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Get Free AccessAn elevator is a machine that vertically transports people between different levels of a building. It is a typical confined space for contracting airborne diseases, such as coronavirus disease 2019 (COVID-19). The purpose of this study is to examine the dispersion of sneeze particles on the infection risk among passengers in a public elevator. A computational fluid dynamics (CFD) model representing an elevator was constructed. A CFD model was verified and validated based on the onsite measurement data. Renormalization Group (RNG) k-ε turbulence model developed based on the Eulerian tracking approach was used to simulate the airflow, while the discrete phase model (DPM) developed based on the Lagrangian tracking approach was used to simulate the particle dispersion during sneezing process. Simulation results show that particle concentration increased by 10 % and 2 % in case 2 (ceiling-mounted air supply diffuser and one low-level exhaust outlet) and case 3 (ceiling-mounted air supply diffuser and exhaust outlet), respectively. In contrast, case 4 shows that integration of the ceiling-mounted air supply diffuser with two-sided low-level exhaust outlet) minimized the particle adherence by 34 % on the manikin, from the time of 0 s – 5 s. Thus, ventilation strategy demonstrated in case 4 is sufficient to minimize particle dispersion and has a particle reduction rate of 0.006 kg/m3 ∙ s. Although case 1 (ceiling-mounted air supply diffuser and ceiling-mounted exhaust outlet) could prevent the particles trapped on the manikin from 0 s to 5 s, it does not perform well from the time of 5 s – 10 s.
Huiyi Tan, Mohd Hafiz Dzarfan Othman, Hong Yee Kek, Wen Tong Chong, Syie Luing Wong, Garry Kuan Pei Ern, Guo Ren Mong, Wai Shin Ho, Pau Chung Leng, Mohamad Nur Hidayat Mat, KengYinn Wong (2023). Would sneezing increase the risk of passengers contracting airborne infection? A validated numerical assessment in a public elevator. Energy and Buildings, 297, pp. 113439-113439, DOI: 10.1016/j.enbuild.2023.113439.
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Type
Article
Year
2023
Authors
11
Datasets
0
Total Files
0
Language
English
Journal
Energy and Buildings
DOI
10.1016/j.enbuild.2023.113439
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