This book illustrates the computational framework based on knowledge of flow and mass transfer together with optimization techniques to solve problems relevant to micromixing technology. The authors provide a detailed analysis of the different numerical techniques applied to the design of micromixers. Flow and mixing analysis is based on both the Eulerian and Lagrangian approaches; relative advantages and disadvantages of the two methods and suitability to different types of mixing problems are analysed. The book 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 computational fluid dynamics (CFD) packages, relevant details of these packages to the mixing problem using them are presented. Numerical optimization techniques coupled with CFD analysis of flow and mixing have proved to be an important tool for micromixers design, and therefore, are animportant part of the book. These techniques are presented briefly, and focus is on surrogate modeling and optimization applied to design of micromixers.

Autorentext

Arshad Afzal received his B. Tech in Mechanical Engineering and M. Tech in Thermal Engineering degrees from Aligarh Muslim University, India in 2006 and 2009, respectively. He obtained his Ph.D. degree in Thermodynamics & Fluid Mechanics from Inha University, South Korea in 2015. He was awarded Postdoctoral Fellowships under the prestigious Brain - Korea 21 (BK-21) program from Inha University and Yonsei University, South Korea. Presently, he is working as INSPIRE Faculty in the Department of Mechanical Engineering at Indian Institute of Technology Kanpur, India. His research interests are mixing at microscale, surrogate modeling, optimization, neural networks, and design of fluid and thermal systems. Dr. Afzal is a member of the American Society of Mechanical Engineers (ASME) and Mathematical Optimization Society (MOS).

Kwang-Yong Kim received his B.S. degree from Seoul National University in 1978, and his M.S. and Ph.D. degrees from the Korea Advanced Institute of Science and Technology (KAIST), Korea, in 1981 and 1987, respectively. He is currently an Inha Fellow Professor in the Department of Mechanical Engineering and was a Dean of Engineering College of Inha University, Incheon, Korea. He is also an associate editor of ASME Journal of Fluids Engineering. He served as the co-editor-in-chief of the International Journal of Fluid Machinery and Systems, the editor-in-chief of the Transactions of Korean Society of Mechanical Engineers, the president of Korean Society for Fluid Machinery, and the chairman of the Asian Fluid Machinery Committee. He is a fellow of the Korean Academy of Science and Technology, a member of National Academy of Engineering of Korea, a fellow of the American Society of Mechanical Engineers (ASME), an associate fellow of the American Institute of Aeronautics and Astronautics (AIAA), and a recipient of order of science and technology merit, Doyak Medal from Republic of Korea.

He is interested in applications of the numerical optimization techniques using various surrogate models and computational fluid dynamics to the designs of fluid machinery, heat-transfer augmentation devices, micro heat sinks, micro mixers, coolant channels in nuclear reactors, etc. He has published 398 peer-reviewed journal papers, and presented 560 papers at international/domestic conferences.

Inhalt

1. 1. Mixing at Microscale

Micromixers are important components of lab-on-a-chip and micro-total analysis systems (-TAS) used for a variety of chemical and biological applications such as sample preparation and analysis, protein folding, DNA analysis, cell separation, among others. Due to small characteristics dimension of the micromixers, the flow is laminar with Reynolds number ranging 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. Mixing is dominated by molecular diffusion, and the time scale for diffusion based on the characteristic length scale of microchannels and typical values of diffusivity constant encountered in microfluidic applications is much higher than the timescales associated with fluid motion. This limitation results in prohibitively long channel lengths to achieve complete mixing. Therefore, for high performance lab-on-a-chip and micro-total analysis systems (-TAS), it is essential to develop and devise methods to achieve fast and compact mixing at the micro-scale. This chapter provides an introduction to application of micromixers, flow dynamics and mixing in micromixers, and use of dimensionless numbers to characterize flow and mixing regimes.

2. 2. Active and Passive micromixers

Micromixers are classified into two types: active and passive. The active type promotes mixing using moving parts or some external agitation/energy to stir the fluids. Magnetic energy, electrical energy, pressure disturbance, and ultrasonic are some of the applied sources of external energy to enhance mixing inside the microchannel. 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 micro-total analysis systems (-TAS). In this chapter, both active and passive types will be discussed, but the major emphasis will be laid on passive micromixer designs and mechanisms. Passive micromixers will be categorized based on the mixing mechanism employed viz. chaotic advection, flow separation, hydrodynamic focusing, and split-and-recombine flows.

3. 3. Computational analysis of flow and mixing in micromixers

This chapter discusses the computational framework for the analysis of flow and mixing in micromixers. It aims to provide a detailed analysis on the different numerical techniques applied to the design of micromixers. Flow and mixing analysis will be based on both the Eulerian and Lagrangian approaches; relative advantages and disadvantages of the two methods and suitability to different types of mixing problems will be analysed. The chapter will also discuss the various facets of numerical schemes subjected to discretization errors and computational grid requirements. Since a large number of studies are based on commercial computational fluid dynamics (CFD) packages, relevant details of these packages to the mixing problem using them will be presented. The chapter will conclude with mixing characterization techniques using velocity and concentration data obtained on a finite computational grid, and form the basis for performance evaluation of different micromixer designs.

4. 4. Design optimization of micromixers

The mixing performance of passive micromixers is sensitive to geometrical shape of flow passage. Therefore, it is important to determine optimal configuration which will maximize the mixing performance of micromixers. But unfortunately, in some micromixers, enhancement of mixing performance is accompanied by a corresponding increase in pressure drop and thereby affects the overall performance of the micromixers. Therefore, it is important to determine several configurations which represent the trade-off between mixing efficiency and pressure drop penalty. Numerical optimization techniques coupled with CFD analysis of flow and mixing have proved to be an important tool for micromixers design. These techniques will be presented briefly, and focus will be on emphasizing the coupling between CFD code and optimization methodology throughout the design process.

5. 5. Epilogue

This chapter will summar…