Identification method of gas-liquid two-phase flow regime based on characteristics of image moment invariant

Gas-liquid two-phase flow and heat transfer character are extremely influenced by the flow regimes, and the accurate identification of flow regimes is important for the operation and design of interrelated instruments. According to the characteristic that moment invariant can effectively recognize the images by translation, rotation and scaling invariants, a flow regime identification method based on image moment invariant and probabilistic neural network was proposed. Gas-liquid two-phase flow images were captured by digital high-speed video systems in horizontal pipe. The image moment invariant eigenvectors were extracted by using image processing techniques. The probabilistic neural network was trained by using these eigenvectors as flow regime samples, and the flow regime intelligent identification was realized. The test results show that successfully-trained probabilistic neural network can quickly and accurately identify seven typical flow regimes of gas-water two-phase flow in horizontal pipe. The whole identification accuracy is 99.3%. It is a new and effective method for online flow regime identification.