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