Search Results (7,877)

To address the challenges of complex wellbore trajectories in horizontal gas wells and the significant differences in droplet entrainment laws across various well sections, which make it difficult to accurately predict the most critical location for liquid loading, this study establishes a prediction model for the critical liquid-carrying flow rate in different well sections. The model is based on droplet force balance and Kelvin–Helmholtz wave theory, considering droplet deformation and energy losses due to wall collisions and friction. By integrating the critical liquid-carrying flow rate models for each section with a four-field coupled wellbore prediction model, a coupled temperature-pressure and liquid-carrying prediction model is developed. Sensitivity analysis was performed on factors influencing the critical liquid-carrying flow rate, and a field data analysis was conducted on 43 gas wells. The results indicate that the proposed model provides accurate predictions, with only one well being misjudged. For four wells near the liquid loading state, the predictions were within a ±15% error range, with an average deviation of only 5.9%. The research results provide a theoretical basis for the accurate prediction of liquid loading in horizontal gas wells. Full article

This paper presents an iterative graph-analytical procedure for determining the roll center height, one of the most critical design parameters influencing vehicle dynamic behavior during cornering. The conventional approaches generally determine roll center locations from suspension kinematics and then evaluate vehicle behavior using multibody or numerical vehicle dynamics models. By contrast, the proposed method is intended for the preliminary design stage and provides a direct correlation between front and rear target roll center heights using tire test data, load transfer and axle-level equilibrium conditions. The main advantage of the method is that it helps define a feasible design space before detailed geometry optimization or MBD validation is performed. The objective is to achieve stable and neutral handling (avoiding intrinsic understeer or oversteer tendencies) during steady-state cornering at a predefined target lateral acceleration. The methodology integrates (i) lateral force equilibrium at the axle level, (ii) a dynamic load transfer model based on axle roll stiffness and roll center heights, and (iii) experimental tire grip characteristics (lateral force–slip angle curves under varying vertical loads), processed through numerical interpolation. The procedure is demonstrated using a vehicle model with specific geometric and mass parameters. The results indicate that the methodology does not yield a single unique solution, but rather a set of correlated roll center heights, allowing the designer to select the most feasible geometric configuration while maintaining neutral handling. As an example, the paper presents a convergent solution for the front and rear roll center heights that satisfy neutrality conditions at a slip angle of approximately 4°. This study provides a fundamental framework for the geometric design of suspension systems and serves as a basis for subsequent numerical and experimental validation. Full article

Arsenic (As) contamination in groundwater represents a critical global environmental health issue. The reductive dissolution of arsenic-bearing iron oxides by dissimilatory metal-reducing bacteria (DMRB) is a key biogeochemical process driving arsenic mobilization and release in groundwater. However, the mechanism of exogenous electron shuttles in this process remains poorly understood. This study investigated the impact of the quinone-based electron shuttle anthraquinone-2,6-disulfonate (AQDS) on the reductive dissolution of arsenic-loaded goethite by the model DMRB Shewanella putrefaciens CN32 (S.P CN32). The mobilization and transformation behaviors of arsenic and iron were compared under different pH conditions and using different arsenic-loading methods (coprecipitation vs. adsorption). Results demonstrated that AQDS acted as an electron transfer mediator. It significantly enhanced the reductive dissolution of Fe(III). It also significantly enhanced the reduction of As(V). These actions collectively accelerated arsenic release and mobilization. The study also revealed a competitive preferential order in microbial reduction, where the thermodynamically more favorable Fe(III) reduction preceded As(V) reduction. Environmental pH co-regulated this process. Its influence worked through microbial activity and mineral surface properties. A neutral pH was most conducive to the AQDS-mediated bioreduction of arsenic and iron. This study elucidates the critical role of electron shuttles in the biogeochemical cycling of arsenic in contaminated sites, providing a scientific basis for a deeper understanding of the formation mechanisms and risk assessment of high-arsenic groundwater. Full article

The Vehicle-to-Grid (V2G) technology is considered one of the best solutions for integrating renewable energy systems; however, most literature reports favorable economic results using synthetic data, without accounting for seasonal or market limitations. The current research presents the results of the MATLAB R2023b (Version 23.2, MathWorks, Natick, MA, USA) simulation of the 100-household energy community in Debrecen, Hungary, with 30 electric vehicles (EVs) using entirely simulation-based Lithium Iron Phosphate (LiFePO4) batteries, a simulation-based 150 kW solar photovoltaic (PV) system, and a simulation-based 200 kW wind power system, using real meteorological data for January 2024. The optimization of charging/discharging for electric vehicles was performed using a multi-objective genetic algorithm (GA) over 30 days at a 15 min time resolution, accounting for stochastic loads and temperature effects on battery degradation, with a sensitivity analysis of key parameters. The results of the optimized solution for the electric vehicle charging/discharging were unexpected: the total energy cost increased by 68.9% ($4337.65 to $7327.54), the peak demand increased by 266.2% (31.9 to 116.9 kW), the degradation cost was $479.63, the load factor was reduced from 0.847 to 0.722, and the SOC constraint was violated for 0.758% of measurements. The V2G is not economically viable under current Hungarian pricing and Central Europe winter conditions. Results are robust for varying parameters using sensitivity analysis and Pareto front tracing. The break-even point is achieved when ratios of peak-to-off-peak prices are above 3.5:1. Seasonal policies and market reforms are critical for V2G viability. Importantly, the influence of inherent design deficiencies in the optimization model on the reported results cannot be ruled out. Full article

Non-invasive temperature estimation during online operation is a critical challenge in enclosed micro-reaction systems, particularly when the thermal mass of the working fluid varies dynamically or is uncertain. Conventional model-based approaches typically rely on fixed thermal parameters, leading to significant estimation errors when the actual reagent volume deviates from nominal conditions. To address this limitation, this study proposes a volume-adaptive temperature estimation framework applied to an ultrafast quantitative polymerase chain reaction (qPCR) system. By modeling the heat-transfer pathways via a simplified resistance–capacitance (RC) network, a nonlinear least squares (NLS) algorithm within an output-error (OE) framework is employed to identify key thermal parameters online. The framework separates the estimation into an offline calibration stage—where a thermocouple-equipped chip provides ground-truth data—and an online deployment stage that relies solely on non-invasive external measurements. This approach allows the system to explicitly compensate for volume-induced variations in thermal inertia. Validation experiments on an ultrafast qPCR platform with reagent volumes ranging from 100 to 250 $\mathsf{\mu }\mathrm{L}$ and heating rates exceeding 20 °C/s demonstrate that the method achieves robust performance, maintaining a mean absolute error (MAE) of reagent temperature at 0.24 ℃ and restricting the average volume estimation error to within 1.37 $\mathsf{\mu }\mathrm{L}$. DNA gel electrophoresis results further confirm the biological reliability of the temperature prediction strategy by verifying amplification specificity. This work provides a generalised solution for precise thermal management in micro-systems subject to variable thermal loads. Full article

Flexural stiffness identification of prestressed concrete beams plays an important role in evaluating the mechanical performance and damage condition of bridge structures and has become a critical research direction in bridge health monitoring. Accordingly, this paper presented a Physics-Informed Neural Network (PINN)-based method for flexural stiffness identification. In the physical modeling framework, point source forces in the beam-column equation (BCE) were represented by approximating the Dirac delta function with Gaussian functions. This strategy alleviated the convergence issue of the loss function caused by singular behavior and enabled the formulation of a unified governing equation for multi-point loading scenarios. To eliminate the long-term deflection caused by non-load-related factors and self-weight, the BCE was expressed in incremental form. The resulting nondimensional equation was adopted as the target constraint for PINN training to alleviate multi-scale challenges. Furthermore, the residual-based adaptive refinement (RAR) strategy was incorporated during network training to improve computational efficiency and identification accuracy. The proposed method was validated through nine numerical cases without linear relationships and three experimental cases. The results indicate that, even with limited measurement data and under the tested noise levels, the proposed framework can achieve satisfactory flexural stiffness identification under the tested loading conditions. This suggests that the proposed method has promising potential for flexural stiffness identification and may be useful in bridge structural health monitoring under sparse-data conditions. Full article

To address the vulnerability of renewable-dominated power grids to cascading failures under extreme conditions and the limitations of existing methods in jointly handling vulnerability identification, energy storage allocation, and online control, this paper proposes an energy-storage-assisted coordinated defense strategy. First, a source-load uncertainty model is constructed and seven typical extreme operating scenarios are identified. Second, a cascading-failure evolution model that accounts for thermal accumulation is established to identify critical vulnerable branches. Third, for areas prone to local disconnection and weak terminal voltages, a coordinated ESS allocation model is developed by jointly considering active power, energy capacity, and reactive power support to determine candidate deployment locations and capacities. Finally, a graph neural network (GNN) is used to extract time-varying topological and electrical-state features, and proximal policy optimization (PPO) is employed to generate coordinated control commands for multiple ESSs, thereby linking overload suppression with voltage support. The results for the modified IEEE 39-bus system show that the proposed method identifies high-risk branches more accurately and forms an integrated defense chain covering identification, allocation, and control. The method reduces thermal stress in critical sections during the early stage of a fault, mitigates load shedding, and enhances system survivability. Full article

Deep shale of the Lower Cambrian Qiongzhusi Formation in the Sichuan Basin represents a critical frontier for shale gas exploration in China. However, systematic understanding of the multi-scale links among lamination type, mechanical anisotropy, and fracture complexity remains limited. Based on lamination characteristics and total organic carbon (TOC) content, core samples were classified into four types. Using a multi-scale approach (uniaxial compression, Brazilian splitting, in situ CT scanning, QEMSCAN, and SEM), this study elucidates how lamination structure controls mechanical anisotropy, failure modes, and fracture mechanisms. The novelties of this work are threefold: (1) quantitatively linking specific lamination types (ORM, OPM, PAFC, PAF) to anisotropic mechanical responses; (2) introducing 3D fractal dimensions to evaluate fracture network complexity; and (3) integrating micro- (SEM) and macro-scale tests to reveal the coupled control of weak planes and brittle minerals on fracture propagation. Results indicate that laminated shales exhibit pronounced mechanical anisotropy. Loading parallel to laminations induces tensile splitting along weak planes, significantly reducing strength. Conversely, perpendicular loading generates complex fracture networks of horizontal secondary fractures along laminae and vertical main fractures through the matrix. Furthermore, 3D fractal dimension analysis quantifies fracture network complexity as follows: organic-poor clay-feldspar laminated shale > organic-poor clay-feldspar-calcareous laminated shale > organic-rich massive shale. Microscopic observations confirm that fracture propagation is jointly governed by weak plane systems and brittle mineral content, which collectively determine macroscopic failure patterns. These findings clarify how lamination type controls the laboratory mechanical response and fracture morphology of deep shale and provide a laboratory-scale framework for comparing lamination-related differences in mechanical anisotropy and fracture complexity in the Qiongzhusi Formation. Full article

The nominal maximum aggregate size (NMAS) plays a critical role in determining the mechanical performance of cement-stabilized macadam (CSM), yet its meso-mechanical influence mechanism remains insufficiently understood. In this study, three skeleton-dense CSM mixtures with different NMAS values were designed, and a combined experimental–numerical approach was adopted to investigate the macro- and meso-scale mechanical behavior. Uniaxial compression tests and aggregate crushing value tests were conducted to evaluate strength development and load-transfer characteristics, while a three-dimensional discrete element method (DEM) model incorporating realistic aggregate morphology was established to analyze the evolution of contact forces and crack propagation. The results show that increasing NMAS significantly improves the mechanical performance of CSM. Compared with CSM-30, the 7-day compressive strength of CSM-40 and CSM-50 increased by approximately 10.3% and 37.3%, respectively. The stress–strain response indicates that mixtures with larger NMAS exhibit higher stiffness and a higher strain. At the meso-scale, a larger NMAS promotes the formation of a more efficient force-chain network dominated by coarse aggregates. Strong contacts were predominantly carried by aggregates larger than 9.5 mm, and in CSM-50, the proportion of strong contacts in the 37.5–53 mm fraction exceeded 90%, indicating that the largest particles likely form the primary load-bearing skeleton. In addition, increasing NMAS delayed crack initiation, reduced crack propagation rate, and decreased the total number of cracks at failure. These findings demonstrate that macroscopic strength improvement is closely associated with meso-scale optimization of the aggregate skeleton and enhanced load-transfer efficiency. This study provides a mechanistic basis for NMAS selection and gradation optimization in semi-rigid base materials. Full article

Background/Objectives: A robust and scalable manufacturing framework for lipid-based nanocarriers remains a critical challenge, particularly for labile phytochemicals such as curcuminoids in turmeric. This study presents an integrated Quality by Design (QbD)-driven and Outcome-Based Design (ObD) strategy to establish a scalable, resource-efficient manufacturing process for curcuminoids-loaded nanostructured lipid carriers (NLCs). Methods: To overcome the limitations of conventional multivariate design of experiments (DOE), which require extensive experimental runs, a risk-based, knowledge-driven single-factor screening approach was employed. Guided by risk assessment tools, including Ishikawa diagrams and failure mode considerations, 12 representative processing conditions were selected to define the design space. Critical quality attributes (CQAs), namely, particle size, polydispersity index (PDI), and zeta potential, were predefined to establish a robust control strategy. A two-step homogenization process—high-shear homogenization (HSH) for pre-emulsification followed by high-pressure homogenization (HPH) for nanoscale refinement—was systematically optimized. Results: Multivariate data analysis using principal component analysis (PCA) and hierarchical cluster analysis (HCA) identified key critical process parameters (CPPs), particularly HSH speed, processing time, and HPH cycles, as dominant factors influencing nanoparticle characteristics. The optimized 1-h process enabled successful scale-up of NLCs from 100 g to 5000 g, demonstrating the capability to generate nanosized particles within 100–500 nm. The combined HSH–HPH approach produced smaller, more uniform nanoparticles with high encapsulation efficiency and physical stability, outperforming HSH alone. Conclusions: Overall, this study establishes a practical and industrially viable framework that integrates QbD principles with data-driven optimization tools, for enabling reliable translation from laboratories to semi-industrial production. Full article

The dynamic response of the secondary circuit system in nuclear propulsion plants is critical to the power output, safety, and energy efficiency of nuclear-powered ships. High-fidelity thermo-hydraulic simulation models can accurately capture system transients but are computationally expensive and unsuitable for real-time applications. To address this limitation, this study proposes a reduced-order dynamic parameter prediction method that integrates high-fidelity simulation with deep learning. A multi-operating-condition simulation model of a typical nuclear-powered ship secondary circuit system is developed to generate time-series data covering load ramping and propulsion mode switching. Based on this dataset, a conventional recurrent neural network (RNN) and a multilayer long short-term memory (LSTM) network are constructed for multivariate autoregressive prediction of 17 key dynamic parameters, and their performances are systematically compared. Results show that the LSTM significantly outperforms the RNN in capturing long-term temporal dependencies, achieving average RMSE and MAPE values of 0.0228% and 0.365%, respectively. The proposed model completes 50-step-ahead prediction within 0.84 s, satisfying real-time requirements. The hybrid simulation-driven and data-driven framework provides a practical solution for intelligent monitoring and control optimization of nuclear-powered ship propulsion systems. Full article

Subway-hub blocks are critical areas where the pressures of metropolitan populations and environmental quality are closely interconnected. This study constructs a “pressure–context–carrier–response” (PCRC) framework (F1–F7) to systematically reveal the correlations between built-environment characteristics and environmental performance. The results demonstrate that resource allocation (F7) and comprehensive response (F5) display notable “asymmetric differentiation”. The socio-economic environment (F2, F3) considerably influences the concentration of green-space resource allocations (F7) (p < 0.01), with affluent blocks demonstrating a clear advantage in resource distribution. The thermo-ecological composite response (F5), which includes NDVI and LST, demonstrates “statistical convergence” (p = 0.894) across various block types, indicating that resource inputs cannot be linearly transformed into environmental efficiency. This disconnection is ascribed to two physical limitations: firstly, the stochastic nature of spatial distribution (Global Moran’s I ≈ 0) restricts the scale effects of green spaces; secondly, the nonlinear limitations of the physical medium indicate that under conditions of high pressure load (F1) and elevated spatial capacity (F6), the regulatory effectiveness of greening demonstrates a significant diminishing marginal return effect. Therefore, intervention planning must shift from controlling macro-level indicators to optimising micro-level accuracy to address ecological performance constraints in densely populated metropolitan areas. Full article

The literature identifies concrete and steel as the primary contributors to embodied carbon in building structures and highlights a strong relationship between sustainability and structural system geometry. However, existing studies predominantly focus on one-way systems and flat slabs, while research on two-way joist slabs remains limited and often centred on strength optimisation. In particular, there is a lack of systematic life cycle comparisons of alternative beam configurations within this system. This gap is critical, as early-stage design decisions largely determine the environmental impact of structural systems. This study presents a comprehensive, span-dependent evaluation of four beam configurations, namely Without Beam, Internal Beam, Perimeter Beam, and Full Beam, for reinforced concrete two-way joist slabs used in office buildings. A parametric framework was developed using Eurocode-compliant structural design and nonlinear finite element modelling to assess 36 span combinations ranging from 4 × 4 m to 14 × 14 m. Material quantities were extracted from the final designs and converted into embodied carbon values using cradle-to-gate (A1–A3) emission factors derived from the ICE database. The results demonstrate that beam configuration has a significant influence on embodied carbon and construction cost. For spans below approximately 8 m, beamless systems provide the most material-efficient solution. For spans exceeding approximately 10 m, full-beam configurations offer improved structural efficiency and reduced embodied carbon due to enhanced stiffness and load distribution. Full article

Background: Immediate loading of dental implants shortens treatment time and improves early function, but it also exposes the healing peri-implant tissue to a critical mechanical environment. This study compared the biomechanical and mechanobiological response of a conventional threaded implant and a porous scaffold-based implant under immediate and delayed loading conditions. Methods: A three-dimensional finite element model of a bone block with a 0.2 mm peri-implant callus was developed in ABAQUS/Standard. Model A was a threaded Ti-6Al-4V implant, while Model B was a porous implant with 64.26% porosity. Bone tissues were modeled as poroelastic materials. Immediate and delayed loading were simulated through frictional and tied bone-implant interfaces, respectively. Mechanobiological predictions were performed using the Prendergast-Huiskes stimulus. Results: Under immediate loading, the porous implant reduced cortical bone stress (32.5 MPa vs. 88 MPa) and markedly increased callus stimulation (20.5–31.6 MPa vs. about 2.5 MPa) compared with the threaded implant. Mechanobiological analysis showed that Model B promoted higher fractions of immature and mature bone and lower fractions of cartilage and fibrous tissue. In all cases, implant stresses remained below the yield strength of the corresponding materials. Conclusions: The porous implant provided a more favorable mechanical environment for early peri-implant healing, particularly under immediate loading, and may be a promising strategy to enhance callus maturation and reduce stress shielding. Full article

Acoustic emission (AE) and digital image correlation (DIC) techniques enable real-time capture of damage signals and full-field deformation at anchored rock–concrete interfaces under shear loading, which is critical for quantitatively characterizing freeze–thaw (F-T) degradation and preventing geological disasters in cold regions. This study synchronously monitored full-shear-process AE signals using a broadband AE system (150 kHz resonant frequency, 5 MS/s sampling) and captured high-precision full-field deformation via a 5-megapixel monocular DIC system (25 fps). F-T cycle and direct shear tests were conducted on sandstone–concrete anchored specimens with varying F-T cycles and anchor depths to investigate their effects on shear mechanical properties, AE characteristics and failure modes. Results show that AE peak ring count first decreases by 44.9% then increases by 56.5%, while cumulative ring count exhibits a three-stage evolution. Shear crack proportion first decreases then increases, with tensile failure remaining dominant throughout. DIC reveals that F-T cycles shift failure from crack propagation to surface delamination and interface slip, while different anchor depths induce distinct failure patterns. This study confirms that AE and DIC can accurately characterize F-T degradation, providing a reliable non-destructive monitoring method for cold-region anchorage engineering. Full article