航空宇宙技術
Online ISSN : 1884-0477
ISSN-L : 1884-0477
双方向長短期記憶ネットワークによる気液二相流の流動様式判別に関する研究
中尾 圭吾坂野 友香理坂本 勇樹樺山 昂生井上 裕介佐藤 哲也
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2022 年 21 巻 p. 68-76

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Cryogenic fluids such as liquid hydrogen and liquid oxygen used as the rocket propellants easily evaporate and form the gas-liquid two-phase flow. The control of the two-phase flow is difficult due to the large fluctuation of the density. For high-precision control, it is necessary simultaneously to understand flow regimes which represent a gas-liquid distribution pattern. Therefore, in this paper, we developed a classifier that uses Bidirectional LSTM networks, which is part of a family of deep learning methods, with a measured value of a void fraction meter as input and gas-liquid flow rate conditions as output, in order to realize a flow regime classifier in the future. The classifier succeeded in classifying with more than 80% accuracy. In addition, in order to verify what features of the input data the classifier captures, a test data-set of which frequency was artificially changed was classified. As a result, it was confirmed that the classifier would use the frequency component of the input data as one of the basis for classification.

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© 2022 The Japan Society for Aeronautical and Space Sciences
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