Search Results (110)

Accurate prediction of production dynamics in chemical composite cold production (CCCP) for heavy oil reservoirs remains challenging due to complex multi-phase fluid interactions and nonlinear flow regime transitions. Traditional numerical simulations are computationally expensive and rely heavily on detailed geological characterization. To address these limitations, a data-driven predictive framework integrating physical mechanisms with machine learning is proposed. A dual-driven feature selection strategy combining Spearman rank correlation and the Entropy Weight Method (EWM) was applied to quantify nonlinear parameter correlations and data informativeness, identifying injection-production balance and development and maximum adsorption capacity as dominant factors controlling oil production fluctuations. Latin Hypercube Sampling (LHS) was used to construct a representative parameter space, followed by weighted standardization. A Multiple Linear Regression (MLR) model was then trained to jointly predict key production indicators. Field validation shows strong predictive capability, with a coefficient of determination above 0.94 and relative fitting error below 5%. The method reduces computational time by over two orders of magnitude while maintaining high precision. Full article

Liquefied natural gas (LNG) sloshing occurs during marine transportation and storage due to vessel motion or external disturbances, leading to complex fluid–structure interactions within the containment system. This study employs OpenFOAM to develop a numerical model of LNG sloshing. The model solves the incompressible multiphase Navier–Stokes equations and utilizes the Volume of Fluid (VOF) method to capture the dynamic behavior of gas–liquid interface. The numerical model was validated against experimental data. Based on this model, the key hydrodynamic characteristics are investigated for LNG sloshing, including nonlinear free surface, transient pressure distribution on the tank walls due to liquid impact, and energy dissipation mechanisms. By varying excitation frequencies, amplitudes, and the configuration of internal components such as baffles or anti-sloshing devices, the study explores the sloshing response and effective control strategies. The results indicate that appropriately designed baffles can significantly mitigate sloshing-induced impact pressures on tank walls and enhance system stability. In the future, this study could extend to multi-layer fluids, multi-degree-of-freedom motions, and simulations under more complex real-world conditions. Full article

Extended Reality (XR) technologies are increasingly used to create immersive and interactive systems across domains such as education, training, health, and entertainment. As these systems become more complex and multisensory, evaluating user experience (UX) in XR environments requires approaches that go beyond traditional usability assessments and consider perceptual, cognitive, emotional, and interaction-related factors. However, existing UX evaluation efforts in XR often rely on isolated instruments or domain-specific studies, lacking a systematic and reusable evaluation methodology. This paper proposes a human-centered methodology for evaluating user experience in extended reality applications, integrating UX dimensions and XR-specific characteristics into a structured and coherent evaluation process. The methodology is grounded in a multi-phase research process that includes a comprehensive literature review, expert consultation, correlation analysis between UX dimensions and XR features, and formal specification of evaluation phases and activities. Based on this process, the proposed methodology supports evaluators in selecting appropriate UX evaluation methods and instruments according to the characteristics and experiential goals of XR applications. The methodology defines a set of UX dimensions tailored to immersive environments, capturing perceptual, cognitive, emotional, and interaction aspects that are critical for the design and evaluation of adaptive and human-centered XR systems. An expert-based validation was conducted to assess the clarity, usefulness, and applicability of the methodology, leading to refinements in its structure and descriptions. The methodology promotes a human-centered approach by considering user perception, emotional impact, and contextual experience across XR modalities. It additionally contributes to the field by offering a reusable process for UX evaluation in XR, supporting more consistent, transparent, and human-centered assessment practices. It also provides a foundation for future empirical studies and the development of evaluation approaches inspired by natural and adaptive human–environment interactions. Full article

Proton exchange membrane fuel cells (PEMFC) are recognized as promising next-generation energy technology. Yet, their performance is critically limited by inefficient gas transport and water management in conventional flow channels. Current rectangular gas channels (GC) restrict reactive gas penetration into the gas diffusion layer (GDL) due to insufficient longitudinal convection. At the same time, the complex multiphase interactions at the mesoscale pose challenges for numerical modeling. To address these limitations, this study proposes a novel cathode channel design featuring laterally contracted fin-shaped barrier blocks and develops a mesoscopic multiphase coupled transport model using the lattice Boltzmann method combined with the volume-of-fluid approach (LBM-VOF). Through systematic investigation of multiphase flow interactions across channel geometries and GDL surface wettability effects, we demonstrate that the optimized barrier structure induces bidirectional forced convection, enhancing oxygen transport compared to linear channels. Compared with the traditional straight channel, the optimized composite channel achieves a 60.9% increase in average droplet transport velocity and a 56.9% longer droplet displacement distance, while reducing the GDL surface water saturation by 24.8% under the same inlet conditions. These findings provide critical insights into channel structure optimization for high-efficiency PEMFC, offering a validated numerical framework for multiphysics-coupled fuel cell simulations. Full article

Accurate identification of oil–water two-phase flow patterns is essential for ensuring the safety and operational efficiency of oil and gas extraction systems. While traditional methods using empirical models and sensor technologies have provided basic insights, they often struggle to capture the nonlinear features of complex operational conditions. To address the challenge of data scarcity commonly found in experimental settings, this study employs a data augmentation strategy that combines the Synthetic Minority Over-sampling Technique (SMOTE) with Gaussian noise injection, effectively expanding the feature space from 60 original experimental nodes. Next, a physics-constrained attention mechanism model was developed that incorporates a physical constraint matrix to effectively mask irrelevant feature interactions. Experimental results show that while the standard attention model (83.88%) and the baseline BP neural network (84.25%) have limitations in generalizing to complex regimes, the proposed physics-constrained model achieves a peak test accuracy of 96.62%. Importantly, the model demonstrates exceptional robustness in identifying complex transition regions—specifically Dispersed Oil-in-Water (DO/W) flows—where it improved recall rates by about 24.6% compared to baselines. Additionally, visualization of attention scores confirms that the distribution of attention weights aligns closely with fluid-dynamic mechanisms—favoring inclination for stratified flows and flow rate for turbulence-dominated dispersions—thus validating the model’s interpretability. This research offers a novel, interpretable approach for modeling dynamic feature interactions in multiphase flows and provides valuable insights for intelligent oilfield development. Full article

During natural gas field extraction, downhole compressors frequently encounter gas-liquid two-phase flow conditions, yet the internal flow characteristics and performance evolution mechanisms remain insufficiently understood. This paper investigates a small-scale, low-pressure-ratio five-stage axial compressor using a multiphase numerical simulation method based on the Euler-Lagrange framework. The study systematically examines the effects of different inlet pressures (0.1 MPa, 1 MPa, 8 MPa) and liquid mass fraction (0%, 5%, 10%) on its overall and stage-by-stage performance, droplet evolution, and flow field structure. The results indicate that the inlet pressure exerts a decisive influence on the overall efficiency trend of wet compression. The stage efficiency response displays a trend of an initial decrease in the front stages followed by an increase in the rear stages, showing significant variation under different inlet pressures. Flow field analysis reveals that increased inlet pressure intensifies droplet aerodynamic breakup, leading to higher flow losses in the compressor. Simultaneously, under high-pressure conditions, the cumulative cooling effect resulting from droplet heat transfer and evaporation effectively enhances the flow stability in the rear stages. This research elucidates the interstage interaction mechanisms of gas-liquid two-phase flow in low-pressure-ratio multistage compressors and highlights the competing influences of droplet breakup and evaporation effects on performance under different pressure conditions, providing a theoretical basis for the optimal design of downhole wet gas compression technology. Full article

Smoothed particle hydrodynamics (SPHs) is a Lagrangian meshless method with distinct strengths in managing unstable and complex interface behaviors. This study develops an integrated multiphase SPH framework by merging multiple algorithms and techniques to enhance stability and accuracy. The multiphase model is validated by several benchmark examples, including square droplet deformation, single bubble rising, and two bubbles rising. The selection of numerical parameters for multiphase simulations is also discussed. The validated model is then applied to simulate oil–water–gas bubbly flows. Interface behaviors, such as coalescence, fragmentation, deformation, etc., are reproduced, which helps to take into account multiphysics interactions in industrial processes. The rising processes of many oil droplets for oil–water separation are first simulated, showing the advantages and stability of the SPH model in dealing with complex interface behaviors. To fully explore the potential of the model, the model is further extended to the field of wax removal. The melting process of the wax layer due to heat conduction is simulated by coupling the thermodynamic model and the phase change model. Interesting behaviors such as wax layer cracking, droplet detachment, and thermally driven flow instabilities are captured, providing insights into wax deposition mitigation strategies. This study provides an effective numerical model for bubbly flows in petroleum engineering and lays a research foundation for extending the application of the SPH method in other engineering fields, such as multiphase reactor design and environmental fluid dynamics. Full article

Recent advances in Chinese Named Entity Recognition (CNER) have integrated lexical features and factual knowledge into pretrained language models. However, existing lexicon-based methods often inject knowledge as restricted, isolated token-level information, lacking rich semantic and structural context. Knowledge graphs (KGs), comprising relational triples, offer explicit relational semantics and reasoning capabilities, while Graph Convolutional Networks (GCNs) effectively capture complex sentence structures. We propose KGGCN, a unified KG-enhanced GCN framework for CNER. KGGCN introduces external factual knowledge without disrupting the original word order, employing a novel end-append serialization scheme and a visibility matrix to control interaction scope. The model further utilizes a two-phase GCN stack, combining a standard GCN for robust aggregation with a multi-head attention GCN for adaptive structural refinement, to capture multi-level structural information. Experiments on four Chinese benchmark datasets demonstrate KGGCN’s superior performance. It achieves the highest F1-scores on MSRA (95.96%) and Weibo (71.98%), surpassing previous bests by 0.26 and 1.18 percentage points, respectively. Additionally, KGGCN obtains the highest Recall on OntoNotes (84.28%) and MSRA (96.14%), and the highest Precision on MSRA (95.82%), Resume (96.40%), and Weibo (72.14%). These results highlight KGGCN’s effectiveness in leveraging structured knowledge and multi-phase graph modeling to enhance entity recognition accuracy and coverage across diverse Chinese texts. Full article

A coupled CFD-DEM model was adopted to investigate the floating ice accumulation mechanism and its disturbance to the flow field in the pump house of coastal nuclear power plants in cold regions. Based on numerical simulations, the motion, accumulation, and flow interaction characteristics of floating ice under various release positions and heights were analyzed. The results indicate that the release height significantly governs the accumulation morphology and hydraulic response. The release height critically determines ice accumulation patterns and hydraulic responses. For inlet scenarios, lower heights induce a dense, wedge-shaped accumulation at the coarse trash rack, increasing thickness by 57.69% and shifting the accumulation 38.16% inlet-ward compared to higher releases. Conversely, higher releases enhance dispersion, expanding disturbances to the central pump house and intensifying flow heterogeneity. In bottom release cases, lower heights form wall-adhering accumulations, while higher releases cause ice to rise into mid-upper layers, thereby markedly intensifying local vortices (peak intensity 79.68, approximately 300% higher). Spatial release locations induce 2.7–4.8-fold variations in flow disturbance intensity across monitoring points. These findings clarify the combined impact of the release height and location on the ice accumulation and flow field dynamics, offering critical insights for the anti-ice design and flow safety assessment of pump houses. Full article

Molecular dynamics (MD) can dynamically reveal the structural evolution and mechanical response of Zirconium (Zr) at the atomic scale under complex service conditions such as high temperature, stress, and irradiation. However, traditional empirical potentials are limited by their fixed function forms and parameters, making it difficult to accurately describe the multi-body interactions of Zr under conditions such as multi-phase structures and strong nonlinear deformation, thereby limiting the accuracy and generalization ability of simulation results. This paper combines high-throughput first-principles calculations (DFT) with the machine learning method to develop the Deep Potential (DP) for Zr. The developed DP of Zr was verified by performing molecular dynamic simulations on lattice constants, surface energies, grain boundary energies, melting point, elastic constants, and tensile responses. The results show that the DP model achieves high consistency with DFT in predicting multiple key physical properties, such as lattice constants and melting point. Also, it can accurately capture atomic migration, local structural evolution, and crystal structural transformations of Zr under thermal excitation. In addition, the DP model can accurately capture plastic deformation and stress softening behavior in Zr under large strains, reproducing the characteristics of yielding and structural rearrangement during tensile loading, as well as the stress-induced phase transition of Zr from HCP to FCC, demonstrating its strong physical fidelity and numerical stability. Full article

Horizontal annular flow channels are widely applied in various fields, including thermal engineering, drilling engineering, and food engineering. Investigating their internal flow patterns is crucial for optimizing pipeline design, selecting appropriate equipment, and understanding the sedimentation and migration modes of multiphase flows within annular geometries. In practical engineering applications, the operational conditions of annular flow channels during gas drilling are the most complex, involving parameters such as eccentricity, rotation, surface roughness, and multiphase flow interactions. This study focuses on the flow characteristics of horizontal annular channels under real-world engineering conditions, examining variations in operational parameters. The pressure drop in annular pipelines is influenced by factors such as flow velocity, eccentricity, and rotational speed, exhibiting complex variation patterns. However, previous studies have not fully considered the impact of rough wellbore walls and the interactions among various factors. Employing experimental methods, this research analyzes the pressure drop characteristics within annular geometries. The results reveal that surface roughness significantly affects pressure drop, with the inner pipe’s roughness having a greater impact when the outer pipe surface is rough compared to when it is smooth. An increase in eccentricity substantially reduces pressure drop, with both positive and negative eccentricities demonstrating symmetric pressure drop patterns. Moreover, a significant positive correlation exists between the total rough area of the annular channel and pressure drop. Furthermore, this study establishes a predictive model through dimensional analysis. Unlike existing models, this new model incorporates the influences of both roughness and eccentricity, achieving a prediction accuracy of over 99%. This research confirms the critical role of roughness in annular flow systems and provides practical implications for selecting more reliable pump power equipment in engineering fields. Full article

Under extreme operating conditions, the internal lubricating flow field of high-speed gear transmission systems exhibits a transient oil–gas multiphase flow, predominantly governed by cavitation-induced phase transitions and turbulent shear. This phenomenon involves complex mechanisms of nonlinear multi-physical coupling and energy dissipation. Traditional lubrication theories and single-phase flow simplified models show significant limitations in capturing microsecond-scale flow features, dynamic interface evolution, and turbulence modulation mechanisms. To address these challenges, this study developed a cross-scale coupled numerical framework based on the Lattice Boltzmann method and large eddy simulation (LBM-LES). By incorporating an adaptive time relaxation algorithm, the framework effectively enhances the computational accuracy and stability for high-speed rotational flow fields, enabling the precise characterization of lubricant splashing, distribution, and its interaction with air. The research systematically reveals the spatiotemporal evolution characteristics of the internal flow field within the gearbox and focuses on analyzing the nonlinear regulatory effect of baffle geometric parameters on the system’s energy transport and dissipation characteristics. Numerical results indicate that the baffle structure significantly influences the spatial distribution of the vorticity field and turbulence intensity by reconstructing the shear layer topology. Low-profile baffles optimize the energy transfer pathway, effectively reducing the flow enthalpy, whereas excessively tall baffles induce strong secondary recirculation flows, exacerbating vortex-induced energy losses. Simultaneously, appropriately increasing the spacing between double baffles helps enhance global lubricant transport efficiency and suppresses unsteady dissipation caused by localized momentum accumulation. Furthermore, the geometrically optimized double-baffle configuration can achieve synergistic improvements in lubrication performance, oil film stability, and system energy efficiency by guiding the main shear flow and mitigating localized high-momentum impacts. This study provides crucial theoretical foundations and design guidelines for developing the next generation of theory-driven, energy-efficient lubrication design strategies for gear transmissions. Full article

A multiphase model of hydrothermal liquefaction (HTL) using the computational fluid dynamics coupling discrete element method (CFD-DEM) is used to simulate biocrude oil production from sludge flocs in a continuous stirred tank reactor (CSTR). Additionally, the influence of the agitator speed and the slurry flow rate on dynamic biocrude oil production is investigated through full transient CFD analysis in a scaled-up CSTR study. The kinetics of the HTL mechanism as a function of temperature, pressure, and residence time distribution were employed in the model through a user-defined function (UDF). The multiphysics simulation of the HTL process in a stirred tank reactor using the Lagrangian–Eulerian (LE) approach, along with a standard k-ε turbulence model, integrated HTL kinetics. The simulation accounts for particle–fluid interactions by coupling CFD-derived hydrodynamic fields with discrete particle motion, enabling prediction of individual particle trajectories based on drag, buoyancy, and interphase momentum exchange. The three-phase flow using a compressible non-ideal gas model and multiphase interaction as design requirements increased process efficiency in high-pressure and high-temperature model conditions. Full article

The oceanic wind and waves, as well as the resultant ship motions, significantly impact the ship airwake and the operation of shipborne helicopters. A numerical method coupling wind, wave, ship and helicopter is developed using multiphase flow, in which the ship motions are simulated in real time by dynamic fluid body interaction module and the helicopter rotor is modeled using the momentum source approach. By integrating the ONRT ship with the UH-60A helicopter, the unsteady aerodynamic characteristics of the ship airwake and the helicopter rotor while the ship is pitching and heaving at sea state 36 that cover moderate to extreme marine environments are studied, and the time history of rotor thrust and pitch moment at four different sea states and different hovering heights are calculated. It is shown that ship motions and deck displacements in relative sea states are highly nonlinear, making the conditions faced by helicopter landing and take-off operations vary greatly from one sea state to another. The effects of each sea state when coupling waves and ship motions varies greatly. The fluctuation of velocity components and rotor air loads in sea state 6 is up to twice that of in sea state 5, while there are less differences between the velocity fluctuation and the corresponding helicopter airloads among common sea state 3~5. The dynamic aerodynamic interference resulting from the wind–wave–ship–helicopter coupling exhibits pronounced unsteady characteristics, as the hovering rotor continuously traverses areas with varying velocities and vorticities. At the most severe sea state 6, rotor thrust fluctuations can reach up to 20%, and strong perturbations of 5~10 Hz with an amplitude of 1/3 of the total range occur due to oscillating separated shear layers, which endanger the shipborne helicopter operation and needs to be eluded. Full article

This article discusses contemporary strategies to deal with immersed boundary (IB) frameworks useful for analyzing flow–structure interaction in complex settings. It focuses on immense advancements in various fields: biology, oscillation of structures due to fluid flow, deformable materials, thermal processes, settling particles, multiphase systems, and sound propagation. The discussion also involves a review of techniques addressing moving boundary conditions at complex interfaces. Evaluating practical examples and theoretical challenges that have been addressed by these frameworks are another focus of the article. Important results highlight the integration of IB methods with adaptive mesh refinement and high-order accuracy techniques, which enormously improve computational efficiency and precision in modeling complex solid–fluid interactions. The article also describes the evolution of IB methodologies in tackling problems of energy harvesting, bio-inspiration propulsion, and thermal-fluid coupling, which extends IB methodologies broadly in many scientific and industrial areas. More importantly, by bringing together different insights and paradigms from across disciplines, the study highlights the emerging trends in IB methodologies towards solving some of the most intricate challenges within the technical and scientific domains. Full article