Optimization of CO Sensor Carrying Position of Mine Intelligent Inspection Vehicle

doi:10.12068/j.issn.1005-3026.2024.05.015

Abstract

Abstract:

To improve the reliability of CO volume fraction monitoring data collected by mine intelligent inspection vehicles， the wind flow distribution and the spatial‐temporal evolution law of CO volume fraction in the excavation roadway at the -630 m level of Jiaojia Gold Mine in Shandong province are analyzed by using a computational fluid dynamics （CFD） software. The study results show that the wind flow in the zone between the ventilation duct’s mouth and the heading face is unstable， leading to the formation of the vortices that disrupt the volume fraction distribution of gas. The CO transducer should be carried as high as possible in the car body because the CO volume fraction peak is mostly at the top of the roadway under the effect of self‐characterization and laminar flow near the wall. The variation of CO volume fraction on both sides is unstable during the attenuation section， thus the inspection vehicle should be put in the middle of the roadway when travelling. The simulation results are validated by comparing the attenuation variation of CO volume fraction with ventilation time in an excavation roadway.

Key words: mine intelligent inspection vehicle, CO sensor, numerical simulation, excavation roadway, safety engineering

CLC Number:

X 936

Jin-rui ZHANG, Xi-wen YAO, Kai-li XU, Xiu SUN. Optimization of CO Sensor Carrying Position of Mine Intelligent Inspection Vehicle[J]. Journal of Northeastern University(Natural Science), 2024, 45(5): 721-728.

Figures/Tables 12

Fig.1 Geometry model of the construction roadway

Fig.2 Construction roadway after grid discretization

Fig.3 Comparison between the results of simulation and literature at 45 m from the heading face

Fig.4 Comparison between the results of simulation and literature at 35 m from the heading face

Fig.5 Cross section and 3D model of the excavation roadway

Table 1 Fluent numerical simulation parameters settings

边界条件	参数设定
紊流模型	RNG k-ε模型
重力加速度/（m·s^-2）	9.81
时间模型	非稳态
壁面函数	标准壁面函数
压力速度耦合方法	压力隐式算子分裂法
能量方程	开启
温度/K	297
组分输运模型	CO-air
CO扩散系数/（m²·s^-1）	1.9×10^-5
入口边界条件	速度入口
入口速度/（m·s^-1）	17.7
紊流动能/（m²·s^-2）	1.57
紊流耗散率/（m²·s^-3）	19.6
出口边界条件	压力出口
出口表压/Pa	0
CO初始质量分数	0.004 8

Fig.6 Wind flow distribution near the heading face

Fig.7 CO mean volume fraction variation curves

Fig.8 CO volume fraction distribution nephogram of cross section A

Fig.9 CO volume fraction distribution nephogram of cross section C

Fig.10 Inspection vehicle equipped with mechanical arm

Fig.11 CO volume fraction variation at monitoring points

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