Widely used for permitting new emission sources (e.g., power plants) and assessing human exposure, atmospheric dispersion models do not perform well when the facility stack is relatively low and the pollution plume would be affected by the building wake, such as biomass boilers, diesel backup generators and residential woodstoves, which was also known as building downwash. The current building downwash preprocessor, Plume Rise Model Enhancements (PRIME), in a regulatory model, AMS/EPA Regulatory Model (AERMOD), tends to underestimate the ground-level concentrations (GLCs). Our flow visualization indicated that the flow pattern under the oblique wind was featured by a sidewash-downwash (S-D) vortex, which determines the near-wake concentration field. Thus, we introduced the concept of “sidewash” to highlight the effect of oblique wind conditions, which can cause higher GLCs than those under the perpendicular wind. We presented a new parameterization method, named the mixture model, to represent the concentration field in the downstream of a box-shape building with a short stack under different wind directions. This model was developed on the flow and concentration fields analysis using a computational fluid dynamics (CFD) technique, Embedded Large Eddy Simulation (ELES), and evaluated against experimental data. With both downwash and sidewash, the new parameterization showed promising performance for the low stack height under both perpendicular and oblique wind directions and successfully captured the lateral shifts of the plume under oblique wind directions and continuous concentration distribution along the longitudinal ground-level receptors, which could not be reflected by using the current PRIME. We showed that CFD-generated training data do not need to perfect to provide valuable insight into the parameterization.
Publications
Yang, B., Gu, J. and Zhang, K.M. Parameterization of the building downwash and sidewash effect using a mixture model. Building and Environment, 172: 106694, 2020
Gu, J., Yang, B., and Zhang, K. M. Spatial-aware source estimation in building downwash environments, Building and Environment, 134: 146-154, 2018.
