Accurate Void Fraction Estimation by Plural Long Short Term Memory Applying to Multiple Voltage Current System in Gas-Liquid Flows

Plural long short term memory ( pLSTM) applying to a multiple voltage-current (mVC) system has been proposed in order to estimate the void fraction ∧α accurately in gas-liquid flows. The pLSTM consists of two LSTMs, one for flow regime identification (fri-LSTM) and the other for void fraction estimation (vfe-LSTM). The fri-LSTM identifies a flow regime q from current vectors i, corresponding to gas distribution, measured by mVC system. Based on the identification result and i, the customized vfe-LSTM to each q estimates ∧α. For training, i are experimentally measured at 36 points of the true void fraction α, which is calculated by the drift flux model. On the other hand, i for test data are measured under 12 points of α. Two parameters of each LSTM, one is sequence length S representing time dependence length considered within the LSTM and the other is the number of LSTM blocks M related to the estimation performance, are optimized so that accurate void fraction estimation is achieved. As a result, pLSTM applying to mVC system achieves a 100% accuracy of flow regime identification and less ±0.00034 standard error of void fraction estimation in liquid single-phase flow, bubble flow, slug flow, and churn flow. Accurate estimation is caused by the fact that pLSTM can consider the time dependence of gas-liquid flows suitable for each flow regime and neglect the effect of the flow regime on i.