In this work, an automatic liquid–liquidtwo‐phase flow pattern recognition platform was developed to help circumvent the difficulties in labor‐intensive hydrodynamics studies. Trained by about 30,000 of human‐labeled flow pattern images, a convolutional neural network was built with the expert‐level ability in the flow pattern recognition tasks and then coupled with automatic pump feeding system and online high‐speed camera monitoring system to realize the high‐throughput experimentation platform for microchannels. Effects of important factors such as flow rate, viscosity, interfacial tension, and so on were studied, and different flow pattern maps were obtained. With these thousands of flow pattern data in hand, we eventually drew the generalized liquid–liquidtwo‐phase flow map and then proposed the relatively prudent criteria for slug flow operation window in the microchannel. This study extended the applications of artificial intelligence on microreactor technology or microfluidics, and in particular facilitated understanding complex hydrodynamics and flow patterns.

中文翻译：

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使用深度学习识别微通道中的液体-液体流动模式

在这项工作中，开发了一个自动的液-液两相流模式识别平台，以帮助解决劳动强度大的流体动力学研究中的困难。经过约30,000张人类标记流模式图像的训练，构建了具有专家级能力的卷积神经网络，以进行流模式识别任务，然后结合自动泵送料系统和在线高速摄像头监控系统来实现高微通道的吞吐量实验平台。研究了流速，粘度，界面张力等重要因素的影响，获得了不同的流型图。有了这数千种流模式数据，我们最终绘制了广义的液-液两相流图，然后针对微通道中的弹状流操作窗口提出了相对谨慎的标准。这项研究扩展了人工智能在微反应器技术或微流控技术上的应用，尤其是促进了对复杂流体力学和流动模式的理解。