ABSTRACT
Airborne pathogen respiratory droplets are the primary route of COVID19 transmission, which are released from infected people. The strength and amplitude of a release mechanism strongly depend on the source mode, including respiration, speech, sneeze, and cough. This study aims to develop a simplified model for evaluation of spreading range (length) in sneeze and cough modes using the results of Eulerian-Lagrangian CFD model. The Eulerian computational framework is first validated with experimental data, and then a high-fidelity Lagrangian CFD model is employed to monitor various scale particles' trajectory, evaporation, and lingering persistency. A series of Eulerian-Lagrangian CFD simulations is conducted to generate a database of bioaerosol release spectrum for the release modes in various thermal conditions of an enclosed space. Eventually, a correlation fitted over the data to offer a simplified airborne pathogen spread model. The simplified model can be applied as a source model for design and decision-making about ventilation systems, occupancy thresholds, and disease transmission risks in enclosed spaces. © 2021 Institute of Physics Publishing. All rights reserved.
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.
ABSTRACT
The outbreak of COVID-19 necessitates developing reliable tools to derive safety measures, including safe social distance and minimum exposure time under different circum-stances. Transient Eulerian–Lagrangian computational fluid dynamics (CFD) models have emerged as a viably fast and economical option. Nonetheless, these CFD models resolve the instantaneous distribution of droplets inside a computational domain, making them incapable of directly being used to assess the risk of infection as it depends on the total accumulated dosage of infecting viruses received by a new host within an exposure time. This study proposes a novel risk assessment model (RAM) to predict the temporal and spatial accumulative concentration of infectious exhaled droplets based on the bio-source’s exhalation profile and droplet distribution using the CFD results of respiratory events in various environmental conditions. Unlike the traditional approach in the bulk movement assessment of droplets’ outreach in a domain, every single droplet is traced inside the domain at each time step, and the total number of droplets passing through any arbitrary position of the domain is determined using a computational code. The performance of RAM is investigated for a series of case studies against various respiratory events where the horizontal and the lateral spread of risky zones are shown to temporarily vary rather than being fixed in space. The sensitivity of risky zones to ambient temperature and relative humidity was also addressed for sample cough and sneeze cases. This implies that the RAM provides crucial information required for defining safety measures such as safety distances or minimum exposure times in different environments. © 2021 by the authors. Li-censee MDPI, Basel, Switzerland.