Using deep learning to recognize liquid–liquid flow patterns in microchannels

Data-driven models for microfluidics: A short review

Microfluidic devices have many unique practical applications across a wide range of fields, making it important to develop accurate models of these devices, and many different models have been developed. Existing modeling methods mainly include mechanism derivation and semi-empirical correlations, but both are not universally applicable. In order to achieve a more accurate and general modeling process, the use of data-driven modeling has been studied recently. This review highlights recent advances in the application of data-driven modeling techniques for simulating and designing microfluidic devices. First, it introduces the application of traditional modeling approaches in microfluidics; subsequently, through different database sources, it reviews studies on data-driven modeling in three categories; and finally, it raises some open issues that require further investigation.

Deep learning with microfluidics for on-chip droplet generation, control, and analysis

Droplet microfluidics has gained widespread attention in recent years due to its advantages of high throughput, high integration, high sensitivity and low power consumption in droplet-based micro-reaction. Meanwhile, with the rapid development of computer technology over the past decade, deep learning architectures have been able to process vast amounts of data from various research fields. Nowadays, interdisciplinarity plays an increasingly important role in modern research, and deep learning has contributed greatly to the advancement of many professions. Consequently, intelligent microfluidics has emerged as the times require, and possesses broad prospects in the development of automated and intelligent devices for integrating the merits of microfluidic technology and artificial intelligence. In this article, we provide a general review of the evolution of intelligent microfluidics and some applications related to deep learning, mainly in droplet generation, control, and analysis. We also present the challenges and emerging opportunities in this field.

The Recognition Algorithm of Two-Phase Flow Patterns Based on GoogLeNet+5 Coord Attention

The two-phase flow in a microchannel consists of liquid-liquid and gas-liquid material components. The automatic recognition of flow patterns using deep learning approaches has been emerging. This study aimed to improve the recognition accuracy of flow patterns in the two-phase flow images. The different convolutional kernels in the GoogLeNet algorithm extracted the image features with different scales. In order to strengthen the important channel and spatial features, this paper proposes the combined five-layer Coord attention and GoogLeNet algorithm to enhance the accuracy of the new algorithm. The optimized algorithm model was derived from image datasets with different liquid-liquid two-phase flows (NaAlg-Oil, GaInSn-Water), and its accuracy was 95.09% in training and 98.12% in testing. This new model was also applied to predict the flow patterns, with a recognition accuracy of more than 97% in both the liquid-liquid and gas-liquid two-phase flows (water-soybean oil, water-lubricating oil, and argon-water).