Abstract
In the digital microfluidic experiments, the droplet characteristics and flow patterns are generally identified and predicted by the empirical methods, which are difficult to process a large amount of data mining. In addition, due to the existence of inevitable human invention, the inconsistent judgment standards make the comparison between different experiments cumbersome and almost impossible. In this paper, we tried to use machine learning to build algorithms that could automatically identify, judge, and predict flow patterns and droplet characteristics, so that the empirical judgment was transferred to be an intelligent process. The difference on the usual machine learning algorithms, a generalized variable system was introduced to describe the different geometry configurations of the digital microfluidics. Specifically, Buckingham’s theorem had been adopted to obtain multiple groups of dimensionless numbers as the input variables of machine learning algorithms. Through the verification of the algorithms, the SVM and BPNN algorithms had classified and predicted the different flow patterns and droplet characteristics (the length and frequency) successfully. By comparing with the primitive parameters system, the dimensionless numbers system was superior in the predictive capability. The traditional dimensionless numbers selected for the machine learning algorithms should have physical meanings strongly rather than mathematical meanings. The machine learning algorithms applying the dimensionless numbers had declined the dimensionality of the system and the amount of computation and not lose the information of primitive parameters.
Highlights
The microfluidic technology has the advantages of generating micron or even nanometer droplets with uniform size, and it has been widely used in the fields of biology [1,2]chemistry [3], heat transfer [4], petroleum engineering [5], etc
The prediction models [7] of flow pattern and droplet characteristics [8,9] had been established by the common method of multivariate regression analysis based on the primitive parameters in experiments
The paper studied the machine learning (ML) model accuracy by inputting the primitive parameters and the dimensionless numbers. All data from both Experiment 1 and 2 had been divided into the training and testing data by the ratio of 4:1, whatever they were in the solution sets with dimensionless numbers (Set1, Set2, and Set3) or primitive parameters (Set4)
Summary
The microfluidic technology has the advantages of generating micron or even nanometer droplets with uniform size, and it has been widely used in the fields of biology [1,2]chemistry [3], heat transfer [4], petroleum engineering [5], etc. Two-phase flow and threephase flow [6] are common microfluids. Countless papers have been focused on the flow-state modeling of microchannel two-phase flow, especially on the flow patterns and droplet characteristics that governed the droplet generation. The prediction models [7] of flow pattern and droplet characteristics [8,9] (including the droplet length and frequency) had been established by the common method of multivariate regression analysis based on the primitive parameters in experiments. The physical mechanism of two-phase flow in microchannels was very complex, and the flow patterns and droplet characteristics were much sensitive to the experimental variables [10,11,12]. The prediction models were highly dependent on the experiments and could not be in extrapolation
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