Pattern Identification and Prediction of Air-Water Flow in Small Channel with LSTM Recurrent Neural Network

The air-water flow in a horizontal ring of small organic-glass tube,3 mm in hydraulic diameter,was investigated with the lab-built test platform,comprising pressure-difference sensor,photoelectric position sensor,high-speed video camera and host computer.Four distinctive flow patterns,including the annular,layered,intermittent and slug flow-patterns.In addition,the fluctuation signals of pressure difference,involving the four flow patterns,were analyzed and predicted in a short term with the model of Long and Short Term Memory(LSTM) recurrent neural network.The results show that when it comes to four flow-patterns,on line prediction with LSTM recurrent neural network model was relatively accurate.To be specific,for the annular/layered/intermittent/slug flow-patterns,the mean-square errors were estimated to be 0.004,0.0099,0.0075 and 0.0156,respectively.