Abstract
Optimal structure of the micromixer with a two-layer serpentine crossing device was accomplished by a multiobjective genetic algorithm and surrogate modeling based on a Navier–Stokes analysis using the trade-off objective functions behavior. The optimization analysis was conducted with three design parameters, i.e., channel width to the pitch span ( w / P ) ratio, major channel width to the pitch span (H/P) ratio, and channel depth to the pitch span (d/P) ratio. Two objective functions (i.e., mixing index and pressure drop) with trade-off characteristics have been used to solve the multiobjective optimization problem. The design domain was predetermined by a parametric investigation; afterward, the Latin hypercube sampling method was employed to select the appropriate design points surrounded by the design domain. The numerical data of the thirty-two design points were used to create the surrogate model; among the different surrogate models, in this study, the Kriging metamodel has been used. The concave pareto-optimal curve signifies the trade-off characteristics linking the objective functions.
Highlights
Proficient and fast mixing of the liquids is mainly a very difficult task in the enhancement of lab-on-a-chip (LOC) as well as μ-TAS investigations
To compute the values of the objective functions, the 3D Navier–Stokes analysis was used at each design points. e surrogate model has been formulated on the basis of the objective functions values
Optimal structure of the micromixer with a two-layer serpentine crossing device was accomplished by a multiobjective genetic algorithm and surrogate modeling based on a Navier–Stokes analysis using the trade-off objective functions behavior. e optimization analysis was conducted with three design parameters, i.e., channel width to the pitch span (w/P) ratio, major channel width to the pitch span (H/P) ratio, and channel depth to the pitch span (d/P) ratio
Summary
Proficient and fast mixing of the liquids is mainly a very difficult task in the enhancement of lab-on-a-chip (LOC) as well as μ-TAS investigations. Different commercial softwares have become a very reliable and convenient tool to investigate the fluid flow and mixing performance in microfluidic devices [7,8,9]. Structural optimization of a micromixer with slanted groove was performed [9] using the electroosmotic flow mechanism; their study illustrates the enhanced mixing performance. E structural optimization of the micromixer with a pattern grooves microchannel was performed [21] with four different parameters; analysis confirms that the performance of the micromixer was reasonably enhanced with the design parameters. E structural optimization of the staggered herringbone micromixer (SHG) with the pattern grooves microchannel was performed using two different objective functions [23, 24], i.e., mixing performance at the exit and pressure drop. Two objective functions (i.e., mixing index and pressure drop) with trade-off characteristics have been used to solve the multiobjective optimization problem. e numerical data of the thirty-two design points were used to create the surrogate model; among the different surrogate models, in this study, the Kriging metamodel has been used. e concave pareto-optimal curve signifies the trade-off characteristics linking the objective functions
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