Computational urban flow predictions with Bayesian inference: Validation with field data

Abstract Urban areas are projected to expand at a rapid pace. In the context of supporting sustainable design of cities and buildings, computational fluid dynamics (CFD) can be used to provide detailed information on the urban flow field. However, the complexity and natural variability of atmospheric boundary layer flows can limit the predictive performance of CFD. In this paper, we present a validation study for a Bayesian inference method that estimates the inflow boundary conditions for Reynolds-averaged Navier-Stokes (RANS) simulations of urban flow by assimilating data from urban sensor measurements. The method employs the ensemble Kalman filter to iteratively estimate the probability density functions of the incoming wind and improve the subsequent RANS prediction. The measurements used in this study were obtained during a full-scale experimental campaign on Stanfords campus. Six sonic anemometers were deployed at roof and pedestrian level; a subset of the sensors was used for data assimilation while the remaining ones were used for validation. The accuracy of the proposed inference method is compared to the conventional approach that defines the boundary conditions based on weather station data. The hit rates increased by a factor of two when using the inference method, and the predicted mean values were ∼ 20% more likely to be within the 95% confidence interval of the experimental data. An analysis of the impact of the number of sensors and their location indicates that the assimilation approach can consistently improve the predictions, as long as the inlet flow properties are identifiable from the sensor measurements.