RESUMEN
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. Large-Eddy Simulations have been used to assess the impact of each separate effect on the thermal puff and particle cloud evolution over the first 2 s after the onset of the exhalation. Results obtained during this short-term period suggest that due to the rapid mixing of the turbulent puff, buoyancy forces play a moderate role on the ability of the particles to disperse. Because of the enhanced mixing, buoyancy reduces the range and increases the vertical size of the small particle clouds. In comparison to the fixed frame case, head rotation has been found to notably affect the size and shape of the cloud by enhancing the vertical transport as the exhalation axial direction sweeps vertically during the exhalation. The impact of the upper airway geometry, in comparison to an idealized mouth consisting in a pipe of circular section, has been found to be the largest when it is considered along with the head rotation.
RESUMEN
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 k-ϵ turbulence model. In both cases, the dispersed phase has been modelled as spherical Lagrangian particles using the one-way coupling assumption. Overall, the URANS model is capable of reproducing the observed tendency of light particles under 64 µm in diameter to rise due to the action of the drag exerted by the buoyant puff generated by the cough. Both DNS and URANS found that particles above 64 µm will tend to describe parabolic trajectories under the action of gravitational forces. Grid independence analysis allows to qualify the impact of increasing mesh resolution on the particle cloud statistics as flow evolves. Results suggest that the k-ϵ model overpredicts the horizontal displacement of the particles smaller than 64 µm while the opposite occurs for the particles larger than 64 µm.
RESUMEN
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. While the dynamics of the largest droplets are dominated by gravitational effects, the smaller aerosol particles, mostly transported by means of hydrodynamic drag, form clouds that can remain afloat for long times. In subsaturated air environments, the dependence of pathogen-laden particle dispersion on their size is complicated due to evaporation of the aqueous fraction. Particle dynamics can significantly change when ambient conditions favor rapid evaporation rates that result in a transition from buoyancy-to-drag dominated dispersion regimes. To investigate the effect of particle size and evaporation on pathogen-laden cloud evolution, a direct numerical simulation of a mild cough was coupled with an evaporative Lagrangian particle advection model. The results suggest that while the dispersion of cough particles in the tails of the size distribution are unlikely to be disrupted by evaporative effects, preferential aerosol diameters (30-40 µm) may exhibit significant increases in the residence time and horizontal range under typical ambient conditions. Using estimations of the viral concentration in the spewed fluid and the number of ejected particles in a typical respiratory event, we obtained a map of viral load per volume of air at the end of the cough and the number of virus copies per inhalation in the emitter vicinity.
RESUMEN
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. Here, we numerically investigate the hydrodynamics produced by a violent expiratory event resembling a mild cough. Coughs can be split into an initial jet stage during which air is expelled through mouth and a dissipative phase over which turbulence intensity decays as the puff penetrates the environment. Time-varying exhaled velocity and buoyancy due to temperature differences between the cough and the ambient air affect the overall flow dynamics. The direct numerical simulation (DNS) of an idealized isolated cough is used to characterize the jet/puff dynamics using the trajectory of the leading turbulent vortex ring and extract its topology by fitting an ellipsoid to the exhaled fluid contour. The three-dimensional structure of the simulated cough shows that the assumption of a spheroidal puff front fails to capture the observed ellipsoidal shape. Numerical results suggest that, although analytical models provide reasonable estimates of the distance traveled by the puff, trajectory predictions exhibit larger deviations from the DNS. The fully resolved hydrodynamics presented here can be used to inform new analytical models, leading to improved prediction of cough-induced pathogen-laden aerosol dispersion.
RESUMEN
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. The working dataset is assembled by combining heterogeneous data including time series from Clinical Information Systems, patient demographics, medical records and respiratory event logs. Three classification learners have been compared: Logistic Discriminant Analysis, Gradient Boosting Method and Support Vector Machines. Standard methodologies have been used for preprocessing, hyperparameter tuning and resampling. RESULTS: The Support Vector Machine classifier is found to correctly predict the outcome of an extubation with a 94.6% accuracy. Contrary to current decision-making criteria for extubation based on Spontaneous Breathing Trials, the classifier predictors only require monitor data, medical entry records and patient demographics. CONCLUSIONS: Machine Learning-based tools have been found to accurately predict the extubation outcome in critical patients with invasive mechanical ventilation. The use of this important predictive capability to assess the extubation decision could potentially reduce the rate of extubation failure, currently at 9%. With about 40% of critically ill patients eventually receiving invasive mechanical ventilation during their stay and given the serious potential complications associated to reintubation, the excellent predictive ability of the model presented here suggests that Machine Learning techniques could significantly improve the clinical outcomes of critical patients.
