2% but increases the local maximum erosion rate by 7.1–25.1%. The comprehensive evaluation indicates that the condition with 30% RS injected from layer AB, mixed with coal, yields the best overall performance. The corrosion assessment is limited to sulfidic corrosion risks associated with reducing atmospheres and does not explicitly model alkali- or chlorine-induced corrosion. This study provides a theoretical foundation for biomass cofiring optimization and offers practical guidance for boiler operational safety and maintenance. Full article

Stick–slip vibration, reversal, axial impact, and dynamic instability are major challenges in deep drilling operations and are closely associated with nonlinear bit–rock interaction. To investigate these phenomena, this study develops a nonlinear axial–torsional coupled dynamic model of a drill-string system by integrating rock surface morphology evolution with a Stribeck dry friction model. The drill string is discretized into a distributed lumped-parameter model with coupled axial and torsional degrees of freedom. A surface morphology matrix is introduced to simulate the rock-cutting process, while the Stribeck friction model is employed to characterise the nonlinear frictional behaviour at the bit–rock interface. Time-domain simulations, bifurcation analysis, and frequency spectrum analysis are performed to investigate the dynamic responses of the system. The results indicate that rock surface morphology evolution significantly influences the contact conditions and frictional behaviour at the bit–rock interface, and together with dry friction induces transitions among steady-state, multi-periodic, and chaotic motions. Stick–slip vibration is accompanied by axial impact, bit bounce, and a reduction in the dominant torsional vibration frequency. In addition, variations in both driving and frictional parameters can trigger dynamic instability and state transitions. The proposed model provides an effective framework for analysing nonlinear drilling dynamics and offers theoretical guidance for drill-string vibration suppression, drilling parameter optimisation, and efficient drilling in complex formations. Full article

Climate change introduces severe non-stationarity and high-frequency noise into hydro-meteorological data. This noise degrades the predictive accuracy of traditional data-driven streamflow models. We propose a physics-inspired data enhancement framework coupling the CEEMDAN-based Hilbert-Huang Transform (HHT) with Stochastic Resonance (SR). We applied this framework to the Lanzhou section of the upper Yellow River. HHT isolates the dominant characteristic frequency of the basin’s streamflow system at 0.0026 cycles/day. Using this frequency as a target, we constructed a Bayesian-optimized SR system. The system converts the energy of high-frequency meteorological noise into low-frequency periodic components, facilitating frequency alignment between the meteorological inputs and the hydrological response. We evaluated the SR-enhanced meteorological inputs across three machine learning architectures: Random Forest, XGBoost, and LSTM. All algorithms demonstrated an improved performance. The SR-LSTM model achieved a Nash-Sutcliffe Efficiency (NSE) of 0.91 ± 0.03. This represents a 19% improvement over the baseline LSTM score of 0.79 ± 0.02. The SR-LSTM demonstrated robust accuracy during extreme hydrological events; it achieved a high-flow NSE of 0.89 and effectively mitigated the common peak-underestimation issue by constraining relative peak magnitude errors to approximately −5.08%. Overall, this study presents a practical data enhancement approach for streamflow forecasting under complex climatic conditions. Full article

While terrestrial carbon storage is vital for achieving global carbon neutrality, its spatiotemporal evolution in ecologically fragile regions—such as the Pisha sandstone area—is complicated by intense erosion and complex environmental drivers. Widely known as the Pisha sandstone area, often referred to as the “Earth’s ecological cancer” due to its unique geological instability (“hard as rock when dry, soft as mud when wet”), this area is a critical but vulnerable carbon sink in the Yellow River Basin. This study aims to clarify these dynamics and identify their non-linear driving mechanisms by integrating a coupled PLUS–InVEST model with an XGBoost–SHAP framework to simulate land-use cover change and quantify carbon sequestration potential from 1990 to 2040. Our results reveal: (1) a robust path dependence in land use, where grassland remained the dominant landscape matrix (>75%), which partly explains the stable regional carbon-stock structure and the moderate FoM value of the PLUS validation; (2) carbon storage followed a fluctuating but overall increasing trajectory, projected to reach a peak of 3.19 × 10⁵ tC by 2040 under the Ecological Conservation Scenario (ECS), which significantly outperforms the economic-driven and natural growth modes; (3) hot spot analysis showed that statistically notable low-carbon cold spots were concentrated mainly along valley corridors, marginal transition zones, and locally disturbed patches, whereas high-carbon hot spots were spatially limited; and, (4) crucially, XGBoost–SHAP results should be interpreted as model-based associations rather than direct causal proof; the whole-region model and the regional models jointly suggest that topography, water availability, socioeconomic pressure, and erosion-related factors contribute differently across bare, loess-covered, and sand-covered Pisha sandstone units. These findings support differentiated land-use and restoration strategies rather than uniform regional management. The findings suggest that future management in the Pisha sandstone area should transition from general restoration toward targeted and differentiated regulation to improve regional ecosystem services. Full article

Previous studies on maglev vehicle–bridge coupled systems have mostly described the bridge using classical boundary conditions, while the effects of general constrained boundaries and uncertain parameters have not been fully considered. In this study, an energy-based dynamic model of a maglev vehicle–bridge coupled system is established. The boundary constraints of the bridge are introduced through equivalent springs, so that complex boundary conditions can be represented in a unified form. The proposed model is verified by comparison with published results. On this basis, Monte Carlo simulations are carried out to investigate the effects of random suspension parameters and random control parameters on the dynamic responses of the system. Two simplified electromagnetic-force models are also considered, and Sobol sensitivity analysis is used to evaluate the contributions of different parameters to the vibration responses and vibration energy. The results indicate that the suspension and control parameters affect different response quantities in different ways. The two electromagnetic-force models also lead to different sensitivity results, especially when the vibration energy is used as the evaluation index. The proposed method provides a useful tool for analyzing the stochastic vibration mechanism and optimizing the parameters of maglev vehicle–bridge coupled systems under general constrained boundaries. Full article

The Atlantic Meridional Overturning Circulation (AMOC), as the core component of the global thermohaline circulation, exerts a profound influence on the Northern Hemisphere climate. Recent observations show that AMOC intensity has weakened by approximately 15% over the past 40 years, yet the traditional theoretical framework dominated by the North Atlantic Oscillation (NAO) cannot fully explain its spatial heterogeneity. This study systematically quantifies the independent driving mechanism of the Arctic Oscillation (AO) on AMOC decline for the first time by integrating multi-source reanalysis data (ERA5, ORAS5) and CMIP6 model output. Theoretical analysis shows that the AO positive phase regulates the stability of AMOC through two coupled pathways: (1) anomalous wind stress curl leads to the weakening of Ekman suction in the subpolar seas (contribution: 42 ± 6%), inhibiting deep-water formation in the Labrador Sea; and (2) increased freshwater flux through the Fram Strait triggers a negative salinity advection feedback, which leads to shoaling of the North Atlantic high-latitude mixed layer by up to 30 m. The cross-scale interaction reveals that the AO interannual variability amplifies the modulation of the AMOC interdecadal trend. This amplification occurs through the positive feedback of sea-ice albedo. When AO and NAO are locked in opposite phases (AO+/NAO−), the AMOC weakening rate increases to 1.8 Sv/decade (1 Sv = 10⁶ m³/s), whereas the same-phase negative condition (AO−/NAO−) yields a moderate decline of 0.5 Sv/decade. This mechanism corrects the underestimation of the traditional wind-driven circulation theory for high-latitude processes and provides a physical attribution for the CMIP6 models’ systematic underestimation of AMOC sensitivity. The study further constructs the “Arctic Oscillation–subpolar basin–AMOC” three-pole coupling theoretical model and confirms that the Arctic amplification effect enhances the AO–AMOC coupling strength by a factor of 2.3 over the full study period (1979–2020; R² = 0.71, p < 0.01), with an even more pronounced enhancement of 2.1 times during the recent two decades (2000–2020; R² increased from 0.28 to 0.59). These findings have direct implications for coastal risk assessment, as AMOC weakening may accelerate sea-level rise along the North American East Coast and increase the frequency of extreme winter storm surges in European coastal areas. The results provide a dynamic basis for IPCC climate risk assessment and have practical application value for the early warning of extreme cold-wave events. Full article

Ultra-low-phosphorus high-purity iron (ULP-HPFe) is essential for advanced electromagnetic, aerospace, and defense systems, yet stabilizing basic-oxygen-furnace (BOF) dephosphorization remains challenging. To address this instability, we present an intelligent partition-and-prediction framework (iDePP) that first auto-classifies 5102 industrial data records into medium-phosphorus (iDePP-MP), low-phosphorus (iDePP-LP), and ultra-low-phosphorus (iDePP-ULP) subsets, and dedicated ensemble prediction models are then developed for each subset based on representative machine learning algorithms, including random forest (RF), extreme gradient boosting (XGBoost), and neural networks (NNs). Compared with a single global predictor, iDePP reduces the mean absolute error from 0.0018% to 0.0011%, 0.0007%, and 0.0004% for the three classes, respectively, and increases the iDePP-ULP hit rate (HR) to 82.7% within ±6 ppm. Shapley additive explanations (SHAP) and quantitative feature coupling analysis reveal two critical mechanisms governing extreme dephosphorization: limestone-induced thermal penalties and furnace-age effects. Guided by these insights, three consecutive 200-ton BOF industrial trials preliminarily verified the practical feasibility of producing ULP-HPFe, with model plant deviations of approximately 4 ppm, 1 ppm, and 1.5 ppm, respectively. Notably, this work demonstrates the value of automatic domain partitioning combined with subset-specific ensemble learning for complex BOF control, highlighting the potential applicability of iDePP to other data-sparse industrial processes. Full article

To reveal the evolution laws of shear mechanical behavior and size effect of prestressed Recycled Aggregate Concrete (RAC) beams under dynamic loading conditions, a three-dimensional five-phase meso-scale numerical model was established based on the ABAQUS2020 software. The bond–slip behavior between steel bars and concrete was considered, and prestress was applied using the temperature cooling method. The effects of prestress level, cross-sectional size and strain rate on failure modes, load–displacement curves, ultimate shear capacity and nominal shear strength were systematically investigated. The results show that increasing the prestress level can significantly restrain the initiation and propagation of diagonal cracks, reduce the brittleness of the failure mode, and effectively mitigate the shear size effect. The nominal shear strength decreases obviously with an increase in cross-sectional size but increases significantly with an increase in strain rate, exhibiting a pronounced strain rate hardening characteristic. Large-scale beams are more sensitive to strain rate, and a high strain rate can reduce the disparity in shear performance among members of different sizes and further weaken the size effect. Based on Bažant’s Size Effect Law (SEL), a modified formula for dynamic shear strength considering the coupled effects of prestress level, strain rate and cross-sectional size was proposed by introducing a prestress enhancement coefficient γ and a strain rate enhancement coefficient β. The calculated results of this formula are in good agreement with the numerical results obtained in this study. Within the investigated parameter range, the present work can provide a reference for the shear design and safety assessment of prestressed recycled aggregate concrete beams under dynamic loading. Full article

Recent global disruptions have exposed the fragility of tightly coordinated supply chains, particularly under transportation and information delays, motivating the need for analytical tools to assess their stability limits. This study analyzes a two-echelon supply chain system to determine how delays affect stability and performance, with an emphasis on the role of feedback coordination. A continuous-time delay-differential modeling framework was developed to examine both uncoupled and coupled configurations. Stability is analyzed through characteristic equations, and explicit closed-form expressions for the critical delay threshold are derived as functions of the coupling gain and shipment rate. The uncoupled system is shown to exhibit delay-independent marginal stability but lacks the ability to regulate downstream inventory. In contrast, the coupled system achieves inventory regulation but introduces delay-dependent stability with a critical delay, beyond which oscillations grow unbounded. A key result revealed an inverse relationship between coupling strength and delay tolerance, highlighting a trade-off between responsiveness and robustness. An optimal control formulation further demonstrates that the stability constraints limit the achievable performance. These findings provide a theoretical explanation for the vulnerability of just-in-time systems and offer practical guidelines for resilient logistics design, enabling supply chain practitioners to quantify stability margins and balance coordination efficiency with robustness to transportation delays. Full article

As automation increases, mechatronic manufacturing systems require supervisory solutions that combine precise control, intelligent diagnostics, and intralogistics awareness. This paper presents a networked sensor–actuator–information architecture integrating model predictive control (MPC), Random Forest (RF)-based diagnostics, and logistics-aware coordination for automated mechatronic manufacturing systems. The main contribution is the explicit coupling of logistics-related supervisory variables with the predictive control problem and the diagnostic feature space. Buffer occupancy, transport delay, and logistics-induced waiting state are incorporated into an augmented reduced-order model to support constrained control and health-state interpretation. The framework is evaluated through a comparative simulation-based feasibility study using a low-order model of a robotic production axis affected by disturbances, degradation, and logistics-related constraints. The proposed approach is compared with classical feedback control, predictive control without diagnostics, and predictive control with diagnostics but without explicit intralogistics coupling. In the reduced-order simulation scenario, the proposed method achieved the lowest mean RMSE of 0.330 ± 0.015 and the lowest mean constraint violation rate of 3.133 ± 0.280% across 40 repeated simulation runs. However, the improvement in nominal tracking accuracy over the strongest diagnostic-assisted MPC baseline was marginal. Adding logistics-related diagnostic features improved mean accuracy from 0.848 ± 0.014 to 0.874 ± 0.012 and mean F1-score from 0.844 ± 0.016 to 0.872 ± 0.013. The main advantage of the proposed architecture was observed in reliability- and continuity-oriented indicators, including reduced downtime, lower final damage accumulation, fewer cooling cycles, and improved differentiation between machine-related and logistics-induced abnormal conditions. Full article

Extreme precipitation and compound hazards can increase the risk of inundation and accidental release of untreated effluent from water treatment facilities, with potential downstream impacts within a short emergency-response window. Few studies have linked site-scale surface-runoff behavior, feasible emergency-response scenarios, and resilience-based decision support for critical water infrastructure. This study presents a GIS-based scenario-comparison framework that couples high-resolution surface-runoff simulation with an AHP-informed resilience interpretation to evaluate untreated effluent runoff and temporary flood-defense strategies at a water treatment plant in Jeollabuk-do, South Korea. A 1 m digital elevation model derived from drone-based LiDAR data was used in ArcGIS Pro to simulate two-dimensional unsteady surface-runoff propagation, producing water-depth and flow-velocity fields at 30 s intervals over 20 min. Three scenarios were compared under identical topographic, release, and hydraulic assumptions, no response, primary defense-line deployment, and secondary defense-line deployment, adding a 335 m barrier along the downstream road. Under the no-response scenario, released water reached the river after approximately 6 min, with a cumulative river inflow of 329.27 m³. The primary defense line reduced cumulative river inflow by 16.8%, and the secondary defense line by 78.2%, while delaying river arrival to 8 min and 30 s. An approximate surface-water balance and time-series analysis showed that the defense lines primarily redistribute water into temporary upstream storage rather than eliminate it. The simulation-derived indicators were linked to four resilience components whose relative importance was estimated using the Analytic Hierarchy Process (AHP) from 205 expert and practitioner responses, which identified recovery speed as the highest-priority component; the weighted normalized indicators are summarized as a transparent scenario-level composite resilience indicator that increases from the no-response to the primary and secondary defense-line scenarios. Because the stormwater drainage network, pollutant transport, and operational deployment uncertainties were not explicitly modeled, the results should be interpreted as a comparative assessment of water-volume transport risk rather than a deterministic prediction of inundation or pollution impact. Within these stated assumptions, the results indicate that a strategically placed secondary defense line can substantially reduce downstream river inflow and secure additional response time, providing preliminary decision support for disaster-risk reduction and emergency-response planning at critical water infrastructure. Full article