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Targeted nasal drug delivery can provide improved efficacy for drug formulations to be delivered at high efficacy rates. Some parameters that influence drug delivery have a dependency on the patient's technique of administration and the spray device itself. When the different parameters, each having a specific range of values are combined, the combinatory permutations for studying its effects on particle deposition become large. In this study, we combine six input spray parameters (the spray half-cone angle, the mean spray exit velocity, the breakup length from the nozzle exit, the diameter of the nozzle spray device, the particle size, and the sagittal angle of the spray) with a range of values to produce 384 combinations of spray characteristics. This was repeated for three inhalation flow rates of 20, 40, and 60 L/min. To reduce the computational costs of a full transient Large Eddy Simulation flow field, we create a time-averaged frozen field and perform the time integration of particle trajectories through the flow field to determine the particle deposition in four anatomical regions of the nasal cavity (anterior, middle, olfactory and posterior) for each of the 384 spray field. A sensitivity analysis determined the significance of each input variable on the deposition. It was found the particle size distribution significantly affected deposition in the olfactory and posterior regions, while the spray device insertion angle was significant for deposition in the anterior and middle regions. Five machine learning models were evaluated based on 384 cases and it was found that despite the small sample dataset the simulation data was sufficient to provide accurate machine-learning predictions. [Display omitted] [ABSTRACT FROM AUTHOR]

The nasal valve is the narrowest part of the nasal airway which is responsible for the largest part of the nasal resistance. Even little changes in the aperture can affect the flow downstream through the nose significantly. Its principal function is to limit airflow for example during a rapid and short inhalation, also called a sniff. Coupling Computational Fluid Dynamics (CFD) with Fluid–Structure Interaction (FSI) allows solving and exchanging force and displacement between the solid and fluid domains and offers a more accurate representation of the physical system in confined flow cases. Furthermore, particle transport and deposition are performed in this study to reveal the effect of the complex coupling on the nasal cavity deposition of inhaled aerosols. Two different configurations are used to model the nasal valve and differences in magnitudes in deformations are observed during the sniff. A comparison between FSI results and the in-vivo evaluation of the deformation shows an acceptable agreement as to the first step of validation. In addition, the results demonstrated that FSI increases significantly the particle deposition in the nasal cavity and the micro-particle diameter is the critical range parameter to enhance deposition with nasal valve deformation during a sniff. • The results demonstrated that FSI increases significantly the particle deposition in the nasal cavity. • The micro-particle diameter is the critical range parameter to enhance deposition with nasal valve deformation during a sniff. • The Comparison between FSI results and the in-vivo evaluation of the deformation shows an acceptable agreement as first step of validation. [ABSTRACT FROM AUTHOR]

In view of the ongoing COVID-19 pandemic and its effects on global health, understanding and accurately modelling the propagation of human biological aerosols has become crucial. Worldwide, health professionals have been one of the most affected demographics, representing approximately 20% of all cases in Spain, 10% in Italy and 4% in China and US. Methods to contain and remove potentially infected aerosols during Aerosol Generating Procedures (AGPs) near source offer advantages in reducing the contamination of protective clothing and the surrounding theatre equipment and space. In this work we describe the application of computational fluid dynamics in assessing the performance of a prototype extraction hood as a means to contain a high speed aerosol jet. Whilst the particular prototype device is intended to be used during tracheotomies, which are increasingly common in the wake of COVID-19, the underlying physics can be adapted to design similar machines for other AGPs. Computational modelling aspect of this study was largely carried out by Barcelona Supercomputing Center using the high performance computational mechanics code Alya. Based on the high fidelity LES coupled with Lagrangian frameworks the results demonstrate high containment efficiency of generated particles is feasible with achievable air extraction rates. • Evaluation of a new device to reduce the risk of infection during Aerosol Generating Procedures for use in COVID-19 surgical tracheotomy. • High fidelity LES coupled with Lagrangian frameworks is used as results to demonstrate the efficiency. • Accurate numerical modelling of the propagation of human biological aerosols. [ABSTRACT FROM AUTHOR]

Accurate prediction of deformation and collapse of the upper airway during breathing is required for effective and personalised treatment of obstructive sleep apnoea (OSA). While numerical modelling techniques such as fluid–structure interaction (FSI) are promising, an outstanding challenge is to accurately predict the deformation of the airway during breathing and thus the occurrence of OSA. These difficulties arise because the effective stiffness of the soft tissue in the human upper airway varies due to neuromuscular effects on the stiffness of the underlying muscles. In addition, both the elasticity and anisotropy of the soft tissues along the upper airway are poorly characterised. Finally, gravitational effects on anatomic features are yet to be considered. In this study, a validated FSI technique is introduced that allows prediction of the extent and position of the major deformation in the upper airway. This technique is used to analyse the behaviour of the upper airway in the two most common sleeping positions and for a range of effective tissue stiffnesses. The results demonstrate that sleeping position, gravity and soft tissue stiffness (used here as a proxy for neuromuscular effects) are the main factors that affect upper airway collapse. Therefore, this study provides new insights into the mechanisms of OSA and a new methodology that significantly advances the patient-specific treatment of OSA. [ABSTRACT FROM AUTHOR]