Flow regime identification for wet gas flow based on WPT and RBFN

A novel noninvasive approach to the on-line flow regime identification for wet gas flow in a horizontally mounted pipeline is proposed in this paper. Research into the flow-induced vibration response for the wet gas flow with the conditions of pipe diameter 50 mm, pressure from 0.25 MPa to 0.35 MPa, Lockhart-Martinelli parameter from 0.02 to 0.6, and gas Froude number from 0.5 to 2.7, was conducted. The flow-induced vibration signals were measured by a vibration transducer installed by outside wall of pipe, and then the features from the vibration signals were extracted through wavelet package transform (WPT). A radial basis function network (RBFN) classifier with Gaussian basis function and the extracted features as inputs was developed to identify the three typical flow regimes including stratified wavy flow, annular mist flow, and slug flow for wet gas flow. The results show that the method can identify flow patterns effectively and its identification accuracy arrives at above 89%.