Can CFD establish a connection to a milder COVID-19 disease in younger people? Aerosol deposition in lungs of different age groups based on Lagrangian particle tracking in turbulent flow

A Critical Analysis of the CFD-DEM Simulation of Pharmaceutical Aerosols Deposition in Upper Intra-Thoracic Airways: Considerations on Aerosol Transport and Deposition

The reliability and accuracy of numerical models and computer simulations to study aerosol deposition in the human respiratory system is investigated for a patient-specific tracheobronchial tree geometry. A computational fluid dynamics (CFD) model coupled with discrete elements methods (DEM) is used to predict the transport and deposition of the aerosol. The results are compared to experimental and numerical data available in the literature to study and quantify the impact of the modeling parameters and numerical assumptions. Even if the total deposition compares very well with the reference data, it is clear from the present work how local deposition results can depend significantly upon spatial discretization and boundary conditions adopted to represent the respiratory act. The modeling of turbulent fluctuations in the airflow is also found to impact the local deposition and, to a minor extent, the flow characteristics at the inlet of the computational domain. Using the CFD-DEM model, it was also possible to calculate the airflow and particles splitting at bifurcations, which were found to depart from the assumption of being equally distributed among branches adopted by some of the simplified deposition models. The results thus suggest the need for further studies towards improving the quantitative prediction of aerosol transport and deposition in the human airways.

Optimal Treatment of Tumor in Upper Human Respiratory Tract Using Microaerosols

Lung cancer is a frequently diagnosed respiratory disease caused by particulate matter in the environment, especially among older individuals. For its effective treatment, a promising approach involves administering drug particles through the inhalation route. Multiple studies have investigated the flow behavior of inhaled particles in the respiratory airways of healthy patients. However, the existing literature lacks studies on the precise understanding of the transportation and deposition (TD) of inhaled particles through age-specific, unhealthy respiratory tracts containing a tumor, which can potentially optimize lung cancer treatment. This study aims to investigate the TD of inhaled drug particles within a tumorous, age-specific human respiratory tract. The computational model reports that drug particles within the size range of 5-10 μm are inclined to deposit more on the tumor located in the upper airways of a 70-year-old lung. Conversely, for individuals aged 50 and 60 years, an optimal particle size range for achieving the highest degree of particle deposition onto upper airway tumor falls within the 11-20 μm range. Flow disturbances are found to be at a maximum in the airway downstream of the tumor. Additionally, the impact of varying inhalation flow rates on particle TD is examined. The obtained patterns of airflow distribution and deposition efficiency on the tumor wall for different ages and tumor locations in the upper tracheobronchial airways would be beneficial for developing an efficient and targeted drug delivery system.

On the efficacy of facial masks to suppress the spreading of pathogen-carrying saliva particles during human respiratory events: Insights gained via high-fidelity numerical modeling

Respiratory fluid dynamics is integral to comprehending the transmission of infectious diseases and the effectiveness of interventions such as face masks and social distancing. In this research, we present our recent studies that investigate respiratory particle transport via high-fidelity large eddy simulation coupled with the Lagrangian particle tracking method. Based on our numerical simulation results for human respiratory events with and without face masks, we demonstrate that facial masks could significantly suppress particle spreading. The studied respiratory events include coughing and normal breathing through mouth and nose. Using the Lagrangian particle tracking simulation results, we elucidated the transport pathways of saliva particles during inhalation and exhalation of breathing cycles, contributing to our understanding of respiratory physiology and potential disease transmission routes. Our findings underscore the importance of respiratory fluid dynamics research in informing public health strategies to reduce the spread of respiratory infections. Combining advanced mathematical modeling techniques with experimental data will help future research on airborne disease transmission dynamics and the effectiveness of preventive measures such as face masks.

Characterization of mass distribution in a biomimetic aerosol exposure system

OBJECTIVE

Lack of biomimicry in geometry and flow conditions of emissions systems for analytical testing and biological exposure has led to fundamental limitations, including a poor understanding of dose delivered to specific airway locations. This work characterizes mass distribution of a JUUL® brand e-cigarette in a Biomimetic Aerosol Exposure System (BAES).

MATERIALS AND METHODS

A combination of mass balance, direct measurements, and inferences based on direct measurements were used to characterize regional and local dose as a function of system flow path configuration and emissions topography profile.

RESULTS

Doses produced by the emissions topography profile with only puffing were significantly different from profiles with clean air inhalation following puffs. Mass characterization results support that dose can be manipulated using flow path geometry. Local and regional deposition was mapped throughout the system.

DISCUSSION AND CONCLUSIONS

We estimate the fraction of yield to the mouth deposited at several locations throughout the system for a variety of puffing and respiration topographies and show that emissions topography profile and system flow path geometry affect dose. This work provides proof-of-concept for assessing mass distribution as a function of aerosol generator (e-cigarette product), user airway geometry, and inhalation and puffing topography.

Numerical analysis of micro lunar dust deposition in the human nasal airway

The toxicity of Lunar dust (LD) is well-known to harm astronauts' health. However, the characteristics of micro-LD deposition in the human nasal airway remains unknown, and studying it through experiments is challenging. Therefore, this study employs numerical investigations to address this issue. Our findings reveal that LD larger than 4 µm primarily (>50%) deposit in the nasal cavity at an inspiration flow rate of Q= 40 L/min, while LD smaller than 8 µm are more likely (>50%) to enter the lung lobe at Q= 15 L/min. The right upper lung lobe receives a higher deposit fraction of LD compared to other lobes, reaching a maximum of 31%. The ratio of deposition fraction in the right lung and left lung can reach to 3.0. Accurately predicting LD deposition in the upper airway and entire lung is possible using mathematical expressions, but the prediction becomes more challenging for the bronchial airway and lung lobes. These results indicate that micro-LD deposition characteristics in the human nasal airway are influenced by LD size and astronauts' activity level. The deposition fractions can be used to assess the health risk from lunar dust to astronauts and provide insights into developing protective measures against LD exposure.

Development of multi-generation lower respiratory tract model and insights into the transport and deposition characteristics of inhalable particles

Airborne particles can spread quickly and enter human respiratory system via inhalation, causing chronic diseases, even cancer. Although recent studies have informed of toxicity of various pollutants, understanding the transport and deposition characteristics of particles in lower respiratory tract is still challenging. The current study proposes a novel model to simulate flow field change from the entrance of lower respiratory tract to pulmonary acinus, while studying particle transport and deposition characteristics. This model for lower respiratory tract with several bronchial extensions containing virtual pulmonary acinus is calculated using computational fluid dynamics and dynamics mesh. The results showed that in the first 10 generations of the lower respiratory tract, vortices and gravity interfered with particles' trajectory, affecting particle deposition distribution. For the first to the tenth-generation respiratory tract, coarse particles were deposited throughout almost the whole respiratory tract model. In contrast, ultrafine particles did not deposit in the higher-generation respiratory tract. The particle enrichment ability of various lobes was uneven with three particle deposition fraction variation patterns. Virtual pulmonary acinus influenced particle deposition and distribution because of vortex ring's trapped ability during expansion and contraction. This new attempt to build a virtual pulmonary acinus model to simulate particle deposition effects in human respiratory system may provide a reference for studying the toxicities of inhalable particles in the exposed human body.

In Silico Quantification of Intersubject Variability on Aerosol Deposition in the Oral Airway

The extrathoracic oral airway is not only a major mechanical barrier for pharmaceutical aerosols to reach the lung but also a major source of variability in lung deposition. Using computational fluid dynamics, deposition of 1−30 µm particles was predicted in 11 CT-based models of the oral airways of adults. Simulations were performed for mouth breathing during both inspiration and expiration at two steady-state flow rates representative of resting/nebulizer use (18 L/min) and of dry powder inhaler (DPI) use (45 L/min). Consistent with previous in vitro studies, there was a large intersubject variability in oral deposition. For an optimal size distribution of 1−5 µm for pharmaceutical aerosols, our data suggest that >75% of the inhaled aerosol is delivered to the intrathoracic lungs in most subjects when using a nebulizer but only in about half the subjects when using a DPI. There was no significant difference in oral deposition efficiency between inspiration and expiration, unlike subregional deposition, which shows significantly different patterns between the two breathing phases. These results highlight the need for incorporating a morphological variation of the upper airway in predictive models of aerosol deposition for accurate predictions of particle dosimetry in the intrathoracic region of the lung.

Anatomy matters: The role of the subject-specific respiratory tract on aerosol deposition - A CFD study

The COVID-19 pandemic is one of the greatest challenges to humanity nowadays. COVID-19 virus can replicate in the host's larynx region, which is in contrast to other viruses that replicate in lungs only, i.e. SARS. This is conjectured to support a fast spread of COVID-19. However, there is sparse research in this field about quantitative comparison of virus load in the larynx for varying susceptible individuals. In this regard the lung geometry itself could influence the risk of reproducing more pathogens and consequently exhaling more virus. Disadvantageously, there are only sparse lung geometries available. To still be able to investigate realistic geometrical deviations we employ three different digital replicas of human airways up to the 7 th level of bifurcation, representing two realistic lungs (male and female) as well as a more simplified experimental model. Our aim is to investigate the influence of breathing scenarios on aerosol deposition in anatomically different, realistic human airways. In this context, we employ three levels of cardiovascular activity as well as reported experimental particle size distributions by means of Computational Fluid Dynamics (CFD) with special focus on the larynx region to enable new insights into the local virus loads in human respiratory tracts. In addition, the influence of more realistic boundary conditions is investigated by performing transient simulations of a complete respiratory cycle in the upper lung regions of the considered respiratory models, focusing in particular on deposition in the oral cavity, the laryngeal region, and trachea, while simplifying the tracheobronchial tree. The aerosol deposition is modeled via OpenFOAM\protect \relax \special {t4ht=®} by employing an Euler-Lagrangian frame including steady and unsteady Reynolds Averaged Navier-Stokes (RANS) resolved turbulent flow using the k- ω -SST and k- ω -SST DES turbulence models. We observed that the respiratory geometry altered the local deposition patterns, especially in the laryngeal region. Despite the larynx region, the effects of varying flow rate for the airway geometries considered were found to be similar in the majority of respiratory tract regions. For all particle size distributions considered, localized particle accumulation occurred in the larynx of all considered lung models, which were more pronounced for larger particle size distributions. Moreover, it was found, that employing transient simulations instead of steady-state analysis, the overall particle deposition pattern is maintained, however with a stronger intensity in the transient cases.

How long and effective does a mask protect you from an infected person who emits virus-laden particles: By implementing one-dimensional physics-based modeling

SARS-CoV-2 spreads via droplets, aerosols, and smear infection. From the beginning of the COVID-19 pandemic, using a facemask in different locations was recommended to slow down the spread of the virus. To evaluate facemasks' performance, masks' filtration efficiency is tested for a range of particle sizes. Although such tests quantify the blockage of the mask for a range of particle sizes, the test does not quantify the cumulative amount of virus-laden particles inhaled or exhaled by its wearer. In this study, we quantify the accumulated viruses that the healthy person inhales as a function of time, activity level, type of mask, and room condition using a physics-based model. We considered different types of masks, such as surgical masks and filtering facepieces (FFPs), and different characteristics of public places such as office rooms, buses, trains, and airplanes. To do such quantification, we implemented a physics-based model of the mask. Our results confirm the importance of both people wearing a mask compared to when only one wears the mask. The protection time for light activity in an office room decreases from 7.8 to 1.4 h with surgical mask IIR. The protection time is further reduced by 85 and 99% if the infected person starts to cough or increases the activity level, respectively. Results show the leakage of the mask can considerably affect the performance of the mask. For the surgical mask, the apparent filtration efficiency reduces by 75% with such a leakage, which cannot provide sufficient protection despite the high filtration efficiency of the mask. The facemask model presented provides key input in order to evaluate the protection of masks for different conditions in public places. The physics-based model of the facemask is provided as an online application.

Towards understanding respiratory particle transport and deposition in the human respiratory system: Effects of physiological conditions and particle properties

Fly ash is a common solid residue of incineration plants and poses a great environmental concern because of its toxicity upon inhalation exposure. The inhalation health impacts of fly ash is closely related to its transport and deposition in the human respiratory system which warrants significant research for health guideline setting and inhalation exposure protection. In this study, a series of fly ash transport and deposition experiments have been carried out in a bifurcation airway model by optical aerosol sampling analysis. Three types of fly ash samples of different morphologies were tested and their respiratory deposition and transport processes were compared. The deposition efficiencies were calculated and relevant transport dynamics mechanisms were discussed. The influences of physiological conditions such as breathing rate, duration, and fly ash physical properties (size, morphology, and specific surface area) were investigated. The deposition characteristics of respiratory particles containing SARS-CoV-2 has also been analyzed, which could further provide some guidance on COVID-19 prevention. The results could potentially serve as a basis for setting health guidelines and recommending personal respiratory protective equipment for fly ash handlers and people who are in the high exposure risk environment for COVID-19 transmission.

COVID-19: respiratory disease diagnosis with regularized deep convolutional neural network using human respiratory sounds

Human respiratory sound auscultation (HRSA) parameters have been the real choice for detecting human respiratory diseases in the last few years. It is a challenging task to extract the respiratory sound features from the breath, voice, and cough sounds. The existing methods failed to extract the sound features to diagnose respiratory diseases. We proposed and evaluated a new regularized deep convolutional neural network (RDCNN) architecture to accept COVID-19 sound data and essential sound features. The proposed architecture is trained with the COVID-19 sound data sets and gives a better learning curve than any other state-of-the-art model. We examine the performance of RDCNN with Max-Pooling (Model-1) and without Max-Pooling (Model-2) functions. In this work, we observed that RDCNN model performance with three sound feature extraction methods [Soft-Mel frequency channel, Log-Mel frequency spectrum, and Modified Mel-frequency Cepstral Coefficient (MMFCC) spectrum] for COVID-19 sound data sets (KDD-data, ComParE2021-CCS-CSS-Data, and NeurlPs2021-data). To amplify the models' performance, we applied the augmentation technique along with regularization. We have also carried out this work to estimate the mutation of SARS-CoV-2 in the five waves using prognostic models (fractal-based). The proposed model achieves state-of-the-art performance on the COVID-19 sound data set to identify COVID-19 disease symptoms. The model's learnable parameter gradients have vanished in the intermediate layers while optimizing the prediction error which is addressed with our proposed RDCNN model. Our experiments suggested that 3 × 3 kernel size for regularized deep CNN (without max-pooling) shows 2-3% better classification accuracy compared to RDCNN with max-pooling. The experimental results suggest that this new approach may achieve the finest results on respiratory diseases.

Computer Added Simulation of the Spread of Multidrug-Resistant Bacteria in an Radiation Therapy Shelter Based on Computational Fluid Dynamics

Purpose

Due to the poor ventilation and air stagnation in the radiation therapy ward, it is easy to cause respiratory disease transmission, which brings about the public health safety problem of infection. In order to alleviate this problem, we propose a research method based on computational fluid dynamics (CFD).

Method

A three-dimensional model of a radiation therapy ward is established, and the CFD software framework is used to numerically simulate the air flow field in the constrained radiation therapy ward environment. We computed the influence of the spray speed, particle size, and inlet content of respiratory droplets on the flow and spread of multidrug-resistant bacteria.

Results

In the range of the horizontal transmission line X from 0 to 3 meters, when the transmission speed (V) is 35 m/s, the multidrug-resistant bacteria concentration reaches the highest value. In the range of the vertical transmission line Y from 0 to 3 meters, when V is 35 m/s, the multidrug-resistant bacteria concentration reaches the highest value.

Conclusion

A large amount of data shows that there is a positive correlation between the respiratory droplet spray velocity, inlet content, and the multidrug-resistant bacteria flow propagation speed and concentration distribution. The respiratory droplet size mainly affects the peak concentration of the multidrug-resistant bacteria flow propagation.

A Model for Translation and Rotation Resistance Tensors for Superellipsoidal Particles in Stokes Flow

In this paper, forces and torques on solid, non-spherical, orthotropic particles in Stokes flow are investigated by using a numerical approach on the basis of the Boundary Element Method. Different flow patterns around a particle are considered, taking into account the contributions of uniform, rotational and shear flows, to the force and the torque exerted on the particle. The expressions for the force and the toque are proposed, by introducing translation, rotation and deformation resistance tensors, which capture each of the flow patterns individually. A parametric study is conducted, considering a wide range of non-spherical particles, determined by the parametric superellipsoid surface equation. Using the results of the parametric study, an approximation scheme is derived on the basis of a multivariate polynomial expression. A coefficient matrix for the polynomial model is introduced, which is used as a tunable parameter for a minimization problem, whereby the polynomials are fitted to the data. The developed model is then put to the test by considering a few examples of particles with different shapes, while also being compared to other, currently available solutions. On top of that, the full functionality of the model is demonstrated by considering an example of a pollen grain, as a realistic non-spherical particle. First, a superellipsoid, which best fits the actual particle shape, is found from the considered range. After that, the coefficients of the translation, rotation and deformation resistance tensors are obtained from the present model and compared to the results of other available models. In the conclusion, a superior accuracy of the present model, for the considered range of particles, is established. To the best of the authors knowledge, this is also one of the first models to extend the torque prediction capabilities beyond sphere and prolate particles. At the same time, the model was demonstrated to be simple to implement and very conservative with the computational resources. As such, it is suitable for large scale studies of dispersed two-phase flows, with a large number of particles.

A Multiscale Approach for the Numerical Simulation of Turbulent Flows with Droplets

A multiscale approach for the detailed simulation of water droplets dispersed in a turbulent airflow is presented. The multiscale procedure combines a novel representative volume element (RVE) with the Pseudo Direct Numerical Simulation (P-DNS) method. The solution at the coarse-scale relies on a synthetic model, constructed using precomputed offline RVE simulations and an alternating digital tree, to characterize the non-linear dynamic response at the fine-scale. A set of numerical experiments for a wide range of volume fractions, particle distribution sizes, and external shear forces in the RVE are carried out. Quantitative results of the statistically stationary turbulent state are obtained, and the turbulence modulation phenomenon due to the presence of droplets is discussed. The developed synthetic model is then employed to solve global scale simulations of flows with airborne droplets via the P-DNS method. Improved predictions are obtained for flow conditions where turbulence modulation is noticeable.

Risk Assessment of Infection by Airborne Droplets and Aerosols at Different Levels of Cardiovascular Activity

Since end of 2019 the COVID-19 pandemic, caused by the SARS-CoV-2 virus, is threatening humanity. Despite the fact that various scientists across the globe try to shed a light on this new respiratory disease, it is not yet fully understood. Unlike many studies on the geographical spread of the pandemic, including the study of external transmission routes, this work focuses on droplet and aerosol transport and their deposition inside the human airways. For this purpose, a digital replica of the human airways is used and particle transport under various levels of cardiovascular activity in enclosed spaces is studied by means of computational fluid dynamics. The influence of the room size, where the activity takes place, and the aerosol concentration is studied. The contribution aims to assess the risk of various levels of exercising while inhaling infectious pathogens to gain further insights in the deposition behavior of aerosols in the human airways. The size distribution of the expiratory droplets or aerosols plays a crucial role for the disease onset and progression. As the size of the expiratory droplets and aerosols differs for various exhaling scenarios, reported experimental particle size distributions are taken into account when setting up the environmental conditions. To model the aerosol deposition we employ OpenFOAM  by using an Euler-Lagrangian frame including Reynolds-Averaged Navier-Stokes resolved turbulent flow. Within this study, the effects of different exercise levels and thus breathing rates as well as particle size distributions and room sizes are investigated to enable new insights into the local particle deposition in the human airway and virus loads. A general observation can be made that exercising at higher levels of activity is increasing the risk to develop a severe cause of the COVID-19 disease due to the increased aerosolized volume that reaches into the lower airways, thus the knowledge of the inhaled particle dynamics in the human airways at various exercising levels provides valuable information for infection control strategies.