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U-Net-Based Surrogate Model for Evaluation of Microfluidic Channels
International Journal of Computational Methods ( IF 1.7 ) Pub Date : 2021-07-24 , DOI: 10.1142/s0219876221410188
Tuyen Quang Le 1 , Pao-Hsiung Chiu 1 , Chinchun Ooi 1
Affiliation  

Microfluidics have shown great promise in multiple applications, especially in biomedical diagnostics and separations. While the flow properties of these microfluidic devices can be solved by numerical methods such as computational fluent dynamics (CFD), the process of mesh generation and setting up a numerical solver requires some domain familiarity, while more intuitive commercial programs such as fluent and StarCCM can be expensive. Hence, in this work, we demonstrated the use of a U-Net convolutional neural network as a surrogate model for predicting the velocity and pressure fields that would result for a particular set of microfluidic filter designs. The surrogate model is fast, easy to set-up and can be used to predict and assess the flow velocity and pressure fields across the domain for new designs of interest via the input of a geometry-encoding matrix. In addition, we demonstrate that the same methodology can also be used to train a network to predict pressure based on velocity data, and propose that this can be an alternative to numerical algorithms for calculating pressure based on velocity measurements from particle image velocimetry measurements. Critically, in both applications, we demonstrate prediction test errors of less than 1%, suggesting that this is indeed a viable method.



中文翻译:

用于评估微流体通道的基于 U-Net 的替代模型

微流体在多种应用中显示出巨大的前景,特别是在生物医学诊断和分离方面。虽然这些微流体装置的流动特性可以通过计算流体动力学 (CFD) 等数值方法求解,但网格生成和设置数值求解器的过程需要对领域有一定了解,而更直观的商业程序(如 fluent 和 StarCCM)则可以贵。因此,在这项工作中,我们展示了使用 U-Net 卷积神经网络作为替代模型来预测特定微流体过滤器设计的速度和压力场。代理模型很快,易于设置,可用于通过输入几何编码矩阵来预测和评估新设计感兴趣的域内的流速和压力场。此外,我们证明了相同的方法也可用于训练网络以基于速度数据预测压力,并建议这可以替代基于粒子图像测速测量的速度测量来计算压力的数值算法。至关重要的是,在这两种应用中,我们都展示了小于 1% 的预测测试误差,这表明这确实是一种可行的方法。并建议这可以替代基于粒子图像测速测量的速度测量来计算压力的数值算法。至关重要的是,在这两种应用中,我们都展示了小于 1% 的预测测试误差,这表明这确实是一种可行的方法。并建议这可以替代基于粒子图像测速测量的速度测量来计算压力的数值算法。至关重要的是,在这两种应用中,我们都展示了小于 1% 的预测测试误差,这表明这确实是一种可行的方法。

更新日期:2021-07-24
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