Search

CFD has emerged as a promising diagnostic tool for clinical trials, with tremendous potential. However, for real clinical applications to be useful, overall statistical findings from large population samples (e.g., multiple cases and models) are needed. Fully resolved solutions are not a priority, but rather rapid solutions with fast turn-around times are desired. This leads to the issue of what are the minimum modelling criteria for achieving adequate accuracy in respiratory flows for large-scale clinical applications, with a view to rapid turnaround times. This study simulated a highly-resolved solution using the large eddy simulation (LES) method as a reference case for comparison with lower resolution models that included larger time steps and no turbulence modelling. Differences in solutions were quantified by pressure loss, flow resistance, unsteadiness, turbulence intensity, and hysteresis effects from multiple cycles. The results demonstrated that sufficient accuracy could be achieved with lower resolution models if the mean flow was considered. Furthermore, to achieve an established transient result unaffected by the initial start-up quiescent effects, the results need to be taken from at least the second respiration cycle. It was also found that the exhalation phase exhibited strong turbulence. The results are expected to provide guidance for future modelling efforts for clinical and engineering applications requiring large numbers of cases using simplified modelling approaches. [ABSTRACT FROM AUTHOR]

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)