IMECH-IR  > 非线性力学国家重点实验室
Machine learning assisted fast prediction of inertial lift in microchannels
Su JH(苏敬宏)1,2,3; Chen XD(陈晓东)4; Zhu YZ(朱勇铮)2,3; Hu GQ(胡国庆)1
Corresponding AuthorHu, Guoqing(ghu@zju.edu.cn)
Source PublicationLAB ON A CHIP
2021-05-11
Pages13
ISSN1473-0197
AbstractInertial effect has been extensively used in manipulating both engineered particles and biocolloids in microfluidic platforms. The design of inertial microfluidic devices largely relies on precise prediction of particle migration that is determined by the inertial lift acting on the particle. In spite of being the only means to accurately obtain the lift forces, direct numerical simulation (DNS) often consumes high computational cost and even becomes impractical when applied to microchannels with complex geometries. Herein, we proposed a fast numerical algorithm in conjunction with machine learning techniques for the analysis and design of inertial microfluidic devices. A database of inertial lift forces was first generated by conducting DNS over a wide range of operating parameters in straight microchannels with three types of cross-sectional shapes, including rectangular, triangular and semicircular shapes. A machine learning assisted model was then developed to gain the inertial lift distribution, by simply specifying the cross-sectional shape, Reynolds number and particle blockage ratio. The resultant inertial lift was integrated into the Lagrangian tracking method to quickly predict the particle trajectories in two types of microchannels in practical devices and yield good agreement with experimental observations. Our database and the associated codes allow researchers to expedite the development of the inertial microfluidic devices for particle manipulation.
DOI10.1039/d1lc00225b
Indexed BySCI
Language英语
WOS IDWOS:000651138000001
WOS KeywordSPHERICAL-PARTICLE ; SPIRAL MICROCHANNEL ; CELL-SEPARATION ; POISEUILLE FLOW ; CROSS-SECTION ; MIGRATION ; MICROFLUIDICS ; MANIPULATION ; NETWORKS
WOS Research AreaBiochemistry & Molecular Biology ; Chemistry ; Science & Technology - Other Topics ; Instruments & Instrumentation
WOS SubjectBiochemical Research Methods ; Chemistry, Multidisciplinary ; Chemistry, Analytical ; Nanoscience & Nanotechnology ; Instruments & Instrumentation
Funding ProjectNatural Science Foundation of China[11832017] ; Natural Science Foundation of China[11772343] ; Chinese Academy of Sciences Key Research Program of Frontier Sciences[QYZDB-SSW-JSC036] ; Chinese Academy of Sciences Strategic Priority Research Program[XDB22040403] ; Beijing Institute of Technology Research Fund Program for Young Scholars
Funding OrganizationNatural Science Foundation of China ; Chinese Academy of Sciences Key Research Program of Frontier Sciences ; Chinese Academy of Sciences Strategic Priority Research Program ; Beijing Institute of Technology Research Fund Program for Young Scholars
Classification一类
Ranking1
ContributorHu, Guoqing
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Document Type期刊论文
Identifierhttp://dspace.imech.ac.cn/handle/311007/86612
Collection非线性力学国家重点实验室
Affiliation1.Zhejiang Univ, Dept Engn Mech, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310027, Peoples R China;
2.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech LNM, Beijing 100190, Peoples R China;
3.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China;
4.Beijing Inst Technol, Sch Aerosp Engn, Beijing 100081, Peoples R China
Recommended Citation
GB/T 7714
Su JH,Chen XD,Zhu YZ,et al. Machine learning assisted fast prediction of inertial lift in microchannels[J]. LAB ON A CHIP,2021:13.
APA 苏敬宏,陈晓东,朱勇铮,&胡国庆.(2021).Machine learning assisted fast prediction of inertial lift in microchannels.LAB ON A CHIP,13.
MLA 苏敬宏,et al."Machine learning assisted fast prediction of inertial lift in microchannels".LAB ON A CHIP (2021):13.
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