Reduced-order modeling of transport of infectious aerosols in ventilated rooms

A new approach to numerical modeling of airborne transmission of respiratory infections, such as COVID-19, influenza, or those caused by common rhinoviruses, is presented. The focus is on the long-range transport of infectious aerosol particles by air flows in indoor environments. The approach is ba...

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Bibliographic Details
Published in:Physics of fluids (1994) 2023-07, Vol.35 (7)
Main Authors: Xiang, Linyan, Lee, Cheol W., Zikanov, Oleg, Abuhegazy, Mohamed, Poroseva, Svetlana V.
Format: Article
Language:English
Subjects:
Aerosols
Air flow
Algorithms
Computational efficiency
Computational fluid dynamics
Computing costs
Cost analysis
Fluid dynamics
Indoor environments
Mathematical models
Model reduction
Numerical models
Ozone
Physics
Reduced order models
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Summary:A new approach to numerical modeling of airborne transmission of respiratory infections, such as COVID-19, influenza, or those caused by common rhinoviruses, is presented. The focus is on the long-range transport of infectious aerosol particles by air flows in indoor environments. The approach is based on the Eulerian description of the aerosol field and the reduced-order modeling (ROM) applied to reduce the computational cost of analysis. The ROM is based on the projection of a computational fluid dynamics (CFD) solution onto a Krylov subspace by an Arnoldi-type algorithm. The algorithm does not require access to the original discretization matrix and, therefore, can be applied to solutions of Eulerian transport problems by general-purpose CFD software, in which such a matrix is often unavailable. The model is validated for a realistic setting via direct comparison of its predictions with the results of the full-order CFD solution based on the Eulerian model and the data of Lagrangian tracking of aerosol particles. Applicability of the ROM to simulation of long-term evolution of the aerosol field and to assessment of infection hazard is demonstrated. Computational tests show that use of ROM reduces the computational cost of analysis by a factor of about 103 without a significant loss in the accuracy of the results.
ISSN:1070-6631
1089-7666
DOI:10.1063/5.0158941