2019
DOI: 10.3390/pr7050313
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In Situ Measurements and CFD Numerical Simulations of Thermal Environment in Blind Headings of Underground Mines

Abstract: In order to gain a knowledge of the heat emitted from a variety of sources at the blind heading of an underground gold mine, the present study conducts an in situ measurement study in a blind heading within the load haul dumps (LHDs) that are operating. The measurements can provide a reliable data basis for the setting of numerical simulations. The results demonstrate that the distances between the forcing outlet and the mining face (denoted as Zm), as well as the heat generation from LHDs (denoted as QL), has… Show more

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Cited by 19 publications
(18 citation statements)
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“…The excavation equipment also influences the range of air backflow effects, which is confirmed by the results of other studies [29,30]. Therefore, it is essential to consider equipment in the roadway during numerical modelling, especially with such a complex The excavation equipment also influences the range of air backflow effects, which is confirmed by the results of other studies [29,30]. Therefore, it is essential to consider equipment in the roadway during numerical modelling, especially with such a complex ventilation as in overlap auxiliary ventilation systems.…”
Section: Resultssupporting
confidence: 85%
“…The excavation equipment also influences the range of air backflow effects, which is confirmed by the results of other studies [29,30]. Therefore, it is essential to consider equipment in the roadway during numerical modelling, especially with such a complex The excavation equipment also influences the range of air backflow effects, which is confirmed by the results of other studies [29,30]. Therefore, it is essential to consider equipment in the roadway during numerical modelling, especially with such a complex ventilation as in overlap auxiliary ventilation systems.…”
Section: Resultssupporting
confidence: 85%
“…The realizable k-epsilon model considers two equation models that deal with turbulent kinetic energy, k , and its rate of dissipation, ε , which are coupled with turbulent viscosity. This model is represented as follows [ 32 ]: where G k represents the generation of turbulence kinetic energy due to the mean velocity gradients, m 2 /s 2 ; C 1 ε and C 2 ε are model constants; σ k and σ ε are the turbulent Prandtl numbers corresponding to the k equation and the ε equation, respectively; ρ is the density of the fluid, kg/m 3 ; µ t is the turbulent viscosity, represented by the following: …”
Section: Numerical Analysismentioning
confidence: 99%
“…The geometric model grid after optimization is shown in Figure 3, and the statistics of the skewness and orthogonal quality are shown in Table 1. The maximal skewness was lower than 0.95 and the minimal orthogonal quality was higher than 0.1, suggesting that a high-quality grid had been generated [32]. The efficiency and accuracy of a numerical simulation process are closely related to grid quality, thus establishing grid independence is a necessary condition for analysis [33].…”
Section: Grid Generation and Independence Testsmentioning
confidence: 99%
“…K-Epsilon model considers two equation model that deals with turbulent kinetic energy, k, and its rate of dissipation, ε, which is coupled with turbulent viscosity. This model is given as [ 30 ]: where G k represents the generation of turbulence kinetic energy due to the mean velocity gradients, m 2 /s 2 ; C 1ε and C 2ε are model constants; σ k and σ ε are the turbulent Prandtl numbers corresponding to the k equation and the ε equation, respectively; ρ is the density of the fluid, kg/m 3 ; µ t is turbulent viscosity given by: …”
Section: Modeling and Simulationmentioning
confidence: 99%