ABSTRACT
Existing studies on ventilation in closed spaces mainly considered the average inlet velocity and ignored the influence of inlet turbulent fluctuation. However, the variation in inlet turbulence intensity (TI) is considerable and significantly affects the dispersion of contaminants. This study conducts numerical simulations verified by experiments to investigate the effect of the inlet TI on train contaminants dispersion and analyze infection probability variation. Firstly, the unsteady Reynolds-averaged Navier-Stokes (URANS) method and improved delayed detached eddy simulation (IDDES) method are compared in simulating the internal airflow characteristics based on the on-site measurement. The results indicate that the latter dominates in capturing airflow pulsations more than the former, although the mean airflow results obtained from both methods agree well with experimental results. Furthermore, the IDDES method is employed to investigate the effect of the inlet TI on contaminant dispersion, and the infection risks are also assessed using the improved probability model. The results show that, with the increase of TI from 5 % to 30 %, the contaminant removal grows considerably, with the removal index rising from 0.23 to 1.86. The increased TI leads to the overall and local infection risks of occupants descending significantly, wherein the former decreases from 1.53 % to 0.88 % with a reduction rate of 42 %, and the latter drops from 3.30 % to 2.16 % with a mitigation rate of 35 %. The findings can serve as solid guidelines for numerical method selection in accurately capturing the indoor dynamic airflow distribution and for the ventilation parameters design regarding TI inside trains to mitigate the airborne infection risk.
ABSTRACT
The wear detection of the guide pair (GP) plays a key role in the safe operation of the mine hoist system. Due to the actual working conditions of the well, manual detection is still the main detection method for GP wear, which has the problems of time consumption, low detection accuracy, and being unable to realize real-time detection. In view of this situation, this paper studies a machine vision-based wear detection method of GP in a mine hoisting system. First, the wear detection algorithm of GP is designed by means of image correction, image preprocessing, and edge extraction. Then, the hardware of the detection system is selected and designed, and the interface of the upper computer is designed by LABVIEW. Finally, according to the actual underground working conditions, a test platform for the wear detection system is built, and the detection experiment is carried out. The results show that the method can detect the wear and the location of the GP's wear in real time. The maximum average error of the detection under three different wear conditions is 3.54%, which meets the requirements of the specified measurement accuracy. It can provide technical support for the automatic detection of the wear of GP in mine hoisting systems.
