Search Results (2,484)

316LN stainless steel is widely used in critical nuclear fusion structural components due to its excellent mechanical properties and machinability. However, its high thermal expansion coefficient and low thermal conductivity promote welding distortion, while work hardening causes residual stress accumulation. Thermo-elastic–plastic finite element modeling (FEM) is the primary numerical method for predicting these effects. Yet, despite hardware advances, full-scale simulations—especially for thick plates with multi-pass welds—remain computationally expensive, hindering the balance between efficiency and accuracy. To address the inherent trade-off between welding efficiency and dimensional accuracy in multi-pass, multi-layer welding of thick-section components, this study employs MSC. Marc to develop a finite element model of a 15 mm thick butt-welded joint fabricated from 316LN stainless steel. Three distinct heat source models—instantaneous, enhanced moving, and moving element-set—are systematically implemented to simulate transient temperature fields, residual stress distributions, and welding deformation. All numerical predictions are rigorously validated against experimental measurements to comprehensively assess both accuracy and computational efficiency. Results indicate that: (i) the predicted molten pool geometries and characteristic thermal cycle profiles from all three models exhibit strong agreement with experimental observations; (ii) longitudinal residual stress distributions predicted by all models align closely with measured values; (iii) transverse residual stresses predicted by the moving element-set and enhanced moving heat sources agree well with experiments, whereas those from the instantaneous heat source show marked deviation; (iv) angular distortion predictions from the moving element-set heat source achieve over 90% conformity with experimental data, while the instantaneous heat source substantially underestimates angular distortion, and the enhanced moving heat source yields approximately 65% agreement; and (v) in terms of computational efficiency, the instantaneous heat source requires only ~40% of the computation time needed by the moving heat source. Full article

Injecting CO₂ into gas reservoirs is a crucial approach for enhancing natural gas recovery and achieving CO₂ geological storage, where the gas–gas diffusion behavior between CO₂ and CH₄ directly influences gas mixing efficiency. Direct observation of the spatiotemporal evolution of concentration fields during diffusion remains insufficient. In this study, a gas–gas diffusion experimental system capable of multi-time and multi-space stratified sampling within a high-temperature high-pressure PVT cell was established based on real reservoir fluid compositions. Non-equilibrium diffusion experiments were conducted under different pressures, different initial CO₂ mole fractions, and different diffusion times. A diffusion model was developed according to Fick’s second law. The results suggest that the gas column can be divided into a natural gas zone, a transition zone, and a CO₂ zone by the dimensionless concentration gradient threshold. At 5 MPa, the transition zone width expands rapidly within the first 4 h (dimensionless width increases from 0 to 0.6902), after which growth slows. Increasing pressure significantly inhibits diffusion, reducing transition zone width and prolonging equilibration time. Rising initial CO₂ concentration also suppresses diffusion mixing, particularly in the later stage. Component profile analysis confirms that, under high pressures and high CO₂ concentrations, the diffusion flux across the interface is weakened. Compared to CH₄, the diffusion equilibration time of CO₂ is shorter and more sensitive to pressure changes. The obtained diffusion coefficients (CH₄: 2.92 × 10⁻⁸ to 4.79 × 10⁻⁸ m²/s; CO₂: 3.91 × 10⁻⁸ to 6.08 × 10⁻⁸ m²/s) are on the order of 10⁻⁸ m²/s, consistent with bulk-phase PVT literature data, validating the reliability of the experimental method and inversion model. This study lays an experimental foundation for predicting multi-component gas mass transfer under conditions of CO₂-enhanced gas recovery and CO₂ geological storage. Full article

We used thermodynamic modeling methods to calculate the stability of auroselenide AuSe_(s) in hydrothermal solutions at different temperatures (25–350 °С), pressures (1–165 bar), salinities (0–5 m NaCl), and acidity–alkalinity (0.00001–0.1 m НCl or NaОН). Gold selenide dissolves congruently in near-neutral solutions. In acidic chloride solutions, AuSe_(s) dissolves incongruently to form selenium Se_(s,l), and in alkaline solutions, to form gold Au_(s). Gold selenide has a low solubility at temperatures of 25–200 °С. With increasing temperature, the solubility of AuSe_(s) increases and at 350 °С the concentration of dissolved gold in highly acidic solutions (without NaCl) reaches 10⁻⁶ m, while in near-neutral and alkaline solutions, it varies from 2·10⁻⁷ to 6·10⁻⁷ m. At concentrations of NaCl and HCl higher than 0.01 m, the solubility of AuSe_(s) increases by half an order of magnitude owing to the formation of gold chloride complexes. In low acidic, near-neutral, and alkaline solutions, gold hydroxocomplex is predominant. We constructed diagrams for the Au–Se–Н₂О system at various temperatures (25, 100, 200 and 300 °С), which show the stability fields of AuSe_(s), Au_(s) + AuSe_(s), Se_(s,l) + AuSe_(s) and Au_(s) on lg ƒO₂–pH. Gold chalcogenides are characteristic minerals of epithermal deposits. The relationships of auroselenide with native selenium and native gold and other minerals in the Au-Ag ores of the Gaching ore occurrence (Kamchatka Peninsula, Russia) and the Bleïda Far West Au-Pd deposit (Morocco) were studied. It was revealed that auroselenide occurs in the peripheral parts of native gold grains, and, less often, in the form of inclusions and intergrowths with other gold chalcogenides in the core of native gold grains. The presence of solidified microdroplets of composition ranging from Te_0.97Se_0.03 to Te_0.71Se_0.28S_0.01 and Se_0.58Te_0.41S_0.01 in the ore minerals at these and other golddeposits suggests participation of chalcogens existing at temperatures of 217–449 °C. The formation of auroselenide and other gold chalcogenides is likely with a decrease in temperature and neutralization of highly acidic or highly alkaline solutions, or with the participation of melts or chalcogen gas particles. The results of thermodynamic calculations are confirmed by the data on the composition of mineral associations with auroselenide from gold deposits. The presence of auroselenide in the ores from Au-Ag epithermal and other gold deposits with Au–Se–Te–S mineralization is predicted. Full article

The Gravity Recovery and Climate Experiment (GRACE) mascon data relies on minor gravitational field variations to map terrestrial water storage anomaly (TWSA). However, the coarse spatial resolution of three degrees by three degrees restricts their application for evaluating small-scale changes in water storage. To address this challenge, in this study, GRACE and GRACE Follow-On (GRACE-FO) data from 2003 to 2023 were downscaled to 800-m resolution across the Coastal Lowland Aquifer System (CLAS) in Texas, Louisiana, Mississippi, Alabama, and Florida. This downscaling used machine learning (ML) models, including Random Forest (RF), Artificial Neural Network (ANN), and Deep Neural Network (DNN). These models incorporated variables such as anomalies in total precipitation (APT), mean temperature (ATM), normalized difference vegetation index (ANDVI), evapotranspiration (AET) from 2003 to 2023, Shuttle Radar Topography Mission DEM, slope angle, soil type, and lithology to generate monthly 800-m TWSA maps. The ANN model showed strong predictive performance (R² = 0.869–0.989 with low RMSE), although the DNN achieved slightly better statistical accuracy and spatial evaluation metrics; however, ANN was selected for its more realistic and spatially consistent outputs regionally. Building on this improved spatial resolution, analysis of the downscaled TWSA data from 2003 to 2023 identified an overall declining trend in water storage. Trend analysis using linear regression shows that the western CLAS—particularly the Gulf Coast aquifer in Texas and western Louisiana—experiences the strongest depletion, with rates of −0.30 and −0.17 cm/year in Zones 1 and 2, respectively, with Zone 1 being statistically significant. In contrast, the eastern CLAS shows relatively stable conditions, with weak, non-significant increases (+0.05 to +0.18 cm/year), likely reflecting natural variability rather than sustained long-term gain. Therefore, ML-based downscaling of GRACE data enables high-resolution TWS assessment and provides a framework for future extraction of groundwater storage anomalies (GWSA), supporting improved groundwater management. Full article

Infrared radiation spectra produced by vibration–rotation transitions in multicomponent gases within the vacuum plume of attitude and orbital control engines constitute crucial radiation sources for optical target identification and space maneuver recognition, and rapid prediction of these signatures is essential for real-time forecasting. This study introduces an axisymmetric vacuum plume flow field model based on a simplified point-source approach that accommodates multicomponent combustion gases. Using the Maxwellian velocity distribution and a velocity–position angle algorithm, normalized number density, velocity, and temperature distributions are derived. A plume–freestream interaction model founded on noncentral fully elastic collision theory is incorporated, and overall plume properties are obtained via density-weighted averaging. Neglecting non-equilibrium radiation effects, the high-temperature gas absorption coefficient is calculated using a statistical narrowband model and radiative transfer is solved via the line-of-sight method. The model is validated against direct simulation Monte Carlo results for single-gas and MBB bipropellant plumes and confirmed using infrared spectral data in the 2.0–4.5 μm band. The proposed framework achieves 10²–10³-fold higher computational efficiency than conventional DSMC approaches. Freestream effects on plume diffusion and momentum exchange diminish with increasing altitude, as does the freestream velocity’s enhancement of radiation intensity, whereas greater plume expansion at higher altitudes increases overall radiation intensity. Full article

Mass concrete is prone to cracking induced by high early-age temperature rise and significant shrinkage stress, which severely compromises structural durability and safety. Aiming to achieve “low temperature rise and high crack resistance,” this study systematically optimized raw material selection and conducted experimental investigations on mix proportioning and the influence of critical parameters. The proposed design was subsequently validated through a field application. The results indicate that a fly ash content of 35% effectively improves workability, mitigates early-age shrinkage and reduces the heat of hydration. The incorporation of a high-performance expansive agent not only retards the hydration process and delays the temperature peak but also generates compensatory expansion at early ages, significantly reducing shrinkage during the cooling phase. Additionally, a polypropylene fiber dosage of 1.2 kg/m³ was found to optimally balance workability with crack resistance enhancement, resulting in less than 5% reduction in early-age strength. Field applications demonstrate that the concrete with the optimized mix proportion exhibits excellent workability and rapid early strength development. Specifically, the expansive agent delayed the temperature peak to 78 h and generated significant chemical expansion, effectively compensating for shrinkage caused by cooling. The findings provide critical insights into the construction-stage behavior of mass concrete, enabling improved safety control through better prediction and mitigation of early-age thermal and shrinkage effects. This study offers theoretical and technical support for the design of mass concrete characterized by low temperature rise and high crack resistance. Full article

Digital twin technology is rapidly gaining traction in the semiconductor industry for its ability to model manufacturing processes, including packaging engineering, to monitor and optimise performance cost-effectively. This paper focuses on two key areas of development. The first part explores the potential of digital design and additive manufacturing to produce high-performance, compact thermal management solutions that significantly reduce device junction temperatures and enhance operational efficiency. The second part presents the development of surrogate models to predict junction temperatures of electronic packages under varying operating and geometrical conditions. These models, trained using deep learning, were integrated into a user-friendly COMSOL Multiphysics application builder version 6.3. The proposed digital twin framework enables fast and accurate full-thermal field predictions in comparison to conventional 3D finite element simulations. Full article

Due to their unique topography, deep open-pit coal mines are prone to temperature inversions, which, in turn, exacerbate dust pollution. To characterize this phenomenon, we combined field measurements with FLUENT-based numerical simulations to analyze how inversion layer properties and dust transport patterns respond to varying conditions. The results show that the temperature contrast between the pit walls is positively correlated with the inversion layer’s temperature difference, thickness, and strength. In contrast, ambient wind speed is negatively correlated with the layer’s temperature difference and strength, yet positively correlated with its thickness. Surface temperature has no significant effect on the inversion layer’s temperature difference or thickness and exhibits only a weak correlation with its strength. Furthermore, higher wall temperature contrasts lead to increased dust concentration, whereas stronger winds promote dispersion and lower concentrations. These findings confirm that temperature inversion intensifies pollution, with stronger inversions causing more severe contamination. Therefore, mitigating the formation of inversion layers is crucial for effective dust control in deep pits. Unlike previous phenomenological observations, this study provides novel quantitative data on the thermal-aerodynamic coupling within deep open pits. Specifically, it establishes exact mathematical correlations between discrete rock wall temperature differentials and inversion layer thickness, providing critical thresholds for predicting severe dust retention. Full article

In this paper, a ternary Tb₆₅Co₂₅Ni₁₀ amorphous tape was successfully prepared, and the glass forming ability (GFA), magnetic properties, and magnetocaloric characteristics of the amorphous tape were studied in detail. The values of the reduced glass transition temperature T_rg, parameter γ and critical section thickness Z_c indicate the good GFA of the Tb₆₅Co₂₅Ni₁₀ amorphous tape. The Tb₆₅Co₂₅Ni₁₀ amorphous tape exhibits spin-glass-like behavior, with a Curie temperature of 83 K and a spin-freezing temperature (T_f) of 73 K, and a large coercivity below T_f. The spin-glass-like behavior significantly deteriorates the magnetic entropy change (−∆S_m) of the Tb₆₅Co₂₅Ni₁₀ amorphous tape at low temperatures, resulting in the deviation of magnetic entropy change behavior from the predicted results. However, the Tb₆₅Co₂₅Ni₁₀ amorphous tape still shows an excellent magnetocaloric effect (the peak value of −∆S_m of 9.46 J kg⁻¹ K⁻¹ and the refrigeration capacity of 569.5 J kg⁻¹ under 5 T, both of which are higher than those of most other heavy rare earth-based amorphous alloys), indicating the great application potential in the field of magnetic refrigeration for the amorphous tape. Full article

Virtual flow metering (VFM) serves as an effective alternative to traditional physical flow meters, significantly reducing gas-field metering costs and operational complexity. However, conventional VFM typically employs a single-modeling approach, failing to address metering requirements across varying production conditions and data types. Focusing on wellhead choke equipment, four mechanistic models (MModels) based on choke-flow dynamics are constructed using piecewise linear regression, alongside six machine learning models. Hyperparameters are optimized via grid search and cross-validation, establishing a hybrid mechanistic and data-driven multi-model VFM method for gas wells. Systematic testing utilizes field data from gas wells in the Southwest Oil and Gas Field, with the Shapley additive explanations (SHAP) method quantifying feature contributions. MModel results indicate superior overall performance by the temperature-difference piecewise linear model, yielding a training R² of 0.91 and a mean test error of 4.59%. Under different valve-position conditions, the downstream-temperature piecewise linear model demonstrates better predictive capability when the valve position is equal to 100, whereas the valve-position piecewise linear model achieves higher accuracy when the valve position is less than 100. MLModel results reveal that among ten feature parameters, “Date” and “Valve Position Indication” contribute most significantly to prediction accuracy, accounting for over 50% of cumulative contribution in GBoost (extreme gradient boosting) and CatBoost (categorical boosting) models. Notably, the XGBoost model exhibits optimal predictive performance, achieving a training R² of 0.979 and a mean test error of merely 0.13%. Random sampling results show coefficient of variation values below 0.1 for all metrics, demonstrating exceptional robustness, providing an effective technical solution and solid theoretical support for gas-field VFM. Full article

The increasing demand for sustainable and lightweight structural systems has motivated the development of alternative materials for wind turbine tower applications, where conventional steel structures are associated with high material consumption and environmental impact. In this study, a novel steel-reinforced recycled thermoplastic composite system is proposed as an alternative structural solution. To enable the design and practical application of such composite systems, the mechanical properties of the recycled thermoplastic matrix were experimentally characterized. Compression and tensile tests revealed average yield strengths of approximately 32 MPa in compression and 7.8 MPa in tension. To account for the environmental conditions encountered in field applications, the temperature-dependent mechanical behavior of the material was investigated. Since the critical mechanical response of the thermoplastic matrix in the composite system is governed by compression rather than tension, the study was limited to compression tests under elevated temperatures. The results show that the compressive yield strength decreases to approximately 31 MPa at 55 °C. An analytical model based on the transformed-section approach was also developed to predict the flexural behavior of the composite section and was validated through three-point bending tests, with an analytically predicted yield load of approximately 31.5 kN showing good agreement with experimental results. To assess structural applicability at a larger scale, a full-scale composite wind turbine tower was designed and manufactured, and its dynamic performance was evaluated through field measurements under natural wind loading conditions. The results indicate that the composite tower exhibits comparable dynamic behavior to a conventional steel tower, with a first natural frequency of approximately 3.08 Hz compared to 2.89 Hz for the steel tower, along with enhanced damping characteristics. These findings demonstrate that steel-reinforced recycled thermoplastic composites offer a promising and sustainable alternative for wind turbine tower applications, with potential for broader use in structural systems. Full article

The SF₆ circuit breaker is an essential piece of high-voltage equipment in ensuring the safe operation of the power grid. Regarding the arc-extinguishing chamber, as the most essential component, its performance is directly related to the breaking capacity of the circuit breaker. This study applies the Double Distribution Function Lattice Boltzmann Method (DDF-LBM), combined with the Smagorinsky sub-grid scale (SGS) model, to systematically simulate the dynamic breaking process of a 252 kV SF₆ arc-extinguishing chamber under 50 kA breaking current conditions. Two independent distribution functions are employed to describe the fluid field and the temperature field, respectively, thereby simulating the physical flow–heat coupling process. A dynamic simulation framework is constructed using the D2Q9 model to describe the mechanical motion of the contacts and the fluid flow. The description of contact movement is achieved by dynamically updating the geometric mesh, thereby realizing fluid–solid transformation. The research results indicate that the proposed method can simulate the pressure variation of the fluid field during the breaking process. The value of the Smagorinsky constant (C_s) exhibits a non-negligible influence on the pressure field predictions. The optimal value of C_s = 0.10 is determined through analysis, and the peak pressures at the upstream and throat measurement points reach 1.11 MPa and 1.37 MPa, respectively. Numerical simulations are conducted on the dynamic breaking process of the arc-extinguishing chamber, revealing the evolution of the pressure field upstream of the nozzle and at the throat regions. This study provides new numerical simulation methods for the investigation of SF₆ arc-extinguishing chambers and establishes a foundation for the application of the Lattice Boltzmann Method in the field of high-voltage electrical appliances. Full article

Cairo’s Islamic architectural heritage represents one of the world’s most significant concentrations of pre-industrial environmental ingenuity. For over a millennium, an integrated suite of passive climate-control systems—the Mašrabiya latticework screen, the open courtyard (Ṣaḥn), the wind-scoop (Malqaf), and stalactite vaulting (Muqarnas)—has moderated temperature, humidity, and airflow with remarkable effectiveness. Today, these inherited solutions are under unprecedented stress from urban densification, chronic particulate pollution, climate-driven temperature rise, and growing visitor footfall. This study investigates indoor environmental quality (IEQ) in six Fatimid- and Mamlūk-era buildings in Historic Cairo through the integrated IQAD-IAH framework, combining IoT field monitoring (January–December 2023) of temperature, relative humidity, CO₂, and PM2.5 with CNN-based deterioration image analysis and Random Forest predictive modeling. Results document critical summer thermal buffering failures reaching 28% of occupied hours above the ASHRAE 55 adaptive comfort limit; hygrothermal stress cycles exceeding the EN 15757 ±10% RH safe threshold for up to 38% of annual hours; and PM2.5 courtyard concentrations of 40–61 µg/m³ under normal conditions, surging to 180–320 µg/m³ during Ḫamāsῑn-seasonal wind events. Machine-learning projections indicate all three principal passive elements will cross the critical deterioration threshold of 70/100 under RCP 8.5 before 2050. A precautionary intervention window is identified between 2025 and 2032. Evidence-based management recommendations compatible with UNESCO World Heritage obligations are presented. Full article

Buried natural gas pipeline leakage poses significant risks to public safety and environmental sustainability. A three-dimensional transient computational fluid dynamics model is established to investigate gas leakage and diffusion behavior, with a focus on quantifying the differences between standalone soil models and soil–atmosphere-coupled models. First danger time (FDT), farthest danger range (FDR), and ground danger range (GDR) are adopted as standardized safety metrics that translate complex concentration fields into actionable emergency response parameters. Simulation results indicate that the coupled model predicts an FDT 4.9% earlier and a GDR 39.7% smaller than the standalone soil model under no-wind conditions, highlighting the necessity of coupling. Increasing temperature reduces leakage mass flow rate by 8.4% but produces only marginal changes in FDT (6.01–6.47 s) and GDR (1.888–1.973 m) across the −10 °C to 40 °C envelope. Wind speed exhibits a non-monotonic, time-dependent effect: the shortest FDT occurs at 5 m/s (5.71 s), while the worst-case spatial hazard occurs at 2 m/s (FDR = 3.423 m at 60 s, 3.304 m at 100 s). A crosswind configuration reduces GDR by merely 1.6% when compared with an along-wind configuration, indicating weak directional sensitivity. Topography dominates hazard onset timing: a canyon shortens FDT to 0.33 s, while a hill delays it to 18.48 s—a 43-fold span. Offset obstacles with upwind configuration maximize FDT delay to 8.07 s (+30.6%) while enabling late-stage wake-driven GDR expansion. These findings provide quantitative guidance for risk assessment, emergency response, and monitoring layout of buried natural gas pipelines. Full article

To improve the efficiency of comparative process-window screening in selective laser melting (SLM), this study developed a finite element simulation-driven machine learning framework for 316L stainless steel. A simulation dataset covering laser power (LP), scanning speed (SS), heat-source diameter (HSD), and substrate preheating temperature (SPH) was generated using ANSYS and used to train nine regression models. In the present work, the primary machine learning target was defined as the simulated average thermal stress, σ_avg, which is used as a simulation-derived comparative thermal stress indicator for ranking process conditions within the investigated parameter window rather than as a direct prediction of the final residual-stress field. Among the evaluated models, the Backpropagation Neural Network (BPNN) showed the best predictive performance and was selected as the representative surrogate model because of its strong predictive accuracy, stable behavior, and direct applicability to the present structured tabular dataset. Shapley additive explanations (SHAP) and partial dependence plots (PDPs) indicated that LP is the dominant variable governing the σ_avg-based response, followed by SPH, whereas SS and HSD mainly affect the response through secondary or coupled effects. Within the investigated parameter window, conditions near 180–200 W corresponded to a relatively lower predicted σ_avg level. Experimental observations provided limited but meaningful trend-level support for the simulation-guided screening results: metallographic examination showed improved forming quality near 200 W, while XRD-derived macroscopic stress estimates exhibited a similar variation trend to the simulated σ_avg values under the tested LP–SS conditions. These results suggest that the proposed framework can serve as an efficient surrogate-based tool for comparative parameter screening in SLM-fabricated 316L stainless steel within the assumptions and parameter range of the present model. Full article