Improving urban flow predictions through data assimilation

Detailed aerodynamic information of local wind flow patterns in urban canopies is essential for the design of sustainable and resilient urban areas. Computational Fluid Dynamics (CFD) can be used to analyze these complex flows, but uncertainties in the models can negatively impact the accuracy of the results. Data assimilation, using measurements from wind sensors located within the urban canopy, provides exciting opportunities to improve the quality of the predictions. The present study explores the deployment of several wind sensors on Stanford's campus to support future validation of CFD predictions with uncertainty quantification and data assimilation. We focus on uncertainty in the incoming wind direction and magnitude, and identify optimal sensor placement to enable accurate inference of these parameters. First, a set of Reynolds-averaged Navier-Stokes simulations is performed to build a surrogate model for the local velocity as a function of the inflow conditions. Subsequently, artificial wind observations are generated from realizations of the surrogate model, and an inverse ensemble Kalman filter is used to infer the inflow conditions from these observations. We investigate the influence of (1) the sensor location, (2) the number of sensors, and (3) the presence of noise or a bias in the measurement data. The analysis shows that multiple roof level sensors should enable robust assimilation of the inflow boundary conditions. In the future field experiment, sensors will be placed in these locations to validate the methodology using actual field measurement data.