1.5 General Comments
9
1.5 General Comments
The literature survey reveals that there has been a large number of researches on
mixing in micromixers from mathematical, theoretical and application perspectives.
Various successful micromixers have been reported for enhanced mixing performance over wide ranges of operating conditions. However, there is still room for
further improvement in the mixing performance.
Computational fluid dynamics (CFD) has been proved to be reliable for both
qualitative and quantitative analyses of species concentration, flow structures, and
mixing behavior in micromixers. Preliminary evaluation of micromixer designs can
be performed by a parametric study using numerical analysis, which finds the design
parameters to which the mixing performance is sensitive. A suitable optimization
strategy can be employed to maximize or minimize performance measures, and
determine robust and efficient designs of micromixers. In this book, the primary
focus is on the application of CFD and optimization techniques to the analysis,
design and development of micromixers.
This book is organized as follows: In Chap. 2, both active and passive micromixers
are introduced with suitable examples, focusing mainly on design and development
of passive micromixers. Chapter 3 deals with the numerical formulation for the analyses of flow and mixing in micromixers, and method for quantification of mixing
is introduced. The mixing problem is looked from both Eulerian and Lagrangian
approaches; the important features are discussed with appropriate references for the
understanding of readers. Various approaches to the design of micromixers based on
CFD and optimization techniques are introduced in Chap. 4. Among the approaches,
special importance is given to the surrogate-based optimization framework, and
a detailed discussion on various facets of the optimization problem is presented.
Finally, Chap. 5 summarizes the details of what have been covered in the previous
chapters, and suggests how to pursue the researches on micromixers further.
References
1. Service RF (2009) Miniaturization puts chemical plants where you want them. Science 282:400.
https://doi.org/10.1126/science.282.5388.400
2. Gambin Y, VanDelinder V, Ferreon ACM, Lamke EA, Groisman A, Deniz AA (2011) Visualizing one-way protein encounter complex by ultrafast single-molecule mixing. Nature Methods
8: 239–241. https://doi.org/10.1038/nmeth.1568
3. Ottino JM, Wiggins S (2004) Introduction: mixing in microfluidics. Phil Trans R Soc Lond
362:923–935. https://doi.org/10.1098/rsta.2003.1355
4. Li Y, Xu F, Liu C, Xu Y, Feng X, Liu BF (2013) A novel microfluidic mixer based on dualhydrodynamic focusing for interrogating the kinetics of DNA-protein interaction. Analyst
138:4475–4482. https://doi.org/10.1039/c3an00521f
5. Hessel V, Hofmann C, Lob P, Lohndorf J, Lowe H, Ziogas A (2005) Aqueous Kolbe-Schmitt
synthesis using resorcinol in a microreactor laboratory rig under high-p, T conditions. Org
process Res Div 9:479–489. https://doi.org/10.1021/op050045q
9
1.5 General Comments
The literature survey reveals that there has been a large number of researches on
mixing in micromixers from mathematical, theoretical and application perspectives.
Various successful micromixers have been reported for enhanced mixing performance over wide ranges of operating conditions. However, there is still room for
further improvement in the mixing performance.
Computational fluid dynamics (CFD) has been proved to be reliable for both
qualitative and quantitative analyses of species concentration, flow structures, and
mixing behavior in micromixers. Preliminary evaluation of micromixer designs can
be performed by a parametric study using numerical analysis, which finds the design
parameters to which the mixing performance is sensitive. A suitable optimization
strategy can be employed to maximize or minimize performance measures, and
determine robust and efficient designs of micromixers. In this book, the primary
focus is on the application of CFD and optimization techniques to the analysis,
design and development of micromixers.
This book is organized as follows: In Chap. 2, both active and passive micromixers
are introduced with suitable examples, focusing mainly on design and development
of passive micromixers. Chapter 3 deals with the numerical formulation for the analyses of flow and mixing in micromixers, and method for quantification of mixing
is introduced. The mixing problem is looked from both Eulerian and Lagrangian
approaches; the important features are discussed with appropriate references for the
understanding of readers. Various approaches to the design of micromixers based on
CFD and optimization techniques are introduced in Chap. 4. Among the approaches,
special importance is given to the surrogate-based optimization framework, and
a detailed discussion on various facets of the optimization problem is presented.
Finally, Chap. 5 summarizes the details of what have been covered in the previous
chapters, and suggests how to pursue the researches on micromixers further.
References
1. Service RF (2009) Miniaturization puts chemical plants where you want them. Science 282:400.
https://doi.org/10.1126/science.282.5388.400
2. Gambin Y, VanDelinder V, Ferreon ACM, Lamke EA, Groisman A, Deniz AA (2011) Visualizing one-way protein encounter complex by ultrafast single-molecule mixing. Nature Methods
8: 239–241. https://doi.org/10.1038/nmeth.1568
3. Ottino JM, Wiggins S (2004) Introduction: mixing in microfluidics. Phil Trans R Soc Lond
362:923–935. https://doi.org/10.1098/rsta.2003.1355
4. Li Y, Xu F, Liu C, Xu Y, Feng X, Liu BF (2013) A novel microfluidic mixer based on dualhydrodynamic focusing for interrogating the kinetics of DNA-protein interaction. Analyst
138:4475–4482. https://doi.org/10.1039/c3an00521f
5. Hessel V, Hofmann C, Lob P, Lohndorf J, Lowe H, Ziogas A (2005) Aqueous Kolbe-Schmitt
synthesis using resorcinol in a microreactor laboratory rig under high-p, T conditions. Org
process Res Div 9:479–489. https://doi.org/10.1021/op050045q