Abstract
Background: Obstructive sleep apnea is a sleeping disorder that has troubled a sizeable population. There is an active area of research on obstructive sleep apnea that intends to better understand airflow behaviors and therefore treat patients more effectively. This paper aims to investigate the airflow characteristics of the upper airway in an obstructive sleep apnea (OSA) patient under light and heavy breathing conditions by using Turbulent Kinetic Energy (TKE), an accurate method in expressing the flow concentration mechanisms of sleeping disorders. It is important to visualize the concentration of flow in the upper airway in order to identify the severity level of the obstruction during sleep. Methods: Computational fluid dynamic (CFD) analysis was used as a solution tool to evaluate the airflow during light and heavy breathing conditions. A medical imaging technique was used to extract the 3D model from the CT scan images. Additionally, mesh generation and simulation were carried out via CFD software to evaluate the light and heavy breathing characteristics related to obstructive sleep apnea. Steady state Reynolds averaged Navier-Stoke (RANS) with the k-omega shear stress transport (SST) turbulence model was utilized. The airflow characteristics were quantified using parameters such as pressure distribution, skin friction coefficient, velocity profile, Reynolds number, turbulent Reynolds number and turbulence kinetic energy. Results: Contour plots at different planes were used to visualize the airflow distribution as it passed through different cross-sectional areas of the airway. The results revealed that the presence of a smaller cross-sectional area of the airway caused an increase in airflow parameters, especially during heavy breathing. Furthermore, turbulent airflow conditions along the airway were noticed during heavy breathing. The severity of OSA could be measured by the turbulent kinetic energy which is able to show the behavior and concentration of mean flow. This study is expected to provide crucial and important results by visualizing the concentration of airflow mechanisms and characteristics of a patients airway during light and heavy breathing. These findings enable TKE to be used as a new tool for characterizing the severity of obstructive sleep apnea in the upper airways of patients.