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.

我们提出了一种基于计算机视觉和机器学习方法的方法来识别振荡条件下具有复杂流动模式的两相流。设计、搭建了可视化实验台，用于模拟低速柴油机活塞内冷却通道的实际往复运动。我们提出的方法的结果表明，从光流图像中提取的特征向量为两相流中的速度向量提供了有价值的参考。我们表明，可以使用贝叶斯网络分类器识别关于雷诺数从 10568 到 31704 的振荡两相流视频，最佳准确率为 94%。本文中的方法不仅可以用于提供数值模拟结果的验证来源，