A CFD Approach for Risk Assessment Based on Airborne Pathogen Transmission
The outbreak of COVID-19 necessitates developing reliable tools to derive safety measures, including safe social distance and minimum exposure time under different circumstances. Transient Eulerian–Lagrangian computational fluid dynamics (CFD) models have emerged as a viably fast and economical opti...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-07-01
|
| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/12/8/986 |
| _version_ | 1797524652863520768 |
|---|---|
| author | Hamid Motamedi Zoka Mohammad Moshfeghi Hadi Bordbar Parham A. Mirzaei Yahya Sheikhnejad |
| author_facet | Hamid Motamedi Zoka Mohammad Moshfeghi Hadi Bordbar Parham A. Mirzaei Yahya Sheikhnejad |
| author_sort | Hamid Motamedi Zoka |
| collection | DOAJ |
| description | The outbreak of COVID-19 necessitates developing reliable tools to derive safety measures, including safe social distance and minimum exposure time under different circumstances. Transient Eulerian–Lagrangian computational fluid dynamics (CFD) models have emerged as a viably fast and economical option. Nonetheless, these CFD models resolve the instantaneous distribution of droplets inside a computational domain, making them incapable of directly being used to assess the risk of infection as it depends on the total accumulated dosage of infecting viruses received by a new host within an exposure time. This study proposes a novel risk assessment model (RAM) to predict the temporal and spatial accumulative concentration of infectious exhaled droplets based on the bio-source’s exhalation profile and droplet distribution using the CFD results of respiratory events in various environmental conditions. Unlike the traditional approach in the bulk movement assessment of droplets’ outreach in a domain, every single droplet is traced inside the domain at each time step, and the total number of droplets passing through any arbitrary position of the domain is determined using a computational code. The performance of RAM is investigated for a series of case studies against various respiratory events where the horizontal and the lateral spread of risky zones are shown to temporarily vary rather than being fixed in space. The sensitivity of risky zones to ambient temperature and relative humidity was also addressed for sample cough and sneeze cases. This implies that the RAM provides crucial information required for defining safety measures such as safety distances or minimum exposure times in different environments. |
| first_indexed | 2024-03-10T09:00:32Z |
| format | Article |
| id | doaj.art-904ba5a3a0314551bbb519340d84f3c6 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T09:00:32Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-904ba5a3a0314551bbb519340d84f3c62023-11-22T06:47:23ZengMDPI AGAtmosphere2073-44332021-07-0112898610.3390/atmos12080986A CFD Approach for Risk Assessment Based on Airborne Pathogen TransmissionHamid Motamedi Zoka0Mohammad Moshfeghi1Hadi Bordbar2Parham A. Mirzaei3Yahya Sheikhnejad4Department of Mechanical Engineering, Tarbiat Modares University, Tehran 14155-6343, IranDepartment of Mechanical Engineering, Sogang University, Seoul 04107, KoreaSchool of Engineering, Aalto University, 02150 Espoo, FinlandArchitecture & Built Environment Department, University of Nottingham, University Park, Nottingham NG7 2QL, UKDepartment of Mechanical Engineering, Universidade de Aveiro, 3810-193 Aveiro, PortugalThe outbreak of COVID-19 necessitates developing reliable tools to derive safety measures, including safe social distance and minimum exposure time under different circumstances. Transient Eulerian–Lagrangian computational fluid dynamics (CFD) models have emerged as a viably fast and economical option. Nonetheless, these CFD models resolve the instantaneous distribution of droplets inside a computational domain, making them incapable of directly being used to assess the risk of infection as it depends on the total accumulated dosage of infecting viruses received by a new host within an exposure time. This study proposes a novel risk assessment model (RAM) to predict the temporal and spatial accumulative concentration of infectious exhaled droplets based on the bio-source’s exhalation profile and droplet distribution using the CFD results of respiratory events in various environmental conditions. Unlike the traditional approach in the bulk movement assessment of droplets’ outreach in a domain, every single droplet is traced inside the domain at each time step, and the total number of droplets passing through any arbitrary position of the domain is determined using a computational code. The performance of RAM is investigated for a series of case studies against various respiratory events where the horizontal and the lateral spread of risky zones are shown to temporarily vary rather than being fixed in space. The sensitivity of risky zones to ambient temperature and relative humidity was also addressed for sample cough and sneeze cases. This implies that the RAM provides crucial information required for defining safety measures such as safety distances or minimum exposure times in different environments.https://www.mdpi.com/2073-4433/12/8/986CFDEulerian–Lagrangian modelingrespiratory dropletsCOVID-19risk assessment |
| spellingShingle | Hamid Motamedi Zoka Mohammad Moshfeghi Hadi Bordbar Parham A. Mirzaei Yahya Sheikhnejad A CFD Approach for Risk Assessment Based on Airborne Pathogen Transmission Atmosphere CFD Eulerian–Lagrangian modeling respiratory droplets COVID-19 risk assessment |
| title | A CFD Approach for Risk Assessment Based on Airborne Pathogen Transmission |
| title_full | A CFD Approach for Risk Assessment Based on Airborne Pathogen Transmission |
| title_fullStr | A CFD Approach for Risk Assessment Based on Airborne Pathogen Transmission |
| title_full_unstemmed | A CFD Approach for Risk Assessment Based on Airborne Pathogen Transmission |
| title_short | A CFD Approach for Risk Assessment Based on Airborne Pathogen Transmission |
| title_sort | cfd approach for risk assessment based on airborne pathogen transmission |
| topic | CFD Eulerian–Lagrangian modeling respiratory droplets COVID-19 risk assessment |
| url | https://www.mdpi.com/2073-4433/12/8/986 |
| work_keys_str_mv | AT hamidmotamedizoka acfdapproachforriskassessmentbasedonairbornepathogentransmission AT mohammadmoshfeghi acfdapproachforriskassessmentbasedonairbornepathogentransmission AT hadibordbar acfdapproachforriskassessmentbasedonairbornepathogentransmission AT parhamamirzaei acfdapproachforriskassessmentbasedonairbornepathogentransmission AT yahyasheikhnejad acfdapproachforriskassessmentbasedonairbornepathogentransmission AT hamidmotamedizoka cfdapproachforriskassessmentbasedonairbornepathogentransmission AT mohammadmoshfeghi cfdapproachforriskassessmentbasedonairbornepathogentransmission AT hadibordbar cfdapproachforriskassessmentbasedonairbornepathogentransmission AT parhamamirzaei cfdapproachforriskassessmentbasedonairbornepathogentransmission AT yahyasheikhnejad cfdapproachforriskassessmentbasedonairbornepathogentransmission |