Combining Wind-Tunnel and Field Measurements of Street-Canyon Flow via Stochastic Estimation

Abstract

We demonstrate how application of the stochastic estimation method can be employed to combine spatially well-resolved wind-tunnel particle image velocimetry measurements with instantaneous velocity signals from a limited number of sensors (six sonic anemometers located within the canyon in the present case) to predict full-scale flow dynamics in an entire street-canyon cross-section. The investigated configuration corresponds to a street-canyon flow in a neutrally stratified atmospheric boundary layer with the oncoming flow being perpendicular to the main canyon axis. Data were obtained during both full-scale and 1:200-scale wind-tunnel experiments. The performance of the proposed method is investigated using both wind-tunnel data and signals from five sonic anemometers to predict the velocity from the sixth one. In particular, based on analysis of the influence of the high-frequency velocity fluctuations on the quality of the reconstruction, it is shown that stochastic estimation is able to correctly reproduce the large-scale temporal features of the flow with the present set-up. The full dataset is then used to spatially extrapolate the instantaneous flow measured by the six sonic anemometers and perform detailed analysis of instantaneous flow features. The main features of the flow, such as the presence of the shear layer that develops over the canyon and the intermittent ejection and penetration events across the canyon opening, are well predicted by stochastic estimation. In addition, thanks to the high spatial resolution made possible by the technique, the intermittency of the main vortical structure existing within the canyon is demonstrated, as well as its meandering motion in the canyon cross-section. It is also shown that the canyon flow, particularly its spanwise component, is affected by large-scale fluctuations of low temporal frequency along the canyon axis. Finally, the proposed techniques based on wind-tunnel data can prove useful for a priori design of field experiments to determine the optimum location of sensors beforehand.