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Design Optimization for a Microfluidic Crossflow Filtration System Incorporating a Micromixer

In this study, we report on a numerical study on design optimization for a microfluidic crossflow filtration system incorporated with the staggered herringbone micromixer (SHM). Computational fluid dynamics (CFD) and the Taguchi method were employed to find out an optimal set of design parameters, m...

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Autores principales: Jung, Seon Yeop, Park, Jo Eun, Kang, Tae Gon, Ahn, Kyung Hyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952986/
https://www.ncbi.nlm.nih.gov/pubmed/31801229
http://dx.doi.org/10.3390/mi10120836
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author Jung, Seon Yeop
Park, Jo Eun
Kang, Tae Gon
Ahn, Kyung Hyun
author_facet Jung, Seon Yeop
Park, Jo Eun
Kang, Tae Gon
Ahn, Kyung Hyun
author_sort Jung, Seon Yeop
collection PubMed
description In this study, we report on a numerical study on design optimization for a microfluidic crossflow filtration system incorporated with the staggered herringbone micromixer (SHM). Computational fluid dynamics (CFD) and the Taguchi method were employed to find out an optimal set of design parameters, mitigating fouling in the filtration system. The flow and the mass transfer characteristics in a reference SHM model and a plain rectangular microchannel were numerically investigated in detail. Downwelling flows in the SHM model lead to backtransport of foulants from the permeable wall, which slows down the development of the concentration boundary layer in the filtration system. Four design parameters — the number of grooves, the groove depth, the interspace between two neighboring grooves, and the interspace between half mixing periods — were chosen to construct a set of numerical experiments using an orthogonal array [Formula: see text] from the Taguchi method. The Analysis of Variance (ANOVA) using the evaluated signal-to-noise (SN) ratios enabled us to identify the contribution of each design parameter on the performance. The proposed optimal SHM model indeed showed the lowest growth rate of the wall concentration compared to other SHM models.
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spelling pubmed-69529862020-01-23 Design Optimization for a Microfluidic Crossflow Filtration System Incorporating a Micromixer Jung, Seon Yeop Park, Jo Eun Kang, Tae Gon Ahn, Kyung Hyun Micromachines (Basel) Article In this study, we report on a numerical study on design optimization for a microfluidic crossflow filtration system incorporated with the staggered herringbone micromixer (SHM). Computational fluid dynamics (CFD) and the Taguchi method were employed to find out an optimal set of design parameters, mitigating fouling in the filtration system. The flow and the mass transfer characteristics in a reference SHM model and a plain rectangular microchannel were numerically investigated in detail. Downwelling flows in the SHM model lead to backtransport of foulants from the permeable wall, which slows down the development of the concentration boundary layer in the filtration system. Four design parameters — the number of grooves, the groove depth, the interspace between two neighboring grooves, and the interspace between half mixing periods — were chosen to construct a set of numerical experiments using an orthogonal array [Formula: see text] from the Taguchi method. The Analysis of Variance (ANOVA) using the evaluated signal-to-noise (SN) ratios enabled us to identify the contribution of each design parameter on the performance. The proposed optimal SHM model indeed showed the lowest growth rate of the wall concentration compared to other SHM models. MDPI 2019-11-30 /pmc/articles/PMC6952986/ /pubmed/31801229 http://dx.doi.org/10.3390/mi10120836 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jung, Seon Yeop
Park, Jo Eun
Kang, Tae Gon
Ahn, Kyung Hyun
Design Optimization for a Microfluidic Crossflow Filtration System Incorporating a Micromixer
title Design Optimization for a Microfluidic Crossflow Filtration System Incorporating a Micromixer
title_full Design Optimization for a Microfluidic Crossflow Filtration System Incorporating a Micromixer
title_fullStr Design Optimization for a Microfluidic Crossflow Filtration System Incorporating a Micromixer
title_full_unstemmed Design Optimization for a Microfluidic Crossflow Filtration System Incorporating a Micromixer
title_short Design Optimization for a Microfluidic Crossflow Filtration System Incorporating a Micromixer
title_sort design optimization for a microfluidic crossflow filtration system incorporating a micromixer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952986/
https://www.ncbi.nlm.nih.gov/pubmed/31801229
http://dx.doi.org/10.3390/mi10120836
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