Large-eddy simulation of street canyons and urban microclimate using Uintah:MPMICE
Thursday, 18 December 2014
Urban microclimate plays an important role in urban water use, energy use, pollutant transport, and the general comfort and well-being of urban inhabitants. The microclimate interacts locally with urban morphology, water levels, properties of urban surfaces, and vegetation cover all of which contribute significantly to the strong spatial variability observed in urban areas. Considerable parts of urban open spaces take the form of street canyons. These urban street canyons play a remarkable role in creating urban microclimates. Within street canyons themselves, a wide variety of phenomena contribute to complex flow patterns. These include various flow structures such as wake fields, circulation zones, isolated roughness flow, wake interference and skimming flows. In addition, heat fluxes from the buildings and the surrounding area enhance the complexity of the flow field inside the canyon. Here, we introduce Uintah:MPMICE for the simulation of fluid structure interactions in urban flows. Uintah:MPMICE has been developed in a massively parallel computational infrastructure, uses material points to represent buildings, and the large-eddy simulation (LES) technique to represent momentum and scalar transport. To validate Uintah:MPMICE, simulations of typical street canyons are compared against published wind tunnel particle imaging velocimetry (PIV) data for the cases of step-up and step-down street canyons. Our findings show promising results in capturing major flow features, namely wake fields, recirculation zones, wake interference, vortex structures, and flow separation in street canyons. LES results demonstrate the ability of the simulations to predict flow topology details such as secondary circulation zones and wall-originating elevated shear layers in step-up and step-down cases, respectively. Furthermore, mean flow and variance statistics indicate sensitivity to inlet boundary conditions; upstream turbulence generation method, in particular, has a significant impact on the LES results.