Numerical simulation of diffusion process in T-shaped micromixer using Shan-Chen Lattice Boltzmann Method

Dauyeshova, Bagdagul; Rojas‐Solórzano, Luis; Monaco, Ernesto

doi:10.1016/j.compfluid.2018.03.029

Cited by 19 publications

(6 citation statements)

References 30 publications

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“…The domain was discretized with the same Cartesian grid as for the FVM. Following the approach from [ 48 ], the Shan–Chen method [ 49 ] was used to solve the multi-component problem. This method adds an artificial force to the fluid elements that mimics the separation of different components.…”

Section: Conducted Simulationsmentioning

confidence: 99%

Simulation of Pressure-Driven and Channel-Based Microfluidics on Different Abstract Levels: A Case Study

Takken

Wille

2022

Sensors

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A microfluidic device, or a Lab-on-a-Chip (LoC), performs lab operations on the microscale through the manipulation of fluids. The design and fabrication of such devices usually is a tedious process, and auxiliary tools, such as simulators, can alleviate the necessary effort for the design process. Simulations of fluids exist in various forms and can be categorized according to how well they represent the underlying physics, into so-called abstraction levels. In this work, we consider simulation approaches in 1D, which are based on analytical solutions of simplified problems, and approaches in 2D and 3D, for which we use two different Computational Fluid Dynamics (CFD) methods—namely, the Finite Volume Method (FVM) and the Lattice-Boltzmann Method (LBM). All these methods come with their pros and cons with respect to accuracy and required compute time, but unfortunately, most designers and researchers are not aware of the trade-off that can be made within the broad spectrum of available simulation approaches for microfluidics and end up choosing a simulation approach arbitrarily. We provide an overview of different simulation approaches as well as a case study of their performance to aid designers and researchers in their choice. To this end, we consider three representative use cases of pressure-driven and channel-based microfluidic devices (namely the non-Newtonian flow in a channel, the mixing of two fluids in a channel, and the behavior of droplets in channels). The considerations and evaluations raise the awareness and provide several insights for what simulation approaches can be utilized today when designing corresponding devices (and for what they cannot be utilized yet).

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Section: Conducted Simulationsmentioning

confidence: 99%

Simulation of Pressure-Driven and Channel-Based Microfluidics on Different Abstract Levels: A Case Study

Takken

Wille

2022

Sensors

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“…One of phase separation principles is to utilize inherent immersion in an anti-solvent way of separating, for example, emulsification (Mukherjee et al, 2019) (Fournanty et al, 2008), nuclear condensates (Zhang et al, 2019), droplet formation, and movement in microchannels. So, the underlying principle of phase separation involves utilizing the two or more fluids, such that they separate into their respective phases (Dauyeshova et al, 2018). Its fully understanding will serve as the emulsified behavior occurring in heavy oil cold production and other areas.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of Phase Separation Using the Lattice Boltzmann Method

You

2021

Front. Earth Sci.

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Phase separation is widely observed in multiphase systems. In this study, it has been investigated using Shan–Chen lattice Boltzmann method. The adhesion parameter in SC model leads to the desired fluid–fluid phenomenon, which was varied to specify the strength of separation between two phases to present emulsified performance in oil production. In order to describe such behaviors quantitatively, graphical distributions were described with time and were corresponded with a statistical index–Fourier structure factor that is able to predict complex phase separation behaviors, thereby providing a measurement for calculating such random distribution during the process of separation as well as evaluating heterogeneous degrees of the entire domain. The repulsive interactions are specified as low, intermediate, and high values. Phase separations with clear boundaries have been observed and each stage of separation evolvement has been discussed in this study. Magnitudes of structure factors are increased with higher degrees of fluctuations.

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“…Vatankhah et al (2018) analyzed the parametric study of the plane spiral shape mixer. Dauyeshova et al (2018) analyzed the effect of cylindrical and rectangular obstacles in the T-junction micromixer. Veldurthi et al (2015) demonstrated the mixing phenomena in the cylindrical chamber having a micro-rotor at the top of the chamber by numerical simulation.…”

Section: Introductionmentioning

confidence: 99%

Numerical study of mixing index in offset inlets 3-D T mixer with bend shape mixing channel

Prakash¹,

Zunaid²,

Samsher³

2021

JER is an international, peer-reviewed journal that publishes f

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The objective of this paper is the computational analysis on the mixing index of simple T shape mixer, offset inlets T shape mixer, and offset inlets T mixer with bend shape mixing channel by CFD simulation. Computational fluid dynamics software package solves conservation of mass equation, conservation of momentum equation, and conservation of energy equation. In the case of offset inlets T shape mixer, as the aspect ratio (height to width ratio) of mixing channel increased so mixing quality also increased and offset inlets T mixer with bend shape is a combination of increased aspect ratio as well as chaotic advection mechanisms, so it provides advanced mixing index than offset inlets T shape mixer and simple T shape mixer. Pressure fall in offset inlets T shape mixer is excess than simple T shape mixer but narrowly degraded than offset inlets T mixer with bend shape. Chaotic advection rooted microchannel generates secondary flow because of which motives a high-pressure drop in the microchannel.

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Numerical simulation of diffusion process in T-shaped micromixer using Shan-Chen Lattice Boltzmann Method

Cited by 19 publications

References 30 publications

Simulation of Pressure-Driven and Channel-Based Microfluidics on Different Abstract Levels: A Case Study

Simulation of Pressure-Driven and Channel-Based Microfluidics on Different Abstract Levels: A Case Study

Quantitative Analysis of Phase Separation Using the Lattice Boltzmann Method

Numerical study of mixing index in offset inlets 3-D T mixer with bend shape mixing channel

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