Computational fluid dynamics modeling of cough transport in an aircraft cabin
Abstract To characterize the transport of respiratory pathogens during commercial air travel, Computational Fluid Dynamics simulations were performed to track particles expelled by coughing by a passenger assigned to different seats on a Boeing 737 aircraft. Simulation data were post-processed to ca...
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Nature Portfolio
2021
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oai:doaj.org-article:8e4a0b76f5a04587bd411c321d596e4c2021-12-05T12:14:24ZComputational fluid dynamics modeling of cough transport in an aircraft cabin10.1038/s41598-021-02663-82045-2322https://doaj.org/article/8e4a0b76f5a04587bd411c321d596e4c2021-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02663-8https://doaj.org/toc/2045-2322Abstract To characterize the transport of respiratory pathogens during commercial air travel, Computational Fluid Dynamics simulations were performed to track particles expelled by coughing by a passenger assigned to different seats on a Boeing 737 aircraft. Simulation data were post-processed to calculate the amounts of particles inhaled by nearby passengers. Different airflow rates were used, as well as different initial conditions to account for random fluctuations of the flow field. Overall, 80% of the particles were removed from the cabin in 1.3–2.6 min, depending on conditions, and 95% of the particles were removed in 2.4–4.6 min. Reducing airflow increased particle dispersion throughout the cabin but did not increase the highest exposure of nearby passengers. The highest exposure was 0.3% of the nonvolatile mass expelled by the cough, and the median exposure for seats within 3 feet of the cough discharge was 0.1%, which was in line with recent experimental testing.Malia ZeeAngela C. DavisAndrew D. ClarkTateh WuStephen P. JonesLindsay L. WaiteJoshua J. CumminsNels A. OlsonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021) |
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Medicine R Science Q Malia Zee Angela C. Davis Andrew D. Clark Tateh Wu Stephen P. Jones Lindsay L. Waite Joshua J. Cummins Nels A. Olson Computational fluid dynamics modeling of cough transport in an aircraft cabin |
description |
Abstract To characterize the transport of respiratory pathogens during commercial air travel, Computational Fluid Dynamics simulations were performed to track particles expelled by coughing by a passenger assigned to different seats on a Boeing 737 aircraft. Simulation data were post-processed to calculate the amounts of particles inhaled by nearby passengers. Different airflow rates were used, as well as different initial conditions to account for random fluctuations of the flow field. Overall, 80% of the particles were removed from the cabin in 1.3–2.6 min, depending on conditions, and 95% of the particles were removed in 2.4–4.6 min. Reducing airflow increased particle dispersion throughout the cabin but did not increase the highest exposure of nearby passengers. The highest exposure was 0.3% of the nonvolatile mass expelled by the cough, and the median exposure for seats within 3 feet of the cough discharge was 0.1%, which was in line with recent experimental testing. |
format |
article |
author |
Malia Zee Angela C. Davis Andrew D. Clark Tateh Wu Stephen P. Jones Lindsay L. Waite Joshua J. Cummins Nels A. Olson |
author_facet |
Malia Zee Angela C. Davis Andrew D. Clark Tateh Wu Stephen P. Jones Lindsay L. Waite Joshua J. Cummins Nels A. Olson |
author_sort |
Malia Zee |
title |
Computational fluid dynamics modeling of cough transport in an aircraft cabin |
title_short |
Computational fluid dynamics modeling of cough transport in an aircraft cabin |
title_full |
Computational fluid dynamics modeling of cough transport in an aircraft cabin |
title_fullStr |
Computational fluid dynamics modeling of cough transport in an aircraft cabin |
title_full_unstemmed |
Computational fluid dynamics modeling of cough transport in an aircraft cabin |
title_sort |
computational fluid dynamics modeling of cough transport in an aircraft cabin |
publisher |
Nature Portfolio |
publishDate |
2021 |
url |
https://doaj.org/article/8e4a0b76f5a04587bd411c321d596e4c |
work_keys_str_mv |
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1718372143460253696 |