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Pan Hui, Li Haiguang, Wu Xuan. Pattern Identification and Prediction of Air-Water Flow in Small Channel with LSTM Recurrent Neural Network[J]. CHINESE JOURNAL OF VACUUM SCIENCE AND TECHNOLOGY, 2020, 40(6): 591-597. DOI: 10.13922/j.cnki.cjovst.2020.06.18
Citation: Pan Hui, Li Haiguang, Wu Xuan. Pattern Identification and Prediction of Air-Water Flow in Small Channel with LSTM Recurrent Neural Network[J]. CHINESE JOURNAL OF VACUUM SCIENCE AND TECHNOLOGY, 2020, 40(6): 591-597. DOI: 10.13922/j.cnki.cjovst.2020.06.18
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Pattern Identification and Prediction of Air-Water Flow in Small Channel with LSTM Recurrent Neural Network

  • Inner Mongolia University of Science and Technology, Baotou 014010, China

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  • Received Date: November 19, 2019
  • Available Online: September 12, 2023
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    • Abstract
  • 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.
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    • References (24)
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