Search Results (6,779)

Dominant flow channels formed during the late stages of waterflooding can severely reduce sweep efficiency and intensify ineffective interwell circulation. Conventional identification approaches, including tracer testing, well testing, and numerical simulation, often suffer from high operational cost, long execution time, or limited adaptability to heterogeneous interwell connectivity. Although ant colony optimization (ACO) is suitable for path-search problems in reservoir networks, its performance depends strongly on hyperparameter settings, and sample-by-sample parameter tuning introduces substantial online computational overhead. This study proposes a structure-informed GIN–ACO framework for adaptive dominant flow channel identification in connection-element reservoir graphs. A physics-constrained benchmark model is first established using Darcy’s law and the connection element method to provide reference flow paths. A geometry-based surrogate model is then developed to approximate flow splitting coefficients efficiently while preserving the main physical trends. Based on graph topology and geometric descriptors, a graph isomorphism network is trained to predict task-specific ACO parameters, replacing iterative online search with direct parameter inference. Experiments on 1000 synthetic reservoir graphs show that the proposed method achieves a 100% success rate with an average online computation time of 143.5 ms, outperforming fixed-parameter ACO, PSO-ACO, and BO-ACO. On 20 semi-realistic SPE10 reservoir models, GIN–ACO achieves a success rate of 92 ± 1% with an average runtime of 160.3 ± 5 ms. Ablation studies further confirm that graph-structure learning, combined topology–geometry features, and GIN-based parameter prediction are essential for robust performance. The proposed framework provides a promising and computationally efficient route for structure-aware dominant channel identification in connection-element reservoir models. Full article

Urban festivals attract substantial numbers of tourists, which consequently imposes significant strain on host cities through spatial overcrowding, uneven pressure on infrastructure, and diminished quality of the visitor experience. Destination management organizations (DMOs) require effective tools to redistribute tourist flows; however, the influence of social media on tourists’ actual destination choices remains insufficiently understood. We ask whether social media discussion intensity (“buzz”) causally influences tourists’ destination choices and whether the effect grows stronger during festivals when information asymmetry is at its peak. Combining 95,692 taxi GPS trajectories with 5995 geotagged Twitter records from the 2019 Songkran Festival in Bangkok, we constructed an exponentially weighted moving average (EWMA) buzz variable with a temporal lag that establishes causal ordering. A conditional logit model shows that district-level buzz significantly raises destination choice probability and that the effect is amplified during the festival. Causal identification rests on a triangulated strategy that combines temporal lag, placebo permutation, and Bartik shift-share instrumental variables. The festival-period IV-corrected estimate (${\widehat{\beta }}^{\mathrm{IV}}=+0.019$, $p<0.001$) is 51% larger than the within-period OLS estimate (${\widehat{\beta }}^{\mathrm{OLS}}=+0.012$, $p<0.001$), a gap consistent with classical measurement-error attenuation in sparse social-media data, and a panel 2SLS analysis at the district–day level isolates a causal visitation channel confirming that cascades reinforce spatial concentration at the tourist-flow level. The aggregate Gini coefficient of spatial concentration declines over the study window in a statistically significant monotonic trend. The positive district-level correlation between buzz and congestion does not survive district and date fixed effects, which indicates that it reflects underlying differences in attractiveness across districts rather than a direct within-district channel. These findings provide an empirical foundation for information-based visitor flow management by identifying the underlying behavioral mechanism rather than evaluating a designed intervention. Full article

Significance: This study addresses critical industrial and biomedical applications including glass blowing (thermal management of shrinking sheets), polymer sheet extrusion (controlled cooling), magnetic drug delivery (nanoparticle targeting), and nuclear reactor cooling (enhanced heat transfer). Aim: We present a novel nonlinear analysis of magnetohydrodynamic (MHD) boundary layer flow of a Jeffery Al${}_2$O${}_3$ nanofluid over a shrinking permeable plate with convective boundary conditions, uniquely integrating mixed convection, Ohmic dissipation, heat generation, Brownian motion, and thermophoresis within a non-Newtonian nanofluid framework. Methodology: The governing partial differential equations are transformed using similarity transformations and solved via the Adomian decomposition method (ADM). Comprehensive validation against RK4, RK45, and bvp4c demonstrates excellent agreement with maximum relative errors below $5\times {10}^{-4}$. Key Contribution: (i) Normal velocity decreases by 15–25% as the Biot number increases from $Bi=0.4$ to $0.6$; (ii) tangential velocity decreases by 20–30% as the magnetic parameter increases from $M=5$ to 15; (iii) temperature increases by 30–40% as the Eckert number increases from $Ec=0.5$ to $2.5$; (iv) ADM converges within 12–15 terms with $L_2$ errors $<{10}^{-5}$; (v) skin friction coefficient increases from $C_f=3.02713$ to $3.90082$ as $Q_0$ increases from 1 to 4; (vi) Nusselt number values: $Nu/\sqrt{Re}=0.4621$ at $Pr=0.7$, $0.8954$ at $Pr=2$, $3.2890$ at $Pr=20$. These quantitative findings provide design guidelines for engineers in thermal management and biomedical applications. Full article

To facilitate accurate physical interpretation and representation of various design concepts for supercritical carbon dioxide (sCO₂) axial turbines and to improve both the efficiency and accuracy of their thermodynamic design, two conceptual design approaches were proposed in this study. One approach was developed based on the steam turbine concept design method, which involved iterative calculations of dimensionless parameters, such as the velocity ratio and degree of reaction. The other was derived from the gas turbine concept design method, which involved iterative calculations of the flow and loading coefficients. The physical implications of the thermodynamic calculation procedures and the characteristics of the loss models associated with different turbine-stage design methodologies were systematically investigated. Combined with a one-dimensional thermodynamic design case study of an sCO₂ axial turbine stage, the applicability and critical implementation steps of each method in the sCO₂ axial turbine design were validated. To address the current limitations of sCO₂ axial turbine design methodologies, a unified framework for multiple turbine loss models was introduced, and high-precision loss model parameters were employed to iteratively correct the velocity coefficient. Based on the steam turbine design concept, a novel multi-parameter iterative methodology was developed for the design of sCO₂ axial turbine stages. This approach enables comprehensive one-dimensional thermodynamic design. Various performance parameters were examined, and multi-parameter iterations were conducted to derive the optimal design. The results provide a useful basis for the preliminary design, parametric screening, and one-dimensional optimization of sCO₂ axial turbine stages. Full article

The alloy with a chemical composition of 64 at. % Fe and 36 at. % Ni is known as Invar36 and is characterized by a coefficient of thermal expansion (CTE) less than 2 × 10⁻⁶ °C⁻¹ below Curie temperature (about 250 °C). The conventional method of obtaining Invar36 alloys consists of melting and casting, followed by a series of heat treatments. In recent years, research has focused on unconventional technologies for Invar36 preparation such as the sintering of Fe and Ni elemental powders. Also, Invar36 in powder form can be synthesized by mechanical alloying (MA). The aim of this paper is the characterization of Invar36 compacts obtained by conventional sintering of mechanically alloyed Fe and Ni elemental powders. MA was performed in a high-energy planetary ball mill (Ar atmosphere). Mechanically alloyed powders were densified by conventional sintering (simple and double). The sintering parameters used are those specific to the sintering of ferrous parts. After simple sintering, the relative density was 74%. Re-pressing and double sintering lead to an increase in the relative density to 78.6%. The microstructure of Invar36 compacts consists of two phases. The coefficient of thermal expansion (CTE) was determined for Invar36 compacts obtained by both simple and double sintering at 1120 °C in endogas. The CTE values of Invar36 simple sintered (α = 0.6 × 10⁻⁶ °C⁻¹) and double sintered (α = 0.5 × 10⁻⁶ °C⁻¹) are very low, up to 195 and 225 °C, respectively. HV_0.05 values of the Invar-ss sample are lower than the values of the Invar-ds sample. Thus, the HV_0.05 value in areas where the γ phase predominates increases from 203 to 218, while in areas where the α phase is predominant it increases from 257 to 271. The results of this study have potential applicability in obtaining Invar parts by sintering under the specific conditions used for ferrous parts, without requiring any modification of the production flow. Full article

Freeze–thaw cycles lead to undesirable gelation in egg yolk, which negatively affects its functional properties, restricting its application in the food industry. This study aimed to investigate whether enzymatic treatment can prevent the freeze-induced gelation of egg yolk, thereby maintaining its desirable quality attributes. Egg yolk samples were treated with an enzyme preparation (Biocatalysts Flavorpro™ 750MDP) at concentrations of 0.05, 0.3, and 0.5 w/w%, homogenized, and incubated at 40 °C for 120 min, followed by rapid cooling and freezing at −24 ± 1 °C for 60 d. Control samples without enzyme treatment were subjected to the same processing steps as the other samples. After thawing, all samples were analyzed for pH, color, rheological and thermophysical properties, turbidity and visual appearance. The results demonstrated that although enzymatic treatment and its combination with freezing significantly altered color, turbidity, rheological and thermophysical properties of egg yolk, it effectively inhibited freezing-induced gel formation, particularly at 0.3 w/w%. The parameters characterizing rheological behavior—yield stress, consistency coefficient, and flow behavior index—were preserved close to those of fresh yolk after the freeze–thaw process. These findings suggest that exopeptidase treatment is a promising approach for controlling freeze–thaw-induced gelation in egg yolk, supporting its wider use in frozen and processed egg products. Full article

The development of future electrical aircraft, such as the Hybrid-Electric Regional Aircraft (HERA) platform, presents challenging cooling demands due to the heat generated by electric powerplants, fuel cells and power electronics. Traditional heat exchangers in ram air channels may not be sufficient, necessitating alternative solutions like Skin Heat Exchangers (SHXs) to enhance heat transfer and reduce parasitic drag. Aircraft drag reduction and efficiency increase are expected with the integration of SHXs in two-phase cooling systems. This study employs Computational Fluid Dynamics (CFD) models, specifically the Volume of Fluid (VOF) multiphase model together with the Lee model, to simulate the condensation process of two Hydrofluoroolefin (HFO) refrigerants in SHX channels (R1233zd(E) and R1234yf). An analytical model based on empirical equations is used to preliminarily correlate and validate the CFD results, showing deviations below 15%. The simulations reveal distinct flow behaviours for each refrigerant, influenced by the differences in liquid and gas densities. The study also establishes a basis for understanding and selecting the inverse of the relaxation time coefficient, which is crucial for multiphase CFD modelling. The CFD models used in this article could be of great importance for future SHX design optimization. Full article

Blade tip leakage flow in gas turbines is associated with aerodynamic loss and local heat transfer variation in the tip region. In this study, the flow structure, total pressure loss coefficient, heat transfer coefficient (HTC), and film cooling effectiveness (FCE) were numerically investigated for a plane tip (PLN) and five squealer tip geometries: a conventional squealer tip (SQR), cutback squealer tip (CBS), multi-cavity squealer tip (MCS), triangular-grooved suction-side squealer tip (GSS), and multi-cavity triangular-grooved suction-side squealer tip (MGS). All configurations were compared under the same cascade geometry, tip-clearance condition, and inlet/outlet boundary conditions to examine the geometry-dependent relationship among aerodynamic loss, heat transfer, and film cooling performance. Film cooling was evaluated at blowing ratios of M = 1 and 2 using a camber-line hole arrangement, and the effect of hole rearrangement was further examined at the same blowing ratio and with the same number of cooling holes. The results indicate that the aerodynamic and thermal characteristics of the tip region vary with the leakage-flow path, cavity recirculation, and reattachment behavior formed by each tip geometry. Under the present conditions, SQR showed the lowest downstream total pressure loss coefficient, with a 7.27% reduction relative to PLN, whereas MGS showed the lowest geometry-normalized heat transfer rate among the tested geometries. Increasing the blowing ratio tended to increase FCE, although local cooling performance was affected by high-pressure or reattachment-dominated regions where coolant ejection, surface attachment, or lateral spreading was limited. Compared with the camber-line arrangement, the rearranged hole configuration increased local FCE by up to 29.6% for CBS and 23.3% for MGS at the same blowing ratio. These results may be used as comparative data for evaluating squealer tip geometries and cooling-hole placement during preliminary blade tip cooling design. Full article

Purely data-driven machine-learning methods are currently limited by weak physical interpretability; meanwhile, the sparsity of well-site data in oil and gas fields further degrades the prediction performance of deep learning models for reservoir seepage simulation. To overcome this bottleneck, this study embeds Darcy’s law-based seepage equations as physical constraints into the loss function of a deep learning framework, thereby constructing a physics-informed neural network (PINN) for seepage flow in porous media of oil and gas reservoirs. Numerical simulations are performed in heterogeneous porous media to compare the predictive performance of the proposed PINN against conventional purely data-driven approaches, via evaluation metrics including the coefficient of determination (R²) and root mean square error (RMSE). The results show that both models achieve comparable predictive accuracy with sufficient training samples. In contrast, the PINN retains high predictive accuracy even with a reduced number of samples, and it delivers prominent superiority under conditions of sparse well data and strong reservoir heterogeneity. This study clarifies the applicable scenarios of the two aforementioned methods (physics-informed neural networks and purely data-driven machine-learning models) for fluid flow simulation in porous media and provides a solid theoretical and technical foundation for the accurate prediction of reservoir seepage fields and the optimization of oil and gas reservoir development. This work also offers a validated physics-constrained deep learning framework to guide the deployment of intelligent algorithms in practical subsurface flow engineering. Full article

Continuous monitoring of impurity content and breakage rate in combine harvester grain flow remains challenging because representative samples are difficult to acquire online, and the visual targets are small, dense, and imbalanced. In this study, a prototype monitoring system integrating sample collection, controlled conveying, image acquisition, and embedded processing was developed for online grain-quality sensing during harvesting. To satisfy the requirement for sidewall sampling from the vertical grain conveying auger, centrifugal sampling and screw conveying were used to extract and transport grain-flow samples, and a stable imaging environment was established using an industrial camera and dedicated illumination. Pixel-area-to-mass mapping models were established for broken grains and impurity targets, with coefficients of determination higher than 0.93. In addition, a lightweight improved YOLOv8-Seg model was developed to recognize and segment broken grains and impurity targets under dense small-target conditions. Bench-scale validation showed that the relative error of impurity content ranged from 1.02% to 13.04%, with an average of 6.09%, while the absolute error of breakage rate ranged from 0.01 to 0.02 percentage points. These results demonstrate the feasibility of the proposed method for online estimation of impurity content and breakage rate under bench-scale conditions and provide a basis for future machine integration and field validation. Full article

Operating room ventilation is a key engineering factor in maintaining clean air environments. This study presents an integrated three-part methodology combining Computational Fluid Dynamics parametric analysis, performance assessment with effect size analysis and multi-criteria decision analysis using quantitative engineering metrics, and surrogate modeling for thermal effect propagation in an orthopedic operating room. Simulations were conducted in ANSYS Fluent 2020 R₂, benchmarking an existing local operating room against an ASHRAE 170-2021 compliant model, followed by parametric evaluation of four ceiling inlet configurations. The existing system exhibited critically low velocities (0.05–0.10 m/s) with a coefficient of variation of 0.73, indicating severe flow non-uniformity. The proposed Multi-Velocity Ceiling Diffuser—featuring a high-velocity core (0.40 m/s) over the surgical area and a low-velocity peripheral frame (0.20 m/s)—achieved 85% coverage of the ASHRAE-recommended velocity range (0.20–0.30 m/s), a coefficient of variation of 0.14 (81% improvement), and 62 air changes per hour, representing an 86% reduction in supply airflow compared to a full-ceiling system. Effect size analysis confirmed that MVCD performance shows large practical differences from smaller inlet designs (Cohen’s d ≥ 0.41) and negligible difference from full-ceiling systems (Cohen’s d = 0.05). Multi-criteria decision analysis—with feasibility and cost quantified using engineering estimates (ductwork area, downtime days, standardized cost data)—ranked MVCD as optimal under the modeled assumptions (composite score = 0.84), outperforming the existing system (0.59) and full-ceiling design (0.51). To address the isothermal assumption limitation, a Random Forest surrogate model was implemented as a differentiable approximation strategy for parametric uncertainty propagation. Under non-isothermal conditions, the MVCD is predicted to maintain a spatial median velocity of 0.19 m/s (5th–95th percentile range: 0.17–0.21 m/s) and 71% ASHRAE compliance (parameter sampling range across literature-derived distributions: 63–78%). Achieving ASHRAE velocity criteria is an engineering surrogate for ventilation effectiveness; the relationship between these metrics and clinical infection outcomes depends on multiple factors beyond airflow, including surgical technique, patient factors, and antimicrobial prophylaxis. No clinical inference is permitted from the present results. Experimental measurement in a physical MVCD-equipped operating room is required to validate these predictions prior to clinical implementation. Full article

Accurate prediction of the behavior of alloys and metal matrix composites during high-temperature deformation requires strict consideration of the loading history. To address this problem, a hybrid rheological model for flow stress prediction has been developed, combining a phenomenological description of the yield stress with a recurrent neural network based on the NARX (Nonlinear AutoRegressive with eXogenous inputs) architecture. The memory effect is formed by expanding the input parameters with the response values from the previous step. The identification of the weight coefficients of the NARX neural network is implemented by training an equivalent multilayer perceptron. To improve the generalization ability of the model and eliminate its dependence on a fixed discretization step, the training dataset includes data obtained under non-monotonic changes in the strain rate over time and a variable time interval. The article justifies the structure of the model input parameters, excluding the accumulated strain from the input set due to its lack of informativeness during active softening processes. Verification of the hybrid model on the 7075/2.5% TiC composite in the temperature range of 300–500 °C demonstrated an average relative error of 1.5% when predicting modes that were not involved in the training. The predicted flow stress values fall within the experimental scatter interval of ±5% and accurately reproduce the local features of the flow stress curves. The proposed model and its identification technique provide correct consideration of the deformation history under the complex interaction of hardening and softening processes. Full article

Climate change poses a significant challenge to food security, as it alters crop productivity, distribution patterns, and the overall food supply. This study modelled the emergence of Amaranthus hybridus L. in bean (Phaseolus vulgaris L.) and maize (Zea mays L.) production systems in the Brazilian state of Minas Gerais, in the cities of Coimbra, Paracatu, São João del-Rei, and Uberaba, under the Coupled Model Intercomparison Project Phase 6 (CMIP6) SSP1-2.6 and SSP5-8.5 scenarios. Using Hydrothermal Time (HTT), computational modelling, and nonlinear Weibull regression, weed emergence was simulated under current and future climate scenarios for 2050 and 2070. Although biological triggers such as temperature and base water potential remain constant, higher average temperatures accelerate HTT accumulation. Thus, this results in earlier and more intense emergence flows. The highest and lowest cumulative emergence were observed in Uberaba and Paracatu, respectively. The SSP5-8.5 scenario projects high emergence windows for 2070. This reduces the time available for management interventions. The root-mean-square error (RMSE) associated with the coefficient of determination (R²) of the models validates HTT as an essential tool in computational agriculture. The integration of these models into decision-support systems is essential to mitigating productivity losses and it will increase control efficiency amid future climate uncertainties. Full article

This study examines the deformation behaviour of laser powder bed fusion-produced A20X aluminium alloy and its accurate representation using flow curve models that account for die–specimen friction. Tests across multiple strain rates at room temperature were conducted on a Gleeble 3800; force–displacement data were friction-corrected to derive constitutive flow curves. A mathematical model was developed to capture barrelling and its impact on the stress–strain response, yielding corrected stresses significantly lower than measured values and validating the correction. An equation linking key post-deformation geometric parameters to their mathematical representation correlated well with a calibrated 2D finite element model, which reliably predicted plastic strain and deformation. The model’s friction factors agreed with experimental data, enabling efficient determination of the friction coefficient. Microstructural analysis and micrographs supported the predicted plastic strain distributions. Together, the corrected experiments and validated simulations provide a robust description of A20X’s response and inform performance and application potential. Full article

In this paper, a flexible steady-state model of a highly integrated, natural convection-driven condensing methanol reactor was developed. The flowsheet model includes 1D submodels of the different sections of the integrated reactor–condenser and includes a method to estimate the maximum possible natural convection-driven flow. Experimental data are used to create a shortcut description for the heat transfer coefficients in the model. The model results indicate that when heat losses can be mitigated, autothermal operation is possible. The major part of the heat integration takes place in the economizer section; however, a significant amount of heat transfer occurs at the catalyst bed also. The model predicts that the loop mass flow and single-pass conversion strongly depend on the catalyst bed inlet temperature. Experimentally measured catalyst preheater and condenser duties suggest, however, that the model-calculated mass flow is likely too low and that it is less dependent on the catalyst bed inlet temperature than the model predicts. A possible cause for this is the neglect of radial temperature gradients in the catalyst bed in the model, overestimating the conversion. Another possible cause is a measurement error in the bed inlet temperature, causing the actual temperature to be lower than the measured value. Natural convection calculations show that the maximum achievable flow strongly depends on the single-pass conversion and that given a single-pass conversion, a minimum temperature difference is required for flow in the right direction. Sensitivity analyses (neglecting heat losses to the environment) show that with the current heat transfer description, the feasible operating range for autothermal, natural convection-driven flow is sizeable. However, at lower recycle mass flows, heat transfer is too fast, leading to premature condensation in the economizer section. If the heat transfer coefficient is smaller than the currently predicted value, autothermal operation is possible in a wide range of conditions. If heat losses are mitigated, the maximum productivity of 2000 ${\mathrm{kg}}_{\mathrm{MeOH}}{\mathrm{m}}_{\mathrm{cat}.}^{-3}{\mathrm{h}}^{-1}$ is achievable at high pressure, a moderate catalyst bed inlet temperature and a low condenser temperature. Full article