Abstract
AbstractThe dispersion of particulate pollutants around buildings raises concerns due to adverse health impacts. Accurate prediction of particle dispersion is important for evaluating health risks in urban areas. However, rigorous validation data using particulate tracers is lacking for numerical models of urban dispersion. Many prior studies rely on gas dispersion data, questioning conclusions due to differences in transport physics. To address this gap, this study utilized a combined experimental and computational approach to generate comprehensive validation data on particulate dispersion. A wind tunnel experiment using particulate tracers measured airflow, turbulence, and particle concentrations around a single building, providing reliable but sparse data. Validated large eddy simulation expanded the data. This combined approach generated much-needed validation data to evaluate numerical particle dispersion models around buildings. Steady Reynolds-averaged Navier–Stokes (SRANS) simulations paired with Lagrangian particle tracking (LPT), and drift-flux (DF) models were validated. SRANS had lower accuracy compared to LES for airflow and turbulence. However, in this case, SRANS inaccuracies did not prevent accurate concentration prediction when LPT or a Stokes drift-flux model were used. The algebraic drift-flux model strongly overpredicted the concentration for large micron particles, indicating proper drift modeling was essential.
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
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