Abstract
Accurate and reliable computational fluid dynamics (CFD) simulation of pollutant dispersion is essential for protecting human health, and the choice of turbulence model is an important parameter determining the accuracy of simulation results. This paper evaluates the ability of Reyonds stress model (RSM) to predict dispersion of carbon dioxide (CO2) cloud, which is a typical type of heavy gas and similar to some particulate pollutants, in flat and urban terrains. The RSM simulation is conducted with stress-ω model, whereas SST k-ω two-equation model is selected as the benchmark. The simulation results are compared with the available wind tunnel measurements, and statistical performance indicators are used to obtain a comprehensive and quantitative evaluation of the performances of the two turbulence models. The results reveal that stress-ω model exhibits different capacities in flat terrain and urban terrain. Specifically, stress-ω model can present better results than SST k-ω model in flat terrain, and it performs better in the far-field region than in the near-field region. Although SST k-ω model can describe CO2 dispersion more accurately in urban terrain, the concentration distribution reproduced by stress-ω model is still within acceptable range.
Highlights
Dispersion of pollutant represents a major threat to human health, and the precise prediction of pollutant concentration distribution is a prerequisite for avoiding adverse air quality impacts (Zhang et al, 2011; Dodla et al, 2017; Guttikunda and Jawahar, 2018)
Accurate and reliable computational fluid dynamics (CFD) simulation of pollutant dispersion is essential for protecting human health, and the choice of turbulence model is an important parameter determining the accuracy of simulation results
This paper evaluates the ability of Reyonds stress model (RSM) to predict dispersion of carbon dioxide (CO2) cloud, which is a typical type of heavy gas and similar to some particulate pollutants, in flat and urban terrains
Summary
Dispersion of pollutant represents a major threat to human health, and the precise prediction of pollutant concentration distribution is a prerequisite for avoiding adverse air quality impacts (Zhang et al, 2011; Dodla et al, 2017; Guttikunda and Jawahar, 2018). Numerical simulation of CO2 cloud dispersion in flat and urban terrains were carried out by means of SST k-ω model and stress-ω model, and the results were tested against the available experimental data obtained from the literatures (Xing et al, 2013; Tan et al, 2018).
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