A VOF-CFD investigation of mucus transport dynamics with non-Newtonian rheology in a patient-specific tracheobronchial airway across the respiratory cycle.

Computational fluid dynamics (CFD) simulations investigating mucus transport in human lung during the resting respiratory cycle remain limited-particularly under pathological conditions such as chronic obstructive pulmonary disease (COPD). In this study, a three-dimensional tracheobronchial geometry is reconstructed from computed tomography (CT) images of a patient with COPD. Then, volume of fluid (VOF) model and CFD simulations with distinct pathophysiological scenarios are performed on the airway model to investigate the air-mucus interaction during resting respiration, including the formation of mucus plaque and bronchial muco-obstruction, as well as the dynamics of mucus transport and the efficiency of mucus clearance. The effects of mucus non-Newtonian characteristics on mucus transport are investigated using the Herschel-Bulkley model. In addition, the influences of mucus rheology, ciliary motion, and mucus secretion on airflow and mucus transport are investigated quantitatively. The simulation results demonstrate that there are significant differences (p < 0.05) in the temporal variations of mucus thickness along the airway wall between expiration phase and inspiration phase especially at the trachea bifurcation. The mucus plugging may enhance the mucus viscosity due to the shear-thinning effect and consequently exacerbate the challenges associated with mucus clearance in COPD patients. Due to the pathological changes in mucus production and rheological characteristics, the mucus clearance efficiency in COPD patients is less than half of that in healthy individuals under resting respiratory condition. In addition, the declined ciliary motion exerts detrimental effects on mucus transport and clearance, particularly under condition of high mucus viscosity. The VOF-CFD model developed in this study enables quantitative analysis of mucus transport dynamics across diverse pathophysiological conditions and can be further refined to support personalized therapeutic assessment through predictions of mucus clearance efficacy.