Reconstructing Urban Wind Flows for Urban Air Mobility using Reduced-Order Data Assimilation

Uncrewed Aircraft Systems (UASs) are integrating the National Airspace System to conduct missions ranging from transport of people and goods to surveillance/inspection and data collection. The concept of UAS integration into urban environments has attracted large attention and investment in recent years and multiple challenges still exist to ensure the safety and efficiency of these operations. One challenge is accurately characterizing and predicting the microscale urban wind which, once addressed, will improve situational awareness, mission scheduling and client comfort. Multiple efforts have been dedicated to resolve this issue using mesoscale-microscale CFD coupled simulations. These simulations are A non-negligible source of uncertainty in these simulations is the initial conditions because of error propagation and growth. Herein, we implement a data assimilation approach to reduce discrepancies between the predicted urban wind speed using a CFD model with real-world, limited and sparse observations. An order reduction technique is used to reduce the memory cost of the computational process. These observations are simulated using a large eddy simulation (LES). This approach leads to error reduction throughout the simulated domain and the reconstructed field is different than the initial guess by ingesting wind speeds at sensor locations. The spatio-temporal impacts of the assimilation process are quantified. Different locations where wind sensors can be installed are discussed in terms of their impact on the resulting wind field. The impact of the assimilation on the hazardous areas for UAS navigation are discussed.