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多孔微通道中流体特性与气体传输的直接模拟蒙特卡洛研究

Direct Simulation Monte Carlo investigation of fluid characteristics and gas transport in porous microchannels.

作者信息

Shariati Vahid, Ahmadian Mohammad Hassan, Roohi Ehsan

机构信息

Department of Mechanical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, P.O. Box 91775-1111, Mashhad, Iran.

出版信息

Sci Rep. 2019 Nov 20;9(1):17183. doi: 10.1038/s41598-019-52707-3.

DOI:10.1038/s41598-019-52707-3
PMID:31748601
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6868203/
Abstract

The impetus of the current research is to use the direct simulation Monte Carlo (DSMC) algorithm to investigate fluid behaviour and gas transport in porous microchannels. Here, we demonstrate DSMC's capability to simulate porous media up to 40% porosity. In this study, the porous geometry is generated by a random distribution of circular obstacles through the microchannel with no interpenetration between the obstacles. The influence of the morphology along with rarefaction and gas type on the apparent permeability is investigated. Moreover, the effects of porosity, solid particle's diameter and specific surface area are considered. Our results demonstrate that although decreasing porosity intensifies tortuosity in the flow field, the tortuosity reduces at higher Knudsen numbers due to slip flow at solid boundaries. In addition, our study on two different gas species showed that the gas type affects slippage and apparent gas permeability. Finally, comparing different apparent permeability models showed that Beskok and Karniadakis model is valid only up to the early transition regime and at higher Knudsen numbers, the current data matches those models that take Knudsen diffusion into account as well.

摘要

当前研究的目的是使用直接模拟蒙特卡洛(DSMC)算法来研究多孔微通道中的流体行为和气体传输。在此,我们展示了DSMC模拟孔隙率高达40%的多孔介质的能力。在本研究中,多孔几何结构是通过圆形障碍物在微通道中的随机分布生成的,障碍物之间不相互穿透。研究了形态以及稀薄度和气体类型对表观渗透率的影响。此外,还考虑了孔隙率、固体颗粒直径和比表面积的影响。我们的结果表明,尽管孔隙率降低会加剧流场中的曲折度,但由于固体边界处的滑移流,在较高的克努森数下曲折度会降低。此外,我们对两种不同气体种类的研究表明,气体类型会影响滑移和表观气体渗透率。最后,比较不同的表观渗透率模型表明,贝斯科克和卡尔尼亚达基斯模型仅在早期过渡区域有效,在较高的克努森数下,当前数据也与考虑克努森扩散的模型相匹配。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b61/6868203/200e8174a993/41598_2019_52707_Fig18_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b61/6868203/200e8174a993/41598_2019_52707_Fig18_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b61/6868203/bbb80dc7a017/41598_2019_52707_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b61/6868203/11189accf2f2/41598_2019_52707_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b61/6868203/87c073ad1cbe/41598_2019_52707_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b61/6868203/f3a4e0e37ee6/41598_2019_52707_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b61/6868203/11927226430e/41598_2019_52707_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b61/6868203/0d78cef28965/41598_2019_52707_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b61/6868203/acb6abe1c2df/41598_2019_52707_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b61/6868203/72f0e2f1019e/41598_2019_52707_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b61/6868203/23ef113e4c54/41598_2019_52707_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b61/6868203/11fe565acb10/41598_2019_52707_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b61/6868203/8bcc07103645/41598_2019_52707_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b61/6868203/bca3a5320884/41598_2019_52707_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b61/6868203/412ac7ac26de/41598_2019_52707_Fig17_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b61/6868203/200e8174a993/41598_2019_52707_Fig18_HTML.jpg

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