A GPU-accelerated computational fluid dynamics solver for assessing shear-driven indoor airflow and virus transmission by scale-resolved simulations

TL;DR

This paper introduces DNSLABIB, a GPU-accelerated LES CFD solver in MATLAB, validated against OpenFOAM, to analyze indoor airflow and virus transmission, revealing significant deviations from ideal mixing assumptions and demonstrating infection risk reduction through ventilation.

Contribution

A novel GPU-accelerated LES CFD solver in MATLAB for indoor airflow analysis, validated against existing software, enabling detailed virus transmission modeling.

Findings

CFD simulations show 75-150% deviation from theoretical ACH.

CO₂ distribution indoors is non-uniform, affecting ventilation efficiency.

Ventilation significantly reduces infection risk, halving or decupling it at higher ACH.

Abstract

We explore the applicability of MATLAB for 3D computational fluid dynamics (CFD) of shear-driven indoor airflows. A new scale-resolving, large-eddy simulation (LES) solver titled DNSLABIB is proposed for MATLAB utilizing graphics processing units (GPUs). The solver is first validated against another CFD software (OpenFOAM). Next, we demonstrate the solver performance in three isothermal indoor ventilation configurations and the results are discussed in the context of airborne transmission of COVID-19. Ventilation in these cases is studied at both low (0.1 m/s) and high (1 m/s) airflow rates corresponding to $Re=5000$ and $Re=50000$. An analysis of the indoor CO$_2$ concentration is carried out as the room is emptied from stale, high CO$_2$ content air. We estimate the air changes per hour (ACH) values for three different room geometries and show that the numerical estimates from 3D CFD simulations may differ by 80-150 % ($Re=50000$) and 75-140 % ($Re=5000$) from the theoretical ACH value based on the perfect mixing assumption. Additionally, the analysis of the CO$_2$ probability distributions (PDFs) indicates a relatively non-uniform distribution of fresh air indoors. Finally, utilizing a time-dependent Wells-Riley analysis, an example is provided on the growth of the cumulative infection risk being reduced rapidly after the ventilation is started. The average infection risk is shown to reduce by a factor of 2 for lower ventilation rates (ACH=3.4-6.3) and 10 for the higher ventilation rates (ACH=37-64). The results indicate a high potential for DNSLABIB in various future developments on airflow prediction.