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. 2021 Dec 2;11(1):23329.
doi: 10.1038/s41598-021-02663-8.

Computational fluid dynamics modeling of cough transport in an aircraft cabin

Malia Zee  1 , Angela C Davis  1 , Andrew D Clark  1 , Tateh Wu  1 , Stephen P Jones  1 , Lindsay L Waite  1 , Joshua J Cummins  1 , Nels A Olson  2
Affiliations

Computational fluid dynamics modeling of cough transport in an aircraft cabin

Malia Zee et al. Sci Rep. .

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.

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Conflict of interest statement

All authors are employees of The Boeing Company and have no other competing interests. Funding and other resources for this work were provided by The Boeing Company. In-kind contribution was provided by Ansys through supply of licenses for Fluent software.

Figures

Figure 1
Figure 1
Airflow design in narrow-body aircraft. (a) Airflow delivery features in the 737 Boeing Sky Interior cabin used in simulations. (b) Idealized airflow pattern in the passenger cabin.
Figure 2
Figure 2
Velocity vectors of initial conditions at 100% flow rate with the highest velocities shown in red and the lowest ones in blue. Time offset and initial condition index as follows: (a) 0 s, Initial Condition 1, (b) 90 s, Initial Condition 2, (c) 120 s, Initial Condition 3.
Figure 3
Figure 3
Decay of expiratory particles over time after cough discharge. (a) Airflow at 100% with different index seats and initial conditions. (b) Airflow at 55–100% with index passenger in aisle seat 3D.
Figure 4
Figure 4
Amount of expiratory material present in the breathing zones of susceptible passengers over time after cough discharge from an index passenger in middle seat 3E.
Figure 5
Figure 5
Seat chart for the 5-row cabin section used in the model, colored by distance of susceptible passengers from an index passenger seated in either (a) the middle or (b) the aisle seat.
Figure 6
Figure 6
Mass of expiratory material inhaled by susceptible subjects in different seats for (a–c) different airflow conditions and (a,d,e) different index seat assignments at 100% airflow. Range bars in (a) represent values over three different initial conditions.
Figure 7
Figure 7
Particle exposure in the computational model using the 737 airframe and in experimental testing using 767 and 777 airframes. Jittered datapoints represent exposures of individual susceptible passengers within bins by distance to the index subject.
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