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流体体积(VOF)法作为研究微通道中液滴形成的一种合适方法。

Volume of Fluid (VOF) Method as a Suitable Method for Studying Droplet Formation in a Microchannel.

作者信息

da Silva Felipe Santos Paes, Lisboa-Filho Paulo Noronha

机构信息

Department of Physics, School of Science, State University of São Paulo, Bauru 17033-360, SP, Brazil.

出版信息

Micromachines (Basel). 2025 Jun 27;16(7):757. doi: 10.3390/mi16070757.

DOI:10.3390/mi16070757
PMID:40731666
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12299916/
Abstract

Microfluidics is a rapidly advancing field focused on optimizing microdevices for applications such as organ-on-a-chip systems and enhancing laboratory analyses. Understanding the physical parameters of droplet generation is crucial for these devices. Computational fluid dynamics (CFD) techniques are essential for providing insights into the limitations and efficiency of numerical methods for studying fluid dynamics and improving our understanding of various application conditions. However, the influence of different numerical methods on the analysis of physical parameters in problems involving droplet generation in microchannels remains an area of ongoing research. This study implements the Volume of Fluid (VOF) method to investigate key physical parameters, including droplet size and the effect of the capillary number on fluid regimes, in droplet generation within a microchannel featuring a T-junction geometry. We compare the VOF method with the widely used Level Set Method (LSM) to evaluate its suitability for this context. The results show that the VOF method agrees with the LSM in fundamental outcomes, such as the reduction in droplet diameter as the flow rate ratio increases and the identification of the capillary number's influence on fluid regime classification. The VOF method provides a clearer understanding of transitions between fluid regimes by detecting stages of non-uniformity in droplet size. It identifies a transition region between regimes with variations in droplet size, proving to be effective at mapping fluid flow regimes. This study highlights the potential of the VOF method in offering more detailed insights into instabilities and transitions between fluid regimes at the microscale.

摘要

微流控技术是一个快速发展的领域,专注于优化用于诸如芯片上器官系统等应用的微器件,并增强实验室分析。了解液滴生成的物理参数对于这些器件至关重要。计算流体动力学(CFD)技术对于深入了解研究流体动力学的数值方法的局限性和效率以及增进我们对各种应用条件的理解至关重要。然而,在涉及微通道中液滴生成的问题中,不同数值方法对物理参数分析的影响仍然是一个正在进行研究的领域。本研究采用流体体积(VOF)方法来研究在具有T型结几何形状的微通道内液滴生成过程中的关键物理参数,包括液滴尺寸以及毛细管数对流体状态的影响。我们将VOF方法与广泛使用的水平集方法(LSM)进行比较,以评估其在此背景下的适用性。结果表明,VOF方法在基本结果上与LSM一致,例如随着流速比增加液滴直径减小以及确定毛细管数对流体状态分类的影响。VOF方法通过检测液滴尺寸的不均匀阶段,能更清晰地理解流体状态之间的转变。它识别出液滴尺寸变化的状态之间的过渡区域,在绘制流体流动状态方面证明是有效的。本研究突出了VOF方法在提供对微尺度下流体状态的不稳定性和转变更详细见解方面的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8907/12299916/20432fcc6574/micromachines-16-00757-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8907/12299916/cbfbecca1fe7/micromachines-16-00757-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8907/12299916/a7078af83108/micromachines-16-00757-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8907/12299916/0ba76472df3e/micromachines-16-00757-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8907/12299916/b6311ea15255/micromachines-16-00757-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8907/12299916/ed8e85f5560b/micromachines-16-00757-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8907/12299916/a6488a636392/micromachines-16-00757-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8907/12299916/0df0755fdb88/micromachines-16-00757-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8907/12299916/acb3c18b390d/micromachines-16-00757-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8907/12299916/504648128d2e/micromachines-16-00757-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8907/12299916/20432fcc6574/micromachines-16-00757-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8907/12299916/cbfbecca1fe7/micromachines-16-00757-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8907/12299916/a7078af83108/micromachines-16-00757-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8907/12299916/0ba76472df3e/micromachines-16-00757-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8907/12299916/b6311ea15255/micromachines-16-00757-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8907/12299916/ed8e85f5560b/micromachines-16-00757-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8907/12299916/a6488a636392/micromachines-16-00757-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8907/12299916/0df0755fdb88/micromachines-16-00757-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8907/12299916/acb3c18b390d/micromachines-16-00757-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8907/12299916/504648128d2e/micromachines-16-00757-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8907/12299916/20432fcc6574/micromachines-16-00757-g010.jpg

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