| dc.contributor.author |
Watcharanat P. |
|
| dc.contributor.author |
Vengsungnle P. |
|
| dc.contributor.author |
Naphon P. |
|
| dc.date.accessioned |
2022-12-14T03:17:51Z |
|
| dc.date.available |
2022-12-14T03:17:51Z |
|
| dc.date.issued |
2022 |
|
| dc.identifier.issn |
23690739 |
|
| dc.identifier.uri |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129972576&doi=10.18280%2fmmep.090203&partnerID=40&md5=d6458326ab9fb6d6927cc296da4003a8 |
|
| dc.identifier.uri |
https://ir.swu.ac.th/jspui/handle/123456789/27638 |
|
| dc.description.abstract |
The risk of spreading the virus largely depends on the airflow behavior and the change in direction caused by the air supply and location of the exhaust air. The generated particles may travel long during sneezing, adversely affecting human bodies to defend against COVID-19 infectious diseases. This paper examines airflow path and airborne pollutant distribution in the nursing caring office room for COVID-19 patients ward at Princess Maha Chakri Sirindhorn Medical Center, Nakhornnayok province by computational fluid dynamics modeling and field measurement. Fifteen dummies of nursing staff stay in the room, and only one dummy (Patient) generated COVID-19. It is found that the generated particles during sneezing may travel a long distance as compared with the normal respiration and the ventilation system can effectively remove contaminants from the room and distribution in the room. From the obtained results, the keeping a social distancing should be more than 1.5 m for preventing the spread of the COVID-19 from person to person. © 2022. Mathematical Modelling of Engineering Problems.All Rights Reserved |
|
| dc.language |
en |
|
| dc.publisher |
International Information and Engineering Technology Association |
|
| dc.subject |
Air ventilation |
|
| dc.subject |
Contaminant distribution |
|
| dc.subject |
Office room |
|
| dc.subject |
Patient |
|
| dc.title |
COVID-19 Distribution Predicting in Nursing Caring Office Room: A Case Study at Princess Maha Chakri Sirindhorn Medical Center |
|
| dc.type |
Article |
|
| dc.rights.holder |
Scopus |
|
| dc.identifier.bibliograpycitation |
Eye (Basingstoke). Vol , No. (2022) |
|
| dc.identifier.doi |
10.18280/mmep.090203 |
|