Experimental measurements of airflow features and velocity distribution exhaled from sneeze and speech using particle image velocimetry.
Particle image velocimetry
Sneeze
Speech
Spread angle
Time-averaged velocity
Velocity profile
Journal
Building and environment
ISSN: 0360-1323
Titre abrégé: Build Environ
Pays: England
ID NLM: 101562928
Informations de publication
Date de publication:
Nov 2021
Nov 2021
Historique:
received:
15
07
2021
revised:
24
08
2021
accepted:
24
08
2021
entrez:
15
12
2021
pubmed:
16
12
2021
medline:
16
12
2021
Statut:
ppublish
Résumé
Airflow exhaled from sneeze and speech is an important source of viruses and droplets in daily life and may cause imperceptible virus propagation. The velocities of sneeze and speech airflow exhaled from 10 healthy young participants repeatedly using high-frequency (2986 Hz) particle image velocimetry are measured. The parameters for describing the dynamic process of sneeze airflow, such as sneeze duration time (SDT), peak velocity time (PVT), maximum velocities, and sneeze spread angle, are analyzed. The sneeze airflow lasts 430 ms (SDT) and reaches the peak velocity in the first 20 ms (PVT). The maximum sneeze airflow velocity is approximately 15.9 m/s. The temporal variation of the sneeze velocity exhibits the gamma distribution. For speech airflow, the maximum instantaneous velocity and maximum time-averaged velocity are reported. The maximum instantaneous velocity is approximately 6.25 m/s, whereas the time-averaged value is only 0.208 m/s owing to the extremely small airflow velocity among syllables. The vertical/horizontal spread angles of the airflow are 15.1°/15.4° for sneeze and 52.9°/42.9° for speech. The difference in airflow features based on gender is generally slight for both sneeze and speech. Subsequently, an ensemble-average operation is conducted to obtain the general and representative velocity distributions. We report each component of the temporal and spatial velocity distributions of the sneeze airflow and the time-averaged velocity distribution of the speech airflow. These detailed distribution data can provide a comprehensive understanding of sneeze and speech airflow movement mechanisms as well as a detailed database for future sneeze and speech computational fluid dynamics simulations.
Identifiants
pubmed: 34908645
doi: 10.1016/j.buildenv.2021.108293
pii: S0360-1323(21)00692-2
pmc: PMC8663001
doi:
Types de publication
Journal Article
Langues
eng
Pagination
108293Informations de copyright
© 2021 Elsevier Ltd. All rights reserved.
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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