Machine learning assisted fast prediction of inertial lift in microchannels
Machine learning assisted fast prediction of inertial lift in microchannels
复制标题
机器学习辅助快速预测微通道中的惯性升力
DOI：
10.1039/d1lc00225b
复制
发表时间：
2021
期刊：
影响因子：
6.1
通讯作者：
Hu Guoqing
中科院分区：
文献类型：
--
作者：
Su Jinghong;Chen Xiaodong;Zhu Yongzheng;Hu Guoqing
在微流控平台中，惯性效应被广泛应用于操纵工程颗粒和生物胶体。惯性微流控装置的设计在很大程度上依赖于对粒子运动的精确预测，而粒子运动的准确预测是由作用于粒子的惯性升力决定的。直接数值模拟虽然是精确计算升力的唯一手段，但其计算量往往很大，当应用于复杂几何形状的微通道时甚至变得不实用。在这里，我们提出了一种结合机器学习技术的快速数值算法，用于分析和设计惯性微流控器件。通过对具有三种截面形状(包括矩形、三角形和半圆形)的直微通道中的各种操作参数进行数值模拟，首次建立了惯性升力数据库。然后开发了一个机器学习辅助模型，通过简单地指定截面形状、雷诺数和颗粒阻塞率来获得惯性升力分布。将所得到的惯性升力与拉格朗日跟踪方法相结合，在实际装置中对两种微通道中的粒子轨迹进行了快速预测，得到了与实验观测相一致的结果。我们的数据库和相关代码使研究人员能够加快用于粒子操纵的惯性微流控设备的开发。
Inertial 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.