Numerical modeling of particle deposition in a realistic respiratory airway using CFD–DPM and genetic algorithm - Khaksar, Saba; Paknezhad, Mehrad; Saidi, Maysam; Ahookhosh, Kaveh (Biomechanics and modeling in mechanobiology, 2024)
In this study, a realistic model of the respiratory tract obtained from CT medical images was used to solve the flow field and particle motion using the Eulerian–Lagrangian approach to obtain the maximum particle deposition in the bronchial tree for the main purpose of optimizing the performance of drug delivery devices. The effects of different parameters, including particle diameter, particle shape factor, and air velocity, on the airflow field and particle deposition pattern in different zones of the lung were investigated. In addition, a genetic algorithm was employed to obtain the maximum particle deposition in the bronchial tree and the effect of the aforementioned parameters on particle deposition. Reverse flow, vortex formation, and laryngeal jet all affect the airflow structure and particle deposition pattern. The mouth–throat region had the highest deposition fraction at various flow rates. A change in the deposition pattern with an increased deposition fraction in the throat was observed owing to the increased diameter and shape factor of the particles, resulting from the higher inertia and drag force, respectively. The particle deposition analysis showed that three parameters, shape factor, diameter, and velocity, are directly related to particle deposition, and the diameter is the most effective parameter for particle deposition, with an effect of 60% compared to the shape factor and velocity. Finally, the prediction of the genetic algorithm reported a maximum particle deposition in the bronchial tree of 17%, whereas, based on the numerical results, the maximum particle deposition was reported to be 16%. Therefore, there is a 1% difference between the prediction of the genetic algorithm and the numerical results, which indicates the high accuracy of the prediction of the genetic algorithm.