A Probabilistic Representation of Wind Data for Natural Ventilation Estimation

With the focus on low energy and sustainable buildings today, building designers, engineers and researchers alike increasingly attempt to incorporate natural ventilation in innovative building practices. Despite the interests and collective effort, one key component of natural ventilation, the wind, has proven to be a difficult riddle to solve due to its unsteady nature. One difficulty with predicting wind-driven airflow is the determination of the amount of wind power available as a driving force for ventilation purposes. While it is common practice to assume the wind is steady and often hourly averages from TMY3 weather data are used for wind estimation, such assumption could be error prone due to variability of wind within the one-hour interval provided by the TMY3 data.To specifically investigate this issue, this study incorporates the much finer wind data from the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) Automated Surface Observing System (ASOS), location-specific weather datasets with 1-minute resolution. Incorporation of such small time steps allows a probabilistic interpretation of how wind would have impact the nature ventilation design. Furthermore, computational fluid dynamics (CFD) was used to investigate the potential wind-driven ventilation flow rate based on this new statistics based wind data, and a comparison to the current state of the art estimation is provided.