Optimising Air Change Rates: A CFD Study on Mitigating Pathogen Transmission in Aircraft Cabins

Amid the COVID-19 pandemic, understanding airborne pathogen transmission within confined spaces became critically important. The release of infectious aerosols through activities such as breathing, speaking, and coughing poses significant health risks, especially in confined spaces like airplane cabins. This study addresses gaps in the research by evaluating the impact of air changes per hour (ACH) on pathogen transmission in an aircraft cabin using computational fluid dynamics (CFD) simulations. A detailed computer-aided design (CAD) model representing half of a four-row section of a Boeing 737 cabin was developed, utilising symmetry boundary conditions to optimise the computational resources while maintaining accuracy. Using ANSYS Fluent 2024, four scenarios were simulated at ACH rates of 15, 20, 25, and 30, with 4 µm pathogens injected into the cabin from a single infector. Airflow patterns and pathogen residence times were analysed for each case. The results indicate that ACH 15 presents the highest risk of pathogen transmission, while increasing the ACH to 20 significantly reduces this risk, with diminishing returns observed beyond ACH 20. Thus, this study underscores the importance of balancing ventilation efficiency, energy consumption, and passenger comfort. The findings provide valuable insights into optimising the ventilation systems to mitigate the airborne transmission in aircraft cabins. Future research should explore higher ACH rates, validate their impact, and conduct a comprehensive optimisation study to further improve the infection control measures.

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Publication Details

Journal
Fluids
Published
2025-03-20
DOI
https://doi.org/10.3390/fluids10030074
Citations
1
Primary Topic
Infection Control and Ventilation
Type
article
Field-Weighted Citation Impact
1.52

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article

Optimising Air Change Rates: A CFD Study on Mitigating Pathogen Transmission in Aircraft Cabins

Lin Tian, Jaydon Benn
1 citations
Fluids
Infection Control and Ventilation
1.52
article

Optimising Air Change Rates: A CFD Study on Mitigating Pathogen Transmission in Aircraft Cabins

Lin Tian, Jaydon Benn
article en
1 citations

Abstract

Amid the COVID-19 pandemic, understanding airborne pathogen transmission within confined spaces became critically important. The release of infectious aerosols through activities such as breathing, speaking, and coughing poses significant health risks, especially in confined spaces like airplane cabins. This study addresses gaps in the research by evaluating the impact of air changes per hour (ACH) on pathogen transmission in an aircraft cabin using computational fluid dynamics (CFD) simulations. A detailed computer-aided design (CAD) model representing half of a four-row section of a Boeing 737 cabin was developed, utilising symmetry boundary conditions to optimise the computational resources while maintaining accuracy. Using ANSYS Fluent 2024, four scenarios were simulated at ACH rates of 15, 20, 25, and 30, with 4 µm pathogens injected into the cabin from a single infector. Airflow patterns and pathogen residence times were analysed for each case. The results indicate that ACH 15 presents the highest risk of pathogen transmission, while increasing the ACH to 20 significantly reduces this risk, with diminishing returns observed beyond ACH 20. Thus, this study underscores the importance of balancing ventilation efficiency, energy consumption, and passenger comfort. The findings provide valuable insights into optimising the ventilation systems to mitigate the airborne transmission in aircraft cabins. Future research should explore higher ACH rates, validate their impact, and conduct a comprehensive optimisation study to further improve the infection control measures.

FluidsVol. 10(3)
RMIT University (AU)
Australian Research Council
Openalex Percentile: Top 21%
Infection Control and Ventilation
1.52
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Optimising Air Change Rates: A CFD Study on Mitigating Pathogen Transmission in Aircraft Cabins — Lin Tian, Jaydon Benn · Fluids (2025) | TGRS Research Map | TGRS