Unsupervised-feature extraction of gas-liquid two-phase flow pattern based on convolutional autoencoder:principle and application
In two-phase flow measurements,the accurate identification of the flow pattern is the basis for the measurement of pressure drop,heat transfer and other thermal parameters.Traditional two-phase flow methods have limited applicability under different operating conditions due to the limitations of test conditions and data.Artificial intelligence algorithms can take into account both efficiency and accuracy,but the feature extraction method is still the difficulty in its identification.The accurate identification of flow patterns is of great significance for interpreting data,improving models,and improving application effects.Therefore,this paper proposes a feature extraction method based on unsupervised learning of convolutional autoencoder,which inputs the extracted features into random forest,support vector machine,and back propagation neural network classifiers for classification,respectively.The experimental results show that the recognition accuracy for all four stream types reaches more than 99%,which indicates that the convolutional autoencoder feature extraction method can significantly improve the accuracy of the classification algorithm,has good compatibility with different classifiers,and also provides help for the feature extraction method for subsequent popular recognition.
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