Prediction of Extrathoracic Aerosol Deposition using RANS-Random Walk and LES Approaches

摘要

Computational Fluid Dynamics (CFD) has emerged as a powerful and economical alternative to empiricism in the prediction of aerosol deposition inside the extrathoracic airways (ETA). In RANS-type treatments, a main difficulty is the specification of turbulent fluid fluctuations experienced by the particles, and hence recent research has concentrated on Large Eddy Simulations (LES) in conjunction with Lagrangian Particle Tracking (LPT). While providing close agreement with data, LES/LPT approaches are extremely time consuming, thus the motivation to investigate whether better Lagrangian stochastic models can help RANS-based treatments achieve comparable accuracy. In this study, the RANS-RSM model is used to obtain the mean carrier flow field, whereas turbulent fluid velocities are defined through a stochastic Continuous Random Walk (CRW) model based on the normalized Langevin equation. With extensive validation against flow field and particle deposition data, we demonstrate that RANS, combined with the Langevin CRW provides accuracy which compares very favorably with the more computationally intensive LES approaches.