4.3 Article

U-Net-Based Surrogate Model for Evaluation of Microfluidic Channels

出版社

WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0219876221410188

关键词

Computational fluid dynamics; microfluidics; convolutional neural network; surrogate model

资金

  1. Agency for Science, Technology and Research (A*STAR), Singapore [A1820g0084]

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This study demonstrates the feasibility of using a U-Net convolutional neural network as a surrogate model to predict the velocity and pressure fields of microfluidic filter designs. The method is fast, easy to set up, and can be used to evaluate the performance of new designs. Additionally, the method can be used to predict pressure based on velocity data.
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 (CPD), 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.

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