A simplified tempo-spatial model to predict airborne pathogen release risk in enclosed spaces: An Eulerian-Lagrangian CFD approach
| Autor: | Mohammad Moshfeghi, Hadi Bordbar, Parham A. Mirzaei, Yahya Sheikhnejad, Hamid Motamedi |
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| Přispěvatelé: | University of Nottingham, Sogang University, Tarbiat Modares University, University of Aveiro, Department of Civil Engineering, Aalto-yliopisto, Aalto University |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Environmental Engineering tempo-spatial risk model 010504 meteorology & atmospheric sciences COVID19 Geography Planning and Development Airborne pathogen transmission Computational fluid dynamics 01 natural sciences Article 010305 fluids & plasmas Eulerian lagrangian symbols.namesake Spatial model 0103 physical sciences medicine Source model Trajectory (fluid mechanics) airborne pathogen transmission Respiratory disease 0105 earth and related environmental sciences Civil and Structural Engineering business.industry Eulerian-Lagrangian-CFD Eulerian path Building and Construction Mechanics medicine.disease respiratory disease Airborne disease Tempo-spatial risk model symbols Environmental science business artificial neural network |
| Zdroj: | Building and Environment |
| ISSN: | 1873-684X 0360-1323 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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