Two‐phase Flow Regime Identification Combining Conductivity Probe Signals and Artificial Neural Network

Important aspects of the hydrodynamics and thus, the correct identification of the flow regime could enhance safety and overall performance in multiphase flow systems. Several works on flow regime identification have been carried out in the past. Most of them consist, in a first stage, on measuring certain flow parameters that can be used as good flow regime indicators and, then, developing a flow regime map using these indicators. In this work, a vertical two‐phase flow loop facility was used, whereby local conductivity signals were recorded and utilized for the development of an Artificial Neural Network (ANN) based method for the flow regime classification. The experimental database consists of a total number of 125 test cases covering a wide range of situations in the loop working area. Each experiment flow regime was identified by visual inspection, and classified into bubbly (B), cap‐bubbly (CB), slug (S), churn turbulent (CT) or annular (A). The bubble chord length cumulative probability function (...