Neural network based two-phase flow classification in a vertical narrow rectangular channel

Abstract

Flow Regimes in a vertical narrow rectangular channel of cross-section $20\times 1{\text{cm}}^2$ are investigated up-to wispy-annular flow using a dense test matrix and double sensor conductivity probe at section mid point. The data from the probe is used to calculate void fraction, velocity of interfaces, and chord length of various flow structures. A five unit self organizing neural network is used to identify various flow regimes by using single point geometrical data of flow structures. Six separate flow regimes are found to exist. A new flow regime is identified and is called rolling-wispy flow. A discussion on boiling crisis is given regarding this flow regime. The resultant flow regime map is compared with various existing maps.