Analysis of Bubble Flow in an Inclined Tube and Modeling of Flow Prediction

Abstract

The lubricating oil system is a significant component of aviation engine lubrication and cooling, and the scavenge pipe is an essential component of the lubricating oil system. Accurately identifying and understanding the flow state of the scavenge pipe is very important. This article establishes a visualization test bench for a 45-degree inclined scavenge pipe, with upward and downward flow directions, respectively. The test temperature is 370 K, and a high-speed camera captures the changes in the two-phase flow inside the pipeline. Based on high-speed photography photos, we develop software for analyzing the flow characteristics of bubbles inside the tube and explore the influence of gas phase conversion velocity and liquid phase conversion velocity on the apparent velocity of bubbles inside the tube. Multiple algorithms were used to develop the model by combining machine learning with speed and accuracy to establish a data regression prediction model for the apparent velocity of bubbles inside the tube. Through calculation and analysis, it was found that the root mean square error of the prediction model using the BP neural network algorithm was the lowest, and the decision coefficient of the prediction model using the support vector machine algorithm was the highest.

Keywords

• oil-gas flow
• visual test
• machine learning
• prediction model