Journal Home > Volume 14 , Issue 6

The evolution of coronavirus disease (COVID-19) into a pandemic has severely hampered the usage of public transit systems. In a post-COVID-19 world, we may see an increased reliance on autonomous cars and personal rapid transit (PRT) systems, with inherent physical distancing, over buses, trains and aircraft for intracity, intercity, and interstate travel. However, air travel would continue to be the dominant mode of intercontinental transportation for humans. In this study, we perform a comprehensive computational analysis, using ANSYS Fluent, of typical intercontinental aircraft ventilation systems to determine the seat where environmental factors are most conducive to human comfort with regards to air quality, protection from orally or nasally released pollutants such as CO2 and coronavirus, and thermal comfort levels. Air velocity, temperature, and air pollutant concentration emitted from the nose/mouth of fellow travelers are considered for both Boeing and Airbus planes. In each plane, first class, business class, and economy class sections were analyzed. We present conclusions as to which is the optimum seat in each section of each plane and provide the data of the environmental conditions to support our inferences. The findings may be used by the general public to decide which seat to occupy for their next intercontinental flight. Alternatively, the commercial airliners can use such a model to plan the occupancy of the aircraft on long-duration intercontinental flights (viz., Airbus A380 and Boeing B747).


menu
Abstract
Full text
Outline
Electronic supplementary material
About this article

On COVID-19-safety ranking of seats in intercontinental commercial aircrafts: A preliminary multiphysics computational perspective

Show Author's information Prathamesh S. Desai1( )Nihar Sawant2Andrew Keene3
Mechanical Engineering, W. M. Rice University, Houston, TX, USA
Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
Department of Mechanical Engineering, Carnegie Mellon University, PA, USA

Abstract

The evolution of coronavirus disease (COVID-19) into a pandemic has severely hampered the usage of public transit systems. In a post-COVID-19 world, we may see an increased reliance on autonomous cars and personal rapid transit (PRT) systems, with inherent physical distancing, over buses, trains and aircraft for intracity, intercity, and interstate travel. However, air travel would continue to be the dominant mode of intercontinental transportation for humans. In this study, we perform a comprehensive computational analysis, using ANSYS Fluent, of typical intercontinental aircraft ventilation systems to determine the seat where environmental factors are most conducive to human comfort with regards to air quality, protection from orally or nasally released pollutants such as CO2 and coronavirus, and thermal comfort levels. Air velocity, temperature, and air pollutant concentration emitted from the nose/mouth of fellow travelers are considered for both Boeing and Airbus planes. In each plane, first class, business class, and economy class sections were analyzed. We present conclusions as to which is the optimum seat in each section of each plane and provide the data of the environmental conditions to support our inferences. The findings may be used by the general public to decide which seat to occupy for their next intercontinental flight. Alternatively, the commercial airliners can use such a model to plan the occupancy of the aircraft on long-duration intercontinental flights (viz., Airbus A380 and Boeing B747).

Keywords: COVID-19, indoor air quality, thermal comfort, airborne coronavirus particles, multiphysics simulation, intercontinental aircraft

References(28)

Adwibowo A (2020). Computational fluid dynamics (CFD), air flow- droplet dispersion, and indoor CO2 analysis for healthy public space configuration to comply with COVID 19 protocol. medRxiv, 2020.07.02.20145219
Anderson M, McKee M, Mossialos E (2020). Developing a sustainable exit strategy for COVID-19: health, economic and public policy implications. Journal of the Royal Society of Medicine, 113: 176-178.
ANSYS (2009). ANSYS FLUENT 12.0: Theory Guide. Canonsburg, PA, USA: ANSYS Inc.
ASHRAE (2009). ASHRAE Handbook Fundamentals. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers.
Bhatia D, De Santis A (2020). A preliminary numerical investigation of airborne droplet dispersion in aircraft cabins. Open Journal of Fluid Dynamics, 10: 198-207.
Bosbach J, Kühn M, Rütten M, et al. (2006). Mixed Convection in a full scale aircraft cabin mock-up. In: Proceedings of the 25th Congress of International Council of the Aeronautical Sciences, 3 - 8 September 2006, Hamburg, Germany
Conceição ST, Pereira ML, Tribess A (2011). A review of methods applied to study airborne biocontaminants inside aircraft cabins. International Journal of Aerospace Engineering, 2011: 824951.
Ferris R (2020). Why Hertz landed in bankruptcy court when its rivals didn’t? Available at https://www.cnbc.com/2020/08/17/why-hertz-landed-in-bankruptcy-court-when-its-rivals-didnt.html.
Garbey M, Joerger G, Furr S (2020). A systems approach to assess transport and diffusion of hazardous airborne particles in a large surgical suite: potential impacts on viral airborne transmission. International Journal of Environmental Research and Public Health, 17: 5404.
Garner RP, Wong KL, Ericson SC, et al. (2004). CFD validation for contaminant transport in aircraft cabin ventilation flow fields. Federal Aviation Administration Oklahoma City Ok Civil Aeromedical Inst.
Günther G, Bosbach J, Pennecot J, et al. (2006). Experimental and numerical simulations of idealized aircraft cabin flows. Aerospace Science and Technology, 10: 563-573.
Li F, Liu J, Ren J, et al. (2016). Numerical investigation of airborne contaminant transport under different vortex structures in the aircraft cabin. International Journal of Heat and Mass Transfer, 96: 287-295.
Li Y, Qian H, Hang J, et al. (2020). Evidence for probable aerosol transmission of SARS-CoV-2 in a poorly ventilated restaurant. medRxiv 2020.04.16.20067728
Liu W, Mazumdar S, Zhang Z, et al. (2012a). State-of-the-art methods for studying air distributions in commercial airliner cabins. Building and Environment, 47: 5-12.
Liu W, Wen J, Chao J, et al. (2012b). Accurate and high-resolution boundary conditions and flow fields in the first-class cabin of an MD-82 commercial airliner. Atmospheric Environment, 56: 33-44.
Liu W (2014) Experimental and numerical study of the air distribution in an airliner cabin. Master Thesis, Purdue University, USA.
Mittal R, Ni R, Seo JH (2020). The flow physics of COVID-19. Journal of Fluid Mechanics, 894: F2.
Perella P, Tabarra M, Hataysal E, et al. (2020). Minimising exposure to droplet and aerosolised pathogens: A computational fluid dynamics study. medRxiv 2020.05.30.20117671.
Singh A, Hosni MH, Horstman RH (2002). Numerical simulation of airflow in an aircraft cabin section/Discussion. ASHRAE Transactions, 108(1): 1005-1013.
Vuorinen V, Aarnio M, Alava M, et al. (2020). Modelling aerosol transport and virus exposure with numerical simulations in relation to SARS-CoV-2 transmission by inhalation indoors. Safety Science, 130: 104866.
Wang A, Zhang Y, Sun Y, et al. (2008). Experimental study of ventilation effectiveness and air velocity distribution in an aircraft cabin mockup. Building and Environment, 43: 337-343.
Wilbur M, Ayman A, Ouyang A, et al. (2020). Impact of COVID-19 on Public Transit Accessibility and Ridership. arXiv preprint arXiv:2008.02413.
Wu C, Ahmed NA (2011). Numerical study of transient aircraft cabin flowfield with unsteady air supply. Journal of Aircraft, 48: 1994-2001.
Yan W, Zhang Y, Sun Y, et al. (2009). Experimental and CFD study of unsteady airborne pollutant transport within an aircraft cabin mock-up. Building and Environment, 44: 34-43.
Zhai ZJ, Zhang Z, Zhang W, et al. (2007). Evaluation of various turbulence models in predicting airflow and turbulence in enclosed environments by CFD: part 1—Summary of prevalent turbulence models. HVAC&R Research, 13: 853-870.
Zhang Z, Zhang W, Zhai ZJ, et al. (2007). Evaluation of various turbulence models in predicting airflow and turbulence in enclosed environments by CFD: part 2—Comparison with experimental data from literature. HVAC&R Research, 13: 871-886.
Zhang TT, Yin S, Wang S (2010). An under-aisle air distribution system facilitating humidification of commercial aircraft cabins. Building and Environment, 45: 907-915.
Zhang TT, Li P, Wang S (2012). A personal air distribution system with air terminals embedded in chair armrests on commercial airplanes. Building and Environment, 47: 89-99.
File
12273_2021_774_MOESM1_ESM.pdf (5.3 MB)
Publication history
Copyright
Acknowledgements

Publication history

Received: 28 August 2020
Revised: 05 January 2021
Accepted: 26 January 2021
Published: 11 January 2021
Issue date: December 2021

Copyright

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021

Acknowledgements

Dr. S. Singh, Associate Teaching Professor, Carnegie Mellon University for his guidance and brainstorming activities with the authors. Pittsburgh Supercomputing Center (PSC) for computational resources.

Return