Effects of upper respiratory tract anatomy and head movement on the buoyant flow and particle dispersion generated in a violent expiratory event.

In the wake of the COVID-19 pandemic, interest in understanding the turbulent dispersion of airborne pathogen-laden particles has significantly increased. The ability of infectious particles to stay afloat and disperse in indoor environments depends on their size, the environmental conditions and the hydrodynamics of the flow generated by the exhalation. In this work we analyze the impact of three different aspects, namely, the buoyancy force, the upper airways geometry and the head rotation during the exhalation on the short-term dispersion.

Comparison between fully resolved and time-averaged simulations of particle cloud dispersion produced by a violent expiratory event.

In this work we compare the DNS results (Fabregat et al. 2021, Fabregat et al. 2021) for a mild cough already reported in the literarure with those obtained with a compressible URANS equations with a turbulence model.

Direct numerical simulation of turbulent dispersion of evaporative aerosol clouds produced by an intense expiratory event.

Airborne particles are a major route for transmission of COVID-19 and many other infectious diseases. When a person talks, sings, coughs, or sneezes, nasal and throat secretions are spewed into the air. After a short initial fragmentation stage, the expelled material is mostly composed of spherical particles of different sizes.

Direct numerical simulation of the turbulent flow generated during a violent expiratory event.

A main route for SARS-CoV-2 (severe acute respiratory syndrome coronavirus) transmission involves airborne droplets and aerosols generated when a person talks, coughs, or sneezes. The residence time and spatial extent of these virus-laden aerosols are mainly controlled by their size and the ability of the background flow to disperse them. Therefore, a better understanding of the role played by the flow driven by respiratory events is key in estimating the ability of pathogen-laden particles to spread the infection.

A Machine Learning decision-making tool for extubation in Intensive Care Unit patients.

Background And Objective: To increase the success rate of invasive mechanical ventilation weaning in critically ill patients using Machine Learning models capable of accurately predicting the outcome of programmed extubations.

Methods: The study population was adult patients admitted to the Intensive Care Unit. Target events were programmed extubations, both successful and failed.