Modified Accuracy of RANS Modeling of Urban Pollutant Flow within Generic Building Clusters Using a High-Quality Full-Scale Dispersion Dataset

Abstract

To improve the reliability of the computational fluid dynamics (CFD) models of wind-driven pollutant dispersion within urban settings, a re-calibration study is conducted to optimize the standard k−ε model. A modified optimization framework based on the genetic algorithm is adapted to alleviate the computational expenses and to further identify ranges for each empirical coefficient to achieve the most reliable and accurate predictions. A robust objective function is defined, incorporating both the flow parameters and pollutant concentration through several linear and logarithmic measures. The coefficients are trained using high-quality and full-scale tracer experiments in a mock urban arrangement simulating a building array. The proposed ranges are 0.14≤Cμ≤0.15, 1.30≤Cε1≤1.46, 1.68≤Cε2≤1.80, 1.12≤σε≤1.20, and 0.87≤σk≤1.00. A thorough evaluation of the predicted flow and concentration fields indicates the modified closure is effective. The fraction of predictions within the acceptable ranges from measurements has increased by 8% for pollutant concentration and 27% for turbulence kinetic energy. The generality of the calibrated model is further tested by modeling additional cases with different meteorological conditions, in which the calculated validation metrics attest to the noteworthy improvements in predictions.