Computational Fluid Dynamics Modeling of Nasal Airflow to Understand Drug Delivery Process

Abstract

Computational Fluid Dynamics (CFD) is off late used in multidisciplinary studies like respiratory flows in understanding the flow mechanisms and in optimizing therapeutic and surgical treatments in patients with respiratory disorders. This study is one such attempt to understand nasal drug delivery processes. Three dimensional anatomically accurate nasal airway model is reconstructed from axial Computed Tomography (CT) scans of a patient using MIMICS®. Computational volume for the nasal airway model is discretized using TGRID® and Gambit®. Flow and Particle tracking simulations are carried for a range of peak inspiratory flow rates 7.5, 15, 30, 45 and 60 lpm and particle diameters in range of 0.5 30 µm using commercial CFD package FLUENT®. Flow field is solved using a steady RANS k-ω SST turbulence closure model. For discrete phase modeling (DPM), a stochastic random walk model with a random eddy life time is used for turbulent dissipation. Effect of particle deposition efficiencies in nasal airway with several factors like particle diameter, particle density, turbulence intensity, injection types, flow rate, spray half-cone angles were studied. Increase in particle diameter, particle diameter or flow rate or all increases impaction factor and also chances of total particle deposition. Increase in turbulence intensities shows only small improvement in particle deposition and that too for smaller particle diameters only (<10 µm). Modes of particle injection studied in this report have not so significant improvement in terms of total deposition; however local deposition varies with modes of injections. In spray injection, half cone-angles appear to have little influence on total particle depositions.