Micromixing in oscillatory baffled flows

McDonough, Jonathan; Oates, MF; Law, Richard; Harvey, Adam P.

doi:10.1016/j.cej.2018.12.088

Cited by 38 publications

(32 citation statements)

References 31 publications

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“…McDonough et al reported the micromixing time (which is somehow equivalent to turbulent time scale) for a meso-scale stationary baffle OBRs. Despite the different values of the parameters in their work comparing to the turbulent time scale in this study, the micromixing time with oscillatory Reynolds number trend is very similar to the trends shown in Figure 7 [49]. They both show large values for the turbulent time scale in low oscillatory Reynolds numbers and approach zero values in high oscillatory Reynolds numbers.…”

Section: Velocity Ratio Turbulent Length Scale and Turbulent Time Scalesupporting

confidence: 70%

The Hydrodynamics and Mixing Performance in a Moving Baffle Oscillatory Baffled Reactor through Computational Fluid Dynamics (CFD)

Mortazavi

Pakzad

2020

Processes

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Oscillatory baffled reactors (OBRs) have attracted much attention from researchers and industries alike due to their proven advantages in mixing, scale-up, and cost-effectiveness over conventional stirred tank reactors (STRs). This study quantitatively investigated how different mixing indices describe the mixing performance of a moving baffle OBR using computational fluid dynamics (CFD). In addition, the hydrodynamic behavior of the reactor was studied, considering parameters such as the Q-criterion, shear strain rate, and velocity vector. A modification of the Q-criterion showed advantages over the original Q-criterion in determination of the vortices’ locations. The dynamic mesh tool was utilized to simulate the moving baffles through ANSYS/Fluent. The mixing indices studied were the velocity ratio, turbulent length scale, turbulent time scale, mixing time, and axial dispersion coefficient. We found that the oscillation amplitude had the most significant impact on these indices. In contrast, the oscillatory Reynolds number did not necessarily describe the mixing intensity of a system. Of the tested indices, the axial dispersion coefficient showed advantages over the other indices for quantifying the mixing performance of a moving baffle OBR.

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Section: Velocity Ratio Turbulent Length Scale and Turbulent Time Scalesupporting

confidence: 70%

The Hydrodynamics and Mixing Performance in a Moving Baffle Oscillatory Baffled Reactor through Computational Fluid Dynamics (CFD)

Mortazavi

Pakzad

2020

Processes

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“…In a more recent study, McDonough et al (2019) characterized micromixing in different meso COBR geometries with the Villermaux Dushman test reaction, which is of interest for fast reactions or operations with fast kinetics such as precipitations.…”

Section: Nomenclaturementioning

confidence: 99%

Mixing performance in continuous oscillatory baffled reactors

Avila

Fletcher

Poux

et al. 2020

Chemical Engineering Science

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Areal distribution of mixing intensityIn the current literature, there is limited information on the influence of operating parameters on spatial mixing quality and how a secondary feed should be introduced into continuous oscillatory baffled reac tors (COBR) to achieve good mixing quality. This work explores for the fi rst time the impact of the posi tion of a secondary feed (passive non reactive tracer) on spatial mixing performance in a COBR using transient laminar CFD simulations. Three theoretical feed positions are stuclied covering a range of net flow and oscillatory Reynolds numbers (Re,,.,= 6 27 /Re0 = 24 96), the range being chosen to ensure that the flow field remains two dimensional in ail cases. Macromixing is evaluated by analysing the spa tial uniformity of the tracer with the areal distribution method developed by Alberini et al. (2014a). Introduction of the secondary stream at the reactor wall or upstream of the edge of the first b affle greatly improves mixing quality due to the recirculation edclies, which assist radial mixing. However, introduc tion of the secondary feed at the centreline results in high axial dispersion with limited radial mixing. With an adequate introduction position, mixing quality typically increases with an increment in the velocity ratio. Nevertheless, if the net flow is too low, mixing performance decreases because the sec ondary stream is pushed upstream of the baffles, where it does not benefit from flow recirculation.

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“…It is noted that Equation is valid for both HSI and grayscale images. In time‐series micromixing experiments,

\overset{I}{true}

HSI ,mix can no longer be considered constant. This is because the value of

\overset{I}{true}

HSI ,mix varies with respect to a particular image frame.…”

Section: Introductionmentioning

confidence: 99%

“…In time-series micromixing experiments, [21][22][23][24] I HSI,mix can no longer be considered constant. This is because the value of I HSI,mix varies with respect to a particular image frame.…”

Section: Introductionmentioning

confidence: 99%

Method for determining mixing index in microfluidics by RGB color model

Mahmud

Tamrin

2020

Asia-Pacific J Chem Eng

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Microfluidic mixing is a key process in miniaturized analysis system. Achieving adequate mixing performance is considerably difficult in microfluidic micromixer, as the flow is always associated with unfavorable laminar flow and dominated by molecular diffusion. The mixing performance of these micromixers are generally characterized as a function of mixing index based on dispersion (homogeneity) information, leading to either an overestimated or underestimated mixing index. This paper presents a new method for determining the mixing index of micromixers based on RGB color model by decoding mixing images to their respective red, green, and blue pixel intensities. Several digital composite images were used to perform initial benchmarking, and the proposed method accurately quantified the mixing index significantly better than previously adopted methods. The practicality of the method was further demonstrated by characterizing mixing of well‐known micromixers, namely T‐ and Y‐ micromixers, at varying Reynolds numbers of 5 ≤ Re ≤ 100. The results show that the mixing index decreases with the increase in Reynolds number, whereas the mixing index of the T‐micromixer was superior to that of Y‐micromixer, both agreeing well with the literature. The mixing index of these micromixers at varying Reynolds numbers of 5 ≤ Re ≤ 100 calculated using other methods were also compared and discussed. The proposed method is foreseen handy and robust in characterizing mixing in real time for gradient mixing in networked microchannels and multivortex mixing for the manipulation of fluids, particles, and biological substances.

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Micromixing in oscillatory baffled flows

Cited by 38 publications

References 31 publications

The Hydrodynamics and Mixing Performance in a Moving Baffle Oscillatory Baffled Reactor through Computational Fluid Dynamics (CFD)

The Hydrodynamics and Mixing Performance in a Moving Baffle Oscillatory Baffled Reactor through Computational Fluid Dynamics (CFD)

Mixing performance in continuous oscillatory baffled reactors

Method for determining mixing index in microfluidics by RGB color model

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