Shape optimization of a micromixer with staggered herringbone groove

Quantitative characterization of micromixing simulation

“…2͑b͔͒ of the mixing fluids within the channel show a typical helical flow, where some of the fluids near channel floor fall into the grooves and cause the helical flow within the channel with strong bulk advections near the channel floor and with a rotation of the involved two fluids within the channel. The results are similar to the reported simulation and experimental results for a full size SGM, 3,4,7,9 and show distinctive difference from staggered herringbone micromixer ͑SHM͒, 3,8,11 indicating that the proportional size reduction of the reported SGM will not change its nature of mixing enhancement.…”

“…13 It has been shown that the simulation results are in good agreement with the experimental results of the micromixers. 2,3,[7][8][9] It has also been concluded from the simulations that helical flows are affected by the groove geometries, such as the groove depth, width, and inclination, 8,10,11 and that the helical flows can occur even when the Reynolds number is low and the Peclet number is high. 9,12,14 While there is progress in simulating the micromixing and numerical simulation has become an easy task with available commercial software, it is still impractical to truly design a micromixer in a way that the optimal device structure for a specific application can be easily found through numerical simulations so that experimental trials can be minimized, as demonstrated in the design and fabrication of microelectronic and integrated photonic devices.…”

On multi-objective optimization of geometry of staggered herringbone micromixer

“…Mott et al (2006) present a computational 'toolbox' where the flow field generated by one isolated feature, such as a groove with specific shape and orientation, is determined by solving numerically the governing flow equations, the result is transposed to an advection map that projects a conserved scalar field across the feature to predict the lateral transport of fluid; the sequential application of the maps can predict the outflow distribution in designs that combine the basic features and so they can be used to select the optimal combinations without solving the governing flow equations, Kang et al (2007) and Singh et al (2007) use a mapping method where the sequence of geometric modules called 'mixing protocols' is modified a number of times to generate different periodic or aperiodic modular designs which are then evaluated and their mixing rate and final mixing state compared to identify the optimum design, and (c) the use of numerical optimization techniques to improve the performance of the micromixers, e.g. genetic algorithms have been used to define the optimum frequencies of the transverse injected flows controlled actively in a crosschannel micromixer (Müller et al 2004), as well as to minimize dispersion and reduce mixing time by optimizing the shape of microchannels (Ivorra et al 2006); threedimensional Navier-Stokes analysis and numerical optimization tools, the radial basis neural network (RBNN) method with Sequential Quadratic Programming (SQP) (Ansari and Kim 2007a) and the response surface method (RSM) (Ansari and Kim 2007b), are used to optimize the shape of the grooves of a Staggered Herringbone Mixer (SHM) for enhancing mixing.…”

Evaluation of Floor-grooved Micromixers using Concentration-channel Length Profiles

“…Since the groove depth has been shown to be one of the most effective parameters to affect mixing efficiency [31,32], we compared the effect of groove depth on the mixing performance in both SGM and SHM. As shown in Figure 4, the concentration data were collected at three different representative cross-section locations.…”