Performance Evaluation of the RANS Models in Predicting the Pollutant Concentration Field within a Compact Urban Setting: Effects of the Source Location and Turbulent Schmidt Number

Computational Fluid Dynamics (CFD) is used to accurately model and predict the dispersion of a passive scalar in the atmospheric wind flow field within an urban setting. The Mock Urban Setting Tests (MUST) experiment was recreated in this work to test and evaluate various modeling settings and to form a framework for reliable representation of dispersion flow in compact urban geometries. Four case studies with distinct source locations and configurations are modeled using Reynolds-Averaged Navier-Stokes (RANS) equations with ANSYS CFX. The performance of three widely suggested closure models of standard k - epsilon, RNG k - epsilon, and SST k - omega is assessed by calculating and interpreting the statistical performance metrics with a specific emphasis on the effects of the source locations. This work demonstrates that the overprediction of the turbulent kinetic energy by the standard k - epsilon counteracts the general underpredictions by RANS in geometries with building complexes. As a result, the superiority of the standard k - epsilon in predicting the scalar concentration field over the two other closures in all four cases is observed, with SST k - omega showing the most discrepancies with the field measurements. Additionally, a sensitivity study is also conducted to find the optimum turbulent Schmidt number (Sc-t) using two approaches of the constant and locally variable values.

Automated summary

This study uses computational fluid dynamics to simulate the dispersion of a passive scalar in the urban wind flow and evaluates different modeling settings. The results show that the standard k - epsilon closure model outperforms the other two models in predicting scalar concentration field in complex urban geometries with building complexes.