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Chromium (Cr) exists in multiple oxidation states, with Cr(III) and Cr(VI) being the most environmentally and industrially relevant due to their distinct toxicity profiles and chemical behaviour. This study presents a comprehensive method that combines chemical simulation modelling, emission spectroscopy for quantification, and the controlled laboratory production of Cr species. Key findings include that acid digestion effectively extracted the Cr(III) and total Cr species, while thermodynamic modelling forecasted their stability and speciation under various environmental conditions. Thematic analysis indicates that the current quantification of Cr species is still in early development and remains centralized. Mineralogical and surface investigations showed that samples 1 and 2 have a BET surface area below 1 m²/g, whereas samples 3 and 4 exceed this. All samples are crystalline, with approximately 54.3 weight percent Cr₂O₃, 7.3 weight percent SiO₂, 17.75 weight percent of MgO, and 8.3 weight percent Al₂O₃, suggesting Al and Fe²⁺ replacement of Cr in the spinel structure. Computational fluid dynamics (CFD) modelling revealed that longer residence times are necessary for higher Cr metallization under H₂-CH₄-reducing conditions, and accurately predicted carbon deposition on pellets. These results demonstrate that CFD can optimize the H₂:CH₄ ratio to minimize carbon deposition and enhance gas transport to reaction sites. Full article

The problem of the post-harvest loss of perishable products has been a loss facing food security, especially in areas that lack adequate cold chain facilities. This issue is directly connected with sustainability objectives because post-harvest losses are the major source of food wastage, unneeded energy use, and related greenhouse gas emissions. Cold storage with phase-change material (PCM) is a promising alternative, as it aims at stabilizing temperatures and enhancing energy consumption, but current analyses of performance have been conducted through experimental testing and computational fluid dynamic (CFD) simulations, which are precise but computationally expensive. To handle this drawback, the current work constructs a machine learning predictive model to predict the dynamics of charging and discharging temperature of PCM cold storage systems. Four regression models, namely Random Forest, Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and K-Nearest Neighbors (KNNs), were trained and tested on experimental datasets that were obtained for varying storage layouts. The various error and accuracy measures used to determine model performance comprised MSE, MAE, R², MAPE, and percentage accuracy. The findings suggest that Random Forest provides the best accuracy during both the charging and the discharging process, with the highest R² values of over 0.98 and with minimal mean absolute errors. The KNN model was competitive in the discharge process, especially in cases of consistent thermal recovery patterns, and XGBoost was consistent in layout accuracy. However, SVR had relatively lower robustness, particularly when using nonlinear charged dynamics. Among the evaluated models, the Random Forest algorithm demonstrated the highest predictive accuracy, achieving coefficients of determination (R²) exceeding 0.98 for both charging and discharging processes, with mean absolute errors below 0.6 °C during charging and 0.3 °C during discharging. This paper has proven that machine learning is an efficient surrogate to CFD and experimental-only methods and can be used to predict the thermal behavior of PCM quickly and precisely. The proposed framework will allow for developing cold storage systems based on energy efficiency, low costs, and sustainability, especially in the context of decentralized and resource-limited agricultural supply chains, with the help of quick and data-focused forecasting of PCM thermal behavior. Full article

While fixed, unactuated rotor tilt is increasingly utilised in commercial multi-rotor unmanned aerial vehicles (UAV), its impact on forward flight performance remains poorly documented in the literature. The current work addresses this gap in the literature by systematically quantifying the performance trade-offs of rotor tilt across various airspeeds. Furthermore, a novel rotor configuration is proposed to mitigate some of the tilted rotor configuration’s inherent drawbacks. The different configurations are evaluated using a computationally affordable numerical approach that combines steady-state computational fluid dynamics (CFD) simulations, a simple proportional–integral (P–I) trimming algorithm, and actuator disk rotor modelling (ADM). The findings reveal that the aircraft’s power requirements can be reduced by more than 29% for airspeeds greater than 20 m/s, while its range can be increased by up to 22% with the alternative rotor configurations. However, the modifications were found to have a significantly lesser impact on endurance, as only a 2.9% increase is noted at best. Full article

Natural ventilation, as a key passive strategy in building energy-efficient design, holds potential for reducing energy consumption and improving indoor air quality in high-rise office buildings and contributes directly to the advancement of sustainable urban development. However, its application in cold regions during winter is constrained by the conflict between low outdoor temperatures and indoor heating demands. Perforated panel external windows, as a novel ventilation form, can maintain the integrity and safety of the building curtain wall while ensuring ventilation rates through reasonable perforation design. Nevertheless, their ventilation performance and winter applicability lack systematic research. This paper combines wind tunnel tests and Computational Fluid Dynamics (CFD) simulations to validate the effectiveness of the porous medium model in simulating ventilation through perforated panels and systematically analyzes the impact of window opening size and perforation rate on ventilation effectiveness. Furthermore, taking Beijing as an example, the study explores ventilation effectiveness and the indoor thermal environment under different window opening forms and proportions during winter in cold regions. Results indicate that ventilation effectiveness primarily depends on the effective ventilation area and has little correlation with the window opening size. Under winter conditions, rationally controlling the window opening proportion and perforation rate can achieve effective ventilation while maintaining the indoor minimum temperature (≥18 °C). The ventilation strategies proposed in this paper provide a theoretical basis and practical guidance for the natural ventilation design of high-rise office buildings that balances energy savings and comfort during the cold season. The proposed ventilation strategies provide practical guidance for sustainable design in high-rise office buildings, offering a viable pathway toward energy-saving, healthy, and climate-responsive built environments during the heating season. Full article

High-speed rotating machinery often demands bearings with superior load capacity and thermal stability. Here, a four-chamber radial hydrodynamic sliding bearing using supercritical carbon dioxide (S-CO₂) as a lubricant is investigated to address these requirements. The work is carried out on the ANSYS Workbench 2024 R1 platform. Computational fluid dynamics (CFD) and structural mechanics are combined to build a fluid–structure interaction (FSI) numerical model. The model accounts for real-gas thermophysical property variations. S-CO₂ properties are dynamically retrieved using the REFPROP database and MATLAB curve fitting. Unlike previous studies that mainly focused on hydrostatic structures and general parameters, this research examines hydrodynamic lubrication behavior under ultra-high-speed conditions. It systematically analyzes the effects of rotational speed on oil film pressure distribution, load capacity, friction coefficient, and housing deformation. It also investigates cavitation characteristics in a specific speed range. Simulation outcomes reveal that higher rotational speeds lead to an increase in both oil film load capacity and peak pressure. In particular, when the speed rises from 4000 r/min to 12,000 r/min, the maximum positive pressure increases from about 10 MPa to approximately 10.4 MPa. Meanwhile, the negative pressure region becomes significantly larger, which raises the cavitation risk and indicates a less stable lubrication state at very high speeds. These results confirm that lubrication simulations incorporating real-gas effects can reliably represent the operating behavior and provide useful guidance. It also provides new theoretical support for the design optimization and engineering application of S-CO₂-lubricated bearings in high-speed machinery. Full article

As computational fluid dynamics (CFD) has become more mainstream in production engineering workflows, new demands have been introduced that require high-quality meshes to accurately capture the complex geometries. This evolution has created the need for mesh generation frameworks that help engineers design optimized meshing structures for each new geometry. However, many simulation workflows rely on the experience and intuition of senior engineers rather than systematic frameworks. In this paper, a novel technique for determining mesh convergence is created using machine learning (ML). This method seeks to provide process engineers with a visual feedback mechanism of flow regions that require mesh refinement. The work was accomplished by creating three grid sensitivity studies on various geometries: zero-pressure-gradient flat plate, bump in channel, and axisymmetric free jet. The cases were then simulated using the Reynolds Averaged Navier-Stokes (RANS) models in OpenFOAM (v2306) and had the ML method applied post-hoc using Python (v3.12.6). To apply the method to each case, the flow field was regionalized and clustered using an unsupervised ML model. The ML clustering results were then converted into a similarity score, which compares two grid levels to inform the user whether the region of the flow had converged. To prove this framework, the similarity scores were compared to flow field probes used to determine mesh convergence at key points in the flow. The method was found to be in agreement with the flow field probes on the level of mesh refinement that created convergence. The approach was also seen to provide refinement region recommendations in regions of the flow that align with human intuition of the physics of the flow. Full article

Pintle injectors offer variable thrust capability and combustion stability advantages for liquid rocket engines. This study presents experimental and numerical investigation of spray characteristics for a liquid–liquid pintle injector using water as simulant. Ten cold flow tests covering total momentum ratio (TMR) from 0.36 to 2.76 captured spray angle variations from 26° to 80°. Computational fluid dynamics (CFD) simulations using Ansys Fluent 2025 R1 with the Volume of Fluid method and dispersed interface modeling showed good agreement with experimental spray angles for TMR > 0.74 (error < 8%), but demonstrated increasing discrepancy at lower TMR values (up to 62% error at TMR = 0.36). This deviation indicates limitations of steady-state RANS models in capturing unsteady, fuel-dominated flow regimes. The experimental dataset provides validation benchmarks for CFD modeling and contributes to injector design optimization for sounding rocket applications. Full article

This study evaluates the performance and scalability of fluoride-salt-lubricated hydrodynamic journal bearings used in primary pumps for Fluoride-salt-cooled High-temperature Reactors (FHRs). Because full-scale pump prototypes have not been tested, a scaling analysis is used to relate laboratory results to commercial conditions. Bearings with different length-to-diameter (L/D) ratios were assessed over a range of shaft speeds to quantify geometric and hydrodynamic effects. High-temperature bushing test data in FLiBe at 650 °C were used as inputs to three-dimensional computational fluid dynamics (CFD) simulations in STAR-CCM+. Applied load, friction force, and power loss were computed across operating speeds. Applied load increases linearly with shaft speed due to hydrodynamic pressure buildup, while power loss increases approximately quadratically, indicating greater energy dissipation at higher speeds. The resulting correlations clarify scaling effects beyond small-scale testing and provide a basis for bearing design optimization, prototype development, and the deployment of FHR technology. This work benchmarks speed-scaling relations for fluoride-salt-lubricated hydrodynamic journal bearings within the investigated regime. Full article

Contra-rotating propellers (CRPs) have promising applications in stratospheric airships. However, the aerodynamic interference caused by the airship envelope could lead to thrust loss, efficiency decrease, and even structure fatigue. This paper constructed parameterized computational fluid dynamic (CFD) models to simulate the aerodynamic performance of CRPs at different positions relative to the airship envelope. The total thrust, torque, efficiency, and thrust generated by a single propeller blade all show different degrees of interference. It is advised that such interference be considered in the layout design of stratospheric airships to improve propeller efficiency and ensure a longer structure fatigue life. Full article

Carbon capture and storage (CCS) is essential for achieving net-zero emissions, yet amine-based capture systems face significant challenges including high energy penalties (20–30% of power plant output) and operational costs ($50–120/tonne CO${}_2$). This study develops and validates a novel multi-scale Digital Twin (DT) framework integrating Physics-Informed Neural Networks (PINNs) to address these challenges through real-time optimization. The framework combines molecular dynamics, process simulation, computational fluid dynamics, and deep learning to enable real-time predictive control. A key innovation is the sequential training algorithm with domain decomposition, specifically designed to handle the nonlinear transport equations governing CO${}_2$ absorption with enhanced convergence properties.The algorithm achieves prediction errors below 1% for key process variables (R${}^2$> 0.98) when validated against CFD simulations across 500 test cases. Experimental validation against pilot-scale absorber data (12 m packing, 30 wt% MEA) confirms good agreement with measured profiles, including temperature (RMSE = 1.2 K), CO${}_2$ loading (RMSE = 0.015 mol/mol), and capture efficiency (RMSE = 0.6%). The trained surrogate enables computational speedups of up to four orders of magnitude, supporting real-time inference with response times below 100 ms suitable for closed-loop control. Under the conditions studied, the framework demonstrates reboiler duty reductions of 18.5% and operational cost reductions of approximately 31%. Sensitivity analysis identifies liquid-to-gas ratio and MEA concentration as the most influential parameters, with mechanistic explanations linking these to mass transfer enhancement and reaction kinetics. Techno-economic assessment indicates favorable investment metrics, though results depend on site-specific factors. The framework architecture is designed for extensibility to alternative solvent systems, with future work planned for industrial-scale validation and uncertainty quantification through Bayesian approaches. Full article

The deployment of multi-rotor drones in applications such as package delivery and urban air mobility is increasingly prevalent. Aerodynamic interference between rotors in traditional quadrotor drones impairs performance, and vertical offset is a promising solution to mitigate this interference. This study systematically investigates the aerodynamic characteristics of a quadrotor drone with a vertically offset rear-rotor configuration through computational fluid dynamics (CFD) simulations. By varying the vertical spacing ratio between the front and rear rotors (H/R), quantitative analyses were conducted of key performance metrics, including rotor thrust and power loading, with explanations provided from the perspective of the flow field structure. Furthermore, the underlying physical mechanisms influencing the observed performance variations are explored. The results indicate that, under the operating conditions investigated in this study, which include a single rotor RPM, a 10° inflow angle, and a specific forward-flight speed, the vertically offset configuration demonstrates superior aerodynamic performance at H/R = 1. At this spacing ratio, the rear rotor disk avoids most of the downwash-induced velocity generated by the front rotor, allowing partial recovery of the effective angle of attack. Consequently, vortex-propeller interaction (PVI) is significantly weakened, turbulent kinetic energy (TKE) levels in the interference region are reduced, and premature flow separation on the rear rotor blades is suppressed. These combined effects enhance overall aerodynamic efficiency. This study clarifies the role of vertical rotor spacing in regulating aerodynamic interference in multi-rotor drones, offering valuable insights for the aerodynamic design of compact rotorcraft. Full article

This paper examines the combined effects of intensive melt shearing and magnetic fields on the flow characteristics of molten aluminum alloy during the direct chill (DC) casting process. Using computational fluid dynamics (CFD), we analyze flow fields across varying rotor speeds, diameters, blade counts, stator configurations, and electromagnetic field strengths. The results demonstrate that increasing rotor speed and diameter significantly enhances melt flow velocity and vortex intensity. Conversely, a greater number of rotor blades results in reduced flow velocity due to higher resistance. Moreover, the shape and diameter of the stator opening impact flow patterns, with circular openings producing higher velocities. The interaction of electromagnetic fields and intensive shearing generates three distinct vortices, achieving the highest overall flow velocity and a uniform distribution. Additionally, adjustments in excitation coil current and rotor speed enable further control of melt flow, offering valuable insights for optimizing casting processes and enhancing ingot quality. Full article

Natural ventilation is a common cooling strategy in open dairy barns, but its efficiency largely depends on external wind directions and speeds. Misalignment between external airflow and fan jets often led to non-uniform air distribution, reduced local cooling efficiency, and an elevated risk of heat stress in cows. However, few studies have systematically examined the combined effects of wind directions and speeds on airflow and heat dissipation. Most research isolates natural or mechanical ventilation effects, neglecting their interaction. Accurate computational fluid dynamics (CFD) modeling of the coupling between outdoor and indoor airflow is crucial for designing and evaluating mixed ventilation systems in dairy barns. To address this gap, this study systematically analyzed the effects of external wind directions (0°, 45°, 90°, 135°, 180°) and speeds (1, 3, 5, 7, 10 m s⁻¹) on fan jet distribution and convective heat transfer around dairy cows using the open-source CFD platform OpenFOAM. By evaluating body surface airflow and regional convective heat transfer coefficients (CHTCs), this study quantitatively linked barn-scale airflow to animal heat dissipation. Results showed that both wind directions and speeds markedly influenced airflow and heat exchange. Under 0° wind direction, dorsal airflow reached 6.2 m s⁻¹ and CHTCs increased nearly linearly with wind speeds, indicating strong synergy between the fan jet and external wind. Crosswinds (90° wind direction) enhanced abdominal airflow (approximately 5.2 m s⁻¹), whereas oblique and opposing winds (135–180°) caused stagnation and reduced convection. The dorsal-to-abdominal CHTCs ratio ($R_{d/a}$) increased to about 1.6 under axial winds but decreased to 1.1 under cross-flow, reflecting reduced thermal asymmetry. Overall, combining axial and lateral airflow paths improves ventilation uniformity in naturally or mechanically ventilated dairy barns. The findings provide theoretical and technical support for optimizing ventilation design, contributing to energy efficiency, animal welfare, productivity, and the sustainable development of dairy farming under changing climatic conditions. Full article

Underwater robots demonstrate significant potential for hull biofouling removal. However, achieving uniform and damage-free cleaning remains a persistent challenge. The fixed arrangement of cleaning mechanisms, combined with the inherent non-uniformity of cavitation jet energy distribution, frequently results in inconsistent removal depths, leading to local over-cleaning or under-cleaning. To address this, this paper proposes an optimization framework to coordinate the robot’s motion with its cleaning mechanism. First, the flow field dynamics of the cavitation nozzle are elucidated using the Stress-Blended Eddy Simulation (SBES) turbulence model. Based on the Computational Fluid Dynamic (CFD) data, a Gaussian mapping model is constructed to quantify the relationship between jet erosion efficiency and robotic motion parameters. Furthermore, to resolve the multi-objective coverage parameter optimization problem, an improved hybrid metaheuristic algorithm—the Composite Cycloid Subtraction-Average-Based Optimizer (CCSABO)—is introduced to determine the optimal synchronization of forward and lateral velocities. Numerical simulations demonstrate the framework’s robustness across various fouling thickness scenarios and nozzle parameters. Notably, the CCSABO algorithm achieves a coverage rate of 99% and minimizes the uniformity index to 0.011, demonstrating superior consistency compared to traditional PSO and GWO methods. This improvement effectively mitigates the risk of hull damage while ensuring cleaning quality. Full article

The aerodynamic performance of unmanned aerial vehicles (UAVs) with non-conventional geometries is a growing area of interest, particularly for improving stability and maneuverability. This study investigates the influence of the dihedral angle on the aerodynamic behavior and overall performance of drones configured in an X-wing layout. Four configurations with dihedral angles of 0°, 15°, 30°, and 45° were analyzed to assess how varying the wing inclination affects flight characteristics. Computational fluid dynamics (CFD) simulations were conducted to evaluate the aerodynamic forces and moments acting on each configuration under controlled conditions. Following the aerodynamic analysis, a performance assessment was carried out to determine the implications of each dihedral angle on parameters such as range, endurance, rate of climb, angle of climb or turn rate. The results indicate that increasing the dihedral angle can enhance maneuverability but may lead to trade-offs in aerodynamic efficiency, particularly at higher angles. The 15° and 30° configurations demonstrated a favorable balance between maneuverability and performance. These findings provide insight into the design optimization of X-wing UAVs and highlight the potential of dihedral angle tuning as a means to tailor drone behavior for specific operational needs. Full article