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The wind pressure caused by the high-speed passage of intercity railway trains applies a significant load on the platform screen doors, to ensure the safe of the platform screen doors and achieve reasonable design and construction goals, it is necessary to explore the distribution law and mechanism of wind pressure load on the platform screen doors first. Using Fluent software, RNG k-ε turbulence model was selected, and the control equations were discretized by finite volume method, the second-order upwind scheme and center difference scheme were used to solve the convective and diffusive fluxes, the influence of wind pressure load on platform screen doors caused by intercity train passing through stations was studied. The research results indicates: When a train passes through a station at high speed, the increase in the ross-section area of the enclosed area allows the compressed air to dissipate faster, thereby reducing the wind pressure load on the platform screen doors and minimizing the extreme values of the wind pressure load on the doors; Before the train arrives at the platform screen door, the slight change in the retreat distance between the platform screen door and the platform edge has little effect on the extreme wind pressure load of the platform screen door; After the train arrives at the platform screen door, the airflow changes and distribution in the enclosed area become more severe and complex, the increase in cross-section area makes the wind pressure load of the platform screen door more significantly affected by the change in the retreat distance of the platform screen door; The increase in train passing speed will lead to an increase in the extreme wind pressure load on the platform screen doors; During the process of the train passing through the station, the maximum load at each point of the platform screen door decreases overall along the direction of the train's travel, however, as the speed of the train passing through the station increases, the minimum load (suction) near the end of the platform screen door in the direction of the train's travel tends to increase, and this phenomenon becomes more pronounced as the speed increases. [ABSTRACT FROM AUTHOR]

The wettability and stability of a solution's film on the filler surface are the key factors determining heat and mass transfer efficiency in liquid desiccant air conditioning systems. Therefore, this study investigates the effects of different air parameters on the flow behavior of a lithium chloride solution's film. The effects of air velocity, air flow pattern, and pressure on the wettability and critical amount of spray are discussed. The results show that the main mechanism by which the air velocity affects the wettability is that the shear stress generated by the direction of the air velocity disperses the direction of the surface tension and weakens its effect on the liquid film distribution. In addition, in the counter flow pattern, the air flow blocks the liquid film from spreading longitudinally and destroys the stability of the liquid film at the liquid outlet, which increases the critical amount of spray. The pressure distribution is similar under different operating pressures when the flow is stable; thus, pressure has little effect on wettability. The simulation results under 8 atm are compared with the experimental results. It is found that the sudden increase in the amount of moisture removal when the amount of spray changes from 0.05 to 0.1 m3/(m·h) in the experiment is caused by the change in the liquid film flow state. In addition, the results show that within the range of air flow parameters for the liquid desiccant air conditioning system, air flow shear force is not the main factor affecting the stability of the solution's film, and there is no secondary breakage of the solution's film during the falling-film flow process. [ABSTRACT FROM AUTHOR]

An unmanned underwater vehicle (UUV) powered by a compressed air power system is proposed to address challenges for battery/motor-powered vehicles under high-speed navigation, long endurance, and high mobility. These vehicles actively utilize supercavitation drag reduction by the exhausted gas from the compressed air power system. MATLAB/Simulink and FLUENT are used to establish theoretical models of the compressed air power system and ventilation supercavitation. The relationship between system power and navigation resistance is examined with different air flows, along with a comparison of endurance of different power vehicles at various speeds. The issue of the endurance-enhancing effect of supercavitation at high speed is investigated. The results demonstrate that increasing the air flow leads to higher power and reduced navigation resistance, and there is a balance between them. Furthermore, compared to the battery-powered vehicles with equal energy storage capacity, the compressed air power system shows 210.08% to 458.20% longer endurance times at speeds of 30 kn to 60 kn. Similarly, considering equal energy storage mass, it achieves 42.02% to 148.96% longer endurance times at high speeds (30 kn to 60 kn). The integration of supercavitation and air-powered systems can greatly enhance the endurance and maneuverability of the vehicle at high speeds while ensuring a compact system structure. The investigations could offer valuable ideas for the development and application of compressed air power systems for UUV at 30 kn to 60 kn or higher maneuvering. [ABSTRACT FROM AUTHOR]

Air compressors are widely used in industrial fields. Compressed air systems aggregate air flows and then supply them to places of demand. These huge systems consume a significant amount of energy and generate heat internally. Machine components in compressed air systems are vulnerable to heat, and, in particular, a radiator to cool the heat of the overall air compressor is the core component. Dirty radiators increase energy consumption due to anomalous cooling. To reduce the energy consumption of air compressors, this mechanism emphasizes a machine learning-based radiator fault detection, using features such as RPM, motor power, outlet pressure, air flow, water pump power, and outlet temperature with slight true fault labels. Moreover, the proposed system adds an LSTM-based motor power prediction model to point out the initial judgment of radiator fault possibility. Via the rigorous analysis and the comparison among machine learning models, this meticulous approach improves the performance of radiator fault prediction up to 93.0%, and decreases the mean power consumption of the air compressor around 2.24%. [ABSTRACT FROM AUTHOR]