Simulation and Prediction of Bubble Size and Motion Characteristics of Underwater Horizontal Flow

Abstract

The geometrical size and motion characteristics of bubbles in water are the key parameters which affect the characteristics of gas-liquid two-phase flow and play a key role in engineering applications. There are few types of research on bubble formation by horizontal intake. This paper analyzes the effects of different gas velocities, gas hole diameters, and water temperature on bubbles' size and motion characteristics based on the BP(backpropagation) neural network and VOF(volume of fluid) model. The statistical data are trained by BP neural network to obtain the prediction model. The results show that the gas velocity and diameter are proportional to the bubble size and horizontal displacement amplitude, but the curvature decreases. The water temperature is inversely proportional to the bubble size, and the horizontal displacement amplitude has little change. The prediction model has a high degree of fit and good correlation and can predict bubble size and displacement quickly and accurately.