Predicting transient particle transport in enclosed environments with the combined computational fluid dynamics and Markov chain method
Identifieur interne : 001740 ( Main/Exploration ); précédent : 001739; suivant : 001741Predicting transient particle transport in enclosed environments with the combined computational fluid dynamics and Markov chain method
Auteurs : C. Chen [États-Unis] ; C. Lin [États-Unis] ; Z. Long [République populaire de Chine] ; Q. Chen [États-Unis, République populaire de Chine]Source :
- Indoor Air [ 0905-6947 ] ; 2014-02.
Abstract
To quickly obtain information about airborne infectious disease transmission in enclosed environments is critical in reducing the infection risk to the occupants. This study developed a combined computational fluid dynamics (CFD) and Markov chain method for quickly predicting transient particle transport in enclosed environments. The method first calculated a transition probability matrix using CFD simulations. Next, the Markov chain technique was applied to calculate the transient particle concentration distributions. This investigation used three cases, particle transport in an isothermal clean room, an office with an underfloor air distribution system, and the first‐class cabin of an MD‐82 airliner, to validate the combined CFD and Markov chain method. The general trends of the particle concentrations vs. time predicted by the Markov chain method agreed with the CFD simulations for these cases. The proposed Markov chain method can provide faster‐than‐real‐time information about particle transport in enclosed environments. Furthermore, for a fixed airflow field, when the source location is changed, the Markov chain method can be used to avoid recalculation of the particle transport equation and thus reduce computing costs.
Url:
DOI: 10.1111/ina.12056
Affiliations:
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Chen</name><affiliation wicri:level="1"><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of Mechanical Engineering, Purdue University, IN, West Lafayette</wicri:regionArea><wicri:noRegion>West Lafayette</wicri:noRegion></affiliation></author><author><name sortKey="Lin, C" sort="Lin, C" uniqKey="Lin C" first="C." last="Lin">C. Lin</name><affiliation wicri:level="1"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Environmental Control Systems, Boeing Commercial Airplanes, WA, Everett</wicri:regionArea><wicri:noRegion>Everett</wicri:noRegion></affiliation></author><author><name sortKey="Long, Z" sort="Long, Z" uniqKey="Long Z" first="Z." last="Long">Z. 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Chen</name><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of Mechanical Engineering, Purdue University, West Lafayette, IN</wicri:regionArea><placeName><region type="state">Indiana</region></placeName></affiliation><affiliation wicri:level="3"><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Environmental Science and Engineering, Tianjin University, Tianjin</wicri:regionArea><placeName><settlement type="city">Tianjin</settlement></placeName></affiliation></author></analytic><monogr></monogr><series><title level="j" type="main">Indoor Air</title><title level="j" type="alt">INDOOR AIR</title><idno type="ISSN">0905-6947</idno><idno type="eISSN">1600-0668</idno><imprint><biblScope unit="vol">24</biblScope><biblScope unit="issue">1</biblScope><biblScope unit="page" from="81">81</biblScope><biblScope unit="page" to="92">92</biblScope><biblScope unit="page-count">12</biblScope><date type="published" when="2014-02">2014-02</date></imprint><idno type="ISSN">0905-6947</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0905-6947</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract">To quickly obtain information about airborne infectious disease transmission in enclosed environments is critical in reducing the infection risk to the occupants. This study developed a combined computational fluid dynamics (CFD) and Markov chain method for quickly predicting transient particle transport in enclosed environments. The method first calculated a transition probability matrix using CFD simulations. Next, the Markov chain technique was applied to calculate the transient particle concentration distributions. This investigation used three cases, particle transport in an isothermal clean room, an office with an underfloor air distribution system, and the first‐class cabin of an MD‐82 airliner, to validate the combined CFD and Markov chain method. The general trends of the particle concentrations vs. time predicted by the Markov chain method agreed with the CFD simulations for these cases. The proposed Markov chain method can provide faster‐than‐real‐time information about particle transport in enclosed environments. Furthermore, for a fixed airflow field, when the source location is changed, the Markov chain method can be used to avoid recalculation of the particle transport equation and thus reduce computing costs.</div></front></TEI><affiliations><list><country><li>République populaire de Chine</li><li>États-Unis</li></country><region><li>Indiana</li></region><settlement><li>Tianjin</li></settlement></list><tree><country name="États-Unis"><noRegion><name sortKey="Chen, C" sort="Chen, C" uniqKey="Chen C" first="C." last="Chen">C. Chen</name></noRegion><name sortKey="Chen, Q" sort="Chen, Q" uniqKey="Chen Q" first="Q." last="Chen">Q. Chen</name><name sortKey="Lin, C" sort="Lin, C" uniqKey="Lin C" first="C." last="Lin">C. Lin</name></country><country name="République populaire de Chine"><noRegion><name sortKey="Long, Z" sort="Long, Z" uniqKey="Long Z" first="Z." last="Long">Z. Long</name></noRegion><name sortKey="Chen, Q" sort="Chen, Q" uniqKey="Chen Q" first="Q." last="Chen">Q. Chen</name><name sortKey="Long, Z" sort="Long, Z" uniqKey="Long Z" first="Z." last="Long">Z. Long</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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