Two-phase flow regime identification based on the liquid-phase velocity information and machine learning

Abstract

Two-phase flow regime identification in a horizontal pipe was realized based on the liquid phase velocity information and the machine learning method. Ultrasound Doppler velocimetry was employed to measure the liquid velocity. Statistical features extracted from the velocity time series data, such as mean, root mean square, and power spectral density, were used to realize real-time flow regime identification. In addition, two novel parameters—maximum velocity ratio and maximum velocity difference ratio—were proposed to identify plug and decaying slug flow. Different classification algorithms were employed to achieve a high identification accuracy. Moreover, transient flow regime identification with a fast response was realized based on two classification algorithms—long–short term memory and convolutional neural network. The results show that the accuracy of real-time flow regime identification based on a flow regime map can reach up to 93.1% using support vector machine, the maximum velocity ratio and maximum velocity difference ratio are effective in identifying plug and decaying slug flow, and transient flow regime identification under slug flow condition can be realized with an accuracy of 94% based on a convolutional neural network (CNN). Decaying slugs with long lengths confuse the CNN and are responsible for the error in identification. The results presented herein are expected to expand the available knowledge on two-phase flow regime identification.