Adjoint Solver-Based Analysis of Mouth–Tongue Morphologies on Vapor Deposition in the Upper Airway

Abstract

Even though inhalation dosimetry is determined by three factors (i.e., breathing, aerosols, and the respiratory tract), the first two categories have been more widely studied than the last. Both breathing and aerosols are quantitative variables that can be easily changed, while respiratory airway morphologies are difficult to reconstruct, modify, and quantify. Although several methods are available for model reconstruction and modification, developing an anatomically accurate airway model and morphing it to various physiological conditions remains labor-intensive and technically challenging. The objective of this study is to explore the feasibility of using an adjoint–CFD model to understand airway shape effects on vapor deposition and control vapor flux into the lung. A mouth–throat model was used, with the shape of the mouth and tongue being automatically varied via adjoint morphing and the vapor transport being simulated using ANSYS Fluent coupled with a wall absorption model. Two chemicals with varying adsorption rates, Acetaldehyde and Benzene, were considered, which exhibited large differences in dosimetry sensitivity to airway shapes. For both chemicals, the maximal possible morphing was first identified and then morphology parametric studies were conducted. Results show that changing the mouth–tongue shape can alter the oral filtration by 3.2% for Acetaldehyde and 0.27% for Benzene under a given inhalation condition. The front tongue exerts a significant impact on all cases considered, while the impact of other regions varies among cases. This study demonstrates that the hybrid adjoint–CFD approach can be a practical and efficient method to investigate morphology-associated variability in the dosimetry of vapors and nanomedicines under steady inhalation.