A comparison of mesh-adaptive LES with wind tunnel data for flow past buildings: Mean flows and velocity fluctuations

In this paper we address two important aspects of micro-scale urban airflow model evaluation: (a) the identification of key flow features as dictated by the physics of the problem and as captured by the simulations, and (b) the comparison of important model output parameters (mean flows and fluctuations) with experimental data. A series of mesh-adaptive large eddy simulations (LES) was carried out for the study of air flows within two intersecting street canyons with varying building configurations. The novelty of the approach lies in the combination of LES with mesh adaptivity, which allows a variable-filter length and the implementation of an anisotropic eddy-viscosity model. Both coarse and fine-mesh simulations were carried out, using single and parallel-processor systems respectively. The simulations showed clearly that the expected flow patterns such as the street canyon recirculation and the street-mouth vortices, as well as the exchange of air flow at the street intersections, can readily be captured by the mesh-adaptive LES. ition, the detailed comparisons of mean flows and fluctuations of the resolved velocity field with the measured data showed that the simulation results agreed well with the patterns and trends of the wind tunnel measurements. In most cases the finer-mesh simulations improved considerably the accuracy of the mean flows, especially for the symmetrical configuration. The improvement in the predicted fluctuations was less obvious, with several detector locations underpredicting the measured values, although the overall comparison was also satisfactory. The typical errors for the mean flows for all three building configurations were less than 30%, whilst for the velocity fluctuations less that 40%. Both the simulated means flows and turbulence levels were generally more accurate in the streets parallel to the wind (streamwise direction) than in the streets normal to the wind.