Online identification of horizontal two-phase flow regimes through gabor transform and neural network processing

The fundamental objective of this work is the development of a specialist system capable of diagnosing different configurations of horizontal two-phase flow regimes. It is important to emphasize that this knowledge is of capital importance to the efficient operation of facilities for the manipulation and transportation of multiphase fluids, and it represents one of the most important challenges in petrochemical and thermonuclear industries today. The working principle of the proposed methodology is based on the signals acquired by a rapid response pressure gradient sensor and their decomposition into Gabor coefficients, followed by processing through a previously trained artificial neural network. The implementation is accomplished in such a way that the diagnosis operation is performed online, from the acquisition of the signal to its post-processing. An experimental campaign was conducted at the facilities of the Thermal and Fluids Engineering Laboratory (NETeF) at the University of S ao Paulo in order to validate the proposed methodology. Experimental pressure gradient signals were obtained for all main horizontal air-water flow regimes (stratified smooth, stratified wavy, intermittent, annular, and bubbly) produced in a 12 m long test section with an internal diameter of 30 mm. Results show that the percentage of correct flow regime diagnosis in steady-state conditions is practically 100%, provided the detection level is adequately set.