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Journal of ZheJiang University (Engineering Science)  2023, Vol. 57 Issue (5): 967-976    DOI: 10.3785/j.issn.1008-973X.2023.05.013
    
Seepage experiment and numerical simulation based on microfluidic chip model
Shao-kai NIE(),Peng-fei LIU,Te BA*(),Yun-min CHEN
Institute of Geotechnical Engineering, Zhejiang University, Hangzhou 310058, China
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Abstract  

Based on the microfluidic chip processing technology and using the microscopy-micromodel experimental system, the seepage experiment was performed by fabricating the quasi-two dimensional microfluidic chip model to imitate the internal skeleton and pore structure of porous media. The permeability of chip model was calculated by measuring and modifying the pressure drop of both end of the chip model. Computational fluid dynamics (CFD) method was adopted to make the numerical simulation of the seepage process compared with the results of experiment. Under the same condition, compared with the chip model with the square arrangement micro-pillar, the chip model with staggered micro-pillar showed that the tortuosity increased with an amplitude of 5.1%—7.9% microscopically, the flow resistance and pressure drop increased and the permeability decreased with an amplitude of 4.5%—7.4% macroscopically. The permeability of chip models was not only related to the internal pore structure and porosity, but also related to particle diameter and particle arrangement. When the porosity of model was 0.327—0.900, the permeability of the chip model obtained by the numerical simulation method was closed to the experimental results with the error of 9.78%—28.43%. Kozeny-Carman (KC) equation could not predict the experiment results correctly and the maximum error was 73.97%. A modified parallel plate duct flow equation was proposed to predict the permeability of quasi-two dimensional microfluidic chip model. The curve of predicted permeability was consistent well with the numerical and experimental data.nt well with the numerical and experimental data.



Key wordsporous media      permeability      microfluidic chip model      Kozeny-Carman equation      parallel plate duct flow     
Received: 10 May 2022      Published: 09 May 2023
CLC:  TU 443  
Fund:  国家自然科学基金资助项目(51988101)
Corresponding Authors: Te BA     E-mail: nsk@zju.edu.cn;ba-te@zju.edu.cn
Cite this article:

Shao-kai NIE,Peng-fei LIU,Te BA,Yun-min CHEN. Seepage experiment and numerical simulation based on microfluidic chip model. Journal of ZheJiang University (Engineering Science), 2023, 57(5): 967-976.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2023.05.013     OR     https://www.zjujournals.com/eng/Y2023/V57/I5/967


基于微流控芯片模型的渗流实验与数值模拟

基于微流控芯片加工技术,采用显微镜-微观模型实验装置,通过制作准二维微流控芯片模型来模拟多孔介质内部的骨架及孔隙结构,开展多孔介质渗流实验. 通过测量芯片模型两端的压降并进行修正,计算芯片模型的渗透率. 采用计算流体力学方法(CFD)对渗流过程进行数值模拟,并与实验结果进行对比分析. 结果表明:在相同条件下,相对于微柱方形排列的芯片模型,微柱错开排列的芯片模型在微观上表现为迂曲度增大,增大的幅值为5.1%~7.9%;在宏观上表现为流阻和压降更大,渗透率更低,降低的幅值为4.5%~7.4%. 芯片模型渗透率不仅与内部孔隙通道结构和孔隙率有关,还与颗粒直径和颗粒排列方式相关. 当模型孔隙率为0.327~0.900时,数值模拟方法所得的微流控芯片模型的渗透率与实验所测结果接近,误差为9.78%~28.43%. Kozeny-Carman (KC)公式不能准确预测实验结果,并且最大误差为73.97%. 提出修正平行板间导管流(平板流)渗流公式预测准二维微流控芯片模型渗透率,预测渗透率曲线与数值模拟和实验数据具有很好的一致性.


关键词: 多孔介质,  渗透率,  微流控芯片模型,  Kozeny-Carman方程,  平板流 
Fig.1 Microscopy-micromodel experimental system
芯片模型 $ L $ $ /\mathrm{m}\mathrm{m} $ $W/\mathrm{m}\mathrm{m}$ $D/{\text{μm}}$ $h/{\text{μm}}$ $D/\text{μ}\mathrm{m}$ $ \varepsilon $
1) 注:Sq为方形排列;St为错开排列;Sq 0.60-500的孔隙率为0.60,微柱直径为 $ 500 $ μm的方形排列芯片模型,其他依此类推,O为孔喉直径,ε为孔隙率.
Sq 0.60-500 20 10 500 50 207.0 0.60
Sq 0.60-1000 20 10 1 000 50 414.0 0.60
Sq 0.60-1500 20 10 1 500 50 621.0 0.60
Sq 0.60-2000 20 10 2 000 50 828.0 0.60
St 0.60-500 20 10 500 50 258.5 0.60
St 0.60-1000 20 10 1 000 50 519.7 0.60
St 0.60-1500 20 10 1 500 50 779.5 0.60
St 0.60-2000 20 10 2 000 50 1 039.3 0.60
St 0.54-500 20 10 500 50 207.0 0.54
St 0.54-1000 20 10 1 000 50 414.0 0.54
St 0.54-1500 20 10 1 500 50 621.0 0.54
St 0.54-2000 20 10 2 000 50 828.0 0.54
Tab.1 Geometrical dimensions of different microfluidic chip models considered in present work
Fig.2 Schematic diagram of numerical simulation physical model
Fig.3 Grid independence test and validation of numerical microfluidic chip model
Fig.4 Variation of unit experimental pressure drop in microfluidic chip models with different velocities under different micropillar diameters when porosity was 0.60
Fig.5 Variation of experimental flow resistance in microfluidic chips with different flow rate under different micro pillar diameter and arrangements when porosity was 0.60
Fig.6 Comparison of permeability obtained by different microfluidic chip models
Fig.7 Comparison of predicted permeability of microfluidic chip models with experimental data and numerical data
Fig.8 Schematic diagram of streamline for chip models
Fig.9 Comparison of average tortuosity in different microfluidic chip models
Fig.10 Effect of rotation angle $ \theta $ on velocity distribution of microfluidic chip models obtained by numerical simulation when flow rate was 10 µL/min, micropillar diameter was 1 000 µm and porosity was 0.60
Fig.11 Effect of rotation angle and pore throat diameter on permeability of microfluidic chip models obtained by numerical simulation under staggered arrangement
Fig.12 Average tortuosity of microfluidic chip models under different rotation angles
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