Theoretical and experimental research on two-phase flow image reconstruction and flow pattern recognition

Abstract

Two-phase flow is a kind of complex fluid flow state, and the flow pattern characteristics are very difficult to obtain accurately. First, the principle of two-phase flow pattern image reconstruction based on electrical resistance tomography technology and the complex flow pattern recognition method are developed. Next, the back propagation (BP), wavelet, and radial basis function (RBF) neural networks are applied to the two-phase flow pattern image identification process. The results show that the RBF neural network algorithm has higher fidelity and faster convergence speed than the BP and wavelet network algorithms, and the fidelity is more than 80%. Then, deep learning of the pattern recognition algorithm fusing the RBF network and convolution neural network is proposed to improve the precision of the flow pattern identification. Additionally, the recognition accuracy of the fusion recognition algorithm is more than 97%. Finally, a two-phase flow test system is constructed, the test is finished, and the correctness of the theoretical simulation model is verified. The research process and results provide important theoretical guidance for the accurate acquisition of two-phase flow patterns.