Analysis and Design Optimization of Micromixers

Micromixers are essential components of lab-on-a-chip and micro-total analysis systems used for a variety of chemical and biological applications such as sample preparation and analysis, protein folding, DNA analysis, and cell separation. Due to the small characteristic dimension of micromixers, the flow is laminar in a Reynolds number range from 0.01 to 100 for typical microfluidic applications. In microfluidic devices, the laminar flow condition poses a challenge for the mixing of liquid samples. Therefore, for high performance lab-on-a-chip and micro-total analysis systems, it is essential to develop and devise micromixers to achieve fast and compact mixing at the micro-scale. Although mixing can involve different phases (solid, liquid and gases), the present book focuses on liquid–liquid mixing such as water–ethanol mixing. This chapter provides an introduction to application of micromixers, flow dynamics and mixing in micromixers, and dimensionless numbers which characterize flow and mixing regimes.

Micromixers are classified into two types: active and passive micromixers. Active micromixers promote mixing using moving parts or some external agitation/energy to stir the fluids. Magnetic energy, electrical energy, pressure disturbance, and ultrasonic are examples of the external energies to enhance mixing. Passive micromixers use geometrical modification to cause chaotic advection or lamination to promote the mixing of the fluid samples, and allow easy fabrication and integration with lab-on-a-chip and μ-TAS. In this chapter, both active and passive micromixers are discussed, but the major emphasis is laid on passive micromixer designs and mechanisms. Extensive referencing on active and passive micromixers is not possible due to the limited length of the book, but the diversity of micromixers is introduced as much as possible.

This chapter introduces the computational framework and provides a detailed analysis on the different numerical techniques for the analyses of flow and mixing in micromixers. Flow and mixing analyses are based on both the Eulerian and Lagrangian approaches; relative advantages and disadvantages of these two approaches and suitability to different types of mixing problems are analyzed. This chapter also discusses the various facets of numerical schemes subjected to discretization errors and computational grid requirements. Since a large number of studies are based on commercial CFD packages, relevant details of these packages to the mixing problem are presented. This chapter concludes with mixing characterization technique using concentration data obtained on a computational grid, and provides the basis for performance evaluation of different micromixer designs. This chapter consists of three sections. Section 3.1 presents the Eulerian approach for flow and mixing analyses, different mixing models, boundary conditions, and the numerical approach employed in obtaining solutions of the governing equations. The Lagrangian approach is presented in Sect. 3.2. In the final section, the method for mixing quantification is discussed.

The mixing performance of a passive micromixer is sensitive to the geometry of the flow passages. Therefore, it is important to determine optimal configuration which maximizes the mixing performance of the micromixer. But, unfortunately, in some micromixers, enhancement of mixing performance is accompanied by a corresponding increase in pressure drop. Therefore, it is important to determine several configurations which represent the trade-offs between mixing efficiency and pressure drop. Numerical optimization techniques coupled with CFD analyses of flow and mixing have been proved to be an important tool for micromixer design. Both the single-objective and multi-objective optimization procedures for the shape optimization of micromixers are presented.

This chapter summarizes the details of what have been covered in the previous chapters, and how to pursue those researches further.