Microchannels are used for delivery of two-or more fluids for multiple purposes, such as drug delivery, where good mixing is desired in a very short time (or distance). For this purpose, many design options are being proposed in the literature. For example herringbone baffles at the bottom of a rectangular channel are commonly proposed to enhance mixing in a drug delivery device. To assess the effectiveness of such devices many experiments need to be performed thus increasing the design cycle time and the cost involved. Computational fluid dynamics (CFD) can and is being used to shorten this design cycle by performing parametric analysis; however, due to numerical errors it is also necessary to verify and then validate the numerical models to ensure that the predictions are indeed accurate. In this study a recently developed microchannel is analyzed using CFD to determine its mixing effectiveness. There are two common ways of assessing the degree of mixing: (a) via calculation of a passive scalar transport equation, (b) by following fluid particle trajectories and calculating the statistics. The first approach suffers from presence of numerical diffusion. The second approach is usually used to only obtain qualitative information rather than quantitative assessment. In this study we explore both approaches and reconcile both of these in terms of extracting quantitative information. Furthermore we assess the results of simulations using both approaches to determine a mixing index that provides directly a measure of mixing quality.
Microchannels are used for delivery of two-or more fluids for multiple purposes, such as drug delivery, where good mixing is desired in a very short time (or distance). For this purpose, many design options are being proposed in the literature. For example herringbone baffles at the bottom of a rectangular channel are proposed to enhance mixing in a drug delivery device [6]. To assess the effectiveness of such devices many experiments need to be performed thus reducing the design cycle time and the cost involved. Computational fluid dynamics (CFD) can and is being used to shorten this design cycle by performing parametric analysis; however, due to numerical errors it is also necessary to verify and then validate the numerical models to ensure that the predictions are indeed accurate. In this study a recently developed microchannel presently in use is analyzed using CFD to determine its mixing efficiency. There are two common ways of assessing the degree of mixing: (a) via calculation of a passive scalar transport equation, (b) by following fluid particle trajectories and calculating the statistics. The first approach suffers from high degree of numerical diffusion. The second approach is usually used to only obtain qualitative information rather than quantitative assessment. In this study we explore both approaches and reconcile both of these in terms of extracting quantitative information. Furthermore we assess the results of simulations using both approaches to determine a mixing index that provides directly a measure of the extent of mixing.
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