Similarity theory for wind and buoyancy combined natural ventilation using CFD simulations

Natural ventilation has gained popularity in response to the increasing need for a sustainable and healthy built environment, but the design of a naturally ventilated building remains challenging due to the high variability in a building's operating conditions, i.e., wind and buoyancy. In the current study, we propose leveraging similarity theory to characterize the operating conditions with one non-dimensional number, the Richardson number, such that we can efficiently deal with this variability in computational fluid dynamics (CFD) models. We first validate high-fidelity large-eddy simulations (LES), which precisely resolve the flow and temperature fields, against full-scale ventilation rate measurements using the tracer concentration decay technique. Then, we verify the existence of an empirical relationship that quantifies the natural ventilation flow rate as a function of the Richardson number under two different scenarios: daytime with indoor temperature stratification and nighttime with uniform indoor temperature. The proposed relationship will enable us to characterize the ventilation rates under a wide range of operating conditions based on a limited number of simulations with different Richardson numbers.