An efficient computational fluid-particle dynamics method to predict deposition in a simplified approximation of the deep lung

Abstract

High-fidelity simulations of the complete airway tree are still largely beyond current computational capabilities. Towards large-scale simulations of the human lung, the current study introduces a numerical methodology to predict particle deposition in a simplified approximation of the deep lung during a full breathing cycle. The geometrical model employed consists of an idealised bronchial tree that represents generations 10 to 19 of the conducting zone and a heterogeneous acinar model created using a space-filling algorithm. The computational cost of the coupled simulation is reduced by taking advantage of the flow similarity across the central conducting regions in order to decompose the bronchial tree into representative subunits. Topological information is used to account for the correct gravitational force on the particles in the representative bifurcations, emulating their transport characteristics in the actual bronchial tree. Eventually, airflow and particle transport are simulated in a single representative bifurcation and a single acinar model, resulting in great savings in computational cost. An Eulerian-Lagrangian approach has been used for solving the flow and particle equations during sinusoidal breathing in the decomposed domain. The resulting deposition estimates agree rather well with the known deposition trends reported in the literature, while offering additional insights. For 1 − 5μm particles, deposition during exhalation is comparable to deposition upon inhalation, suggesting the use of breath-hold maneuvers to further increase sedimentation of these particles. Airway orientation relative to gravity was found to have a significant impact on deposition rates, especially for particles above 2μm and to be higher in the more distal generations, due to the wider range of angles relative to the direction of gravity. In the acinus, particles in the 2 − 5μm range have a quite high average deposition efficiency that reaches approximately 75% and shows considerable variation (12.4%) due to airway orientation. Finally, a simplified semi-analytical approach is introduced that can lead to even further reduction in computational costs, while incurring only a small loss in accuracy. © 2017 Elsevier B.V.