This study aims to investigate the characteristics of gas-liquid-solid three-phase flow in an Internal Circulation (IC) anaerobic reactor during the treatment of wastewater. Through computational fluid dynamics (CFD) simulations of the gas-liquid-solid three-phase in the first reaction chamber and based on the anaerobic granule swarms drag coefficient model, the study investigates the effects of superficial liquid velocity and superficial gas velocity on granules distribution, uniformity index, gas holdup, flow velocities of each phase, and the dimensionless variance of residence time distribution. In addition, the relationship between the fully mixed superficial velocities of gas and liquid in the first reaction chamber is also determined.

Predicting pollutant leakage and diffusion processes is crucial for ensuring people\'s safety. While the deep learning method offers high simulation efficiency and superior generalization, there is currently a lack of research on predicting pollutant leakage and diffusion flow field using deep learning. Therefore, it is necessary to conduct further studies in this area. This paper introduces a two-level network method to model the flow characteristics of pollutant diffusion. The proposed method in this study demonstrates a significant enhancement in flow field prediction accuracy compared to traditional deep learning methods. Moreover, it improves computational efficiency by over 800 times compared to traditional computational fluid dynamics (CFD) methods. Unlike conventional CFD methods that require grid expansion to calculate all operation conditions, the deep learning method is not confined by grid limitations. While deep learning methods may not entirely replace CFD methods, they can serve as a valuable supplementary tool, expanding the versatility of CFD methods. The findings of this research establish a robust foundation for incorporating deep learning methods in addressing pollutant leakage and diffusion challenges.

The present investigation examines the usage of rectangular multi-injectors for fuel injection in a supersonic combustion chamber. To evaluate the fuel jet penetration and distribution, a computational method is applied to model the supersonic compressible flow with cross multi-fuel jets released from annular rectangular nozzles with different nozzle configurations. The main effort of this work is to evaluate the jet interactions in the existence of cross-supersonic flow. Fuel jet penetration and distribution are evaluated for three proposed injector arrangements to attain the more efficient option for better fuel mixing. Our results show that reducing injector space improves fuel mixing inside the combustor via creation of strong vortices. Beside, injection of air from internal nozzle increase fuel interactions and fuel mixing inside combustion chamber.

The velocity field and temperature field are crucial for metal foams to be used as a heat exchanger, but they are difficult to obtain through physical experiments. In this work, the fluid flow behavior and heat transfer performance in open-cell metal foam were numerically studied. Porous 3D models with different porosities (55-75%) and pore sizes (250 μm, 550 μm, and 1000 μm) were created based on the porous structure manufactured by the Lost Carbonate Sintering method. A wide flow velocity range from 0.0001 m/s to 0.3 m/s, covering both laminar and turbulent flow regimes, is fully studied for the first time. Pressure drop, heat transfer coefficient, permeability, form drag coefficient, temperature and velocity distributions were calculated. The calculated results agree well with our previous experimental results, indicating that the model works well. The results showed that pressure drop increased with decreasing porosity and increasing pore size. Permeability increased and the form drag coefficient decreased with increasing porosity, and both increased with increasing pore size. The heat transfer coefficient increased with increasing velocity and porosity, whereas it slightly decreased with increasing pore size. The results also showed that at high velocity, only the metal foam close to the heat source contributes to heat dissipation.

Aspergillus niger (A. niger) spores can induce numerous health problems. Once the airflow-imposed drag force on an A. niger spore exceeds its binding force with the colony, the spore is detached. Turbulent flow may considerably increase the spore detachment. No method is currently available for prediction of the drag force on a spore and its detachment in turbulent flows. This investigation measured the turbulent velocities and detachment of A. niger colonies in a wind tunnel. Computational fluid dynamics (CFD) was employed to model an A. niger unit subjected to turbulent flow blowing. The top 1 % quantile instantaneous velocity of the turbulent flow was specified as the steady entry flow boundary condition for solving the peak velocity distribution and the peak drag forces onto spores. The predicted spore detachment ratios were compared with the measurement data for model validation. The results revealed that the spore detachment ratios with a turbulence intensity of 17 % to 20 % can be twice to triple the ratio with a turbulence intensity of approximately 1 %, when the average velocity for blowing remains the same. The proposed CFD model can accurately predict the detachment ratios of the A. niger spores. ENVIRONMENTAL IMPLICATION: Some people are sensitive to the Aspergillus niger (A. niger) spores, and excessive exposure can cause nasal congestion, skin tingling, coughing, and even asthma. Turbulent flow can considerably increase the spore detachment, due to the increased airflow-imposed drag force on the spores during turbulence. This investigation developed a numerical model to solve for the peak velocity distribution and the peak drag forces onto spores in turbulent flows to predict the spore detachment. With the numerical tool, the airborne fungal spore concentrations would be predictable, which paves a way for intelligent and precise control of fungal aerosol pollution.

Carotid atherosclerosis is a major cause of stroke. Hemodynamic forces, such as shear stress and oscillatory shear, play an important role in the initiation and progression of atherosclerosis. The alteration of the immune microenvironment is the fundamental pathological mechanism by which diverse external environmental factors impact the formation and progression of plaques. However, Current research on the relationship between hemodynamics and immunity in atherosclerosis still lack of comprehensive understanding. In this study, we combined computational fluid dynamics (CFD) and Mass cytometry (CyTOF) technologies to explore the changes in the immune microenvironment within plaques under different hemodynamic conditions. Our results indicated that neutrophils were enriched in adverse flow environments. M2-like CD163+CD86+ macrophages were predominantly enriched in high WSS and low OSI environments, while CD163-CD14+ macrophages were enriched in low WSS and high OSI environments. Functional analysis further revealed T cell pro-inflammatory activation and dysregulation in modulation, along with an imbalance in M1-like/M2-like macrophages, suggesting their potential involvement in the progression of atherosclerotic lesions mediated by adverse flow patterns. Our study elucidated the potential mechanisms by which hemodynamics regulated the immune microenvironment within plaques, providing intervention targets for future precision therapies.

UNASSIGNED: This study aimed to efficiently reduce the release of radon from water bodies to protect the environment.
UNASSIGNED: Based on the sizes of the experimental setup and modular float, computational fluid dynamics (CFD) was used to assess the impact of the area coverage rate, immersion depth, diffusion coefficient, and radon transfer velocity at the gas-liquid interface on radon migration and exhalation of radon-containing water. Based on the numerical simulation results, an estimation model for the radon retardation rate was constructed. The effectiveness of the CFD simulation was evaluated by comparing the experimental and simulated variation values of the radon retardation rate with the coverage area rates.
UNASSIGNED: The effect of radon transfer velocity on radon retardation in water bodies was minor and insignificant according to the appropriate value; therefore, an estimation model of the radon retardation rate of the coverage of a radon-containing water body was constructed using the synergistic impacts of three factors: area coverage rate, immersion depth, and diffusion coefficient. The deviation between the experimental and simulated results was < 4.3%.
UNASSIGNED: Based on the numerical simulation conditions, an estimation model of the radon retardation rate of covering floats in water bodies under the synergistic effect of multiple factors was obtained, which provides a reference for designing covering floats for radon retardation in radon-containing water.

In this paper, a precise and efficient method to optimize corrugated tube heat exchangers is proposed by combining computational fluid dynamics simulation with optimization. The optimization of tubular heat exchangers involves contradictory Colburn coefficient j, and the friction coefficient f, so it is a multi-objective optimization problem. The approximate model is obtained by an extreme learning machine, and the structure parameter of the heat exchanger is optimized by the nondominated sorting genetic algorithm-Ⅱ. Compared to the results between the original and optimized tube, the optimized structure Colburn coefficient increased by 5.1 % and the friction coefficient decreased by 9.3 %. Finally, the internal flow field is compared qualitatively from temperature, pressure, and velocity. The optimization effect is further emphasized by using the field synergy theory.

Presently, the hydroxyl radical oxidation mechanism is widely acknowledged for the degradation of organic pollutants based on hydrodynamic cavitation technology. The presence and production mechanism of other potential reactive oxygen species (ROS) in the cavitation systems are still unclear. In this paper, singlet oxygen (1O2) and superoxide radical (·O2-) were selected as the target ROS, and their generation rules and mechanism in vortex-based hydrodynamic cavitation (VBHC) were analyzed. Computational fluid dynamics (CFD) were used to simulate and analyze the intensity characteristics of VBHC, and the relationship between the generation of ROS and cavitation intensity was thoroughly revealed. The results show that the operating conditions of the device have a significant and complicated influence on the generation of 1O2 and ·O2-. When the inlet pressure reaches to 4.5 bar, it is more favorable for the generation of 1O2 and ·O2- comparing with those lower pressure. However, higher temperature (45 °C) and aeration rate (15 (L/min)/L) do not always have positive effect on the 1O2 and ·O2- productions, and their optimal parameters need to be analyzed in combination with the inlet pressure. Through quenching experiments, it is found that 1O2 is completely transformed from ·O2-, and ·O2- comes from the transformation of hydroxyl radicals and dissolved oxygen. Higher cavitation intensity is captured and shown more disperse in the vortex cavitation region, which is consistent with the larger production and stronger diffusion of 1O2 and ·O2-. This paper shed light to the generation mechanism of 1O2 and ·O2- in VBHC reactors and the relationship with cavitation intensity. The conclusion provides new ideas for the research of effective ROS in hydrodynamic cavitation process.

The \"spatial pattern-wind environment-air pollution\" within building clusters is closely interconnected, where different spatial pattern parameters may have varying degrees of impact on the wind environment and pollutant dispersion. Due to the complex spatial structure within industrial parks, this complexity may lead to the accumulation and retention of air pollutants within the parks. Therefore, to alleviate the air pollution situation in industrial parks in China and achieve the circular transformation and construction of parks, this study takes Hefei Circular Economy Demonstration Park as the research object. The microscale Fluent model in computational fluid dynamics (CFD) is used to finely simulate the wind flow field and the diffusion process of pollutants within the park. The study analyzes the triad relationship and influence mechanism of \"spatial pattern-wind environment-air pollution\" within the park and studies the influence of different spatial pattern parameters on the migration and diffusion of pollutants. The results show a significant negative correlation between the content of pollutants and wind speed inside the industrial park. The better the wind conditions, the higher the air quality. The spatial morphology parameters of the building complex are the main influences on the condition of its internal wind environment. Building coverage ratio and degree of enclosure have a significant negative correlation with wind conditions. Maintaining them near 0.23 and 0.37, respectively, is favorable to the quality of the surrounding environment. Moreover, the average height of the building is positively correlated with the wind environment condition. The rate of transport and dissipation of pollutants gradually increases as the average building height reaches 16 m. Therefore, a reasonable building planning strategy and arrangement layout can effectively improve the wind environment condition inside the park, thus alleviating the pollutant retention situation. The obtained results serve as a theoretical foundation for optimizing morphological structure design within urban industrial parks.