Uncertainty quantification for microscale CFD simulations based on input from mesoscale codes

Accurate predictions of wind and dispersion in the atmospheric boundary layer (ABL) can provide essential information to support design and policy decisions for sustainable urban areas. However, computational fluid dynamics (CFD) predictions of the ABL have several sources of uncertainty that can affect the results. An important uncertainty is the definition of the inflow boundary condition, which is influenced by larger scale weather phenomena. In this paper, we propose a method to quantify the effect of uncertainty in the inflow boundary conditions using input from an ensemble of mesoscale simulations. The mesoscale mean velocity and turbulent kinetic energy at the inflow of the CFD domain are used to define probability density functions for the uncertain wind direction and magnitude. A non-intrusive method is used to propagate these uncertainties to the quantities of interest. The methodology is applied to two different cases for which field experimental data are available: the Askervein hill and the Joint Urban 2003 measurements. For the latter case, the results are similar to those of a previous study that characterized the uncertain input parameters based on measurements. Hence, the results show that the proposed mesoscale simulation-based approach provides a valuable alternative in absence of sufficient measurement data.