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TU Delft COVID-app: A tool to democratize CFD simulations for SARS-CoV-2 infection risk analysis

This work describes a modelling approach to SARS-CoV-2 dispersion based on experiments. The main goal is the development of an application integrated in Ansys Fluent to enable computational fluid dynamics (CFD) users to set up, in a relatively short time, complex simulations of virion-laden droplet...

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Autores principales: Faleiros, David Engler, van den Bos, Wouter, Botto, Lorenzo, Scarano, Fulvio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Authors. Published by Elsevier B.V. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8875768/
https://www.ncbi.nlm.nih.gov/pubmed/35227716
http://dx.doi.org/10.1016/j.scitotenv.2022.154143
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author Faleiros, David Engler
van den Bos, Wouter
Botto, Lorenzo
Scarano, Fulvio
author_facet Faleiros, David Engler
van den Bos, Wouter
Botto, Lorenzo
Scarano, Fulvio
author_sort Faleiros, David Engler
collection PubMed
description This work describes a modelling approach to SARS-CoV-2 dispersion based on experiments. The main goal is the development of an application integrated in Ansys Fluent to enable computational fluid dynamics (CFD) users to set up, in a relatively short time, complex simulations of virion-laden droplet dispersion for calculating the probability of SARS-CoV-2 infection in real life scenarios. The software application, referred to as TU Delft COVID-app, includes the modelling of human expiratory activities, unsteady and turbulent convection, droplet evaporation and thermal coupling. Data describing human expiratory activities have been obtained from selected studies involving measurements of the expelled droplets and the air flow during coughing, sneezing and breathing. Particle Image Velocimetry (PIV) measurements of the transient air flow expelled by a person while reciting a speech have been conducted with and without a surgical mask. The instantaneous velocity fields from PIV are used to determine the velocity flow rates used in the numerical simulations, while the average velocity fields are used for validation. Furthermore, the effect of surgical masks and N95 respirators on particle filtration and the probability of SARS-CoV-2 infection from a dose-response model have also been implemented in the application. Finally, the work includes a case-study of SARS-CoV-2 infection risk analysis during a conversation across a dining/meeting table that demonstrates the capability of the newly developed application.
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spelling pubmed-88757682022-02-25 TU Delft COVID-app: A tool to democratize CFD simulations for SARS-CoV-2 infection risk analysis Faleiros, David Engler van den Bos, Wouter Botto, Lorenzo Scarano, Fulvio Sci Total Environ Article This work describes a modelling approach to SARS-CoV-2 dispersion based on experiments. The main goal is the development of an application integrated in Ansys Fluent to enable computational fluid dynamics (CFD) users to set up, in a relatively short time, complex simulations of virion-laden droplet dispersion for calculating the probability of SARS-CoV-2 infection in real life scenarios. The software application, referred to as TU Delft COVID-app, includes the modelling of human expiratory activities, unsteady and turbulent convection, droplet evaporation and thermal coupling. Data describing human expiratory activities have been obtained from selected studies involving measurements of the expelled droplets and the air flow during coughing, sneezing and breathing. Particle Image Velocimetry (PIV) measurements of the transient air flow expelled by a person while reciting a speech have been conducted with and without a surgical mask. The instantaneous velocity fields from PIV are used to determine the velocity flow rates used in the numerical simulations, while the average velocity fields are used for validation. Furthermore, the effect of surgical masks and N95 respirators on particle filtration and the probability of SARS-CoV-2 infection from a dose-response model have also been implemented in the application. Finally, the work includes a case-study of SARS-CoV-2 infection risk analysis during a conversation across a dining/meeting table that demonstrates the capability of the newly developed application. The Authors. Published by Elsevier B.V. 2022-06-20 2022-02-25 /pmc/articles/PMC8875768/ /pubmed/35227716 http://dx.doi.org/10.1016/j.scitotenv.2022.154143 Text en © 2022 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Faleiros, David Engler
van den Bos, Wouter
Botto, Lorenzo
Scarano, Fulvio
TU Delft COVID-app: A tool to democratize CFD simulations for SARS-CoV-2 infection risk analysis
title TU Delft COVID-app: A tool to democratize CFD simulations for SARS-CoV-2 infection risk analysis
title_full TU Delft COVID-app: A tool to democratize CFD simulations for SARS-CoV-2 infection risk analysis
title_fullStr TU Delft COVID-app: A tool to democratize CFD simulations for SARS-CoV-2 infection risk analysis
title_full_unstemmed TU Delft COVID-app: A tool to democratize CFD simulations for SARS-CoV-2 infection risk analysis
title_short TU Delft COVID-app: A tool to democratize CFD simulations for SARS-CoV-2 infection risk analysis
title_sort tu delft covid-app: a tool to democratize cfd simulations for sars-cov-2 infection risk analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8875768/
https://www.ncbi.nlm.nih.gov/pubmed/35227716
http://dx.doi.org/10.1016/j.scitotenv.2022.154143
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