A CFD-based framework to assess airborne infection risk in buildings.

The COVID-19 pandemic has prompted huge efforts to further the scientific knowledge of indoor ventilation and its relationship to airborne infection risk. Exhaled infectious aerosols are spread and inhaled as a result of room airflow characteristics. Many calculation methods and assertions on risk assume 'well-mixed' flow conditions. However, ventilation in buildings is complex and often not showing well-mixed conditions. Ventilation guidance is typically based on the provision of generic minimum ventilation flow rates for a given space, irrespective of the effectiveness in the delivery of the supply air. Furthermore, the airflow might be heavily affected by the season, the HVAC ventilation, or the opening of windows, which would potentially generate draughts and non-uniform conditions. As a result, fresh air concentration would be variable depending upon a susceptible receptor's position in a room and, therefore, associated airborne infection risk. A computational fluid dynamics (CFD) and dynamic thermal modelling (DTM) framework is proposed to assess the influence of internal airflow characteristics on airborne infection risk. A simple metric is proposed, the hourly airborne infection rate (HAI) which can easily help designers to stress-test the ventilation within a building under several conditions. A case study is presented, and the results clearly demonstrate the importance of understanding detailed indoor airflow characteristics and associated concentration patterns in order to provide detailed design guidance, e.g. occupancy, supply air diffusers and furniture layouts, to reduce airborne infection risk.