Numerical simulation and parameter optimization of micromixer device using fuzzy logic technique

Karthikeyan, Kaliaperumal; Kandasamy, Senthil Kumar; Saravanan, P; Alodhayb, Abdullah

doi:10.1039/d2ra07992e

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…( B ). Category 3 Micromixers: (i) Primary and Secondary Minkowski Fractal Obstacle Micromixer, Chen and Chen [ 74 ]; (ii) Staggered Baffle Micromixer, Niu et al [ 32 ]; (iii) T-type Fractal Obstacle Micromixer, Hou et al [ 33 ]; (iv) ASZMM micromixer, Yuan et al [ 87 ]; (v) Obstacle Serpentine Micromixer, Karthikeyan et al [ 89 ]; (vi) T-type mixer with rectangular inserts, Rudyak and Minakov, [ 34 ]; (vii) Obstructed Grooved Micromixer, Rahmannezhad and Mirbozorgi, [ 31 ]; (viii) Primary and Secondary Koch Fractal Baffle Micromixer, Zhang et al [ 35 ]. ( C ).…”

Section: Figurementioning

confidence: 99%

A Review of Pressure Drop and Mixing Characteristics in Passive Mixers Involving Miscible Liquids

Ganguli,

Bhatt,

Yagodnitsyna

et al. 2024

Micromachines

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The present review focuses on the recent studies carried out in passive micromixers for understanding the hydrodynamics and transport phenomena of miscible liquid–liquid (LL) systems in terms of pressure drop and mixing indices. First, the passive micromixers have been categorized based on the type of complexity in shape, size, and configuration. It is observed that the use of different aspect ratios of the microchannel width, presence of obstructions, flow and operating conditions, and fluid properties majorly affect the mixing characteristics and pressure drop in passive micromixers. A regime map for the micromixer selection based on optimization of mixing index (MI) and pressure drop has been identified based on the literature data for the Reynolds number (Re) range (1 ≤ Re ≤ 100). The map comprehensively summarizes the favorable, moderately favorable, or non-operable regimes of a micromixer. Further, regions for special applications of complex micromixer shapes and micromixers operating at low Re have been identified. Similarly, the operable limits for a micromixer based on pressure drop for Re range 0.1 < Re < 100,000 have been identified. A comparison of measured pressure drop with fundamentally derived analytical expressions show that Category 3 and 4 micromixers mostly have higher pressure drops, except for a few efficient ones. An MI regime map comprising diffusion, chaotic advection, and mixed advection-dominated zones has also been devised. An empirical correlation for pressure drop as a function of Reynolds number has been developed and a corresponding friction factor has been obtained. Predictions on heat and mass transfer based on analogies in micromixers have also been proposed.

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Section: Figurementioning

confidence: 99%

A Review of Pressure Drop and Mixing Characteristics in Passive Mixers Involving Miscible Liquids

Ganguli,

Bhatt,

Yagodnitsyna

et al. 2024

Micromachines

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show abstract

“…The fluid in this study is assumed to be a Newtonian fluid, incompressible, with no chemical reaction occuring between the fluids, and the gravity of the fluid and the volume force in the microchannel ignored. Considering that the inertial force related to fluid acceleration can be ignored in microfluidics at low Reynolds numbers, the flow of microfluidics in a micromixer can be described by the Stokes equation and the continuity equation: −∇ P + μ ∇ 2 italicu = 0 ∇ · italicu = 0 where u is the velocity vector, ρ is the fluid density, μ is the dynamic viscosity (Pa·s), and P is the pressure (Pa).…”

Section: Numerical Simulation Modelingmentioning

confidence: 99%

Design, Implementation, and Optimization of a Novel Chaotic Micromixer

Zheng,

Fang,

et al. 2024

Ind. Eng. Chem. Res.

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The mixing efficiency of a micromixer directly determines the performance of the microfluidic device. In this article, micromixers based on arrow-shaped obstacles are designed and fabricated by applying 3D printing and demolding techniques. A CFD fluid model of the proposed micromixer was established using the finite volume method, and the mechanism of the arrow-shaped obstacle for the fluid perturbation was analyzed. The effects of the key parameters of the triangular pyramid obstacle on the mixing efficiency, velocity vector, and pressure drop were studied using orthogonal experiments, range analysis, and variance analysis methods, and further design optimization of related parameters was carried out. The results show that the bottom length has the most significant effect on the micromixing index at a Reynolds number of 0.1− 10 with more than 90% mixing efficiency at a mixing length of 16 mm.

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Machine learning and metaheuristics in microfluidic transport characterization and optimization:CFDand experimental study integrated with predictive modelling

Kouhkord,

Amirmahani,

Hassani

et al. 2024

Can J Chem Eng

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This study presents a comprehensive numerical and experimental analysis on microfluidic cell lysis through computational fluid dynamics (CFD), data‐driven modelling, and multi‐objective optimization. The proposed intelligent framework integrates artificial intelligence and CFD for data generation and extraction, alongside machine learning analysis and experimental studies for transport phenomena characterization in the cell lysis process. The framework explores compound effects of various inflow Reynolds numbers and geometrical parameters, including obstacle configurations and microchannel thickness. It shows substantial effects on flow patterns and mixing in varied microfluidic designs. A surrogate model, developed via central composite design, exhibits high accuracy in assessing system functionality (). The height of the implemented baffles from its lower value to the upper bound resulted in more than 42% and 14% increase in the mixing index at low and high Reynolds numbers, respectively, with minimal impact on pressure drop. The framework introduces data‐driven modelling coupled with multi‐objective optimization by desirability function (DF), non‐dominated sorting genetic algorithm (NSGA‐II), and differential evolution (DE). In the optimization of microfluidic processes, machine learning algorithms outperform desirability‐based methods, and the DE algorithm surpasses the NSGA‐II. An optimum micromixing reducing the mixing length by over 50% and mixing index above 97% achieved, fabricated, and experimental investigations conducted to validate numerical process. Through the precise control of microfluidic variables and the exploitation of microtransfer phenomena, it is possible to enhance the efficiency and selectivity of cell lysis. This not only improves the accuracy of diagnostic information but also opens up new avenues for personalized medicine and therapeutic development.

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Numerical simulation and parameter optimization of micromixer device using fuzzy logic technique

Abstract: Simulated result of Y-shaped herringbone serpentine channel micromixer with obstacles.

Cited by 4 publications

References 42 publications

A Review of Pressure Drop and Mixing Characteristics in Passive Mixers Involving Miscible Liquids

A Review of Pressure Drop and Mixing Characteristics in Passive Mixers Involving Miscible Liquids

Design, Implementation, and Optimization of a Novel Chaotic Micromixer

Machine learning and metaheuristics in microfluidic transport characterization and optimization:CFDand experimental study integrated with predictive modelling

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