Void fraction measurement is a crucial challenge in two-phase flow, which is related to the measurement of phase flow and the calculation of pressure drop. In this study, a coaxial line phase sensor is designed for void fraction acquisition and the void fraction of the plug measured has a well time-domain and Probability Density Functions (PDF) distribution characteristics. Four typical correlations were selected to compare with the measured values of the sensors, the mean absolute errors (MAPE) are 19.88 %, 18.94 %, 18.25 %, and 17.19 %, respectively. Considering the nonlinear and strong coupling between the flow characteristic parameters and the void fraction, the flow parameters were analyzed and selected by Pearson correlation coefficient. The Kernel ridge regression methods were employed to select effective variables for model training, and the Extreme gradient boosting (XGboost) machine learning model was proposed. The prediction accuracy of the model is 1.65 % (current data) and 4.18 % (literature data), respectively.
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