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 ( ${p}$ LSTM) applying to a multiple voltage-current (mVC) system has been proposed in order to estimate the void fraction $\hat {\alpha }$ accurately in gas-liquid flows. The ${p}$ LSTM 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 $\hat {\alpha }$ . For training, ${i}$ are experimentally measured at 36 points of the true void fraction $\alpha $ , which is calculated by the drift flux model. On the other hand, ${i}$ for test data are measured under 12 points of $\alpha $ . 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, ${p}$ LSTM 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 ${p}$ LSTM can consider the time dependence of gas-liquid flows suitable for each flow regime and neglect the effect of the flow regime on ${i}$ .