Laminar‐to‐turbulent fluid–nanoparticle dynamics simulations: Model comparisons and nanoparticle‐deposition applications

Abstract

SUMMARYRelying on benchmark experimental data sets for flow in conduits with local constrictions, LES and three widely used RANS turbulence models, i.e. the low Reynolds number (LRN) k‐ω model, standard k‐ω model and shear stress transport (SST) transition model, were compared and evaluated to gain new physical insight and provide useful turbulence modeling information. These two geometric test cases may represent stenosed arteries and a segment of the human upper airways where the velocity fields undergo all flow regimes, i.e. from laminar, via transitional, to fully turbulent.The comparison study revealed that the standard k‐ω models amplify the flow instabilities after the constrictions, and hence fail to capture the laminar flow behavior at relatively LRNs. The overall performances of LES, the LRN k‐ω model and SST transition model do not have measurable differences in predicting laminar flows and transition to turbulent flow, while the SST transition model may give a better prediction of turbulence kinetic energy profiles in some cases. Clearly, LES can provide instantaneous velocity fluctuations, which may be significant for turbulent micron particle transport/deposition in the respiratory tract. However, it requires 100‐fold more computational time than RANS turbulence models. The use of different turbulence models has a minor effect on nanoparticle deposition in human upper airways when the inspiratory flow rate is low, say, Q = 10L/min. The relative difference for deposition fraction (DF) of nanoparticles with dp>10nm is measurable at a medium inhalation flow rate (say, Q = 30L/min) when employing different turbulence models. However, the absolute difference in DFs is within 0.5% for all‐sized nanoparticles (i.e. 1nm⩽dp⩽50nm) because the DF in the oral airway is very low (say, <1.5%) when 10 nm and 10 L/min.The modeling and simulation information provided are most useful for computational fluid–particle dynamics practitioners to obtain accurate lung deposition concentrations of inhaled toxic or therapeutic nanoparticles. The physical insight provided sheds additional light on the laminar‐to‐turbulent airflow and nanoparticle transport/deposition in locally constricted conduits. Copyright © 2011 John Wiley & Sons, Ltd.