Chapter 4
Design Optimization of Micromixers
Abstract 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.
Keywords Micromixers · CFD · Design optimization · Surrogate modeling
4.1 Optimization Strategies for Micromixers
For passive micromixers, changes in the geometry can alter the flow and mixing
dynamics inside the channel, and thus affect the performance of the devices. Many
studies have been conducted to analyze the effects of geometrical and flow parameters on mixing performance leading to workable designs of micromixers. Using more
sophisticated techniques such as design optimization [1], more efficient designs can
be realized. There have been various approaches to design of micromixers, such
as parametric study [2–7], layout optimization by solving a variational optimization problem [8], and design optimization using systematic optimization techniques
[9–15].
Aubin et al. [3, 4] studied the effects of geometrical parameters on a SHM using
a particle tracking approach and CFD. Three design parameters, viz. the depth and
width of the grooves, and number of grooves per cycle, were tested. They quantified
the mixing by analyzing the maximum striation thickness and residence time for
various combinations of design parameters. Figure 4.1 shows the effect of groove
width on mixing pattern at different axial locations. Qualitative information on the
secondary flow inside the microchannel can be obtained from such plots. For narrow
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
A. Afzal and K.-Y. Kim, Analysis and Design Optimization of Micromixers,
SpringerBriefs in Computational Mechanics,
https://doi.org/10.1007/978-981-33-4291-0_4
45
Design Optimization of Micromixers
Abstract 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.
Keywords Micromixers · CFD · Design optimization · Surrogate modeling
4.1 Optimization Strategies for Micromixers
For passive micromixers, changes in the geometry can alter the flow and mixing
dynamics inside the channel, and thus affect the performance of the devices. Many
studies have been conducted to analyze the effects of geometrical and flow parameters on mixing performance leading to workable designs of micromixers. Using more
sophisticated techniques such as design optimization [1], more efficient designs can
be realized. There have been various approaches to design of micromixers, such
as parametric study [2–7], layout optimization by solving a variational optimization problem [8], and design optimization using systematic optimization techniques
[9–15].
Aubin et al. [3, 4] studied the effects of geometrical parameters on a SHM using
a particle tracking approach and CFD. Three design parameters, viz. the depth and
width of the grooves, and number of grooves per cycle, were tested. They quantified
the mixing by analyzing the maximum striation thickness and residence time for
various combinations of design parameters. Figure 4.1 shows the effect of groove
width on mixing pattern at different axial locations. Qualitative information on the
secondary flow inside the microchannel can be obtained from such plots. For narrow
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
A. Afzal and K.-Y. Kim, Analysis and Design Optimization of Micromixers,
SpringerBriefs in Computational Mechanics,
https://doi.org/10.1007/978-981-33-4291-0_4
45