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Numerical simulation of bioaerosol deposition and mildew risk under transitional seasonal ventilation in workshop with wet source
https://orcid.org/0009-0004-4370-4646
In this study, a typical workshop with internal water vapour sources was selected as the research object. A coupled Computational Fluid Dynamics model was established to simulate bio-particle diffusion, dry and wet collision and deposition. The study investigated the indoor humidity field and bioaerosol deposition distribution under different supply air parameters in transitional seasons when using a fresh air ventilation system. Additionally, quantitative analysis was conducted on the Mildew Risk Index (MRI) in the workshop under different airflow patterns and supply rates. The main findings of this study are as follows: 1) Higher indoor humidity led to larger MRI, with Beijing having an MRI of only 0.59% compared to Guangzhou’s 56.4%. Correlation analysis showed that MRI had a positive correlation with Absolute Humidity (AH) of supply air (coefficient = 0.92), while it had a negative correlation with Particle Removal Efficiency (PRE) (coefficient = −1.0). 2). There was a negative correlation between PRE and AH (coefficient = −0.93) which indicated that a higher humidity in the supply air could promote bioaerosol deposition indoors and would increase MRI. 3) The regression equations between MRI and PRE were also established in this study, and a variance analysis was performed on them.
Numerical simulation of bioaerosol deposition and mildew risk under transitional seasonal ventilation in workshop with wet source
https://orcid.org/0009-0004-4370-4646
In this study, a typical workshop with internal water vapour sources was selected as the research object. A coupled Computational Fluid Dynamics model was established to simulate bio-particle diffusion, dry and wet collision and deposition. The study investigated the indoor humidity field and bioaerosol deposition distribution under different supply air parameters in transitional seasons when using a fresh air ventilation system. Additionally, quantitative analysis was conducted on the Mildew Risk Index (MRI) in the workshop under different airflow patterns and supply rates. The main findings of this study are as follows: 1) Higher indoor humidity led to larger MRI, with Beijing having an MRI of only 0.59% compared to Guangzhou’s 56.4%. Correlation analysis showed that MRI had a positive correlation with Absolute Humidity (AH) of supply air (coefficient = 0.92), while it had a negative correlation with Particle Removal Efficiency (PRE) (coefficient = −1.0). 2). There was a negative correlation between PRE and AH (coefficient = −0.93) which indicated that a higher humidity in the supply air could promote bioaerosol deposition indoors and would increase MRI. 3) The regression equations between MRI and PRE were also established in this study, and a variance analysis was performed on them.
Numerical simulation of bioaerosol deposition and mildew risk under transitional seasonal ventilation in workshop with wet source
https://orcid.org/0009-0004-4370-4646
In this study, a typical workshop with internal water vapour sources was selected as the research object. A coupled Computational Fluid Dynamics model was established to simulate bio-particle diffusion, dry and wet collision and deposition. The study investigated the indoor humidity field and bioaerosol deposition distribution under different supply air parameters in transitional seasons when using a fresh air ventilation system. Additionally, quantitative analysis was conducted on the Mildew Risk Index (MRI) in the workshop under different airflow patterns and supply rates. The main findings of this study are as follows: 1) Higher indoor humidity led to larger MRI, with Beijing having an MRI of only 0.59% compared to Guangzhou’s 56.4%. Correlation analysis showed that MRI had a positive correlation with Absolute Humidity (AH) of supply air (coefficient = 0.92), while it had a negative correlation with Particle Removal Efficiency (PRE) (coefficient = −1.0). 2). There was a negative correlation between PRE and AH (coefficient = −0.93) which indicated that a higher humidity in the supply air could promote bioaerosol deposition indoors and would increase MRI. 3) The regression equations between MRI and PRE were also established in this study, and a variance analysis was performed on them.
Numerical simulation of bioaerosol deposition and mildew risk under transitional seasonal ventilation in workshop with wet source
Wang, Yifei (author) / Ding, Dexin (author) / Yu, Chuck Wah (author)
Indoor and Built Environment ; 34 ; 367-387
2025-02-01
Article (Journal)
Electronic Resource
English
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