Void Fraction Calculation Model Based on Two-Phase Flow Patterns in the Scavenge Pipe

Void fraction is a critical parameter in the oil-air two-phase flow within the scavenge pipe of the aircraft engine lubrication system. It plays a significant role in analyzing fluid viscosity and density, pressure drop characteristics, and heat transfer performance in the scavenge pipe. Therefore, enhancing the accuracy of void fraction prediction is essential for improving the design precision of the scavenge pipe. Empirical correlations are commonly used in void fraction calculations. This study focuses on the scavenge pipe of the aircraft engine lubrication system and establishes void fraction prediction correlations based on different flow patterns to enhance prediction accuracy. High-speed photography is employed to simultaneously capture front and bottom views of the two-phase flow, constructing three-dimensional fluid images and measuring 247 void fraction data points. The extracted features from the captured images are input into machine learning methods for flow pattern identification, and the void fraction data points are classified according to different flow patterns. The performance of seven commonly used correlations is evaluated based on the classified void fraction data points. The root mean square error (RMSE) is used to determine the optimal result for each flow pattern. Results indicate that the RMSE for the void fraction correlations based on different flow patterns remains below 7.5%, significantly improving the prediction accuracy compared to using a single void fraction correlation. This improvement is of substantial importance for the design of the scavenge pipe.