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Experiments and computations of microfluidic liquid–liquid flow patterns†
Reaction Chemistry & Engineering ( IF 3.9 ) Pub Date : 2019-10-01 , DOI: 10.1039/c9re00332k
Pierre Desir 1, 2, 3, 4 , Tai-Ying Chen 1, 2, 3, 4 , Mauro Bracconi 5, 6, 7, 8, 9 , Basudeb Saha 3, 4, 10 , Matteo Maestri 5, 6, 7, 8, 9 , Dionisios G. Vlachos 1, 2, 3, 4, 10
Affiliation  

We study two-phase liquid–liquid flow patterns in a 500 μm capillary microchannel for four biphasic systems: ethyl acetate/water, 2-pentanol/water, methyl isobutyl ketone/water, and heptane/water. Flow visualization experiments using laser induced fluorescence (LIF) reveal a total of 7 different flow patterns for all solvent pairs, namely slug flow, droplet flow, slug-droplet flow, parallel, annular, dispersed, and irregular flow. A map of different flow patterns was built to delineate the origin of their formation. We find conventional dimensionless groups are insufficient to uniquely identify the flow patterns. Computational fluid dynamics (CFD) modeling in OpenFOAM shows agreement with the experimental flow patterns for most of the two-phase flows. Principal component analysis reduces the dimensionality of potential descriptors of flow patterns and, unlike prior work using two dimensionless numbers, determines six important features that describe >95% of the variance of the experimental flow patterns. These include the total flow rate, the flow rate ratio between the two phases, the capillary and Ohnesorge numbers of the aqueous phase, and the Weber number and velocity of the organic phase. We build a decision-tree model to further regress the data and identify the critical features and demonstrate an accuracy in predicting the flow patterns of up to 93%.

中文翻译:

微流体液-液流动模式的实验和计算

我们在500μm毛细管微通道中研究了四个双相系统的两相液-液流动模式:乙酸乙酯/水,2-戊醇/水,甲基异丁基酮/水和庚烷/水。使用激光诱导荧光(LIF)的流动可视化实验揭示了所有溶剂对共有7种不同的流动模式,即团状流,液滴流,团状液滴流,平行流,环形流,分散流和不规则流。建立了不同流动模式的地图以描绘其形成的起源。我们发现常规的无量纲组不足以唯一地识别流型。OpenFOAM中的计算流体动力学(CFD)建模表明与大多数两相流的实验流模式一致。主成分分析降低了流动模式潜在描述子的维数,并且与先前使用两个无量纲数的工作不同,它确定了六个重要特征,这些重要特征描述了实验流动模式方差的> 95%。这些因素包括总流速,两相之间的流速比,水相的毛细管数和Ohnesorge数以及有机相的Weber数和速度。我们建立了决策树模型,以进一步回归数据并确定关键特征,并证明预测流量模式的准确性高达93%。两相之间的流速比,水相的毛细管数和Ohnesorge数以及有机相的Weber数和速度。我们建立了决策树模型,以进一步回归数据并确定关键特征,并证明预测流量模式的准确性高达93%。两相之间的流速比,水相的毛细管数和Ohnesorge数以及有机相的Weber数和速度。我们建立了决策树模型,以进一步回归数据并确定关键特征,并证明预测流量模式的准确性高达93%。
更新日期:2019-12-18
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