An improved correlation for pressure drop in a tunnel under traffic jam using CFD

Eftekharian, Esmaeel; Abouali, Omid; Ahmadi, Goodarz

doi:10.1016/j.jweia.2015.04.013

Cited by 18 publications

(7 citation statements)

References 15 publications

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“…Standard deviations for values greater than Reynolds number of 3.5*106 vary from 14.2% to 20.3%. Despite that, these results are consistent with those presented in other works (Diego I., Torno S., Toraño J., Menéndez M. 2011;Eftekharian, Abouali, and Ahmadi 2015).…”

Section: Resultssupporting

confidence: 93%

“…Another way to calculate the friction factor is through the CFD, computational fluid dynamics. In this case, knowing the section of the mine, flow and roughness are solved by numerical methods, using the conservative equations of mass, energy and momentum, resulting in the pressure drop and the friction factor (Diego I., Torno S., Toraño J., Menéndez M. 2011;Eftekharian, Abouali, and Ahmadi 2015;Wackernagel, M.;Rees 1996;Sridharan and Sastry 2020). Two numerical methods for solving different equations are used; Finite element if the problem is focused to solids or static fluids and by finite control volume if focuses more fluid moves (flows).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Potash mining friction factor determination using CFD

Felipe

Costa

Bascompte

et al. 2022

Preprint

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Friction factor is a crucial parameter for assessing and modelling the ventilation system in underground mining, becoming essential as we deepen the mining activity. This parameter is related to the loss of energy due to air friction inside the mine and consequently to the energy consumption in the fans. So, it must properly calculate in order to reduce operating costs and thus improve mineral prices. This paper presents the calculation of the friction factor through a CFD model of a potash mine case study for different mean roughness and typical velocities. Results are compared with friction factors calculated through traditional empirical relationships. The comparison of both data has given some differences, especially for relative high Reynolds numbers, for low Reynolds numbers it is verified that there is also a dependence with himself. Thus, it is proposed a correction for calculate friction factor with Colebrook-White and Von Karman expressions for the particular case of potash mines.

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Section: Resultssupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Potash mining friction factor determination using CFD

Felipe

Costa

Bascompte

et al. 2022

Preprint

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“…Their results show that the average CO concentration under trafEc jam condition was higher than the permissible level in the vicinity of human breathing zone. Eftekharian et al 7 also obtained a correlation for pressure drop due to stopped cars inside the tunnel as a function of tunnel average air velocity and tunnel length from CFD simulation results, to optimize the estimation of pressure drop due to vehicles. With the advantage of visualization to provide details that is hard to obtain from onsite measurement, and cost-less than running scaled models, CFD techniques have been widely used to study pollutant transport and distribution in the tunnel under severe traffic jam conditions.…”

Section: Introductionmentioning

confidence: 99%

Numerical simulation of pollutant dispersion in urban roadway tunnels

Dong

Tao

Xiao

et al. 2017

The Journal of Computational Multiphase Flows

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Vehicular toxic emissions can easily contaminate the air quality of the enclosed tunnel environment, especially during rush hours with traffic jam events or low vehicle speeds, which poses serious health hazards to road utilizers. The piston effect generated by moving vehicles was normally considered adequate to discharge vitiated air out of short tunnel based on a typical driving speed. However, complex traffic conditions may yield unexpected consequences on in-tunnel air quality levels. This study numerically investigated the CO 2 concentration to identify the in-tunnel pollutant dispersion under three traffic conditions including severe traffic congestion and traffic flow with low vehicle speeds. Fan conditions were considered to model the influence of mechanical winds on pollutant dispersion and comparison with vehicular piston effect was also performed. The results revealed elevated pollutant concentration regions were found at the vicinity of near-ground region and tunnel downstream. The vehicular piston effect can sufficiently remove the in-tunnel vehicular emissions when vehicles travel at relatively higher speed. However, pollutant accumulation occurs when vehicles are idling or moving at slow speed. Compared with traffic piston effect at high travelling speed, the mechanical ventilation of ceiling mounted fans only generate a limited contribution to the removal of emissions.

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“…For longitudinal ventilation system, the drag coefficients CDi of vehicles are fixed equal to 1 (because the flow direction is from back to front of them, [8]). The cross section of vehicles was assumed equal to 2, 4 and 7 m 2 for cars, vans and trucks, respectively.…”

Section: One-dimensional Model For Ventilation System Designmentioning

confidence: 99%

“…Eftekharian et al [8] provided estimation of drag force related to the presence of vehicles inside the tunnel under traffic jam. Authors investigated several tunnels length under different traffic conditions by CFD model, they provided a new correlation for tunnel pressure drop as a function of average air velocity and tunnel length when air velocity profile was imposed at entrance of the tunnel.…”

Section: Introductionmentioning

confidence: 99%