A simplified tempo-spatial model to predict airborne pathogen release risk in enclosed spaces: An Eulerian-Lagrangian CFD approach

Parham A. Mirzaei*, Mohammad Moshfeghi, Hamid Motamedi, Yahya Sheikhnejad, Hadi Bordbar

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

COVID19 pathogens are primarily transmitted via airborne respiratory droplets expelled from infected bio-sources. However, there is a lack of simplified accurate source models that can represent the airborne release to be utilized in the safe-social distancing measures and ventilation design of buildings.

Although computational fluid dynamics (CFD) can provide accurate models of airborne disease transmissions, they are computationally expensive. Thus, this study proposes an innovative framework that benefits from a series of relatively accurate CFD simulations to first generate a dataset of respiratory events and then to develop a simplified source model.

The dataset has been generated based on key clinical parameters (i.e., the velocity of droplet release) and environmental factors (i.e., room temperature and relative humidity) in the droplet release modes. An Eulerian CFD model is first validated against experimental data and then interlinked with a Lagrangian CFD model to simulate trajectory and evaporation of numerous droplets in various sizes (0.1 μm–700 μm). A risk assessment model previously developed by the authors is then applied to the simulation cases to identify the horizontal and vertical spread lengths (risk cloud) of viruses in each case within an exposure time. Eventually, an artificial neural network-based model is fitted to the spread lengths to develop the simplified predictive source model. The results identify three main regimes of risk clouds, which can be fairly predicted by the ANN model.
Original languageEnglish
Article number108428
Number of pages15
JournalBuilding and Environment
Volume207 A
DOIs
Publication statusPublished - Jan 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • tempo-spatial risk model
  • COVID19
  • airborne pathogen transmission
  • Eulerian-Lagrangian-CFD
  • respiratory disease
  • artificial neural network

Fingerprint

Dive into the research topics of 'A simplified tempo-spatial model to predict airborne pathogen release risk in enclosed spaces: An Eulerian-Lagrangian CFD approach'. Together they form a unique fingerprint.

Cite this