Effectiveness of Direct Lagrangian Tracking Models for Simulating Nanoparticle Deposition in the Upper Airways
Source: Aerosol Science and Technology, Volume 41, Number 4, April 2007 , pp. 380-397(18)
Publisher: Taylor and Francis Ltd
Abstract:Direct Lagrangian particle tracking may provide an effective method for simulating the deposition of ultrafine aerosols in the upper respiratory airways that can account for finite inertia and slip correction effects. However, use of the Lagrangian approach for simulating ultrafine aerosols has been limited due to computational cost and numerical difficulties. The objective of this study is to evaluate the effectiveness of direct Lagrangian tracking methods for calculating ultrafine aerosol transport and deposition in flow fields consistent with the upper respiratory tract. Representative geometries that have been considered include a straight tubular flow field, a 90° tubular bend, and an idealized replica of the human oral airway. The Lagrangian particle tracking algorithms considered include the Fluent Brownian motion (BM) routine, a user-defined BM model, and a user-defined BM model in conjunction with a near-wall interpolation (NWI) algorithm. Lagrangian deposition results have been compared with a chemical species Eulerian model, which neglects particle inertia, and available experimental data. Results indicate that the Fluent BM routine incorrectly predicts the diffusion-driven deposition of ultrafine aerosols by up to one order of magnitude in all cases considered. For the tubular and 90° bend geometries, Lagrangian model results with a user-defined BM routine agreed well with the Eulerian model, available analytic correlations, and experimental deposition data. Considering the oral airway model, the best match to empirical deposition data over a range of particle sizes from 1 to 120 nm was provided by the Lagrangian model with user-defined BM and NWI routines. Therefore, a direct Lagrangian transport model with appropriate user-defined routines provides an effective approach to accurately predict the deposition of nanoparticles in the respiratory tract.
Document Type: Research Article
Affiliations: 1: Department of Mechanical Engineering, Virginia Commonwealth University, Richmond, Virginia, USA,Department of Pharmaceutics, Virginia Commonwealth University, Richmond, Virginia, USA 2: Department of Mechanical Engineering, Virginia Commonwealth University, Richmond, Virginia, USA
Publication date: April 2007