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Deep learning based spraying pattern recognition and prediction for electrohydrodynamic system
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DOI：10.1016/j.ces.2024.120163.png)
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摘要
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Effective recognition and prediction of spraying patterns for electrohydrodynamic (EHD) process are extremely important for its applications in high quality micro/nanoparticles preparation, chip coating, droplet-reactor design, and high precision printing, etc. In this study, six distinct spray patterns, namely dripping, spindle, cone-jet, rotational jet, atomization, and skew jet-atomization, were classified through experiments. Subsequently, 30,000 images were obtained to train a convolutional neural network (CNN) model for recognizing EHD spraying patterns, which exhibited a remarkable accuracy of 99.80%. The CNN model was used to recognize the patterns across a range of experimental variables. Dimensionless groups were established and the generalized spraying pattern maps were drawn efficiently via the model. Finally, a database consisting of 11,650 experimental data points was constructed to train a deep neural network (DNN) model, aiming to reduce the number of experiments. The DNN model with an accuracy of 95.88% was employed to predict the spraying patterns, by which a rapid but comprehensive analysis of the impact of different conditions was achieved.
Keyword:
EHD spraying patterns
Deep neural network
Electrohydrodynamic
Flow pattern recognition
Flow pattern prediction
Multiphase flow
期刊
IF:
4.3
论文数:
2.3W
被引数:
5.5W
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引用论文
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