Spatiotemporal distribution prediction of coughing airflow at mouth based on machine learning—Part II: Boundary inference using neural network

Abstract

In the post-epidemic era, the trajectory of pathogenic airflows and droplets generated by coughing have been widely studied. However, owing to the limitations of measurement methods, there is a lack of detailed data on their spatiotemporal distribution at the mouth during coughing, which are the basis of research and the critical boundary conditions for computational simulation. Previous experiments have determined the velocity distribution of coughing airflow in spaces located far from the mouth. This study aims to collect detailed data at the mouth for use as the Computational Fluid Dynamics (CFD) boundary conditions from the experimental data. In Part I of this study, the critical parameters that describe the boundary conditions at the mouth for CFD simulation were obtained. Based on these parameters, this part infers the detailed temporal and spatial distribution velocity data of the coughing airflow at the mouth using a neural network. We performed CFD simulation on the prediction results with V=10.76 and M=4, and got FAC2=0.56 compared with the experimental values. The results obtained provided a generic detailed boundary condition for coughing airflow at the mouth and appropriate machine-learning parameters. This study can provide more accurate boundary conditions for simulating the propagation of pathogenic airflow and a supplementary database for epidemic prevention research.