The definition of boundary conditions for CFD simulations of urban dispersion is a major challenge, since they are determined by atmospheric conditions that are highly variable and uncertain. Improving the predictive capabilities of environmental flow simulations therefore requires quantifying the effect of these uncertainties on the simulation outcome.
The goal of this project is to establish a novel computational tool that accounts for uncertainties in the inflow boundary conditions and predicts urban air quality with a quantified confidence interval. The main focus is the definition of a parameterization for the inflow boundary conditions for CFD simulations in terms of random variables that can represent the large-scale variability in the atmospheric boundary layer. Initially measurement data was used to inform the definition of the parameterization; at present a method to use results from a Numerical Weather Prediction code (WRF) is being established. The methodology is validated by performing simulations of Oklahoma City, which can be compared with data from the Joint Urban 2003 field measurements. This research will inform more reliable decision-making tools for air quality control, emergency response planning, and urban planning, and is relevant to all wind engineering problems that require realistic predictions of the velocity field in the urban canopy.