TY - GEN
T1 - Accurate CFD prediction of respiratory airflow and dispersion through face mask
AU - Ai, Zhengtao
AU - Jia, Zhongjian
N1 - Conference code: 13
PY - 2023/6
Y1 - 2023/6
N2 - This study develops an accurate modelling framework of flow and dispersion through face mask based on computational fluid dynamics (CFD) theory and method. The influence of gird division, time step size, and turbulence model on simulation accuracy were investigated. The result shows that the viscous resistance coefficient and inertial resistance coefficient of face masks (surgical masks) were 3.65×109 and 1.69×106, respectively. The cell size on the surface of face masks should not be larger than 1.0 mm; the height of the first layer cells near the face masks should not be larger than 0.1 mm; and the time step sizes discretizing the breathing and coughing periods should not be more than 0.01 s and 0.001 s, respectively. The results given by LES model show closer agreement with the experimental data than RANS models, with approximately 10% relative deviation for the air speed near the face mask. Overall, the SST k-ω model performs the best among the RANS models, especially for the air speed. The findings obtained form a CFD modelling framework for an accurate prediction of airflow and dispersion problems involving face masks.
AB - This study develops an accurate modelling framework of flow and dispersion through face mask based on computational fluid dynamics (CFD) theory and method. The influence of gird division, time step size, and turbulence model on simulation accuracy were investigated. The result shows that the viscous resistance coefficient and inertial resistance coefficient of face masks (surgical masks) were 3.65×109 and 1.69×106, respectively. The cell size on the surface of face masks should not be larger than 1.0 mm; the height of the first layer cells near the face masks should not be larger than 0.1 mm; and the time step sizes discretizing the breathing and coughing periods should not be more than 0.01 s and 0.001 s, respectively. The results given by LES model show closer agreement with the experimental data than RANS models, with approximately 10% relative deviation for the air speed near the face mask. Overall, the SST k-ω model performs the best among the RANS models, especially for the air speed. The findings obtained form a CFD modelling framework for an accurate prediction of airflow and dispersion problems involving face masks.
KW - NSB 2023
U2 - 10.54337/aau541592930
DO - 10.54337/aau541592930
M3 - Article in proceeding
VL - 13
BT - NSB 2023 - Book of Technical Papers: 13th Nordic Symposium on Building Physics
A2 - Johra, Hicham
PB - Department of the Built Environment, Aalborg University
CY - Aalborg
T2 - 13th Nordic Symposium on Building Physics, NSB 2023
Y2 - 12 June 2023 through 14 June 2023
ER -