Fluid mechanics is fundamentally important to several aspects of the design of sustainable and resilient urban environments. The flow pattern in an urban canopy for example determines pedestrian wind comfort, urban heat island effects and thermal comfort, air quality and optimal building ventilation strategies, and wind loading on buildings. Computational fluid dynamics (CFD) provides a unique tool to study these problems because of its capability to provide a complete solution for the flow field in complex configurations. However, the significant variability and uncertainty in the operating and design conditions poses a challenge when interpreting results as a basis for design decisions. Our research therefore aims to develop modeling frameworks that can quantify the uncertainty in CFD simulation results, and support designers and policy makers in the design of sustainable building and cities.
The definition of boundary conditions for CFD simulations of urban dispersion is a major challenge, since they are determined by atmospheric conditions that are highly variable and uncertain. This project aims to improve the predictive capabilities of environmental flow simulations by quantifying the effect of these uncertainties on the simulation outcome.
Natural ventilation has the potential to significantly reduce energy consumption in buildings, but the design of a robust natural ventilation system remains a challenging task. The goal of this project is to develop a multi-fidelity computational model that enables predicting the performance of natural ventilation systems, and to validate the results with measurements from the Y2E2 building.
Turbulent heat transfer modeling
Turbulence and turbulent heat or scalar transfer are essential processes in many fluid dynamics problems relevant to sustainable urban environments. The objective of this project is to develop a turbulence model form uncertainty quantification method for RANS simulations with heat transfer.
Wind and Air Quality Measurements
Acquiring field measurement data for validation is of fundamental importance to our overall research goal. In addition, the rapid increase in urban sensing network capabilities creates exciting opportunities for using data assimilation methods to reduce uncertainty in the predictions. This project therefore aims at implementing an array of sensors for wind, temperature and air quality on Stanford’s engineering quad.