Generalizability evaluation of k-ε models calibrated by using ensemble Kalman filtering for urban airflow and airborne contaminant dispersion

Abstract

The low accuracy of Reynolds-averaged Navier-Stokes (RANS) modeling of urban airflow and airborne pollutant dispersion is attributed to model flaws and uncertainty contributed by closure coefficients. Previous studies have attempted to improve the performance of RANS modeling by ad hoc calibration of the turbulence model coefficients specifically for urban problems. However, these models failed to accurately reproduce the key features like the reattachment lengths. In addition, there was a lack of generalizability evaluations of the calibrated models. To optimize the accuracy and generalizability of the turbulence model, this study considered the effects of optimization objectives and model formulations. Six k-ε based turbulence models calibrated using the ensemble Kalman filtering (EnKF) approach were compared. A wind tunnel experiment consisting of key features of the airflow around a single-building model was conducted to provide the training data. The proposed optimization objective prioritizing the reproduction of the reattachment lengths in the calibrations enabled the calibrated models to capture the key features of the airflow with better accuracy. The six turbulence models before and after the calibrations were compared for a single-building test case and a building-array test case with respect to the reattachment lengths, velocity, turbulence kinetic energy, and airborne contaminant concentration. The calibrated models with different formulations exhibit distinctive generalizability. The calibrated Murakami-Mochida-Kondo k-ε model (MMK) exhibited strong potential for generalizability to single-building problems. However, the generalization from the single-building isolated roughness flow regime to the street canyon skimming flow regime is limited.