Two-phase flow regime identification based on the liquid-phase velocity information and machine learning
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RESEARCH ARTICLE
Two‑phase flow regime identification based on the liquid‑phase velocity information and machine learning Yongchao Zhang1,2 · Amirah Nabilah Azman1 · Ke‑Wei Xu1 · Can Kang2 · Hyoung‑Bum Kim1 Received: 28 May 2020 / Revised: 23 August 2020 / Accepted: 29 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
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. Graphic abstract
* Hyoung‑Bum Kim [email protected] Extended author information available on the last page of the article
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1 Introduction Two-phase gas–liquid flows are common in various industrial operations, such as nuclear power generation and petroleum transportation. The complexity of two-phase flow is evident owing to the interfacial structures caused by the different properties and behaviors of the two phases. This flow type can be divided into different flow regimes, such as separated, transitional, and dispersed flow regimes, based on the characteristics of the interfacial structures (Taitel and Dukler 1977; Weisman et al. 1979). Flow regimes affect the characteristics of the operation and the stability of the pipeline system (Dinaryanto et al. 2017). Therefore, the identification and prediction of two-phase flow regimes play an important role in practical engineering applications for analyzing the operations and behaviors of two-phase flow systems. The measurement of the two-phase flow characteristics provides a better understanding on the two-phase flow regime behavior and is a prerequisite for realizing flow regime identification. Flow visualization techniq
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