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 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 10(3) without a significant loss in the accuracy of the results.

Automated summary

A new approach to numerical modeling of airborne transmission of respiratory infections in indoor environments is presented in this paper. The approach is based on the Eulerian description of the aerosol field and uses reduced-order modeling (ROM) to reduce computational cost. The ROM is validated through direct comparison with full-order computational fluid dynamics (CFD) solutions and Lagrangian tracking data. Computational tests show that the ROM reduces computational cost by a factor of about 10³ without significant loss in accuracy.