Search Results (1,493)

During drilling circulation, the wellbore temperature field changes in response to drilling-fluid rheology, borehole geometry, drillstring eccentricity, and cuttings concentration. Conventional wellbore temperature models use fixed heat-transfer parameters and therefore cannot accurately represent real-time heat-transfer conditions. To address this limitation, a transient temperature model was developed. Bottomhole annular-fluid temperatures generated by OLGA were used as synthetic observations for MHE calibration. An MHE-based calibration method was developed to estimate the equivalent heat-transfer correction factor. The temperature model was first benchmarked against the Kabir analytical model and the commercial simulator OLGA. The estimated correction factor was then used by the mechanistic model to update the wellbore temperature field. The results showed that, under different initial values of the correction factor and assumed measurement-noise standard deviations, the equivalent heat-transfer correction factor converged to a steady-state value of approximately 0.941. Additional sensitivity tests showed generally stable calibration performance under moderate parameter settings, whereas stronger observation noise reduced calibration stability and accuracy. Under varying operating conditions, the calibrated bottomhole temperature yielded a mean absolute error of 0.033–0.332 °C, representing a reduction of 45.97–98.73% relative to the corresponding uncalibrated results. When the observation delay was extended to 2400 s (d = 8), the MAE was still reduced by 26.50–68.20% across different operating stages. These numerical results show that the proposed method can use bottomhole temperature observations to dynamically compensate for equivalent heat-transfer model mismatch under the investigated simulation conditions. The method reduces model-prediction errors and improves bottomhole temperature prediction under the tested varying operating conditions. The proposed method has potential applications in wellbore temperature prediction and high-temperature risk assessment for deep-well drilling. Full article

Microalgae-based wastewater treatment represents a sustainable approach for simultaneous nutrient removal and biodiesel feedstock production. This study evaluated the growth, total organic carbon (TOC) removal, FAME productivity, fatty acid composition, and predicted biodiesel properties of Chlorella vulgaris and Scenedesmus obliquus cultivated in urban wastewater and synthetic medium under photoautotrophic and mixotrophic conditions, with and without nitrogen limitation using a two-stage cultivation strategy. S. obliquus showed greater adaptability to urban wastewater, with better growth and the highest TOC removal efficiency (92%) under mixotrophic cultivation with nitrogen limitation. Nitrogen limitation significantly enhanced FAME productivity in both species, with increases of 43.06–79.35% for C. vulgaris and 48.11–69.98% for S. obliquus relative to the corresponding photoautotrophic cultures. Fatty acid analysis revealed higher saturated fatty acid contents in C. vulgaris and higher polyunsaturated fatty acid levels in S. obliquus. Based on the fatty acid composition, C. vulgaris showed lower predicted iodine values and greater predicted oxidative stability than S. obliquus. The predicted cetane numbers were below the minimum values cited in international biodiesel specifications, while predicted kinematic viscosity was within the ranges reported by ASTM D6751 and EN 14214. However, the predicted cold filter plugging point (CFPP) values of 14.9–15.0 °C indicated limited suitability for low-temperature applications, and oxidative stability met the cited EN 14214 threshold only for some C. vulgaris treatments. These results indicate the potential of urban wastewater as a culture medium for sustainable microalgal FAME production and as a feedstock source for biodiesel applications, particularly when combined with fatty acid profile optimization or blending strategies. Full article

This article investigates the numerical simulation of heat-and-aerodynamic cycles in the ventilated air gap of a multilayer composite wall façade system in a hot climate using ANSYS 19/2 Fluent. Standard normative techniques rely on averaged, stationary boundary conditions and account for neither the height-wise inequality of solar exposure nor the dependence of air density and viscosity on barometric pressure and temperature, resulting in significant errors in predicting the actual heating of such structures. The model was calibrated on the authors’ own full-scale, in situ measurements of temperature, air speed and solar exposure in the ventilated gap of a nine-storey building, from which linear height-dependent surface-temperature relations were derived and used as boundary conditions for 3D models of façades 25 and 60 m tall. Thirty-two finite-volume experiments were performed under free convection (Boussinesq approximation), varying gap width (5 and 10 cm), inlet width (20 and 40 cm), barometric pressure (690 and 770 mmHg) and external air temperature (20 and 40 °C). Façade height proved the dominant factor (air speed up to 1.8 times higher, temperature 3–12.1 °C higher), followed by gap width (speed lower by 1.7 times, temperature by 3–5 °C), whereas pressure and inlet width altered the results by no more than 6%. Discrepancies with the standard calculation reached 10 °C in temperature and a two-fold difference in flow speed, confirming the need for verified CFD simulation when designing ventilated composite wall façades in hot climates. Full article

Winter wheat is a cornerstone of China’s grain production, contributing substantially to national food security and overall cereal output. This study modeled the nonlinear associations between multitemporal remote sensing variables and winter wheat yield. To produce high-spatiotemporal-resolution inputs, we used the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to integrate Sentinel-2 normalized difference vegetation index (NDVI) data with MODIS NDVI data, generating NDVI composites at 8-day intervals with a 10-m spatial resolution. The NDVI, actual evapotranspiration (ET), land surface temperature (LST), precipitation (PRE), and soil moisture (SM) were selected as predictors for yield estimation because they are closely associated with winter wheat growth and yield formation during primary growth stages. By integrating the local temporal feature-learning capacity of a one-dimensional convolutional neural network (1-D CNN) with the strength of a bidirectional long short-term memory (BiLSTM) model in capturing temporal dependencies within time series, a BiLSTM–CNN model was constructed for wheat yield estimation and prediction. The BiLSTM–CNN model showed higher estimation accuracy than individual BiLSTM and 1-D CNN models, with an R² of 0.69 and root mean square error (RMSE) of 478.68 kg/hm². The use of all the parameters produced the best estimation performance among all the parameter combinations. Approximately two months before harvest, the model still provided satisfactory yield prediction accuracy. This study provides an important theoretical basis for high-accuracy regional winter wheat yield estimation and pre-harvest forecasting. Full article

Liquid organic hydrogen carriers (LOHCs) offer a safe and scalable solution for hydrogen storage and transportation, overcoming many of the limitations of conventional compressed and liquefied hydrogen systems. Among the available LOHCs, the dibenzyltoluene/perhydro-dibenzyltoluene (DBT/18H-DBT) pair is particularly attractive due to its high theoretical hydrogen storage capacity (6.2 wt.%) and favourable handling characteristics. This study investigates the hydrogenation of DBT over a cost-effective 13 wt.% Ni/Al₂O₃ catalyst using a multiphysics modelling approach developed in COMSOL Multiphysics. A zero-dimensional (0D) kinetic model, assessed against available experimental data, was employed to investigate the effects of temperature and hydrogen pressure on conversion, intermediate formation, and selectivity, and was subsequently extended to a two-dimensional (2D) predictive modelling framework to evaluate reactor behaviour under continuous-flow conditions. The simulations identified an optimum operating window of 505–515 K and 2 MPa, achieving complete DBT conversion and 95–97% selectivity towards the fully hydrogenated product, 18H-DBT, while higher pressures provided only marginal additional benefits. The 2D reactor model further predicted a final 18H-DBT selectivity of 96.8%, confirming the suitability of these conditions for continuous hydrogenation. The developed modelling framework provides valuable insight into reactor-scale performance and offers a robust tool for the design and optimisation of efficient, economically viable LOHC hydrogenation systems based on nickel catalysts. Full article

Precise prediction of pressure drop and drag-reduction performance is essential for improving the hydraulic efficiency and reducing the energy demand of crude-oil pipeline transportations. Therefore, this study aims to develop an integrated computational fluid dynamics (CFD)–machine learning (ML) framework for predicting pressure drop (∆p), drag reduction (DR), pumping power reduction (PPR), energy savings (ES), and flow-rate enhancement (Q) in turbulent crude-oil pipeline flow containing drag-reducing agents (DRAs). The investigated system considers the effect of pipeline length (L), diameter (D), surface roughness (ε), operating temperature (T), and DRA concentration (25–200 ppm). The Reynolds-average Navier–Stokes equations (RANS) were solved using the shear stress transport (SST) k-ω turbulence model approaching near-wall resolution of y⁺ ≈ 1 for DRA3 at 20 ppm. The CFD modelling was first used to validate an experimental benchmark and subsequently used to expand the available dataset over the investigated operating conditions. The combined experimental–CFD dataset was then employed to develop a multi-output Kolmogorov–Arnold network (KAN) surrogate model. The proposed framework predicted DR up to 44.2%, PPR of approximately 55 W, ES of 30%, and flow-rate enhancement up to 5–10(${\displaystyle \frac{\mathrm{L}}{\mathrm{d}\mathrm{a}\mathrm{y}}}$). The KAN model effectively captured the nonlinear relationships among DRA characteristics, pipeline geometry, and operating conditions, achieving R² = 0.9318 for PPR prediction. The novelty of the proposed work lies in integrating a validated, near-wall-resolved SST k-ω CFD model with a multi-output KAN surrogate model, combining physics-based flow analysis with rapid data-driven prediction of hydraulic and energy-performance indicators. Full article

Climate change-driven increases in temperature and changes in precipitation regimes are altering the timing and patterns of weed emergence in agricultural systems. Consequently, accurately predicting weed emergence and selecting the best timing for control are becoming more crucial over time. Weeds such as Echinochloa crus-galli (barnyardgrass) are particularly problematic due to their widespread adaptability and competition across varying cultivation environments, making an accurate prediction of the timing of emergence crucial for management. The objective of this study was to characterize the emergence pattern of E. crus-galli under diverse environmental conditions and to develop and evaluate a Gompertz-based thermal-time model for predicting seedling emergence. Increased temperatures enhanced emergence rates and speeds in both growth chamber and greenhouse conditions. The effective accumulated temperature required for 50% emergence was relatively consistent (54–69 °C·d). Furthermore, high emergence percentages were maintained at soil moisture levels of 80% or greater. Across years, emergence responses differed substantially under field conditions. Independent validation using a field dataset collected in 2026 demonstrated that the model developed from the 2025 dataset successfully reproduced observed emergence patterns under field conditions (RMSE = 2.7%p, MAE = 2.3%p). Regional emergence analyses suggested a tendency toward earlier emergence under recent temperature conditions, particularly in warmer regions, although these predictions were based on only two years of field observations. Overall, the present study provides a preliminary evaluation of the applicability of a thermal-time-based approach for describing E. crus-galli emergence under Korean environmental conditions. Additional validation across multiple locations and growing seasons would further strengthen the general applicability of the model. Full article

Pre-packaged fermented soybean whey-based tofu (FSW-tofu) was stored under dynamic temperature conditions (4–20 °C) that simulated typical supermarket and e-commerce cold-chain transport modes. Changes in total viable count (TVC), psychrophilic bacterial count (PBC), hardness, springiness, chewiness, and water-holding capacity were monitored over 35 d, and a hybrid prediction model integrating mechanistic kinetics with machine learning was established. Results indicated that both temperature fluctuation amplitude and frequency significantly affected microbial proliferation and textural degradation. Under the e-commerce mode, exposure to 20 °C accelerated the TVC, reaching 5 lg CFU/g at 17 d, earlier than under the supermarket mode (27 d). However, the sustained low-temperature stress in the supermarket mode caused more profound degradation of the protein gel network, leading to more severe textural deterioration at the equivalent TVC threshold. The Baranyi–Roberts–Ratkowsky non-isothermal growth model and quality response functions served as the base framework, while random forest and gradient boosting trees were used for residual correction, yielding a coupled mechanistic-data-driven model. Independent validation yielded R² > 0.89 and relatively low RMSE, confirming the model’s good generalization and predictive accuracy. This approach combines mechanistic interpretability with machine learning accuracy to provide a rapid assessment tool for the cold-chain quality management of FSW-tofu. Full article

Four-dimensionally printed polymer honeycombs generally exhibit recoverable deformation only above the glass transition temperature, where their load-bearing capacity is greatly reduced. To address this limitation, this study proposes a recoverable kirigami honeycomb enabled by a small-strain crease design. The original non-Euclidean origami honeycomb is transformed into planar corrugated sheets through local cutting, allowing the structure to be fabricated by 3D printing, folding, and bonding. A geometric model is established to describe the relationship between crease rotation and global honeycomb deformation, revealing that large structural deformation can be achieved with limited local crease strain. Polylactic acid (PLA) specimens with weakened creases are designed and tested to evaluate their shape-memory recovery. The results show that PLA maintains good thermally induced recovery after low-temperature bending, with recovery ratios reaching up to 96.7%. Finite element simulations further confirm that the weakened creases remain within a small strain range even under 180° bending, preventing local failure during large deformation. A stable-state prediction model based on the minimum potential energy principle is developed and validated by experiments and simulations. The predicted stable height agrees well with the experimental result. This work provides a feasible strategy for 4D-printed kirigami honeycombs capable of large deformation below the glass transition temperature followed by thermally activated recovery, with potential applications in deployable structures and temperature-responsive mechanical metamaterials. Full article

The dissimilar metal welded joints at the safe ends of nuclear primary circuits are highly susceptible to stress corrosion cracking (SCC) initiation in high-temperature, high-pressure water environments. Existing predictive models are predominantly based on homogeneous material assumptions, making it challenging to accurately evaluate the actual failure behavior of welds caused by mechanical property heterogeneity. Consequently, based on the mechanical gradient obtained from hardness tests, this study constructs a finite element model with continuously varying mechanical properties to quantitatively investigate SCC behavior under different crack characteristics. The analysis demonstrates that mechanical heterogeneity significantly influences the crack tip mechanical fields: When the crack is located proximal to the sub-interface (d = 1 mm), the severe mechanical mismatch induces a sharp increase in creep strain, resulting in a peak SCC propagation rate approximately 14.6% higher than those at d = 3 mm. Furthermore, extending the crack length at the weld center (a/W from 0.45 to 0.60) expands the plastic strain zone along the propagation direction, driving an approximately 43.6% increase in the crack growth rate. The heterogeneous model, accounting for the local mechanical gradient, can more accurately reveal the influence laws of crack position and length on SCC propagation behavior, providing theoretical support for improving life prediction accuracy and in-service inspections. Full article

This study systematically investigates the hot deformation behavior and microstructural evolution of AA3102 aluminum alloy through isothermal uniaxial compression tests conducted at 400–550 °C and strain rates of 0.01–10 s⁻¹. Previous studies on 3xxx-series Al–Mn alloys have mainly considered temperature and strain-rate effects while neglecting strain-dependent material parameter variations, limiting prediction accuracy under large deformation and obscuring dynamic softening mechanisms. Here, the experimental flow stress data were corrected for interfacial friction and adiabatic temperature rise. A sixth-order strain-compensated Arrhenius constitutive model was then developed to describe the coupled effects of temperature, strain rate, and strain. Full-strain-range power dissipation and flow instability maps were constructed using the dynamic material model, while optical microscopy and DEFORM-3D simulations were employed to clarify microstructural evolution and deformation inhomogeneity. The model achieved a correlation coefficient of 0.9841 and an average absolute relative error of 3.67%, demonstrating high predictive accuracy. No flow instability was detected within the investigated range, indicating excellent hot formability. The favorable compression-processing window was identified as 500–550 °C and 0.1–1 s⁻¹, while the peak power-dissipation efficiency increased from 30.55% at $\epsilon = 0.2$ to 32.90% at $\epsilon = 0.8$. Optical-microstructural observations suggest that increasing temperature and decreasing strain rate are associated with an increasing contribution of dynamic recrystallization relative to dynamic recovery. Finite-element results further reveal pronounced spatial variations in strain, temperature, strain rate, and stress during compression. These findings provide baseline constitutive and thermomechanical information for subsequent AA3102 hot-extrusion optimization. Full article

Unsaturated polyester resin concrete (UPC) exhibits high strength and corrosion resistance, and it has been widely used in hydraulic engineering structures. However, the diffusion of water molecules inevitably induces matrix plasticization and debonding at the aggregate–resin interface, which leads to progressive degradation of mechanical performance under long-term aqueous service conditions. To elucidate the water-induced mechanical deterioration mechanism of UPC and to develop a temperature-adaptive model for long-term compressive strength prediction, we prepared UPC specimens using graded quartz sand aggregate, an unsaturated polyester binder, V388 curing agent, and KH570 coupling agent at a fixed mass mixing ratio, followed by 7 days of natural curing after demolding. Accelerated water aging tests were conducted at three temperature levels (25 °C, 40 °C, 60 °C) and four immersion durations (15 d, 30 d, 45 d, 60 d), including water absorption, compressive, splitting tensile, and flexural tests. Based on Fick’s second diffusion law and the Arrhenius equation, we quantitatively analyzed moisture diffusion behavior and the evolution of mechanical degradation. The results indicate that the water absorption of UPC strictly follows Fickian diffusion, and that elevated temperature increases both the water absorption rate and the saturated water absorption capacity. The saturated water absorption ratios reached 0.15%, 0.16%, and 0.22% at 25 °C, 40 °C, and 60 °C, respectively, corresponding to apparent diffusion coefficients of 1.13 × 10⁻⁶, 1.67 × 10⁻⁶, and 4.52 × 10⁻⁶ mm/s. Long-term water aging progressively degrades the mechanical properties of UPC; after 60 d of immersion at 60 °C, the retention rates of compressive, splitting tensile, and flexural strength decreased to 88%, 85%, and 78%, respectively. We developed a physically coupled compressive strength prediction model based on moisture erosion depth and the associated reduction in effective bearing area. The ratio of model predictions to experimental data ranged from 0.93 to 0.98, indicating favorable conservative accuracy for engineering applications. When further combined with the Arrhenius relationship, the model enables extrapolation of the long-term mechanical properties of UPC under arbitrary service temperatures. This work provides theoretical support and a quantitative calculation framework for assessing the durability and predicting the service life of UPC hydraulic structures. Full article

Thermoplastic composites offer substantial lightweighting potential for body-in-white (BIW). However, the application of long-fibre-reinforced thermoplastic direct processing (LFT-D) to deep-drawn rear-body parts remains largely unexplored. This work presents the first documented LFT-D glass-fibre/polypropylene spare wheel well for a production electric vehicle, validated under a full vehicle-level durability programme. Fibre orientation was characterised by X-ray computed tomography, and both isotropic and orthotropic finite element models were built. Prototypes passed six component-level validation tests: stiffness, constrained modal, thermal cycling, low-temperature impact, stone impact, and a 7000 km road simulation; the orthotropic model, validated against these tests, reduced the first natural frequency prediction error to 3.9%. No structural damage occurred in any test. The composite part achieved 35% mass saving at component level and 54% at system level versus the steel assembly. Adding up to 20 wt% regrind retained >89% of virgin tensile strength (95% confidence interval [CI] lower bound: 89.2%) and >92% of impact strength, and cradle-to-gate CO₂ emissions dropped by 42%. These results show that LFT-D can be applied to large, structurally critical BIW components, delivering both lightweighting and closed-loop recyclability for electric vehicles. Full article

The edge-defined film-fed growth (EFG) method is the dominant industrial technique for producing large-area β-Ga₂O₃ single-crystal substrates, but thermal stress-induced dislocation generation remains a critical barrier. This work presents a coupled thermo-mechanical finite-element framework for thermal-stress management in EFG-grown β-Ga₂O₃. The central methodological contribution is a 500-sample gradient-boosting surrogate sensitivity analysis ($R^2=0.955$, mean absolute error (MAE) $=11.3$ MPa) that quantitatively decomposes thermal-stress variance into controllable process factors and irreducible material-property uncertainties. The physical foundation comprises two enabling elements: (i) the full 21-component monoclinic Voigt stiffness matrix with explicit crystal–model coordinate mapping, for which the orthotropic model is rigorously shown to be exact in 2D plane strain through an exact kinematic theorem showing that the 2D plane-strain results of prior orthotropic EFG analyses are unaffected by the coupling terms, while the monoclinic formulation provides the essential foundation for future 3D studies; and (ii) a dimensionless and numerical justification for omitting melt convection, which enables 100% solver convergence (500/500 Latin hypercube samples) with stress errors < 1.5 MPa. Afterheater temperature $T_{\mathrm{AH}}$ is the leading controllable parameter (35.9%), nearly tied with the elastic constant $C_{33}$ (35.5%), followed by the thermal-expansion component ${\alpha }_c$ (15.9%). Elevating $T_{\mathrm{AH}}$ from 1900 K to 1950 K reduces the peak von Mises stress by ∼29% (COMSOL Multiphysics 6.2-verified); the 2D plane-strain baseline anchors the surrogate analysis at ${\sigma }_{max}=223$ MPa, while the afterheater-free 3D configuration gives ${\sigma }_{max}=187$ MPa at the crystal periphery near the solid–liquid interface. The isotropic approximation underestimates peak stress by 39.6%, confirming that directional anisotropy is essential for quantitatively reliable thermal stress prediction in monoclinic oxide crystals. Full article

Reliable cropland, forestland, and grassland maps support resource assessment and ecological management in arid and semi-arid Central Asia. Existing land-cover products often delineate these classes differently, vary in reliability across classes and locations, and may share the same errors even when they agree. This study formulates multi-product fusion as a pixel- and class-specific reliability decision problem. To address this problem, we propose a reliability-adaptive fusion framework, the Discrepancy-Aware Reliability-Adaptive Fusion Network (DRAFNet), using 2020 maps from three global 30 m land-cover products—FROM-GLC Plus, GLC-FCS30D, and GlobeLand30—and variables representing aridity, temperature, precipitation, elevation, and slope. Unlike fixed-weight fusion methods and segmentation models that use the source products only as input channels, DRAFNet retains the categorical source decisions and adjusts each contribution according to its estimated reliability for the assigned class and location. Weight removed from an unreliable source is transferred to a residual expert, which provides an alternative prediction when the source products are unreliable or share the same error. Voting entropy and geo-environmental variables provide contextual information for this decision. On independent test samples from the five Central Asian countries, DRAFNet achieved an overall accuracy (OA) of 0.8275, a Kappa coefficient of 0.7698, a mean intersection over union (mIoU) of 0.7046, and a macro-averaged F1 score (Macro F1) of 0.8241. These values were 0.95–1.38 percentage points higher than those of U-Net++, the strongest benchmark. Local comparisons indicated more coherent spatial patterns and clearer boundaries in areas of pronounced disagreement. The mean and median absolute log-ratio deviations from area statistics reported by the Food and Agriculture Organization of the United Nations (FAO) were 0.618 and 0.450, respectively, both lower than those of the source products. These results support land-resource assessment and ecological management in Central Asia. Full article