Title

Influence of Air Flow on the Performance of a Dry Powder Inhaler using Computational and Experimental Analyses

RIS ID

27212

Publication Details

Coates, M. S., Fletcher, D. F., Chan, H. & Raper, J. A. (2005). Influence of Air Flow on the Performance of a Dry Powder Inhaler using Computational and Experimental Analyses. Pharmaceutical Research, 22 (9), 1445-1453.

Abstract

Purpose The aims of the study are to analyze the influence of air flow on the overall performance of a dry powder inhaler (Aerolizer®) and to provide an initial quantification of the flow turbulence levels and particle impaction velocities that maximized the inhaler dispersion performance. Methods Computational fluid dynamics (CFD) analysis of the flowfield in the Aerolizer®, in conjunction with experimental dispersions of mannitol powder using a multistage liquid impinger, was used to determine how the inhaler dispersion performance varied as the device flow rate was increased. Results Both the powder dispersion and throat deposition were increased with air flow. The capsule retention was decreased with flow, whereas the device retention first increased then decreased with flow. The optimal inhaler performance was found at 65 l min−1 showing a high fine particle fraction (FPF) of 63 wt.% with low throat deposition (9.0 wt.%) and capsule retention (4.3 wt.%). Computational fluid dynamics analysis showed that at the critical flow rate of 65 l min−, the volume-averaged integral scale strain rate (ISSR) was 5,400 s−1, and the average particle impaction velocities were 12.7 and 19.0 m s−1 at the inhaler base and grid, respectively. Correlations between the device flow rate and (a) the amount of throat deposition and (b) the capsule emptying times were also developed. Conclusions The use of CFD has provided further insight into the effect of air flow on the performance of the Aerolizer®. The approach of using CFD coupled with powder dispersion is readily applicable to other dry powder inhalers (DPIs) to help better understand their performance optimization.

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Link to publisher version (DOI)

http://dx.doi.org/10.1007/s11095-005-6155-x