Transient zonal model for predicting indoor airflows in naturally ventilated buildings: A case study of hospital patient rooms

Abstract

Proper ventilation dilutes viral concentrations and reduces infection risk. Advanced simulation methods are needed to understand indoor airflow dynamics in naturally ventilated spaces, like hospital patient rooms. Predicting airflow distribution is complex due to factors such as variable opening sizes, changing weather conditions, and exhaust shaft locations. Simulation methods, such as Computational Fluid Dynamics (CFD), building energy simulation, and analytical mathematical models are used to address these challenges. Zonal models, in particular, bridge the gap between the simplicity of standard perfectly mixed room air assumptions and the computational intensity of CFD simulations. This research presents a case study of patient rooms in a hospital located in Romania. The study focuses on validating a coarse grid zonal model implemented in the building simulation tool IDA ICE for predicting indoor airflow in patient rooms with natural ventilation. The model is validated against field measurements of indoor air parameters in the patient room. This study demonstrates the capability of a one-dimensional transient zonal model integrated into building simulation software to predict main indoor air distribution patterns. This model requires minimal prior knowledge of airflow characteristics, making it a versatile tool for predicting indoor air quality in naturally ventilated hospital buildings. The method can identify risky areas for infection control and optimise ventilation in healthcare facilities.