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Long short-term memory (LSTM) and recurrent neural network (RNN) has achievedgreat successes on time-series prediction. In this paper, a methodology ofusing LSTM-based deep-RNN for two-phase flow regime prediction is proposed,motivated by previous research on constructing deep RNN. The method is featuredwith fast response and accuracy. The built RNN networks are trained and testedwith time-series void fraction data collected using impedance void meter. Theresult shows that the prediction accuracy depends on the depth of network andthe number of layer cells. However, deeper and larger network consumes moretime in predicting.

Two-phase flow regime prediction using LSTM based deep recurrent neural network