Department of Mechanical and Nuclear Engineering, Virginia Commonwealth University, 401 West Main Street, P.O. Box 843015, Richmond, Virginia, 23284-3015, USA.
Department of Pharmaceutics, Virginia Commonwealth University, Richmond, Virginia, USA.
Pharm Res. 2019 May 28;36(8):110. doi: 10.1007/s11095-019-2644-1.
To determine the predictive power of computational fluid dynamics (CFD)-based dispersion parameters in the development of a new inline DPI that is actuated with low volumes of air.
Four new versions of a dose aerosolization and containment (DAC)-unit DPI were created with varying inlet and outlet orifice sizes and analyzed with results from five previous designs. A concurrent in vitro and CFD analysis was conducted to predict the emitted dose (ED; as a % of loaded dose) and aerosol mass median aerodynamic diameter (MMAD) produced by each device when actuated with 10 ml air bursts. CFD simulations of device operation were used to predict flow field and particle-based dispersion parameters.
Comparisons of experimental and CFD results indicated that multiple flow field and particle-based dispersion parameters could be used to predict ED (minimum RMS Error = 4.9%) and MMAD (minimum RMS Error = 0.04 μm) to a high degree of accuracy. Based on experiments, the best overall device produced mean (standard deviation; SD) ED = 82.9(4.3)% and mean MMAD (SD) = 1.73(0.07)μm, which were in close agreement with the CFD predictions.
A unique relationship was identified in the DAC-unit DPI in which reducing turbulence also reduced the MMAD.
确定基于计算流体动力学（CFD）的分散参数在开发新型低容量空气驱动的在线干粉吸入器（DPI）中的预测能力。
使用不同的入口和出口孔尺寸创建了四个新的剂量气溶胶化和容纳（DAC）单元 DPI，并与之前的五个设计的结果进行了分析。进行了同步的体外和 CFD 分析，以预测每个装置在 10ml 空气爆发时产生的发射剂量（ED；作为加载剂量的百分比）和气溶胶质量中值空气动力学直径（MMAD）。设备操作的 CFD 模拟用于预测流场和基于颗粒的分散参数。
实验和 CFD 结果的比较表明，多个流场和基于颗粒的分散参数可用于高度准确地预测 ED（最小均方根误差 = 4.9%）和 MMAD（最小均方根误差 = 0.04μm）。根据实验，性能最佳的总体装置产生的平均（标准偏差；SD）ED=82.9（4.3）%和平均 MMAD（SD）=1.73（0.07）μm，与 CFD 预测值非常吻合。
在 DAC 单元 DPI 中发现了一种独特的关系，即降低湍流也降低了 MMAD。
