A comparison of a range of models for dispersion in a partially stratified room
The QUESTOR Centre and The School of Computer Science, The Queen's University of Belfast, Belfast BT7 1NN, UKEnvironmental Modelling and Software (Impact Factor: 4.42). 04/2008; 23(4):511-519. DOI: 10.1016/j.envsoft.2007.07.003
A comparative study of models used to predict contaminant dispersion in a partially stratified room is presented. The experiments were carried out in a ventilated test room, with an initially evenly dispersed pollutant. Air was extracted from the outlet in the ceiling of the room at 1 and 3 air changes per hour. A small temperature difference between the top and bottom of the room causes very low air velocities, and higher concentrations, in the lower half of the room. Grid-independent CFD calculations were compared with predictions from a zonal model and from CFD using a very coarse grid. All the calculations show broadly similar contaminant concentration decay rates for the three locations monitored in the experiments, with the zonal model performing surprisingly well. For the lower air change rate, the models predict a less well mixed contaminant distribution than the experimental measurements suggest. With run times of less than a few minutes, the zonal model is around two orders of magnitude faster than coarse-grid CFD and could therefore be used more easily in parametric studies and sensitivity analyses. For a more detailed picture of internal dispersion, a CFD study using coarse and standard grids may be more appropriate.
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ABSTRACT: Numerical simulation of air pollution dispersion inside tunnels is a suitable method for studying air pollutants’ spatial distribution and for evaluating the tunnel’s ventilation efficiency. In the present work, Fluent 6.2, a computational fluid dynamic software, has been used for full-scale numerical simulation of air flows and carbon monoxide (CO) concentrations inside the Resalat Tunnel of Tehran. Fans and vehicles are simulated with source of momentum and porous jump boundary conditions, respectively. Also, source of air pollutants, i.e., vehicle emissions, is simulated as uniform area source in the tunnel floor. Modeling results for concentrations of CO are validated by measurement data in 24 points adjacent to the fans inside the tunnel. Calibration of model indicated that the moving wall porous jump method for simulation of vehicle geometry and their effects, momentum source for fans modeling, standard k − ϵ scheme for turbulence modeling, and hexahedral mesh type are proper choices for the developed model. The results show a good correlation (R = 0.9) between modeling and measurement data. Five different scenarios (namely 1–vehicles to be stopped, 2–fans off, 3–two vertical ventilation ducts considered in the tunnel, 4–vehicles with Euro-IV emissions standard instead of Euro-II, and 5–blowing power of fans increased to twice the present) are examined for CO concentrations inside the tunnel. The numerical simulations for these scenarios are modified using a relation between measurement data and modeling results. Following the modification, results show that at the last measurement point (near the end of the tunnel), concentrations of CO is 59, 77, 9, 23, and 14 ppm for the five mentioned scenarios, respectively, and it is 32 ppm for normal condition. Hence, appropriate measures may be undertaken by the city authorities for air quality improvements in urban tunnels.Environmental Modeling and Assessment 10/2012; 17(5). DOI:10.1007/s10666-012-9308-4 · 0.98 Impact Factor
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