A bouncing computational model of particle–mucus interaction for predictive deposition maps in the airways

Abstract

In computational medicine, particle transport dynamics and deposition maps in the airways are of utmost importance in respiratory health. On the one hand, advantages include a better grasp of accurately delivering pharmaceutical drugs, enhancing treatment effectiveness, and advancing personalised medicine. On the other hand, aerosol deposition maps can improve our understanding of how viruses and bacteria infect the respiratory tract and the lung damage caused by pollutants. This work presents a novel statistical computational model to predict the deposition of solid particles in the upper airways. Unlike the classical “deposit-on-touch” condition, where a particle deposits upon touching the nasal wall, the proposed model determines deposition through particle–wall interaction, considering the surface roughness of the mucus layer coating the nasal cavity walls. Upon collision, if the particle velocity is below a critical threshold, it deposits. The model, based on experimental results from the same CT-based 3D nasal geometry, significantly improves deposition accuracy and provides a physical explanation for the deposition mechanism, offering a robust tool for predictive deposition maps in the human respiratory system.