Design of a smart prefabricated sanitising chamber for COVID-19 using computational fluid dynamics
Abstract
The novel coronavirus (SARS-CoV-2) has spread at an unprecedented rate, resulting in a global pandemic (COVID-19) that has strained healthcare systems and claimed many lives. Front-line healthcare workers are among the most at risk of contracting and spreading the virus due to close contact with infected patients and settings of high viral loads. To provide these workers with an extra layer of protection, the authors propose a low-cost, prefabricated, and portable sanitising chamber that sprays individuals with sanitising fluid to disinfect clothing and external surfaces on their person. The study discusses computer-aided design of the chamber to improve uniformity of sanitiser deposition and reduce discomfort due to excessive moisture. Advanced computational fluid dynamics is used to simulate the dispersion and deposition of spray particle, and the resulting wetting pattern on the treated person is used to optimise the chamber design.
Keyword : COVID-19, sanitising chamber, disinfection chamber, computational fluid dynamics (CFD), numerical simulation, computer aided design (CAD), portable, prefabricated
How to Cite
Abu-Zidan, Y., Nguyen, K., Mendis, P., Setunge, S., & Adeli, H. (2021). Design of a smart prefabricated sanitising chamber for COVID-19 using computational fluid dynamics. Journal of Civil Engineering and Management, 27(2), 139-148. https://doi.org/10.3846/jcem.2021.14348
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Wang, C., Abdul-Rahman, H., & Chow, P. S. (2016b). Development and test run of civil engineering schedule acceleration model through ant colony optimization. Journal of Civil Engineering and Management, 22(8), 1009–1020. https://doi.org/10.3846/13923730.2014.945954
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Ghorbel, H., Zannini, N., Cherif, S., Sauser, F., Grunenwald, D., Droz, W., Baradji, M., & Lakehal, D. (2019). Smart adaptive run parameterization (SArP): enhancement of user manual selection of running parameters in fluid dynamic simulations using bio-inspired and machine-learning techniques. Soft Computing, 23(22), 12031–12047. https://doi.org/10.1007/s00500-019-03761-6
Guo, H., Zhou, J., Liu, F., He, Y., Huang, H., & Wang, H. (2020). Application of machine learning method to quantitatively evaluate the droplet size and deposition distribution of the UAV spray nozzle. Applied Sciences, 10(5), 1759. https://doi.org/10.3390/app10051759
Hakim, H., Alam, M. S., Sangsriratanakul, N., Nakajima, K., Kitazawa, M., Ota, M., Toyofuku, C., Yamada, M., Thammakarn, C., Shoham, D., & Takehara, K. (2016). Inactivation of bacteria on surfaces by sprayed slightly acidic hypochlorous acid water: in vitro experiments. The Journal of Veterinary Medical Science, 78(7), 1123–1128. https://doi.org/10.1292/jvms.16-0075
Hindson, J. (2020). COVID-19: faecal–oral transmission? Nature Reviews Gastroenterology & Hepatology, 17(5), 259–259. https://doi.org/10.1038/s41575-020-0295-7
Jiang, S. (2020). Don’t rush to deploy COVID-19 vaccines and drugs without sufficient safety guarantees. Nature, 579, 321–321. https://doi.org/10.1038/d41586-020-00751-9
Johns Hopkins University. (2020). COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). https://coronavirus.jhu.edu/map.html
Junior, C. R., Gomes, P. H., Mano, L. Y., de Oliveira, R. B., de Car valho, A. C. P. de L. F., & Faiçal, B. S. (2017). A machine learning-based approach for prediction of plant protection product deposition [Conference presentation]. 2017 Brazilian Conference on Intelligent Systems (BRACIS), Uberlandia, Brazil. https://doi.org/10.1109/BRACIS.2017.26
Ko, C. H. (2010). An integrated framework for reducing precast fabrication inventory. Journal of Civil Engineering and Management, 16(3), 418–427. https://doi.org/10.3846/jcem.2010.48
Launder, B. E., & Spalding, D. B. (1974). The numerical computation of turbulent flows. Computer Methods in Applied Mechanics and Engineering, 3(2), 269–289. https://doi.org/10.1016/0045-7825(74)90029-2
Lefebvre, A. H., & McDonell, V. G. (2017). Atomization and sprays. CRC Press. https://doi.org/10.1201/9781315120911
Lichtarowicz, A., Duggins, R. K., & Markland, E. (1965). Discharge coefficients for incompressible non-cavitating flow through long orifices. Journal of Mechanical Engineering Science, 7(2), 210–219. https://doi.org/10.1243/JMES_JOUR_1965_007_029_02
Mistcooling Inc. (2020). Dimensions of misting nozzle. https://www.mistcooling.com/mist-nozzles-10-24-thread.html
Nurick, W. H. (1976). Orifice cavitation and its effect on spray mixing. Journal of Fluids Engineering, 98(4), 681–687. https://doi.org/10.1115/1.3448452
Rafiei, M. H., & Adeli, H. (2017). A novel machine learningbased algorithm to detect damage in high-rise building structures. The Structural Design of Tall and Special Buildings, 26(18), e1400. https://doi.org/10.1002/tal.1400
Rafiei, M. H., & Adeli, H. (2018a). Novel machine-learning model for estimating construction costs considering economic variables and indexes. Journal of Construction Engineering and Management, 144(12), 04018106. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001570
Rafiei, M. H., & Adeli, H. (2018b). A novel unsupervised deep learning model for global and local health condition assessment of structures. Engineering Structures, 156, 598–607. https://doi.org/10.1016/j.engstruct.2017.10.070
Ranz, W. E. (1958). Some experiments on orifice sprays. The Canadian Journal of Chemical Engineering, 36(4), 175–181. https://doi.org/10.1002/cjce.5450360405
Santangelo, P. E. (2010). Characterization of high-pressure watermist sprays: Experimental analysis of droplet size and dispersion. Experimental Thermal and Fluid Science, 34(8), 1353– 1366. https://doi.org/10.1016/j.expthermflusci.2010.06.008
Shahrara, N., Çelik, T., & Gandomi, A. H. (2017). Gene expression programming approach to cost estimation formulation for utility projects. Journal of Civil Engineering and Management, 23(1), 85–95. https://doi.org/10.3846/13923730.2016.1210214
Shih, T.-H., Liou, W. W., Shabbir, A., Yang, Z., & Zhu, J. (1995). A new k-ϵ eddy viscosity model for high reynolds number turbulent flows. Computers & Fluids, 24(3), 227–238. https://doi.org/10.1016/0045-7930(94)00032-T
Take, W. A. (2015). Thirty-Sixth Canadian Geotechnical Colloquium: Advances in visualization of geotechnical processes through digital image correlation. Canadian Geotechnical Journal, 52(9), 1199–1220. https://doi.org/10.1139/cgj-2014-0080
van Doremalen, N., Bushmaker, T., Morris, D. H., Holbrook, M. G., Gamble, A., Williamson, B. N., Tamin, A., Harcourt, J. L., Thornburg, N. J., Gerber, S. I., Lloyd-Smith, J. O., de Wit, E., & Munster, V. J. (2020). Aerosol and surface stability of SARSCoV-2 as compared with SARS-CoV-1. New England Journal of Medicine, 382(16), 1564–1567. https://doi.org/10.1056/NEJMc2004973
Wang, C., Abdul-Rahman, H., & Ch’ng, W. S. (2016a). Ant colony optimization (ACO) in scheduling overlapping architectural design activities. Journal of Civil Engineering and Management, 22(6), 780–791. https://doi.org/10.3846/13923730.2014.914100
Wang, C., Abdul-Rahman, H., & Chow, P. S. (2016b). Development and test run of civil engineering schedule acceleration model through ant colony optimization. Journal of Civil Engineering and Management, 22(8), 1009–1020. https://doi.org/10.3846/13923730.2014.945954
Zawidzki, M., & Jankowski, Ł. (2019). Multiobjective optimization of modular structures: Weight versus geometric versatility in a Truss-Z system. Computer-Aided Civil and Infrastructure Engineering, 34(11), 1026–1040. https://doi.org/10.1111/mice.12478
Zhu, P., Wang, X. S., Li, G. C., Liu, Y. P., Kong, X. X., Huang, Y. Q., Zhao, X. D., & Yuan, J. W. (2017). Experimental study on interaction of water mist spray with high-velocity gas jet. Fire Safety Journal, 93, 60–73. https://doi.org/10.1016/j.firesaf.2017.08.005