Abstract
Computational Fluid Dynamics (CFD) tools are used to investigate fluid flow and scalar mixing in micromixers where low molecular diffusivities yield advection dominant transport. In these applications, achieving a numerical solution is challenging. Numerical procedures used to overcome these difficulties may cause misevaluation of the mixing process. Evaluation of the mixing performance of these devices without appropriate analysis of the contribution of numerical diffusion yields over estimation of mixing performance. In this study, two- and four-inlet swirl-generating micromixers are examined for different mesh density, flow and molecular diffusivity scenarios. It is shown that mesh densities need to be high enough to reveal numerical diffusion errors in scalar transport simulations. Two-inlet micromixer design was found to produce higher numerical diffusion. In both micromixer configurations, when cell Peclet numbers were around 50 and 100 for Reynolds numbers 240 and 120, the numerical diffusion effects were tolerable. However, when large cell Peclet number scenarios were tested, it was found that the molecular diffusivity of the fluid is completely masked by false diffusion errors.
Highlights
The use of micromixers in microfluidic devices has become popular due to multiple advantages of small scales, less energy requirement, low cost, and high efficiency [1]
Mesh characterization is a critical step to identify and control numerical errors which fundamentally originate from the properties of the mesh
It was shown that characterization and quantification of false diffusion errors in mesh densities
Summary
The use of micromixers in microfluidic devices has become popular due to multiple advantages of small scales, less energy requirement, low cost, and high efficiency [1]. While active micromixers require an external disturbance (e.g., electrical, magnetic, sound etc.) and present high mixing efficiencies in short distances [3], passive micromixers function using the fluid flow energy within the micromixer and utilize two mixing phenomena—advection and diffusion—in relatively less complicated mixing structures [6]. On the other hand, increasing the flow rate (e.g., Re > 1) creates advection dominant flow within the micromixer whereby mixing becomes even more challenging unless the contact surface area between fluid bodies is increased by creating secondary flows in the micromixer In this case, high flow velocities along with low molecular diffusivities create complexity in terms of numerical evaluation of mixing using a Computational Fluid Dynamics (CFD) approach which is extensively used in passive micromixer studies [9,10,11]
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