Visual identification of oscillatory two-phase flow with complex flow patterns

We present an approach based on computer vision and machine learning methods to identify two-phase flow with complex flow patterns in oscillatory conditions. A visualization experiment bench was designed, constructed, and used to simulate the actual reciprocating motion of the cooling gallery inside the piston of low-speed diesel engines. The results of our proposed approach show that the feature vectors extracted from the optical flow images provides a valuable reference for the velocity vectors in two-phase flow. We show that it is possible to identify oscillatory two-phase flow videos with respect to Reynolds numbers from 10568 to 31704 using a Bayesian Network classifier, with the best accuracy of 94%. The approach purposed in this paper can not only be used to present the validating sources for numerical simulation results, but also be widely applied in the visualization of multiphase flow, which is a key area to be developed on the basic research of heat transfer systems.

Automated summary

The approach based on computer vision and machine learning can successfully identify two-phase flow with complex flow patterns in oscillatory conditions, with a high accuracy rate of 94% using a Bayesian Network classifier. The method is not only useful for validating numerical simulation results, but also has wide applications in visualizing multiphase flow, especially in the basic research of heat transfer systems.