Identification of gas-liquid two-phase flow patterns in a horizontal pipe based on ultrasonic echoes and RBF neural network

This paper proposes a novel flow pattern identification method using ultrasonic echo signals within the pipe wall. A two-dimensional acoustic pressure numerical model is established to investigate the ultrasonic pulse transmission behavior between the wall-gas and wall-liquid interface. Experiments were also carried out at a horizontal air-water two-phase flow loop to measure the ultrasonic echo pulse signals of stratified flow, slug flow, and annular flow. It is interesting to find that the attenuation of the ultrasonic pulse at the wall-liquid interface is faster than the attenuation at the wall-gas interface. An RBF neural network is constructed for online flow pattern identification. The normalized envelop area and the area ratios of the echo spectrum are selected as the input parameters. The results show that the stratified flow, slug flow, and annular flow can be identified with an accuracy of 94.0%.

Automated summary

This paper proposes a novel method for flow pattern identification using ultrasonic echo signals within the pipe wall, with experiments demonstrating that the attenuation of ultrasonic pulses varies between different flow patterns. An RBF neural network is constructed for online flow pattern identification, achieving an accuracy of 94.0% in identifying stratified flow, slug flow, and annular flow.