Parametric Study of Obstacle Geometry Effect on Mixing Performance in a Convergent-Divergent Micromixer with Sinusoidal Walls
-
Fazlollah Heshmatnezhad
Abstract
This study presents a numerical simulation through computational fluid dynamics on mixing and flow structures in convergent-divergent micromixer with a triangular obstacle. The main concept in this design is to enhance the interfacial area between the two fluids by creating a transverse flow and split, and recombination of fluids flow due to the presence of obstacles. The effect of triangular obstacle size, the number of units, changing the position of an obstacle in the mixing channel and operational parameter like the Reynolds number on the mixing efficiency and pressure drop are assessed. The results indicate that at inlet Reynolds numbers below 5, the molecular diffusion is the most important mechanism of mixing, and the mixing index is almost the same for all cases. By increasing the inlet Reynolds number above 5, mixing index increases dramatically, due to the secondary flows. Based on the simulation results, due to increasing the effect of dean and separation vortices as well as more recirculation of flow in the side branches and behind the triangular obstacle, the highest mixing index is obtained at the aspect ratio of 2 for the triangular obstacle and its position at the front of the mixing unit. Also the highest value of mixing index is obtained by six unit of mixing chamber. The effect of changing the position of the obstacle in the channel and changing the aspect ratio of the obstacle is evident in high Reynolds numbers. An increase in the Reynolds number in both cases (changing the aspect ratio and position of the obstacle) leads to pressure drop increases.
References
1. Ansari MA, Kim K-Y, Anwar K, Kim SM. A novel passive micromixer based on unbalanced splits and collisions of fluid streams. J Micromech Microeng 2010;20(5):55007.10.1088/0960-1317/20/5/055007Search in Google Scholar
2. Sudarsan AP. Multivortex micromixing: novel techniques using Dean flows for passive microfluidic mixing. Texas: Texas A&M University, 2007Search in Google Scholar
3. Capretto L, Carugo D, Mazzitelli S, Nastruzzi C, Zhang X. Microfluidic and lab-on-a-chip preparation routes for organic nanoparticles and vesicular systems for nanomedicine applications. Adv Drug Deliv Rev 2013;65(11-12):1496–532.10.1016/j.addr.2013.08.002Search in Google Scholar
4. Jeong GS, Chung S, Kim C-B, Lee S-H. Applications of micromixing technology. Analyst 2010;135(3):460–73.10.1039/b921430eSearch in Google Scholar
5. Bhagat AA, Peterson ET, Papautsky I. A passive planar micromixer with obstructions for mixing at low Reynolds numbers. J Micromech Microeng 2007;17(5):1017.10.1088/0960-1317/17/5/023Search in Google Scholar
6. Park JM, Seo KD, Kwon TH. A chaotic micromixer using obstruction-pairs. J Micromech Microeng 2010;20(1):15023.10.1088/0960-1317/20/1/015023Search in Google Scholar
7. Chen L, Wang G, Lim C, Seong GH, Choo J, Lee EK, et al. Evaluation of passive mixing behaviors in a pillar obstruction poly (dimethylsiloxane) microfluidic mixer using fluorescence microscopy. Microfluid Nanofluidics 2009;7(2):267–73.10.1007/s10404-008-0386-1Search in Google Scholar
8. Chung C-K, Shih TR. Effect of geometry on fluid mixing of the rhombic micromixers. Microfluid Nanofluidics 2008;4(5):419–25.10.1007/s10404-007-0197-9Search in Google Scholar
9. Wong SH, Bryant P, Ward M, Wharton C. Investigation of mixing in a cross-shaped micromixer with static mixing elements for reaction kinetics studies. Sensors Actuators B Chem 2003;95(1):414–24.10.1016/S0925-4005(03)00447-7Search in Google Scholar
10. Ansari MA, Kim K-Y. Shape optimization of a micromixer with staggered herringbone groove. Chem Eng Sci 2007;62(23):6687–95.10.1016/j.ces.2007.07.059Search in Google Scholar
11. Ansari MA. Parametric study on mixing of two fluids in a three-dimensional serpentine microchannel. Chem Eng J 2009;146(3):439–48.10.1016/j.cej.2008.10.006Search in Google Scholar
12. Hossain S, Ansari MA, Kim KY. Evaluation of the mixing performance of three passive micromixers. Chem Eng J 2009;150:492–501.10.1016/j.cej.2009.02.033Search in Google Scholar
13. Nimafart M, Viktorov V, Matrinelli M. Experimental comparative mixing performance and pressure drop simulation of three passive micromixers. Majlesi J Mechatron Syst 2012;1:4.Search in Google Scholar
14. Chung CK, Shih TR, Chang CK, Lai CW, Wu BH. Design and experiments of a short-mixing-length baffled microreactor and its application to microfluidic synthesis of nanoparticles. Chem Eng J 2011;168:790–8.10.1016/j.cej.2010.12.035Search in Google Scholar
15. Leung WW, Ren Y. Crossflow and mixing in obstructed and width-constricted rotating radial microchannel. Int J Heat Mass Transf 2013;64:457–67.10.1016/j.ijheatmasstransfer.2013.04.064Search in Google Scholar
16. Sadegh Cheri M, Latifi H, Salehi Moghaddam M, Shahraki H. Simulation and experimental investigation of planar micromixers with short-mixing-length. Chem Eng J 2013;234:247–55.10.1016/j.cej.2013.08.067Search in Google Scholar
17. Ghadge SS, Misal N. Design and analysis of micro-mixer for enhancing mixing performance. Int J Emerg Trends Sci Technol 2014;1(08):1342–1346.Search in Google Scholar
18. Alam A, Afzal A, Kim K-Y. Mixing performance of a planar micromixer with circular obstructions in a curved microchannel. Chem Eng Res Des 2014;92(3):423–34.10.1016/j.cherd.2013.09.008Search in Google Scholar
19. Afzal A, Kim K-Y. Passive split and recombination micromixer with convergent–divergent walls. Chem Eng J 2012;203:182–92.10.1016/j.cej.2012.06.111Search in Google Scholar
20. Afzal A, Kim K-Y. Performance evaluation of three types of passive micromixer with convergent-divergent sinusoidal walls. J Mar Sci Technol 2014;22(6):680–6.Search in Google Scholar
21. Afzal A, Kim K-Y. Convergent–divergent micromixer coupled with pulsatile flow. Sensors Actuators B Chem 2015;211:198–205.10.1016/j.snb.2015.01.062Search in Google Scholar
22. Parsa MK, Hormozi F, Jafari D. Mixing enhancement in a passive micromixer with convergent–divergent sinusoidal microchannels and different ratio of amplitude to wave length. Comput Fluids 2014;105:82–90.10.1016/j.compfluid.2014.09.024Search in Google Scholar
23. Parsa MK, Hormozi F. Experimental and CFD modeling of fluid mixing in sinusoidal microchannels with different phase shift between side walls. J Micromechanics Microengineering 2014;24(6):65018.10.1088/0960-1317/24/6/065018Search in Google Scholar
24. N. Kockmann, Transport Phenomena in Micro Process Engineering, Springer, Berlin 2008.Search in Google Scholar
25. Kockmann N, Engler M, Haller D, Woias P. Fluid dynamics and transfer processes in bended microchannels. Heat Transf Eng 2005;26(3):71–8.10.1080/01457630590907310Search in Google Scholar
©2017 by De Gruyter
Articles in the same Issue
- Moisture Diffusivity of Seedless Grape undergoing convective drying
- Transient Analysis of Falling Cylinder in Non-Newtonian Fluids: Further Opportunity to Employ the Benefits of SPH Method in Fluid – Structure Problems
- Parametric Study of Obstacle Geometry Effect on Mixing Performance in a Convergent-Divergent Micromixer with Sinusoidal Walls
- ANFIS based Control Scheme for Binary Distillation Column
- Optimization of microfluidic gallotannic acid extraction using artificial neural network and genetic algorithm
- Simulation of In-Flight Reduction of the Fine Iron Ore Concentrate by Hydrogen
Articles in the same Issue
- Moisture Diffusivity of Seedless Grape undergoing convective drying
- Transient Analysis of Falling Cylinder in Non-Newtonian Fluids: Further Opportunity to Employ the Benefits of SPH Method in Fluid – Structure Problems
- Parametric Study of Obstacle Geometry Effect on Mixing Performance in a Convergent-Divergent Micromixer with Sinusoidal Walls
- ANFIS based Control Scheme for Binary Distillation Column
- Optimization of microfluidic gallotannic acid extraction using artificial neural network and genetic algorithm
- Simulation of In-Flight Reduction of the Fine Iron Ore Concentrate by Hydrogen
