Semi-surrogate modelling of droplets evaporation process via XGBoost integrated CFD simulations.

The applications of machine learning (ML) based approach are emerging as possible tools to accelerate CFD simulations. This study proposed a semi-surrogate model for CFD with integration of the cutting-edge ML algorithm, eXtreme Gradient Boosting (XGB), which enlightened a possible pathway to effectively and efficiently solve and predict those costly but highly repetitive fluid dynamics-related problems. Droplet evaporation, a complex but essential phenomenon in respiratory droplets transport, was studied as the practical case using the proposed model. Droplets evaporation and dynamic size distributions were firstly tracked under various combinations of indoor humidity and temperature using traditional Eulerian-Lagrangian CFD framework, followed by generating several datasets for XGB training. The trained XGB was then used to interpret the evaporated droplets size over time under new combinations of indoor conditions. Outcomes revealed that well-trained XGB-base semi-surrogate model was capable of interpreting complex non-linear relationships between droplets dynamic parameters (diameter and time) and indoor parameters (humidity and temperature). For each specific parameter, the predictive error of well-trained XGB could retain below 5 % and its prediction speed was found nearly 1 million times faster than that of new CFD simulations. Successful applications of XGB in conjunction with CFD demonstrated its great potential on providing rapid and more efficient predictions of complex, costly and repetitive fluid dynamics-related phenomenons (e.g. droplets evaporation). Also, the XGB predicted droplets evaporation data from this study could be further applied as initial conditions into new simulations via the User-defined function (UDF).

Detailed comparative analysis of environmental microparticle deposition characteristics between human and monkey nasal cavities using a surface mapping technique.

Inhaled particulate matter is associated with nasal diseases such as allergic rhinitis, rhinosinusitis and neural disorders. Its health risks on humans are usually evaluated by measurements on monkeys as they share close phylogenetic relationship. However, the reliability of cross-species toxicological extrapolation is in doubt due to physiological and anatomical variations, which greatly undermine the reliability of these expensive human surrogate models. This study numerically investigated in-depth microparticle transport and deposition characteristics on human and monkey (Macaca fuscata) nasal cavities that were reconstructed from CT-images. Deposition characteristics of 1-30µm particles were investigated under resting and active breathing conditions. Similar trends were observed for total deposition efficiencies and a single correlation using Stokes Number was fitted for both species and both breathing conditions, which is convenient for monkey-human extrapolation. Regional deposition patterns were carefully compared using the surface mapping technique. Deposition patterns of low, medium and high inertial particles, classified based on their total deposition efficiencies, were further analyzed in the 3D view and the mapped 2D view, which allows locating particle depositions on specific nasal regions. According to the particle intensity contours and regional deposition profiles, the major differences were observed at the vestibule and the floor of the nasal cavity, where higher deposition intensities of medium and high inertial particles were shown in the monkey case than the human case. Comparisons of airflow streamlines indicated that the cross-species variations of microparticle deposition patterns are mainly contributed by two factors. First, the more oblique directions of monkey nostrils result in a sharper airflow turn in the vestibule region. Second, the monkey's relatively narrower nasal valves lead to higher impaction of medium and high inertial particles on the nasal cavity floor. The methods and findings in this study would contribute to an improved cross-species toxicological extrapolation between human and monkey nasal cavities.

An improved numerical model for epidemic transmission and infection risks assessment in indoor environment.

Social distance will remain the key measure to contain COVID-19 before the global widespread vaccination coverage expected in 2024. Containing the virus outbreak in the office is prioritised to relieve socio-economic burdens caused by COVID-19 and potential pandemics in the future. However, "what is the transmissible distance of SARS-CoV-2" and "what are the appropriate ventilation rates in the office" have been under debate. Without quantitative evaluation of the infection risk, some studies challenged the current social distance policies of 1-2 m adopted by most countries and suggested that longer social distance rule is required as the maximum transmission distance of cough ejected droplets could reach 3-10 m. With the emergence of virus variants such as the Delta variant, the applicability of previous social distance rules are also in doubt. To address the above problem, this study conducted transient Computational Fluid Dynamics (CFD) simulations to evaluate the infection risks under calm and wind scenarios. The calculated Social Distance Index (SDI) indicates that lower humidity leads to a higher infection risk due to weaker evaporation. The infection risk in office was found more sensitive to social distance than ventilation rate. In standard ventilation conditions, social distance of 1.7 m-1.8 m is sufficient distances to reach low probability of infection (PI) target in a calm scenario when coughing is the dominant transmission route. However in the wind scenario (0.25 m/s indoor wind), distance of 2.8 m is required to contain the wild virus type and 3 m is insufficient to contain the spread of the Delta variant. The numerical methods developed in this study provide a framework to evaluate the COVID-19 infection risk in indoor environment. The predicted PI will be beneficial for governments and regulators to make appropriate social-distance and ventilation rules in the office.

Prediction of nasal spray drug absorption influenced by mucociliary clearance.

Evaluation of nasal spray drug absorption has been challenging because deposited particles are consistently transported away by mucociliary clearance during diffusing through the mucus layer. This study developed a novel approach combining Computational Fluid Dynamics (CFD) techniques with a 1-D mucus diffusion model to better predict nasal spray drug absorption. This integrated CFD-diffusion approach comprised a preliminary simulation of nasal airflow, spray particle injection, followed by analysis of mucociliary clearance and drug solute diffusion through the mucus layer. The spray particle deposition distribution was validated experimentally and numerically, and the mucus velocity field was validated by comparing with previous studies. Total and regional drug absorption for solute radius in the range of 1 - 110nm were investigated. The total drug absorption contributed by the spray particle deposition was calculated. The absorption contribution from particles that deposited on the anterior region was found to increase significantly as the solute radius became larger (diffusion became slower). This was because the particles were consistently moved out of the anterior region, and the delayed absorption ensured more solute to be absorbed by the posterior regions covered with respiratory epithelium. Future improvements in the spray drug absorption model were discussed. The results of this study are aimed at working towards a CFD-based integrated model for evaluating nasal spray bioequivalence.

Deposition features of inhaled viral droplets may lead to rapid secondary transmission of COVID-19.

Inhaled viral droplets may immediately be expelled and cause an escalating re-transmission. Differences in the deposition location of inhaled viral droplets may have a direct impact on the probability of virus expelling. This study develops a numerical model to estimate the region-specific deposition fractions for inhalable droplets (1-50 µ m) in respiratory airways. The results identified a higher deposition fraction in the upper airways than the lower airways. Particularly for droplets larger than 10 µ m, the relatively high deposition fraction in the oral/laryngeal combined region warns of its easy transmission through casual talking/coughing. Moreover, considering droplet sizes' effect on virus loading capacity, we built a correlation model to quantify the potential of virus expelling hazards, which suggests an amplified cascade effect on virus transmission on top of the existing transmission mechanism. It therefore highlights the importance of considering the instant expelling possibilities from inhaled droplets, and also implies potentials in restricting a rapid secondary transmission by measures that can lower down droplet deposition in the upper airways.