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We aim to advance understanding and predictive modeling of wind flow in urban areas through collaborative learning and research.
Our research focusses on establishing multi-scale and multi-fidelity modeling frameworks that incorporate uncertainty quantification and data assimilation, and on investigating how these tools can effectively support sustainable urban and building design.
Our research projects span across a variety of applications, including wind loading, natural ventilation, and pollutant dispersion. Most projects involve quantifying and reducing uncertainties, with a focus on inflow condition or turbulence model uncertainty. Our ultimate objective is to validate our CFD predictions with field measurements.

Sensitivity of LES predictions of wind loading on a high-rise building to the inflow boundary condition.

Wireless sensor network installed on the space needle
