The technology and theory of online recognition of gas-liquid two-phase flow regime

It has important industrial background and scientific significance to study the online recognition of gas-liquid two-phase flow regime. The key problem of the online recognition is how to map to the space of flow regimes from the space of character of the parameter fluctuations with the shortest time. Counter propagation network (CPN) can train the results of self-organizes mapping with supervising. At the same time, it takes advantage of simple algorithm and doesn't require lots of training samples. Therefore, it meets the online and automatic recognition of gas-liquid two-phase flow regimes. In the present test case, the online criterion was 8.2s with the FFT of pressure fluctuations from the vertical upward section of the U-type tube. The presented technology and theory has some obvious advantages, which provides a feasible solution of online recognition without databank of flow regime and can overcome partially the problems induced by not enough experiments in advance. In addition, the problems to shorten the identification time and to increase the identifying possibility are discussed in the paper.