Bioaerosol particles in indoor air are related to airborne transmission infections and some pandemic outbreaks such as Severe Acute Respiratory Syndrome (SARS) in 2003 and Middle East Respiratory Syndrome (MERS) in 2015. Several environmental control strategies and parameters for a ventilation system have been suggested to prevent infections in building environments. To design an appropriate ventilation system, the infection risks of the proposed ventilation system were evaluated in the thesis in order to achieve effective infection control. Computational fluid dynamics (CFD) simulation is often used to predict the dispersions and depositions of bioaerosol particles to evaluate the infection risks of ventilation systems. However, there are differences between bioaerosol and aerosol particles in terms of shape, diameter, surface texture and elasticity. In this study, the transport mechanism of a bioaerosol particle was investigated to formulate a bioaerosol particle transport model for CFD simulation. The empirical bioaerosol drag coefficient model was developed in this study to investigate the transport mechanism of bioaerosol particles. A chamber study was used to collate the empirical data from 13 common indoor bioaerosol species with the three common ventilation rates (1.7, 10.3 and 18.8 ACH). By comparing the experimental and numerical data, the empirical drag constants and coefficients were determined for each bioaerosol species. The model (i.e. Kdrag,bp=dbp2/2) was developed by correlating the drag constants Kdrag,bp with the equivalent bioaerosol diameters dbp in a range between 0.054 and 6.9 µm. Several validations were done for the generalization of the model for various bioaerosol species, ventilation rates, enclosures and literature. The model simplifies the transport mechanism of bioaerosol particles, for example, dispersion and deposition, in terms of the equivalent bioaerosol diameter dbp and drag coefficient Cdrag,bp. This is beneficial in that only a single morphological characteristic (i.e. dbp) is required to predict the movement of any bioaerosol species.
|Date of Award||2017|