On multi-objective optimization of geometry of staggered herringbone micromixer

Abstract

A design methodology for micromixers is presented which systematically integrates computational fluid dynamics (CFD) with an optimization methodology based on the use of design of experiments (DOE), function approximation technique (FA) and multi-objective genetic algorithm (MOGA). The methodology allows the simultaneous investigation of the effect of geometric parameters on the mixing performance of micromixers whose design strategy is based fundamentally on the generation of chaotic advection. The methodology has been applied on a Staggered Herringbone Micromixer (SHM) at several Reynolds numbers. The geometric features of the SHM are optimized and their effects on mixing are evaluated. The degree of mixing and the pressure drop are the performance criteria to define the efficiency of the micromixer for different design requirements

Analysis and multi-criteria design optimization of geometric characteristics of grooved micromixer

“…It systematically integrates CFD with an optimization strategy based on the use of design of experiments (DOE), surrogate modelling (SM) and multi-objective genetic algorithm (MOGA) techniques. The study is based on and complements the work of Cortes-Quiroz et al [22]. The effects on mixing and pressure drop of six selected geometric parameters of the SHM have been evaluated and the parameters optimized accordingly.…”

“…Stroock and McGraw [9] approximated the complex three-dimensional flow field using a two dimensional lid-driven cavity model that was tuned to provide qualitative agreement to experimental data and studied the effect of varying two geometric parameters: the asymmetry factor of the groove and the number of grooves in each cycle; Liu et al [10] studied the influence of different fluid properties and a large concentration gradient on mixing at Re = 1 and 10 for a fixed geometry; Aubin et al [11,12] investigated numerically the effect of three geometric parameters: the groove depth, the groove width and the number of grooves per cycle, using a particle tracking method to visualize and quantify the mixing performance; Kang and Kwon [13] applied a coloured particle tracking method to study numerically the mixing performance of three types of grooved micromixers including the SHM; Yang et al [14] studied the effects of varying herringbone groove offset, depth, and angle, as well as the ratio of inlet channel width to mixing channel width by applying CFD to nine configurations defined with an array given by the Taguchi method; Li and Chen [15] used the Lattice Boltzmann method to study numerically the effect on mixing performance of the asymmetry factor and the number of grooves per half-cycle; Hassel and Zimmerman [16] presented a numerical study of the flow through the SHM to characterize the effect of the grooves on moving fluid across the channel, in particular of the groove depth in Re range 0-15 and of the number of grooves per half-cycle; Lynn and Dandy [17] evaluated numerically the generation of helical flows in the slanted groove micromixer (SGM) and its optimization, i.e., the increment of transverse flow, by varying the ratio of the length of the grooves to the neighbouring ridge for a given groove depth and channel aspect ratio, and discussed the implications of translating the optimized parameters to the SHM design; Ansari and Kim [18] used a numerical procedure that combines three-dimensional Navier-Stokes analysis and a numerical optimization technique, the response surface method (RSM), to enhance mixing performance by optimizing the groove using the ratio of groove depth to channel height and angle of the groove; Singh et al [19] introduced a new simplified formulation of the mapping method [20] to make it much simpler to implement and applied the method to optimize three micromixer designs including the SHM for which groove depth and number of grooves per half-cycle were used as parameters; very recently, Cortes-Quiroz et al [21,22] presented a multi-criteria design optimization methodology for micromixers based on the integration of CFD with numerical optimization techniques and applied it for the optimization of four geometries of the SHM to obtain good mixing performance with low pressure loss.…”

A highly uniform lamination micromixer with wedge shaped inlet channels for time resolved infrared spectroscopy

“…Mixing times in the 1 s range can be achieved with these designs in low Reynolds number flow regimes (Hossain et al 2009). To reduce mixing time in T-mixers in the order of one magnitude, pillar structures (Chen et al 2009) or herringbone structured walls (Park et al 2010;Cortes-Quiroz et al 2009) can be used, which introduce chaotic advection of the fluid. As many chemical and biochemical reaction times occur in the millisecond range, fast mixing at low flow rates, and thus small sample consumption, is desirable.…”

Multi-objective and thermodynamic optimisation of a parabolic trough receiver with perforated plate inserts

“…[17,18]. Researchers who have used genetic algorithms for multi-objective optimisation of heat transfer and fluid flow problems include Kim et al [19], Ndao et al [20], Cortes-Quiroz et al [21,22] and Karathanassis et al [23]. Other authors have applied multi-objective optimisation to thermal systems such as refrigeration systems [24] and energy storage systems [25][26][27].…”