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A Markov chain model for predicting transient particle transport in enclosed environments
Obtaining information about particle dispersion in a room is crucial in reducing the risk of infectious disease transmission among occupants. This study developed a Markov chain model for quickly obtaining the information on the basis of a steady-state flow field calculated by computational fluid dy...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7117050/ https://www.ncbi.nlm.nih.gov/pubmed/32288030 http://dx.doi.org/10.1016/j.buildenv.2015.03.024 |
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author | Chen, Chun Liu, Wei Lin, Chao-Hsin Chen, Qingyan |
author_facet | Chen, Chun Liu, Wei Lin, Chao-Hsin Chen, Qingyan |
author_sort | Chen, Chun |
collection | PubMed |
description | Obtaining information about particle dispersion in a room is crucial in reducing the risk of infectious disease transmission among occupants. This study developed a Markov chain model for quickly obtaining the information on the basis of a steady-state flow field calculated by computational fluid dynamics. When solving the particle transport equations, the Markov chain model does not require iterations in each time step, and thus it can significantly reduce the computing cost. This study used two sets of experimental data for transient particle transport to validate the model. In general, the trends in the particle concentration distributions predicted by the Markov chain model agreed reasonably well with the experimental data. This investigation also applied the model to the calculation of person-to-person particle transport in a ventilated room. The Markov chain model produced similar results to those of the Lagrangian and Eulerian models, while the speed of calculation increased by 8.0 and 6.3 times, respectively, in comparison to the latter two models. |
format | Online Article Text |
id | pubmed-7117050 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71170502020-04-02 A Markov chain model for predicting transient particle transport in enclosed environments Chen, Chun Liu, Wei Lin, Chao-Hsin Chen, Qingyan Build Environ Article Obtaining information about particle dispersion in a room is crucial in reducing the risk of infectious disease transmission among occupants. This study developed a Markov chain model for quickly obtaining the information on the basis of a steady-state flow field calculated by computational fluid dynamics. When solving the particle transport equations, the Markov chain model does not require iterations in each time step, and thus it can significantly reduce the computing cost. This study used two sets of experimental data for transient particle transport to validate the model. In general, the trends in the particle concentration distributions predicted by the Markov chain model agreed reasonably well with the experimental data. This investigation also applied the model to the calculation of person-to-person particle transport in a ventilated room. The Markov chain model produced similar results to those of the Lagrangian and Eulerian models, while the speed of calculation increased by 8.0 and 6.3 times, respectively, in comparison to the latter two models. Elsevier Ltd. 2015-08 2015-03-28 /pmc/articles/PMC7117050/ /pubmed/32288030 http://dx.doi.org/10.1016/j.buildenv.2015.03.024 Text en Copyright © 2015 Elsevier Ltd. All rights reserved. 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 Chen, Chun Liu, Wei Lin, Chao-Hsin Chen, Qingyan A Markov chain model for predicting transient particle transport in enclosed environments |
title | A Markov chain model for predicting transient particle transport in enclosed environments |
title_full | A Markov chain model for predicting transient particle transport in enclosed environments |
title_fullStr | A Markov chain model for predicting transient particle transport in enclosed environments |
title_full_unstemmed | A Markov chain model for predicting transient particle transport in enclosed environments |
title_short | A Markov chain model for predicting transient particle transport in enclosed environments |
title_sort | markov chain model for predicting transient particle transport in enclosed environments |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7117050/ https://www.ncbi.nlm.nih.gov/pubmed/32288030 http://dx.doi.org/10.1016/j.buildenv.2015.03.024 |
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