Vertical two-phase flow identification using advanced instrumentation and neural networks

Most two-phase flow measurements, including void fraction measurements, depend on correct flow regime identification. There are two steps taken towards the successful identification of flow regimes: first, develop a non-intrusive instrument to demonstrate area-averaged void fluctuations and second, develop a non-linear mapping approach to perform objective identification of flow regimes. In this paper, an advanced non-intrusive impedance void-meter provides input signals to neural networks which are used to identify flow regimes. After training, both supervised and self-organizing neural network learning paradigms performed flow regime identification successfully. The methodology presented holds considerable promise for multiphase flow diagnostic and measurement applications.