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.
