Search Results (5,338)

Computational Fluid Dynamics (CFD) was applied to an intermittent anoxic/aerobic Moving Bed Biofilm Reactor (MBBR) operated under six different aeration intermittency cycles and dissolved oxygen concentration levels. Experimental results showed that most aeration cycles did not provide a sufficiently long anoxic phase to sustain effective denitrification, thereby limiting NOx removal efficiency. This behavior was not adequately captured by simulations performed using conventional biological models (BioWin), which rely on the assumption of complete mixing. In contrast, the CFD model implemented in ANSYS Fluent 2024 R2 enabled a detailed characterization of reactor hydrodynamics and the identification of several inefficiencies, including short-circuiting, back-mixing, and the presence of dead zones. Notably, the simulations revealed a pronounced asymmetric distribution of carriers within the reactor, with the majority accumulating along one side, leaving a significant fraction of the reactor volume largely unoccupied. Further analysis indicated that this phenomenon was caused by a design flaw—specifically, the asymmetric placement of the aerators—combined with an excessively high air injection flow rate. Full article

The azimuth of a solar greenhouse affects the lighting and the amount of solar radiation received. To investigate the influence of greenhouse azimuth angles on the thermal environment and to ensure an optimal temperature for the growth of warm-season crops such as tomatoes and cucumbers, a naturally ventilated solar greenhouse in Urumqi, Xinjiang was examined. Using computational fluid dynamics (CFD) software (ANSYS 2020), a model of the greenhouse under natural ventilation was constructed. Taking the indoor temperature as the evaluation index, the temperature field inside the greenhouse was simulated at two time points (11:00 and 17:00) during the daytime in spring, under different azimuths (8° west of south, 4° west of south, due south, 4° east of south, and 8° east of south). The indoor measured point temperatures at 11:00 and 17:00 over four consecutive days were compared with the simulated results. The MaxRE, ARE, RMSE, and MAE were all remained within a low range, verifying the accuracy of the constructed CFD greenhouse model. The temperature contour maps of different sections, as well as the indoor average temperature and temperature uniformity in each case, were compared and analyzed. The results indicated that, at 11:00, the greenhouses with azimuths of 8° and 4° east of south exhibited higher average indoor temperatures than those with azimuths of due south and west of south. At 17:00, however, the highest average indoor temperatures occurred in the greenhouses with azimuths of 8° and 4° west of south, exceeding those with azimuths of due south and east of south. The differences in temperature uniformity among different azimuths at the same time were small, but there were significant differences in the temperature uniformity at different times for the same azimuth. According to the climatic characteristics and the temperature requirements of crops of Urumqi, Xinjiang, an azimuth of 4–8° west of south is recommended for solar greenhouses in this region. Full article

As core structural components for deep-sea oil and gas exploitation, deep-sea risers are continuously subjected to wind, wave, and current loads, which readily induce vortex-induced vibration (VIV) and further trigger structural fatigue damage. Furthermore, the progressive exploitation of deepwater and ultra-deepwater oil and gas resources has exacerbated the complexity and risk of riser VIV, rendering it a critical engineering problem that urgently requires effective solutions. This paper presents a comprehensive review of numerical studies on deep-sea riser VIV, systematically elaborating the fundamental principles, research advances, and application scenarios of three mainstream numerical approaches: semi-empirical models, computational fluid dynamics (CFD) models, and computational structural dynamics (CSD) models. The respective accuracy advantages and inherent limitations of each numerical method are thoroughly analyzed. Additionally, this review focuses on key research hotspots and challenging issues, including VIV responses of flexible risers, dynamic fluid–structure boundary coupling, internal–external flow coupling effects, wake interference of multi-riser systems, efficient VIV prediction, and vibration suppression optimization. The current technical bottlenecks in existing research are clarified. This study aims to provide a systematic theoretical framework and methodological reference for subsequent numerical investigations and engineering applications of riser VIV, and offer technical support for the optimal structural design and safety risk prevention of deep-sea riser systems. Full article

This study presents a transient computational fluid dynamics (CFD) analysis of dielectric flow behaviour and debris transport in micro-EDM milling, considering the effects of dielectric properties, inter-electrode gap (IEG) size (20–30 µm), and tool rotational speed (400–850 rpm). Four dielectric media, nitrogen gas, deionized water, HEDMA111 EDM oil, and sunflower seed oil, were investigated using a two-dimensional FEM-based model coupled with particle tracking simulations to evaluate debris mobility within the machining region. The results demonstrate that dielectric properties, particularly viscosity, strongly influence hydrodynamic behaviour and particle transport within the IEG. Under the adopted equal mass flow rate condition, nitrogen gas exhibited the highest flow velocities and the fastest debris evacuation due to the combined effects of its low viscosity and the resulting higher inlet velocity. Deionized water and HEDMA111 oil exhibit comparable intermediate behaviour, indicating that moderate viscosity variations within liquid dielectrics do not significantly alter the overall flow regime. In contrast, sunflower seed oil generates the most damped flow conditions, with reduced velocities and prolonged particle residence due to increased viscous resistance. Variations in IEG size produce only minor changes in evacuation efficiency compared with the dominant influence of dielectric properties, while tool rotational speed primarily affects velocity magnitude without altering qualitative transport behaviour. Full article

Non-exhaust emissions (NEEs), particularly brake wear particles (BWPs), have become a dominant source of traffic-related particulate matter (PM), accounting for approximately 77% of PM10 and 60% of PM2.5 emissions. Accurate quantification of these emissions is essential under increasingly stringent regulations such as Euro 7. However, measurement reliability is strongly influenced by particle transport and sampling losses. This review provides a state-of-the-art analysis of laboratory-scale methodologies for investigating BWP emissions, focusing on pin-on-disc (PoD) tribometers and inertia dynamometer systems. Particular attention is given to chamber design, airflow management, sampling configurations, and the mechanisms governing particle transport efficiency. The literature indicates that PoD systems are often affected by complex and non-uniform flow fields, leading to incomplete particle capture and reduced representativeness, whereas inertia dynamometers, especially when coupled with constant volume sampling (CVS), provide more controlled and reproducible conditions. Key loss mechanisms, including inertial deposition, diffusion, gravitational settling, and non-isokinetic sampling effects, are major contributors to uncertainty. The reviewed studies highlight that aerodynamic limitations in PoD systems, particularly box-shaped chambers, promote flow recirculation and particle losses. Advanced optimization approaches that combine artificial neural networks (ANNs) with computational fluid dynamics (CFD) simulations show strong potential to improve system design and measurement reliability. Full article

Blast furnace (BF) ironmaking is a complex multiphase process involving gas–solid flow, heat transfer, chemical reactions, burden movement, and phase transformation under high-temperature conditions. Since many internal states of the blast furnace cannot be directly observed during operation, numerical simulation and mathematical modeling have become important tools for understanding furnace behavior and optimizing operational parameters. This paper reviews recent advances in blast furnace numerical simulation and internal state reconstruction methods. Existing approaches, including packed-bed flow models, cohesive zone reconstruction methods, burden distribution models, and temperature field prediction methods, are summarized and discussed. In addition, the evolution of blast furnace mathematical models from early one-dimensional steady-state formulations to modern three-dimensional multifluid and hybrid simulation approaches is reviewed. Recent developments in computational fluid dynamics (CFD), the discrete element method (DEM), digital twin, and data-driven modeling are also discussed. Compared with traditional simplified models, modern multidimensional and hybrid approaches show improved capability in describing asymmetric furnace inner states, multiphase transport behavior, and operational parameter effects under industrial conditions. However, challenges still remain in achieving computational efficiency, parameter calibration, multiphase coupling, and real-time industrial application. Future studies are expected to focus on the integration of mechanism-based simulation and intelligent data-driven methods to improve prediction accuracy, operational adaptability, and intelligent control capability in blast furnace ironmaking. Full article

High-fidelity computational fluid dynamics (CFD) remains computationally expensive for steady aerodynamic prediction under multi-condition and variable-geometry configurations, which limits rapid design iteration. To address this issue, this study proposes a data-driven surrogate framework for aircraft surface flow-field prediction on irregular meshes. The framework combines a geometry-unification strategy for variable rudder-deflection configurations with KM-GAT, a hybrid neural architecture that integrates graph attention and KAN-based nonlinear feature transformation. Geometry unification maps the surface flow fields associated with different rudder-deflection states onto a common zero-deflection reference template, thereby establishing consistent mesh correspondence and fixed prediction locations across samples while retaining the rudder angle as an operating-condition variable. The KM-GAT model further combines topology-aware message passing with localized nonlinear refinement, while the Huber loss is adopted to improve training robustness for CFD-derived data. Experiments on the F-22 research model show that the proposed framework achieves lower prediction errors and more concentrated error distributions than baseline MLP and GNN-based models. Qualitative comparisons further indicate that KM-GAT better preserves localized high-gradient structures, including pressure transitions and vortex-dominated regions. These results suggest that the proposed framework provides an effective surrogate modeling strategy for variable-geometry aerodynamic flow field prediction. Full article

The safety and stability of onboard Liquid Hydrogen (LH₂) storage systems depend strongly on gas–liquid two-phase flow, heat transfer, and phase change under sloshing; however, the coupled influence of filling ratio and sloshing on thermo-hydrodynamic behavior remains underexplored. We develop a Volume of Fluid (VOF)-based two-phase Computational Fluid Dynamics (CFD) model in ANSYS Fluent to quantify interfacial dynamics, pressure response, and temperature-field evolution in LH₂ tanks subjected to sinusoidal acceleration for filling ratios from 10% to 90%. Increasing the filling ratio strengthens net condensation in the ullage and thus intensifies depressurization. As the filling ratio increases from 10% to 90%, the pressure reduction over the 2.0 s sloshing process increases from 0.418 kPa to 2.410 kPa, and the corresponding initial depressurization rate rises from 0.209 to 1.205 kPa s⁻¹. Free-surface motion decreases with filling ratio: at 10%, large interface excursions can induce gas-cavity formation and splashing, increasing the risk of intermittent propellant supply, whereas at 90% the interface is constrained and oscillations are suppressed. Higher filling ratios lead to faster ullage cooling and larger temperature oscillations. The liquid warms modestly, and its warming rate decreases nonlinearly with filling ratio, consistent with the larger effective thermal mass at higher fillings. Overall, the obtained mechanistic understanding can support the engineering design of onboard LH₂ tanks, including filling-ratio selection and thermal-management optimization under sloshing conditions. Full article

This study investigates the impact of internal muffler geometry on flow-related dissipation characteristics potentially relevant to acoustic behavior using steady-state Computational Fluid Dynamics (CFD) simulations. Four variants were analyzed: an empty tube, considered to be a baseline model, a three-chamber baffle system, a single spiral channel, and a complex multi-channel insert manufacturable only via advanced additive technologies. Simulations were conducted in SimScale using a compressible flow model with the k-ω SST turbulence formulation. Key outputs included static pressure distribution and turbulent kinetic energy (TKE), both of which were evaluated as qualitative surrogate indicators associated with flow-induced energy dissipation phenomena. The results indicate that geometries incorporating spiral features modify flow redistribution patterns, pressure gradients and localized turbulence intensity, suggesting potential applicability for future acoustic optimization studies. The study highlights how additive manufacturing enables the integration of geometrically complex internal structures otherwise unattainable through conventional methods. By comparing pressure drop and TKE patterns with internal design features, the research offers a preliminary CFD-based framework for geometry screening and conceptual evaluation of muffler insert designs for automotive exhaust systems. This approach provides computational support for rapid comparative assessment prior to experimental validation and detailed acoustic analysis. Full article

The toroidal propeller, as a high-performance propulsor with a unique geometric configuration, presents challenges in parameterizing its complex geometry and conducting design optimization. This paper proposes a hybrid Free-Form Deformation (FFD) based parametric method, which integrates global FFD control with local parameters to achieve flexible and efficient description of the complex surfaces of toroidal propellers. Building upon this, an automated design framework integrating Computational Fluid Dynamics (CFD), a Kriging surrogate model, and a data-driven optimization algorithm is constructed to explore a high-dimensional design space comprising 14 variables. The goal is to minimize torque while satisfying thrust and geometric constraints. Optimization results show that the optimized propeller achieves approximately 3.63% higher propulsive efficiency at the design condition and requires about 4.32% less power for the required thrust, compared with the best design from Design of Experiments (DOE) sampling. Further flow field analysis reveals that the optimized design achieves a more gradual pressure distribution, which effectively suppresses flow separation and cavitation risk, thereby maintaining better performance across a wider operational range. This study provides a systematic parametric modeling method and optimization strategy for the efficient design of toroidal propellers, demonstrating clear engineering application value. Full article

Understanding and accurately predicting the outflow hydrograph from embankment dam breaches is essential for managing the associated flood hazard and improving emergency preparedness. This work simulates the breaching process using a high-resolution 3D computational fluid dynamics (CFD) model, a critical natural hazard for earth-fill dams under overtopping conditions. The model was validated against the experimental data, showing high accuracy in predicting breach development and failure timing. A parametric analysis was performed to assess the influence of the initial breach geometry on erosion dynamics. The results indicated a high sensitivity, while increasing the breach width by 5% led to an average 11% increase in the erosion rate, and decreasing the depth by 5% caused an average 16.5% rise. To enhance predictive capabilities for this hazard, a multilayer neural network (MLNN) was trained on the CFD-generated dataset. The network utilized breach geometry and time as inputs to forecast the peak outflow and erosion rate, achieving excellent accuracy (RMSE = 0.019, R² = 0.99). This integrated modeling strategy combines data-driven learning with physics-based simulation and demonstrates its effectiveness for laboratory-scale dam breach modeling. This approach is a step toward more efficient surrogate-based tools for flood risk analysis, though its extension to full-scale dams and varied material properties requires additional validation and scaling analyses beyond the scope of this work. Full article

The integration of wind energy into urban environments is constrained by low wind speeds, high turbulence, and the recurrent negative torque experienced by lift-driven vertical-axis wind turbines (VAWTs). This study specifically evaluates a straight-bladed H-Darrieus rotor equipped with a single upstream passive flat-plate deflector for the wind regime measured on the campus of the Universidad Nacional Mayor de San Marcos (Lima, Peru). A three-dimensional transient CFD model using the SST k–ω turbulence model was applied to compare the baseline rotor and the deflector-assisted configuration under identical operating conditions; DMST calculations were used only as a low-order cross-check for the bare rotor performance trend, not as a substitute for experimental validation. The deflector was selected after a geometric sensitivity assessment and positioned at 30° relative to the incoming flow, with a span equal to the rotor height and a length comparable to the rotor diameter. At TSR = 2.5, the maximum power coefficient increased from 0.4459 for the bare rotor to 0.6153 with the deflector, equivalent to an improvement of approximately 38%. Velocity and pressure fields show that the deflector accelerates the flow toward the advancing blade while shielding the returning blade, thereby reducing adverse torque and smoothing cyclic torque fluctuations. The results define the applicability of the proposed passive device for low-to-moderate urban wind environments with a dominant wind sector and provide a reproducible numerical basis for subsequent wind-tunnel and field validation. Full article

Air temperature measurements in atmospheric environmental monitoring are susceptible to radiation-induced bias under natural ventilation. This study develops a low-power naturally ventilated air temperature sensor and a correction method combining computational fluid dynamics (CFD) with machine learning. The sensor integrates a Pt100 thin-film platinum resistance probe (Heraeus Holding GmbH, Hanau, Germany), symmetric guide plates, and a dual aluminum-plate radiation shield to reduce radiative heating while improving airflow around the probe. A three-dimensional fluid–solid coupled heat-transfer model was established in ANSYS FLUENT 15.0 to optimize guide-plate spacing and inclination angle and quantify the effects of solar radiation, long-wave radiation, scattered radiation, air density, wind speed, solar elevation angle, and surface albedo on radiation error. CFD results identified a guide-plate spacing of 24 mm and an inclination angle of 45° as the preferred parameters. A multilayer perceptron (MLP) model trained with CFD-derived data was validated in field experiments using a Model 076B aspirated radiation shield (Met One Instruments, Inc., Grants Pass, OR, USA) as the reference. The model predicted radiation error with a root mean square error (RMSE) of 0.052 °C, a mean absolute error (MAE) of 0.042 °C, and a correlation coefficient of 0.92. The proposed sensor and correction method provide a low-power and easy-to-maintain approach for reducing radiation-induced bias in naturally ventilated air-temperature measurements, with potential applications in meteorological observation, air-quality monitoring, and agricultural microclimate assessment. Full article

Lithium-ion batteries used in electric vehicles (EVs) are highly sensitive to temperature variations, and excessive heat accumulation can adversely affect their performance, lifespan, and safety. Therefore, an effective battery thermal management system (BTMS) is essential for maintaining safe operating conditions. This study proposes a novel air-cooled BTMS incorporating circular vent holes in an acrylic enclosure to enhance airflow distribution and convective heat transfer around LiNiCoMnO₂ batteries. A computational fluid dynamics (CFD) model was developed to investigate the effects of discharge rate (1C–2C), inlet air velocity (1.0–3.0 m/s), and inlet air temperature (25–35 °C) on thermal behavior. The results indicate that the proposed BTMS effectively maintains battery temperatures below the critical limit of 40 °C. Optimal cooling performance was achieved at inlet air temperatures of 25–35 °C, 25–30 °C, and 25 °C for discharge rates of 1C, 1.5C, and 2C, respectively. The proposed design provides a simple, effective, and practical BTMS solution for EV applications. These findings confirm that the combination of forced air cooling and optimized vent design significantly improves thermal management performance. Full article

The mineralogical characteristics of low-grade hard-rock uranium ore from the Zoujiashan deposit were systematically investigated via multiple analytical techniques, including chemical analysis, X-ray fluorescence (XRF) spectrometry, uranium occurrence analysis, 3D X-ray micro-computed tomography (CT), an automated mineral identification and characterization system (AMICS), and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS). The results revealed that the uranium grade of the ore was only 0.202%, among which 65.87% existed in the form of independent uranium minerals, while the remaining 34.13% existed in a dispersed ionic state. Except for quartz, most uranium minerals and gangue minerals were finely disseminated and closely intergrown. The pre-concentration of the ore is therefore necessary to separate uranium-rich particles from barren particles at a coarse particle size. Ore density analysis demonstrated that the feed particle size exerted a significant impact on the separation performance, and the optimum feed particle size was determined to be 20 mm. Subsequently, dense medium cyclone (DMC) separation tests were conducted. The experimental results indicated that fine grains were prone to report to low-density products (tailings) during mixed-size beneficiation. Under a tailings yield of 54%, for the −20 + 8 mm coarse fraction, the tailings uranium grade was 0.025% and the uranium recovery of the concentrate was 88.05%. Therefore, classified separation can effectively promote separation efficiency. To reveal the density control mechanism of the particle separation behavior inside the DMC, computational fluid dynamics (CFD) simulations were implemented with the Eulerian–Eulerian multiphase model in ANSYS-Fluent (version 2020R2). The simulation results suggested that a density difference of 8.6% realized effective separation. This work achieved the effective treatment of low-grade hard-rock uranium ore via DMC separation, providing a novel technical route for uranium ore pre-concentration. Full article